[First Draft]
Grading Efficiency: Public versus Private universities in Pakistan
Corresponding Author: Ummad Mazhar Director Shahid Javed Burki Institute of Public Policy, 126-B, Ahmed Block, New Garden Town, Lahore, Pakistan. Email:
[email protected] Tel:+92 42 35975717 +92 42 35913304
Najaf Zahra Research Assistant, Shahid Javed Burki Institute of Public Policy, 126-B, Ahmed Block, New Garden Town, Lahore, Pakistan. Email:
[email protected]
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Abstract Higher education originates research and innovation that not only serves as an important input to the process of economic change but also build the stock of human and non-human capital that strengthen the social and economic foundations of a society. Given the multifaceted importance of higher education many authors argue that only government can provide it adequately and with satisfactory level of service delivery. Surprisingly, however, indicators to gauge the relative validity of these claims are non-existent or scarce. This study attempts to fill this gap. Focusing on institutes of higher education in Pakistan, we emailed a query to 100 universities, and record whether a university responds to our enquiry or not and how long it takes. The values obtained from these indicators are then use to assess the relative efficiency of public and private universities. To make our comparison meaningful all universities in our sample are taken from the Higher Education Commission of Pakistan’s data base of approved universities so as to ensure a minimum level of standard in terms of education quality and service delivery. The results provide new objective indicators of university efficiency operating in private and public sectors, based on a simple and easily accessible service, and allow us to shed light on its determinants. Besides, the analysis also has important implications related to the effective use of information and communication technologies in universities, issues related to e-governance and online service availability. We find that 80 percent of the universities do not respond to our query. Among those who respond, the percentage of public sector universities is larger than those in the private sector. Our econometric evidence suggests that being in public sector and in a big city area increases the probability that university will be more responsive to online enquiries. JEL Classfication: A2, A23, I29, H40 Key Words: University, Efficiency, Email, Education, Quality.
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1. Introduction In any society institutes of higher education and learning serve three important functions: (a) as instruments of enlightenment and change they serve to empower the people and rationalizing their behavior, while at the same time (b) channel the forces of redistribution of power by (c) providing a merit based platform to competing view points (Sen, 1997 and Unterhalter, 2009). As a reflection of the stock of knowledge in a society, the level of education determines the quality of research and development of a society at a given time. From institutional point of view, education determines the cognitive abilities of the people and helps them grapple with the forces of physical and economic uncertainty (North, 2005). From public policy perspective, approaches like endogenous growth theory, human capability approach and social choice theory also assign key role to education in sustainable economic and political progress of a society (Chattopadjay, 2012). From business point of view, education is directly related with the productivity of human factor behind production processes. Given the seminal importance of education and institutes of higher learning in the political economic evolution of a society, it is unsettled whether government or the private sector should be at the commanding heights in delivering this crucial service. For government skeptics, public sector is less dynamic in responding to changing trends in labor market and thus is wasteful of the resources of students and future employers and employees. Those against private sector see paucity of available resources with a single institute plus the public good nature of research and development activities as sufficient reasons to keep government at the center stage at least in higher education services. Amid these extremes, one can observe the pragmatic approach where government sets benchmark institutes and lets the private sector to compete and excel the standards set by the public institutes. This is pragmatic because neither the private sector nor government alone has the capacity to cater all the educational requirements especially in a developing country. In this context, this research attempts to measure efficiency of government and private sector higher education institutes using a novel procedure based on electronic emails. The findings provide insights about the level of efficiency in private and public sector institutes and to what extent they are determined by factors common to both stripes of universities. The approach adopted in this study is simple yet novel. It develops indicators of university efficiency by measuring the time it takes to respond electronically to a simple and uniform task: providing information about the admission process. Using a query to measure the quality of service delivery is not entirely a new idea. Chong et al (2014) has used postal message to measure the efficiency of government in delivering postal service. 3
The email method, compared to postal delivery method, is better as there is no chance of message being lost in email (nonetheless we experience failure delivery notices). It allows us to compare the response time delay as a measure of efficiency rather than any other kind of administrative leakage or crime (e.g. Castillo et al., 2014). Focusing on email, moreover, is in line with the suggestion by “Edward Prescott in the early 1980s that postal economics is more central to understanding the economy than monetary economics”1. The Prescottian idea is resurrected and to take into account the change in time our paper changes mail to email, a change that, we believe, is necessary to reflect the emerging focus on egovernance and online service delivery information especially in health and education sectors (e.g. UN, 2014). At a broader level, the findings of this research are insulated from political and economic forces shaping the government production function such as democracy and accountability. In addition, our findings shed light on the potential role of ICT(information and communication technology) in key services like health care and education. For instance, releasing information at regular intervals and readily supplying it to key stake holders on request are pre-requisites for harmonizing social interaction among various groups in a society. The paper focuses on institutes of higher learning in Pakistan ___ a higher middle income country according to World Bank’s country classification released in 2014. The focus on Pakistan’s higher education sector is justified due to various reasons. Firstly, Pakistan has a large university-student population. The future growth potential of the country, therefore, is enormous if this population is sufficiently invested with competencies required for the twenty first century graduates. Feeling the need of quality education at university level, Higher Education Comission of Pakistan has been investing huge funds and technical expertise to build and strengthen existing educational infrastructure. Consequently, the number of universities in Pakistan has grown at a phenomenal rate over 2002 to 2014 (as shown in Table 1). The number of universities both in private and public sectors has more than doubled: former from 22 to 69 while later from 37 to 87 over 2002 to 2014 period. Unfortunately there is no study that gauges the relative quality of service delivery of universities operating in Pakistan. This study is an important step in this direction. Given the large bulk of youth population, one can expect a growing demand for IT (Information Technology) services as youth not only consumes more IT services and products but are also going to be a persistent source of labor supply into the sector for many years to come. In line with these trends, education sector is offering a number of degrees related to IT and its applications in all fields are 1
Mentioned in Chong et al., 2014 page 278.
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increasing exponentially. It is yet to assess, however, to what extent the IT culture has penetrated in education service delivery itself. This research precisely aims at this objective. Higher education in Pakistan University education in Pakistan is regulated by Higher Education Commission (HEC) of Pakistan. The HEC was established in 2002 and since its inception it has been investing funds to improve the university level education in Pakistan. Improving and expanding the opportunities of higher education are important in Pakistan not only from the perspective of research and development and their role in economic progress but also from the stand point of demographic dynamics in Pakistan. Given the huge bulk of youthful population in South Asian region and above all, in Pakistan, the demand for education services is likely to increase at a high rate in coming years. For instance the median age of population in the most highly populated countries of South Asia (except China) is below 29 years2. More importantly, it is 22.2 years in Pakistan according to World Health Organization (2014). Given the evidence that young people in developing countries are consuming more internet and educational services, one can safely predict a significant increase in the demand for online educational services and queries in coming years3. Moreover, young professionals also are always on the look out to add on to their credentials lest they may be replaced by a more talented new comer or simply to move up the ladder of professional career. Similarly, with the removal of cultural barriers and social changes, girls and women are increasingly entering the job market. To become more competitive they develop their skills and prefer to add more years of education in their portfolio. All these factors will shift the demand curve of higher education upward from its existence level. To provide a sneek preview of our findings, our results suggest that public sector universities are more likely to respond to email queries and that being located in a big city marginally increases the chance that university will be more responsive on its e-messages. Given the above mentioned importance of university education both at present and for the future, higher education sector requires clearer understanding of the extent of relative efficiency for public and private
2
According to World Health Organization (2014), the median age (in years) in South Asian countries in 2012 is as follows: Afghanistan (16.2), Bangladesh (24.7), India (26.07), Iran (27.99), and Pakistan (22.23). (Source https://data.un.org/Data.aspx?q=united+states+of+america&d=WHO&f=MEASURE_CODE%3AWHS9_88%3BC OUNTRY_ISO_CODE%3AUSA) (Accessed 11 March 2015). 3
For instance PEW Global Attitude Survey Q67, Q68 and Q69. Link http://www.pewglobal.org/2015/03/19/1communications-technology-in-emerging-and-developing-nations/ (Accessed 11 March 2015).
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universities. To this end, this study first develops the indicators of university efficiency (section 2), and secondly investigates their determinants (sections 3 and 4). The final section concludes the study.
2. Methodology The idea of measuring the quality of services delivered by institutions has gained tremendous currency in recent years. Among the most glaring and repeated examples of service quality measurement are the World Bank’s Governance Indicators that measure the quality of government across different sectors (Kaufmann
et
al.,
2007).
(https://www.transparency.org/)
One corruption
can
also
perception
mention index
and
Transparency World
International’s
Economic
Forum’s
(http://www.weforum.org/) various measure of people’s freedom. All these measures are based on survey method and capture public’s perceptions about abstract concepts. Though useful, these indexes are based on perceptions __ rather than on actual data ___ that are volatile and may not reflect underlying fundamentals (Glaeser, et al., 2004; Goel and Mazhar, 2015). In this paper we employ a simple but direct way to measure the relative efficiency of universities. Arguably, universities are organizations and, like all self interested organizations, should care about their prospective customers. This being said, we write a simple email message asking about the mid-year enrollment policy and see how much time a university takes to respond, if at all. The message is written in Standard English and requests an early response. We sent emails to universities located in all four provinces of Pakistan. We selected 100 universities: half belonging to public sector and half coming from the pool of universities operating in the private sector. An email query is sent to each university from a randomly selected male or female sender operating from a fake account. That is, one email account uses male email identity while other has a female name. The text of the sent messages, email identities and names of the senders are given in Figure 1 [see Appendix at the end]. All universities we include in our sample are recognized by the HEC (www.hec.gov.pk ), the highest regulatory body of higher education in Pakistan. The universities are thus satisfying the criteria necessary for a degree awarding institute. We measure the proportion of emails actually returned from private and public universities and the time it takes the response to come back from the date of departure. We stop keeping track of responses 3 months after the final email dispatched (i.e., our study period is from November 2014 to February 2015). It is important to note the advantages and disadvantages of our approach. On the positive side, we are focusing on a fairly simple and, in the case of university education, almost a universal service i.e. enquiry 6
about admission 4. Arguably, telephone is the more common medium of getting information but given the international and regional linkages that universities are maintaining, we cannot assume that they can be efficient on telephone while inactive on emails. Secondly, we are comparing public and private universities using a service where corruption plays no role. This is important because in most of the government services the main issue is that of corruption. Evidently, this is not the case in our comparison because no government clerk can ask a bribe from an email sender. Similarly, the university worker cannot serve any personal purpose by deleting the email. Thus focusing on email allows us to zero-in on the more direct measure of efficiency. One can argue that it is more efficient for smaller universities not to respond to email queries to save their limited resources. But in the context of Pakistan this argument should be reversed. Given the fact that small proportion of people in Pakistan use internet compares to telephone, the chance that email would be used by more serious students is larger compared to telephone enquiries. Although our measure of university efficiency is simple and direct it is not free from weaknesses. For instance, one can argue that difference in efficiency is due to different wage structure in public and private sectors. Due to unavailability of data we cannot rule out the possibility of this influence. Additionally, the management policy of a university may also have an impact on university’s overall responsiveness on different media. We try to take into account the management practices of the university using some dummy variables as explained in the following paragraphs but we cannot rule out the weakness due to this aspect. Finally, the indicator we have developed cannot be interpreted to reflect the quality of education in our sampled universities. At the best, it provide us a very imperfect insight into university’s overall responsiveness to online queries and thus, at best can be assume to be a correlate of university’s overall efficiency.
3. Data We sent an email message to 100 HEC recognized universities in Pakistan. The email was sent from two different email accounts specifically created for this purpose. One of the accounts has a male id while second has a female id name. This distinction was observed to overcome some cultural biases that can affect the responsiveness of the email recipient. The decision whether the sender of the message to a particular university is male or female is decided on the basis of a coin toss. Therefore, our randomization scheme makes it equally likely for each university to receive an email from either of the two senders.
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Its universality can be seen from the fact that almost all universities have provided some means of contact to their prospective clients. It is either an email, telephone or both or an online feedback box.
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The emails were sent from these accounts using GMAIL domain server. Both the emails were exactly the same except the name of the sender. The email message is reproduced in Figure 1. The message requests the university to provide information about the midyear enrollment policy. It requests urgent response from the university. The email was sent to the addresses given at the university websites mostly under the link “contact us”5. The name of the senders and their email identities were chosen as simple and common place in Pakistan with the only care that they are explicit in communicating the gender associated with the name. The email does not give any hint about the residence of the sender or weather s/he is a parent of a student or student herself. The names of the email senders are deliberately kept the same as the email ids to dispel any notion that it is a fake email. The emails are sent on week days and during office hours to avoid any issues related to “sender’s mail box is full”. Specifically, out of the total of 100 email messages to HEC recognized universities, half were sent on 25 (Tuesday) and remaining on 26 (Wednesday) of November 2014. Table 2 provides some exploratory statistics. Thus, average response time in our sample is 1902.5 minutes or 32 hours (approx.) or 4 business days. The median response time is less than average response time indicating that smaller values than the average are at a greater distance from the mean than the larger values to the right of the mean. In comparison, public sector universities have smaller (around 3 business days) and more stable response time (with a standard deviation of 4 business days) on average than private sector universities (around 5 days on average with a margin of more than 7 business days). On the basis of the response we divide universities into three categories. The first category comprises of universities with failure delivery status (FailResp). The second category includes universities who never responded to our query and are labeled NoResp. Finally, we have the category constituting universities that responded (Respond)6. In the first category, there are 10 universities from where we received failure delivery notice. We tried second time after a week but we get the same response. Obviously, the failure delivery message either means that email account is no longer functional or it has no capacity to receive new messages. In both the cases the failure delivery message indicates lack of attention to the account. In other words, it indicates lack of attention towards maintenance of service delivery mechanism.
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All the data related to university web addresses, emails, and the time and date of each message is available on request with the corresponding author. 6 There are only two universities who have auto response mechanism. One of these sent manual response later which we record in our analysis. One who does not deliver any manual response is considered in the category of ZeroResp.
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In the second category i.e. NoResp or those with zero reaction to our query, we have 70 universities i.e. this is the biggest category in our sample. Exactly 53 percent institutions in this category are in private sector. In the final category, we have universities that respond to our query (Respond)7. It includes 20 universities or in other words, only 20 percent of the universities responded back to our query. As one can noticed from Table 3 in Appendix I, majority of the universities in the three sub categories are in urban centers (defined as having population in excess of 1 million and represented as BigCityProp). Thus, our sample has 14 big cities namely Karachi, Lahore, Islamabad, Rawalpindi, Hyderabad, Multan, Gujranwala, Sargodha, Peshawar, Bahawalpur, Sialkot, Quetta, Faisalabad and Sukkur. In other words, out of the total of 32 locations where our 100 universities are locating, 14 universities or 44 percent are in urban areas. In the FailResp category, majority or 60 percent are in the private sector and only 40 percent are in the public sector. The same proportion holds in terms of location: 6 of these universities are in big cities. In terms of distribution across provinces, Sindh topped the list with 4 universities, Punjab and Baluchistan ranked second with 2 universities each, and KPK and Azad Kashmir has one university each with FailResp status. One can argue that these universities might be new in business and thus are not very well aware of the tendency of students to send email queries. This argument does not find support in our data: the average age of the group is 19 years while the average enrollment is above 3400 students. Moreover, the universities are not small in terms of number of departments: the average number of departments across universities is 8 which is not too small a number from the average of the whole sample of 11 departments. From Table 3, it is surprising to realize that location or being in big city has no impact on universities’ email response elasticity. It may indicate that big cities are not very different from small cities in terms of technological culture and online service culture. More deeply, it may also reflect the existence of second technological gap in our society i.e. inability to use technology even when it is accessible (UN, 2014). The distribution of universities across federal and provincial capitals is shown in Table 4 across the three categories. Together these five cities account for 68 percent of the total universities in our sample. The biggest city of Pakistan, Karachi, tops the list with 22 universities while Quetta has the lowest number of 3 universities. In terms of FailResp, only Lahore based universities manage to escape this dubious distinction. But Lahore along with Peshawar and Quetta has all those universities who never responded to our query. Among capitals only Islamabad and Karachi based universities respond. In terms 7
We avoid discussing quality of the reply to avoid indulging in issues that are beyond the scope of this study.
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of response rate Islamabad edges over Karachi: former registers a response rate of 0.25 compared to later’s 0.23. All other capitals do not have any influence on response and universities located in them fall in the category of NoResp. Another thing that one can notice from Table 4 is the higher ratio of no response for private sector universities across almost all the capitals. In fact, the ratio is highest for Karachi the biggest urban center and most modern and well connected city of the country. With accessibility out of question, this fact reflects lack of online culture or, as mentioned previously, the existence of second digital gap i.e. inability to use information and communication technology even when it is accessible. As we have mentioned in the previous section, there exist cultural factors that can influence the response behavior of university officials. One such factor is the gender of the email sender. In Table 5 we try to capture the influence, if any, of this factor. As is shown in the Table, the effect of gender does not seem to work: the no response rate is insignificantly different from response across gender. On the contrary, the response rate is higher in the case of male sender (23 percent) compare to female sender (18.5 percent). Thus, it seems to suggest a bias against women queries rather than in their favor. In any case, this issue requires greater attention, information and understanding of the cultural patterns which is beyond the scope of this study. 4. Econometric Analysis As a final step in our analysis we tried to estimate a simple logit model to answer the question of the determinants of university’s response to online queries. The choice of logit model is justified given the readily qualitative interpretation of our dependent variable namely university response. Thus it is defined as 1 for universities falling in the category of Respond. Whereas it assumes a value of zero if university falls in the category of NoResp, while we do not consider FailResp category in our econometric model. We have estimated a number of models with different specifications to see how various factors affect the underlying propensity of the university to respond. These estimates are shown in Table 6. The model (1) offers maximum information (in terms of number of observations) but includes only two regressors: a qualitative variable indicating whether university is operating in a public or private sector (OwnDum); and age of the university (AgeUniv) indicating the number of years since it has been granted university status. Similarly, the maturity of the university is also reflected, albeit imperfectly, in the number of departments university is housing. This influence is controlled in model (2). In models (3) and (4) the influence of being located in a big urban center (BigCityDum) and total number of students’ enrolled (lStudEnrol) in a university are taken into account. It is common observation that big cities offer greater opportunities to
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connect with other parts and regions across the country and world. Similarly, universities compete for good students and so may feel to have more active engagement with their potential students through online service delivery. Our results in Table 6 are of mixed sort. On the one hand, the coefficients per se are insignificant. However, the overall significance of the models (as shown in each case by the probability value of Chisquare test statistic) is good. It is difficult, in this context, to attribute much importance to individual variables. In other words, we are unable to find any significant individual effect of independent variables in our model on the latent propensity of university responsiveness. That is, if the elasticity of response of university is a function of some unobserved factors then our variables are not significantly related with it. In logit models, however, one is more interested in quantifying the influence of independent variables on the probability of occurrence of the event. We can explore, therefore, how the probability of university’s responsiveness is affected by changes in our independent variables. We can, for instance, check how change in each of the independent variable affect the probable responsiveness of university, what is called marginal probabilities in technical terms8. Two interesting proposition that find support in this analysis are presented in the lower panel of Table 6. As mentioned already, one can suspect that private sector universities would be more careful and efficient in responding to their potential clients. However, many factors can sabotage this reasoning. Especially, the lack of e-governance structure and lack of concern for online service deliveries, lack of online data bases, less incentive to improve efficiency due to greater demand of higher education compared to institutions. As most of these factors exist in Pakistan in one or another form so one cannot be sure that private sector will exercise larger positive effect on university efficiency. In fact, given the high cost-benefit ratio associated with the provision of higher education programs one can equally suspect that private sector does not have enough rationale to be efficient and dynamic. As shown in the lower panel of Table 6, the marginal probability of public sector ownership is greater than the marginal probability of being in private sector. As one can see, this result is consistent across all the models estimated in Table 6. Its interpretation is that: if we take a typical university and put it in public sector then it experiences a larger increase in its probability to respond to online queries than if we put it in private sector. This marginal probability is significant and robust against various specifications.
8
For difference in logit estimates and marginal probabilities one can consult Chapter 14 in Cameron and Trivedi (2010).
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The second proposition that we explore with our logit model specification is the big city (BigCityDum) influence on university efficiency. To some extent, one can argue that larger cities are more wellconnected, are facing greater competition and that universities located in larger urban areas can access new technologies and skilled manpower relatively easily so as to be more efficient. On the other hand, being in a densely populated area may put university in high demand so that it may not feel the need to show efficiency to attract greater number of students. So the reasoning goes both ways. The last three models in Table 6 includes qualitative variable that assumes a value of 1 if university is located in one of 13 big urban centers having population in excess of 1 million. Given the fact that our sample universities are coming from 32 different cities, the big city universities are just around 43 percent of the total sample. As shown in the lower half of Table 6, a change of location to big city has greater marginal effect on university efficiency than a converse change i.e. from big city to non-big city location. The effect is significant and robust across different specifications though its magnitude is small. In sum, our empirical models do not permit us enough insights on the individual influence of various factors related to university efficiency. However, focusing on basic specification with ownership structure as explanatory variable and checking its robustness across various specifications we come to know that being in public sector increases the probability that university will respond to email query. To a lesser degree, we also get some evidence that locating in a big city also increases the chances that university will respond to emails.
5. Conclusion Various studies have pointed out the importance of education in the process of long term economic change of a society. According to World Bank (1993), education and accumulation of human capital is one important factor behind the miraculous economic performance of the so called Asian Tiger economies. Arguably, university is an important if not the important institute of higher learning. Unfortunately there are few studies in economics that focuses on education service delivery at the level of higher educational institutes. This is surprising given the focus of economists on good governance and participatory approach to government decision which is not possible without creating effective and transparent service delivery mechanisms. The need for such mechanisms is particularly high in countries where average age is below mid 20s as such countries are going to face the greatest challenge of providing quality higher educational services to its young population.
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As Pakistan is one such country (with median age of 22.3 years) and it has been investing a larger proportion of its education budget on higher education, capacity building, and encouraging a culture of research and learning with university at the center stage. There is a strong need to assess the level of technological sophistication that has already achieved after the efforts of last decade. Moreover, it is also necessary to identify the weak points and areas where we can focus to enhance the service delivery efficiency significantly. This paper attempts to develop simple indicators of university efficiency. We focus on 100 universities approved by Higher Education Commission of Pakistan and thus are maintaining a minimum level of quality. In our sample, half the universities are from private sector and remaining are from public sector. Our method is to write a simple query regarding mid-year enrollment policy and send it to each of these 100 universities through electronic email message and record their response time. Our findings are not very enthusiastic as far as universities’ responses are assumed to be correlated with the level of efficiency in service delivery. Around 80 percent of the universities do not respond over the span of 3 months. Among the 20 percent who respond, we find that majority exist in public sector rather than in the private sector. It suggests relative efficiency of public sector over private sector. This finding is supported by econometrics estimations, and is robust against different specifications. We also find that big city has a slightly positive effect on the response efficiency of university. This effect too is significant and consistent across a couple of specifications. In general, our findings recommend that greater competition and infrastructure in higher education to increases its efficiency. We believe that this critical infrastructure, physical and human, will be available in coming years and higher education system in Pakistan will be performing much more efficiently than at present. References Cameron, A. C., Trivedi, P. K., 2010. Microeconometrics using Stata. Revised Edition. Stata Press. Castillo , M., Petrie , R., Torero , M., & C. Viceisza , A. (2014). Lost in the Mail: A Field Experiment on Crime. Economic Inquiry , 52, 285-303. Chattopadhyay, S. (2012). Education And Economics: Disciplinary Evolution And Policy Discourse. Oxford: Oxford University Press. Chong, A., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2014). Letter grading government efficiency . Journal of European Economic Association , 12 (2), 277-299.
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Glaeser, E. L., La Porta, R., Lopez-de-Silane, F., & Shleifer, A. (2004). Do Institutions Cause Growth? Journal of Economic Growth , 9 (3), 271-303. GoP. (2014). Pakistan Economic survey. Ministry of Finance, www.mof.pk. Kaufmann , D., Kraay , A., & Mastruzzi , M. (2007, July). Governance Matters VI: Governance Indicators for 1996-2006. World bank policy research working paper#4280 . Mazhar, U., & Goel, R. (2015). Corruption and Elections: An emperical study of cross section of countries. Public finance Review , 43 (2), 143-154. North, D. C. (2005). Understanding the Process of Economic Change. New Jersey: Princeton University press. (2014). Pakistan Economic survey. Ministry of Finance, Government of Pakistan. www.mof.gov.pk. Sen, A. (1997). Human capital and human capability. World Development , 25 (12), 1959-1961. UN. (2014). UN E-Government Development Database, Department Of Economic & Social Affairs. Retrieved from http://unpan3.un.org/egovkb/en-us/#.VQ16iPyUcwg Unterhalter, E. (2009). An Introduction to the Human Development and Capability Approach:Freedom and Agency. London,UK: Earthscan. WB. (1993). The East Asian Miracle: Economic growth and Public policy. Washinton D.C: World Bank policy Research Reports. WHO. (2014). World Health Organization Country statistics. Retrieved from https://data.un.org/Data.aspx?q=united+states+of+america&d=WHO&f=MEASURE_CODE%3AWHS9 _88%3BCOUNTRY_ISO_CODE%3AUSA
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Appendix I Table 1. Student Enrollment and Number of Degree Awarding Institutions Year
Number of degree awarding institutions
Student enrollment
Public
Private
Total
2000-01
37
22
59
276,274
2004-05
57
53
110
471,964
2008-09
70
57
127
803,504
2012-13
87
66
153
1,080,000
2013-14
87
69
156
1,230,000
Source: Pakistan Economic Survey (2014)
Table 2. Response Time Comparison: Public versus Private Universities RespTime (min.) Average
St.Dev
Max; Min
Median
Total (20)
1902.5
1212
2614.9
9937; 2
Public (13)
1608.1
1208
1974.7
7148; 2
Private (7)
2449.3
1240
3649.2
9937; 39
Table 3. Universities according to the nature of their responses PvtProp1
BigCityProp2
StudEnrol3
FailResp
0.6
0.60
3457.6
NoResp
0.53
0.80
5061.6
Respond
0.35
0.75
6505
Total
0.50
0.77
5221.9
1. Proportion of private universities in the category 2. Proportion of universities located in big cities. Where big city is defined as one with population in excess of 1 million. 3. Average number of student enrolled. The latest year available is used but no value relates to year before 2010.
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Table 4. Distribution of Universities across Federal and Provincial Capitals City (universities)
FailResp
NoResp
Respond
Response Time
Islamabad (12)
1 (pvt)
4 (pvt); 4(pub)
3
579.3 min
Karachi (22)
3 (pvt)
15 (pvt); 2 (pub)
5
2745.4 min
Lahore (17)
0
9 (pvt); 8 (pub)
0
No Response
Peshawar (8)
1 (pvt)
6 (pvt); 1 (pub)
0
No Response
Quetta (3)
1 (pub)
1 (pvt); 1(pub)
0
No Response
Table 5. Responsiveness vs Sender’s Gender Male Sender1 (Response
Female Sender2 (Response
Rate %)
Rate %)
FailResp
2 (6)
8 (12.3)
NoResp
25 (71)
45 (69.2)
Respond
8 (23)
12 (18.5)
35
65
Total
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Table 6. Determinants of University Efficiency (Logit Estimates) Dependent Variable: Response Time Dummy VARIABLES OwnDum AgeUniv
(1)
(2)
(3)
(4)
(5)
-0.936* (0.558) -0.005 (0.010)
-0.773 (0.539)
-0.794 (0.576)
-0.585 (0.960)
0.007 (0.024)
0.006 (0.026) 0.070 (0.680)
0.020 (0.029) 0.148 (0.812) -0.059 (0.365)
-0.620 (1.033) -0.001 (0.009) 0.019 (0.029) 0.085 (0.875) -0.039 (0.372)
Departments BigCityDum lStudEnrol
Observations
85
82
82
47
46
Wald χ2 (prob)
0.000
0.000
0.000
0.001
0.001
Estimates of Marginal Probabilities1 OwnDum (0)2
0.32*** (0.076) 0.16*** (0.055)
0.31*** (0.073) 0.17*** (0.060)
0.31*** 0.28*** (0.077) (0.077) OwnDum (1) 0.17*** 0.18 (0.061) (0.123) BigCityDum (0) 0.23** 0.23* (0.1000) (0.120) BigCityDum (1)3 0.25*** 0.26** (0.056) (0.077) Robust standard errors in parentheses; *** p
