rafmacalaba/datause-encoder-data huggingface dataset:
including those from the DHS and VLSS themselves. The direct estimates are, however, close to the unadjusted 1999 Census-based estimates shown in Figures 1 and 2.

**Figure 3. Direct estimates of infant and under-five mortality**

<!-- Start of picture text --> 60 ______<br>ui 50<br>40<br>-. \3 1997 DHS IMR<br>oU--1997 DHS U5MR<br>30 1993 VLSS IMR<br>a * n | .^ 1993 vISS U5MR<br>1998 VLSS IMR<br>t 20 l 1988 VLS U5MR<br>z Z110 o<br>0<br>1982 1984 1986 1988 1990 1992 :l994 1996<br><!-- End of picture text -->

## **_Key points_**

The first key point to emerge from this section is that infant and under-five mortality appear to have continued to fall under Doi Moi-there is no sign in the data of any decrease in the rate of reduction. The second key point is that the infant mortality rate would appear to have fallen below the target figure for 2000 of 30%o. Indeed, the evidence suggests this target was probably reached in the mid-1990s, and the figure now may well be around 25%o and may even be below this.

# **3. Changing inequalities in child mortality under Doi Moi**

We turn now to the issue of socioeconomic inequalities in child survival. Previous research by one of the authors (Wagstaff 2000) suggested that by international standards inequalities in infant and under-five mortality between poor and better-off children were extremely low in Vietnam. These results were based, however, on VLSS data covering survival and deaths among children over the period 1S983-92. The question we address in this section is whether under Doi Moi a gap in survival prospects between poor and better-off children has opened up. Have the apparent continued gains in child survival under Doi Moi been spread equally across the population?

7
<mark>outcomes comparable to those of developing economies, Finland now ranks among the top performers in educational achievement and attainment in the world (Sahlberg, 2009, 2011).</mark> <mark>In most educational systems, however, the best and brightest do not choose education majors. Teachers’ academic performance and potential is below than that of their peers, who choose to attend other programs (Giesen and Gold, 1993; Hanushek and Pace, 1995; Podgursky et al., 2004; Denzler and Wolter, 2008). The absence of competitive salaries and benefits, as well as the absence of a meaningful and challenging career path, might discourage talented people from choosing a career in education (Ballou and Podgursky, 1997; Odden and Kelley, 1997; Dolton, 1990, 2006; Dolton and van der Klaauw 1999; Dolton 2006; Mizala and Ñopo, 2014). In Colombia the situation is no different, teachers fare at the bottom of the distribution of standardized test scores (Barón and Bonilla, 2011; Barón et al., 2013). Talented people might get discouraged from choosing a career in education to avoid getting trapped in a profession with low social prestige, bad academic reputation and low salaries (García et al 2014).</mark> There is solid evidence that self-selection plays a role on teachers’ quality. Granted, but, what about the role of higher education? Even if prospective teachers start at disadvantage compared to their peers in other careers, does higher education level the playing field? <mark>To address these questions we analyze</mark> _<mark>relative learning mobility</mark>_ <mark>, understood as the change in score-rankings between two periods, for students who are close to graduating from a university program in education (which we also call throughout the paper</mark> _<mark>teachers</mark>_ <mark>) v</mark> is-à-vis <mark>students who are also about to graduate, but from other university academic programs (which we also call throughout the paper</mark> _<mark>other professionals</mark>_ <mark>). We use data from two standardized tests: Saber 11 and Saber Pro, which provide information on educational outcomes and socio-demographic characteristics for students in Colombia at their senior year of high school and</mark> near the end of their college education.

Our results indicate that after around 5 years of academic training, teachers’ skills vis-à-vis those in other professions relatively deteriorate (or do not improve as much) in quantitative reasoning and 3
|(5)<br>Thailand|-0.698***<br>(0.177)<br>-0.579**|(0.285)|0.589||-0.153<br>(0.256)<br>-1.362***<br>(0.357)|0.636|Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>11,740|ing of each column. The dependent<br>table 4. _∗∗∗, ∗∗,_and _∗_indicate|
|---|---|---|---|---|---|---|---|---|
|Log number of exported products by frm_f_ to country_j_ in year_t_<br>(2)<br>(3)<br>(4)<br>Indonesia<br>Pakistan<br>Philippines|-0.074<br>-0.086<br>-0.044<br>(0.167)<br>(0.140)<br>(0.150)<br>-1.334***<br>-0.918***<br>-0.503**|(0.294)<br>(0.265)<br>(0.255)|0.621<br>0.596<br>0.605|Log export sales by frm_f_ to country_j_ in year_t_|-0.282<br>-0.114<br>-0.062<br>(0.230)<br>(0.198)<br>(0.211)<br>-2.264***<br>-1.851***<br>-1.672***<br>(0.330)<br>(0.308)<br>(0.323)|0.670<br>0.634<br>0.646|Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>9,880<br>12,730<br>11,871|st developed Asian countries: China, India, Indonesia, Pakistan, Philippines and Thailand. The name of the excluded country is presented in the head<br> Fixed effects by frm, country and year and a constant are included in all specifcations. Country level control variables defnitions are reported in|
|(1)<br>India|-0.230<br>(0.145)<br>-0.766***|(0.270)|0.611||-0.318<br>(0.202)<br>-1.855***<br>(0.314)|0.652|Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>11,630|99-2005 for the subsample of lea<br>o country_j_ by frm _f_ in year _t_.|
|Dependent variable|Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_||_R_2|Dependent variable|Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_|_R_2|Foreign competition measures<br>Country level controls<br>Firm fxed effects<br>Country fxed effects<br>Year fxed effects<br>Observations|_Notes_: The regressions are OLS estimations of equation (3) for the period 19<br>variable is the logarithm of the number of exported products (export sales) t<br>signifcance at the 1, 5 and 10 percent levels respectively.|

32
**Figure 1b: Growth of Higher Education Institutions and Enrolment in India**

<!-- Start of picture text --> 20,000 12<br>10<br>16,000<br>Institutions Enrolment 8<br>12,000<br>6<br>8,000<br>4<br>4,000<br>2<br>- 0<br>1947-48 1950-51 1960-61 1970-71 1980-81 1990-91 2000-01 2005-06<br><!-- End of picture text -->

**Figure 1c:  Number and growth of professional education institutions in India between 1999/00 & 2005/06**

<!-- Start of picture text --> Number of institutions in 2005/06<br>Teacher Education 395 5190<br>Pharmacy 208 629<br>Physiotherapy 194 205<br>Engineering 121 1478<br>Hotel management 70 70<br>M BA 55 1052<br>Architecture 51 118<br>M edicine 32 229<br>M CA 25 976<br>0 50 100 150 200 250 300 350 400 450<br><!-- End of picture text --> _<u>Source</u>_ <u>: Agarwal, 2006</u> Notwithstanding the fact that India is endowed with a large and growing base for skilled professionals, there are serious concerns about the uneven quality of its endowment. According to McKinsey (2005), only 25 percent of Indian engineers, 15 percent of its finance and accounting professionals and 10 percent of Indian professionals with general degrees are suitable to work for multinational companies<sup>4</sup> . In fact, faced with shortages of relevant skills, even Indian firms are beginning to recruit abroad.<sup>5</sup> Interviews with Indian professionals working in the US and with human resource managers in Indian companies confirm the heterogeneity in the quality of education and sporadic shortage of professionals with certain skills. There is also broad consensus on the urgent need for reform of higher education in India.<sup>6</sup> 4 This is largely attributed to poor pedagogy, outdated curricula, inadequate interaction between universities and industry, as well as restrictions on the entry of private domestic and foreign education service providers.

5 For example, see Economic Times (June 15, 2006) and Financial Times (June 17, 2006), Christian Science Monitor (May, 2006).

6 See Kaul (2006) and Aggarwal (2006).

5
71 Table 12 International Experience with the VAT (2000 or most recent year available)

|Country|Standard<br>rate (%)|Other<br>rates (%)|Revenue<br>as % GDP|
|---|---|---|---|
|Argentina|21|10.5, 27|6.2|
|Belgium|21|1, 6, 12|6.9|
|Brazil|20.5|10, 12, 36|8.6|
|Chile|18||8.5|
|Czech Republic|22|5|7|
|France|20.6|2.1, 5.5|7.8|
|Germany|16|7|6.9|
|Hungary|25|12|8.1|
|Israel|17|6.5|10.6|
|Italy|19|4, 6, 16|5.4|
|Korea|10|2, 3.5|4.6|
|Mexico|15|10|3.2|
|Netherlands|17.5|6|6.9|
|Norway|23||8.9|
|Peru|18||6.3|
|Poland|22|7, 12|7.9|
|Portugal|17|5, 12|8|
|South Africa|14||5.9|
|Spain|16|4, 7|6.2|
|Sweden|25|6, 12, 21|7.2|
|Turkey|16|1, 8, 25, 40|6.5|
|United Kingdom|17.5|0|6.8|
|Average|18.7||7.0|

Source: Ebrill, Keen, Bodin, and Summers (2001).

For its part, the income tax revenue also suggests widespread evasion. Here, however, the inflation of 2002 may have begun to contribute to indirect reform. Previously the threshold for income tax liability was so high that the great bulk of the population was not subject to the tax. The top rate of 35 percent applied only above 100,000 pesos. Both the minimum threshold and the top bracket will now apply to a larger portion of the population in view of the likely rise in nominal peso incomes following the 2002 inflation (although nominal wages remained stagnant last year). Here again conspicuous enforcement instances would seem salutary to collection.

Evasion is also a major problem in worker and employer contributions. Perhaps more than half of workers are not in the formal system, so their contributions and those of their employers are absent from the payroll revenue. The bulk of the self-employed are in the informal sector, and perhaps over 40 percent of salaried workers are also not in the system. As worker and employer contributions have amounted to about 4.5 percent of GDP (except for the weak 3.1 percent in 2002 with depression and high unemployment), and as aggressive enforcement of contributions would seem capable of raising
Figure 2- Internal Migration Rate by Country <!-- Start of picture text --> Internal Migration (% of Working Age<br>population)<br>70%<br>60%<br>50%<br>40%<br>30%<br>20% 8%<br>10%<br>0%<br>India 2001 Paraguay 2001 Morocco 1998 Honduras 2003 Congo, DR 2005 Armenia 1999 Albania 2005 Vitnam 1992 Colombia 1995 Brazil 2001 Nicaragua 2001 Haiti 2001 Kyrgyz Rep 1997 Croatia 2004 Cambodia 2004 Mongolia 2002 Madagascar 2001 Egypt 2009 Micronesia 2000<br><!-- End of picture text --> Sources: World Development Report 2010; Egypt: Authors’ calculations using Labor Force Survey 2009; India: Bahgat, Ram B. 2009.

Men and women show similar migration rates, with the rate of migration among males (6 percent) slightly lower than that of females (6.2 percent) (see Tables A1 and A2 in the appendix). However, the reasons for migration greatly differ between men women, as described later in the paper.

# **Direction of Migration**

Whether from urban or rural areas, most migrants prefer cities and towns as destinations. Rural migrants have a somewhat higher tendency to choose a rural locality (18.2 percent) compared to urban migrants (13.5 percent). In other words, urban migrants have a higher preference for cities and towns (86.5 percent) than rural migrants (81.7percent; Ssee Table 1).

Table 1 – Direction of Migration – Urban/Rural (classified according to place of origin)

||Current|Location||
|---|---|---|---|
|Previous Location|Urban|Rural|Total|
|Urban|86.5|13.5|100|
|Rural|81.7|18.3|100|
|Total|84.6|15.4|100|

Source: Authors’ calculations using Labor Force Survey 4
Menezes-Filho, N. and M. Muendler (2011). “Labor Reallocation in Response to Trade Reform,” CESifo Working Paper, March.

McFadden, Daniel (1973). “Conditional Logit Analysis of Qualitative Choice Behavior,” in P. Zarembka (ed.) Frontiers in Econometrics, New York, Academic Press.

McFadden, D. (1989). “A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,” Econometrica, 57(5), pp. 995–1026.

Muendler, M. (2010). “Trade Reform, Employment Allocation and Worker Flows,” in G. Porto and B. Hoekman (2010).

Porto, G. and B. Hoekman (2010) (Eds). _Trade Adjustment Costs in Developing Countries: Impacts, Determinants and Policy Responses_ , CEPR and World Bank, Washington DC.

Wacziarg, R. and J. S. Wallack (2004). “Trade Liberalization and Intersectoral Labour Movements,” Journal of International Economics 64, pp. 411-439.

22
# **Figure 2. Debt and Guarantees as Percentage of GSDP**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Debt Guarantees<br>Percent of GSDP<br>1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/01 2001/02 2002/03 2003/04<br><!-- End of picture text --> Source: Government of Tamil Nadu’s Budget documents.

# **Figure 3. Revenue Deficit as Percentage of Fiscal Deficit**

<!-- Start of picture text --> 5000 80%<br>4500 70%<br>4000<br>60%<br>3500<br>3000 50%<br>2500 40%<br>2000 30%<br>1500<br>20%<br>1000<br>500 10%<br>0 0%<br>Capital Outlay & Net Lending Current Deficit Current Deficit as a ratio of Fiscal Deficit<br>1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/01 2001/02 2002/03 2003/04<br><!-- End of picture text --> Source: Government of Tamil Nadu’s Budget documents.

Note: The deficits in 2000/01 and 2001/02 underestimate the actual levels because they do not account for non-pension arrears equaling 1.7 percent of GSDP in 2000/01 and 2 percent of GSDP in 2001/02. The deficit (revenue and fiscal) in 2002/03 and 2003/04 includes partial clearance of payment arrears accumulated from the late 1990s.

4
**Table G7: Relationship between food security measures and weight-for-age z-scores (Pakistan): Expanded Controls Version**

|VARIABLES|(1)|(2)|(3)<br>Weight-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|Per capita kcal|0.002<br>(0.036)||||||
|1-SSR||-0.012|||||
|||(0.044)|||||
|1 - SSEXR|||0.005||||
||||(0.044)||||
|DDS||||-0.054|||
|||||(0.046)|||
|Per capita expenditure|||||-0.004<br>(0.052)||
|Food share||||||0.036<br>(0.042)|
|Child is female|0.236***|0.236***|0.236***|0.232***|0.237***|0.239***|
||(0.081)|(0.081)|(0.081)|(0.081)|(0.081)|(0.081)|
|Age (in months)|0.026**|0.026**|0.026**|0.026**|0.026**|0.026**|
||(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|Age squared|-0.000*|-0.000*|-0.000*|-0.000*|-0.000*|-0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|HH size|-0.005|-0.005|-0.005|-0.004|-0.002|-0.002|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.009)|(0.008)|
|Mother is literate|0.139|0.141|0.139|0.149|0.141|0.139|
||(0.110)|(0.110)|(0.110)|(0.110)|(0.110)|(0.110)|
|Father is literate|-0.209**|-0.208**|-0.209**|-0.208**|-0.204**|-0.197**|
||(0.091)|(0.091)|(0.091)|(0.091)|(0.091)|(0.092)|
|Distance to health|||||||
|service|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Main source of drinking|||||||
|<br>water (tap=1)|0.035|0.037|0.035|0.026|0.031|0.038|
||(0.100)|(0.101)|(0.100)|(0.100)|(0.101)|(0.100)|
|_Sanitation facility (referen_|_ce category:_|_No toilet)_|||||
|Latrine|0.003|0.002|0.003|0.030|0.000|0.000|
||(0.134)|(0.133)|(0.133)|(0.135)|(0.133)|(0.133)|
|Flush toilet|0.060|0.059|0.060|0.067|0.070|0.071|
||(0.124)|(0.124)|(0.124)|(0.124)|(0.124)|(0.124)|

80
from Burkina Faso (2003), Cameroon (2004), Ghana (2003), Kenya (2003), and Tanzania (2003–04), five countries with different HIV/AIDS epidemics (See GSS, NMIMR, and ORC Macro 2005; INS and ORC Macro 2004; INSD and ORC Macro 2004; NBS and ORC Macro 2005; CBS, MOH, and ORC Macro 2004). The five datasets include very similar variables, allowing easy comparisons across countries. The questionnaire content of the DHS is similar to that used by Clark, Bruce, and Dude (2006).

The relation between the HIV infection rate, the dependent variable, and the marital status of women under the age of 24 is examined by dividing the data into three groups of women, those are 15–19, 20–24, and 15–24. The women are further divided into two groups: one that includes all women in an age range, the other that includes only women who self-report being sexually active. Restricting the analysis to sexually active women allows a more natural comparison of the riskiness of sexual activity inside and outside marriage. Looking at all women allows the sexual debut dimension to be integrated. In addition to marital status (ever married versus never married), the control variables included in the regressions are: years of education; dummies for age, urban location, ethnicity (not available in Tanzania), religion, region, and wealth index; and one interaction between ever married and being in a polygamous union.

For 15- to 19-year olds, early marriage seems to be protective of HIV infection in Burkina Faso, among both all women and self-reported sexually active women; ever having been married carries a statistically significant negative coefficient. In the other four countries, the coefficients on ever being married are not statistically significant. For women 20–24, early marriage seems to be protective for women in Burkina Faso and Ghana.

Marriage seems to be associated with a greater risk for HIV infection in women 15–19 in Cameroon, in women 20–24 in Tanzania, and in the pooled group of women 15–24 in both countries. The coefficient on marriage is not significant when the sample is limited to women who self-report as being sexually active.

Overall, except in Cameroon, these results do not support the hypothesis, advanced by Clark (2004), that early marriage increases the HIV risk for women. Getting married at an early age does not seem to put young married women at any greater risk of contracting HIV than women their age who do not get married.

13
productivity (Röller and Waverman, 2001).<sup>10</sup> On the other hand, more people could afford telecommunications services as the income level increases. Similarly, price and quantity are jointly endogenous variables (Trajtenberg, 1989; Berry, 1994; Nevo, 2001; Iimi, 2005). Many African countries have a relatively small number of telephone subscribers and considerably high tariffs together. People cannot afford telecommunications services because of high prices. At the same time, however, the existing high prices may be attributable to the current narrow installed bases. This is a typical demand-and-supply endogeneity problem.

This paper, applying a simple discrete consumer choice model, estimates the demand function for telecommunications services, which are allowed to be differentiated across telephone operators. To examine the reasons for large divergences in Africa’s telecommunications development, the model mainly accounts for network externalities and the termination-based price discrimination effect. Other factors, such as demographics, regulation and market structure, are briefly discussed but not formally taken into account in the model, because these factors primarily affect supply conditions and are reflected in observed market prices. For instance, low population density would likely increase the production costs of telecommunications and thus retail prices. Monopoly and weak regulatory frameworks may also tend to result in higher prices. From the consumer perspective, however, no one makes his telephone subscription decision looking at the regulatory or market structure.

The paper is organized as follows. Section II illustrates recent telecommunications developments in Africa, based on a simple correlation analysis. Section III discusses the price discrimination practices in the region. Section IV discusses the empirical model and several econometric issues. Section V describes data to be used. Section VI then shows the estimated demand equation and the effects of price discrimination and network externalities. The implied price elasticities and price-cost margins are also computed. In

> 10 Röller and Waverman (2001), using a structural and simultaneous approach, estimate the impact of telecommunications infrastructure on economic growth in favor of the causal link from infrastructure to growth. However, their analysis still relies on country-level aggregate data.

7
17 measure public system's asset performance. Virtually all PERS report one datum for 1990 -- that year's total portfolio return (referred to as YIROR), and most also reported their annualized average return over the period 1986-199U (referred to as Y5ROR). Both variables are reported in nominal dollars (the analysis therefore assumes that all plans experienced identical inflation rates). The fact that the investment yield is a-<sup>eraged over the</sup> 5-year span makes it impossible to compute traditional measures of pension performance variability over time, so precise estimates of this concept must await development of panel data. An alternative variability measure used here is the root of the squared deviation beitween YIROR and YSROR as a fraction of Y5ROR, defined here as RETURNSD.

Two dependent variables were developed for the funding analysis. The concept which best captures a plan's stock funding rate in the PENDAT survey expresses pension plan assets as a fraction of the Pension Benefit Obligation, and for<sup>ease of</sup> reference this stock funding measure is termed AST_PBO. An alternative measure focuses on the plan's current funding practices, a concept captured here as FLONVFUND,<sup>or the</sup> ratio of actual to required employer contributions for the year.

Explanatory variables in the analysis are grouped into five main categories: pension Board composition, Board management practices, investment practices, reporting requirements and assumptions, and other factors which reflect regulations at the state<sup>level</sup> governing budget and funding practices. Controls are also included for plan size, type of plan and covered employees, and in some cases portfolio composition is incorporated<sup>on the</sup> grounds that plans with less risky holdings will have lower returns.

Olivia S. Mitchell - Public Pension Governance and Performance
# **Table 1b: The Impact of Local Elections (PILKADA) on District Government Revenue and Fiscal Balance**

# **per capita**

Treatment: T =1 if district held direct election in 2005; T = 0 if district did not have direct election during 2005-07

|_Variable_|_Coeffi-_<br>_cient_|_Revenue from own_<br>_sources_|_Budget surplust_|
|---|---|---|---|
|T*D06||21,675*|575,565**|
|||(11,195)|(257,807)|
|# obs||821|823|
|# districts||174|174|

Notes:

*: 10% level of significance; **:  5% level of significance; ***: 1% level of significance

Standard errors in parentheses estimated based on fixed-effects model with an AR(1) disturbance (Baltagi and Wu, 1999) Districts that held direct elections during 2006 & 2007 are dropped from the sample used in the regressions 32