rafmacalaba/datause-encoder-data huggingface dataset:
the change in these same outcomes for municipalities where the reform was introduced later. This is an estimation strategy that is likely to be applicable in understanding a number of other regulatory reforms, which might be phased in over time.<sup>16</sup> The headline result from both Bruhn (2008) and Kaplan et al. (2007) is that the reform succeeded in increasing registrations. This is where the most simplistic measures of impact would stop. For example, World Bank (2008) reports that following a reduction in the minimum capital requirement, there was an increase in new company registrations of 55 percent in Georgia and 81 percent in Saudi Arabia.17 But to know if a reform worked and why, we want to go beyond did it lead to more businesses, to understand how and why? In the specific example of business registration reform, an important question of interest is whether these new registrations are the results of existing firms registering, or of new firms starting up. Bruhn (2008) finds that the increase in registrations comes from new entry, not from the conversion of existing informal firms.

This result suggests there may be a group of potential self-employed for whom the burden of registering is a barrier to business formation; but once this pool of pent-up demand is exhausted, there may be much less long-term impact. The results here do not support de Soto’s (1989) view that existing informal small business owners are individuals who wish to become formal, but are stymied by high barriers to registration. They are more consistent with the view that the majority of informal businesses are informal by choice, because becoming formal offers no benefit to them. Indeed, McKenzie and Sakho (2009) estimate that for Bolivian small firms, there are huge gains to becoming formal for the subset of informal firms who do not know how to become formal, but that becoming formal would be costly to the remainder of informal firms.

We also want to know what the consequences of these reforms are for the economic outcomes we ultimately care about, such as employment generation, consumer

> 16 Note that the validity of this difference-in-difference estimation strategy relies on an assumption that the municipalities which reform later are a good comparison group for what would have happened to the earlier reform municipalities in the absence of early reform. Bruhn (2008) carries out a number of checks on preexisting trends and municipality characteristics to argue this is the case. This strategy will be less applicable if countries decide to, for example, first introduce the reform in the capital city or business capital, and then roll the reform out to progressively smaller cities.

> 17 Note that these numbers for Georgia and South Africa are not even the true impact on the number of registrations, since they are a simple before-after comparison and do not control for pre-existing trends or concurrent events in the economy.

- 14 -
# **1. INTRODUCTION**

This paper analyzes the affordability and accessibility of tertiary education in four selected Latin American countries (Brazil, Colombia, Mexico and Peru) within an international context.

In the past two decades, countries in Latin America and the Caribbean (LAC) liberalized their economy. Together with technological advances and increased globalization, this policy has increased demand for knowledge and workers with tertiary education (Attanasio and Szekely, 2000; Sanchez-Paramo and Schady, 2003; De Ferranti et al, 2003; Goldberg and Pavcnik, 2005).

LAC governments also have increased investments in education in the last two decades. These investments sought to increase attainment of education and in particular for children of low-income families. As a result, graduation rates of primary and secondary education improved remarkably. This increased the pool of qualified students for tertiary education. Hence, the demand for tertiary education intensified.

As a consequence, the rate of enrollment in tertiary education in LAC has increased annually by 2 percent since 1985 (Holm-Nielsen et al, 2005). In LAC countries, such as Argentina, Chile, Colombia and Mexico, public and non-governmental universities expanded and diversified to absorb some of the increased pool of qualified students from secondary education. However, LAC countries continue to trail high-income countries in terms of enrollment. In 2001, the tertiary education enrollment rate in high-income countries was about 56 percent compared to 23 percent for LAC countries (World Bank, 2002).

What triggers a comparatively low enrollment rate in LAC? The main factors seem to be: (i) affordability and lack of financing of tertiary education, (ii) insufficient and unequal access to secondary education, (iii) the lack of information, and (iv) low expectations of attending tertiary education among youth from low-income families. Based on surveys of families in Colombia and Mexico, affordability and lack of financing seem to be the main obstacles for families (ICETEX, 2006; Suzuki, Blom, and Yammal,2006; UNESCO, 2006; World Bank, 2005).

Surprisingly, we hardly know how affordable tertiary education is in Latin America compared within a regional and global context. We know that Latin American countries differ in their policies on tertiary education finance. Some have tuition in public institutions, others not. Some promote non-governmental tertiary education with full tuition, others frown of non-governmental education. Some run large student assistance programs, others administer smaller programs. However, we do not know how these differences affect affordability and access for the students. Often, discussion of financing of tertiary education is limited to an ideologically driven debate for or against tuition in public universities. Nevertheless, financing of tertiary education is more than a discussion of the tuition level of public tertiary education. One needs to analyze a comprehensive set of factors when analyzing tertiary education finance.

2
# Table E2: Impacts on Secondary Outcomes

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Panel A: Firm Survival**|||||
||Dec 2019|Dec 2020|Dec 2020||
|Assigned to Treatment|0.032|0.053|0.051*||
||(0.026)|(0.034)|(0.030)||
|Control Mean|.96|.93|.93||
|Sample Size|195|195|200||
|Sample|PILA|PILA|PILA+cross-check||
|**Panel B: Formal Employment**|||||
||2018:5-2019:12|2018:5-2020:12|2018:5-2020:12||
|Assigned to Treatment|0.487|0.440|0.211||
||(1.627)|(2.003)|(2.037)||
|Control Mean|81|79|85||
|Sample Size|4000|6400|6067||
|Sample|PILA|PILA|PILA (survivors only)||
|**Panel C: Firm Sales**|||||
||2019 Level|2019 I.H.S.|2020 Levels|2020 I.H.S.|
|Assigned to Treatment|-307|0.467|-1817**|0.319|
||(615)|(0.493)|(767)|(0.392)|
|Control Mean|11138|22.2|14346|22.5|
|Sample Size|173|173|198|198|
|**Panel D: Firm Profits**|||||
||2019 Level|2019 I.H.S.|2020 Levels|2020 I.H.S.|
|Assigned to Treatment|-246|-0.335|-338|-0.875|
||(207)|(1.311)|(207)|(1.357)|
|Control Mean|1020|16.64|1118|17.06|
|Sample Size|200|200|200|200|
|**Panel E: Labor Productivity (Sales pe**|**r Worker)**||||
||2019 Level|2019 I.H.S.|2020 Levels|2020 I.H.S.|
|Assigned to Treatment|-2.7|0.376|-39.3|-0.135|
||(22.2)|(0.263)|(31.1)|(0.100)|
|Control Mean|170|19.04|223|19.52|
|Sample Size<br>Notes:<br>Notes: Robust standard errors in par<br>Panel B uses monthly data and cluste<br>Panels A and B use data on formal em<br>from phone calls and the RUES.<br>Panels C through E use data from the|162<br>entheses. *, **, *** den<br>rs standard errors at th<br>ployment from the PIL<br>RUES. I.H.S. denotes in|162<br>ote significance at t<br>e firm level.<br>A, supplemented in<br>verse hyperbolic sin|190<br>he 10, 5, and 1 percent<br>column 3 of panel A by<br>e transformation.|190<br>levels.<br>cross-checks|

61
-30 -

the occurrence of minor restictions in activity (MRAD). These involve restrictions in activity that do not result in either work loss or bed disability Individual-level health data for 14,000 adults living throughout the United States were combined with data on air pollution and meteorology. The health outcome was based on a two-week recall period. The general regression model included socioeconomic and demographic factors, chronic healdt status, and city-wide variables. A statistically significant association was found between the number of MRAD in a two-week period and ozone concentrtions during a similar period.

For the most general results, using a poisson model, the coefficient on ozone represents the percent change in MRAD per person per two weeks fir a one part per million (ppm) change in ozone. The estimated beta coefficient was 6.883, wiEh a standard error of 3.4 (p **C** .05) Therefore, the centmal estimate becomes: % change in MRAD per two week period = 6.883 * change in two week average of 1-hour daily maximum ozone Since the mean MRAD per two week period is roughly 0.19, the above equation becomes: $ change in MRAD per year = (6.883)(.19)(26) change in annual average of I-hour naximum ozone (ppm) This implies that reducing the average daily maximun ozone concentration by 0.01 ppm for one year would reduce the number of MRAD per person by 0-34. The high and low ranges are developed by using plus and minus one standard error of the estimate. The annual increase in the number of cases per person for a change in the annual average of the 1-hour daily maximum of ozone (ppm) is:
The ideal index of financial reform would attempt to measure both the de-regulatory and the institution building aspects of the process. Unfortunately, short of using outcome measures such as the development of markets as a proxy - an approach leading **in** the present context to unacceptable endogeneity difficulties -- it seems impossible at present to find useful measures of institutional development. For these reasons we have chosen to build our index of reform from explicit policy changes which, though not wholly independent of wider economic conditions, should be less subject to endogeneity problems.

Our index thus summarizes exogenous changes in interest rate regulation, reserve requirements, directed credit, bank ownership (moves toward privatization), liberalization of securities markets, prudential regulation, and intemational financial liberalization. Based on an analysis of the historical evolution in each case we have identified the timing of major moves towards, and sometimes away from, a more liberalized system under each of these headings (Appendix 1 gives the details). This yields a matrix of zero-one variables for each country. Rather than attempting to use all of these variables in the econometrics -- that would use up too many of the available degrees of freedom -- we have constructed for each country the principal components of the matrix. We use the first principal component as our main liberalization index in the regressions of Section IV below. (As an alternative, we also experiment below with a weighted average of the more important principal components, using as weights the fraction of the total standard deviation explained by each component.) In all cases, a higher value of the index in a given year captures an overall more market oriented regime." The resulting index is shown in Figures 1-8, with data on financial depth (M2 or total credit to the private sector as a percentage of Gross National Disposable Income, GNDI), real interest rates, and the private saving rate (measured as a share of GNDI). We have used a definition of the private saving rate, both unadjusted and adjusted for capital losses due to inflation on domestic assets denominated in local currency. For example in Figure la, the index (both versions) captures the partial reversal of reforms in Chile resulting from the twin bankdng and debt crises of 1982.12 See also Demetriades and Luintel (1997) for an application to India of the principal components approach to aggregating the information contained in a combination of policy changes and outcome variables.

12 The Chilean reforms had begun in 1974 with the freeing of interest rates and the beginning of the easing of reserve requirements, and continued in the mid- and late-1970s with bank privatization 11
-4 -

Table 3. Growth in Pakistan's Per CaDim ConsuTmtion - An International Comparison

||1970-80|1980-91<br>m|
|---|---|---|
|Pakistan|__1.1|1.6|
|Idia|0.6|3.2|
|Bangladesh|_0_4<br>._.1.4|.|
|Nepal at|3.2|2.2|
|Sri Lanka|||
|South Asia|0.6|2.9|
|East Asia and Pacific|3.9|4.9|
|Latin America and the Caribbean|3.1<br>-|0.6|
|Middle East and North Africa|-<br>_||
|Sub-Saharan Africa|2.3|-2.4|
|World|1.6|1.3|

a/ Estimated from World Tables 1993.

Source: WDRP 1993.

Although some progress was made in raising life expectancy, reducing infant mortality, and increasing adult literacy, Pakistan's performance stll compares poorly widt other developing countries (see Table 4). For example, the infant mortality rate remains above the average for all other regions of the developing world except Africa. Pakistan's adult illiteracy rate is amongst the highest in the world, exceeding the average in all other regions. Perhaps the most disappointing situation is regarding schooling achievement. Pakistan's primary school enrollment ratio is substantially below the average for any other region in the world. Moreover, there is substantal discrimination against female education.

# Macro-balances

6. Pakistn faced significant inflationary pressures in thie 1970s, but succeeded in reducing these pressures in the 1980s. Indeed, its average inflation rate during the 1980's was amongst the lowest in the developing countries, only bettered by the East Asia and Pacific Region (see Table 5). Pakistan, however, ran substantial fiscal deficits while the current account balance of payments deficits have also been sizeable. As explained later, while initially these deficits did not pose a financial problem because of negative international and domestic real interest rates, more recently in the 1980 the effects of these fiscal and current account balance of payments deficits have shown up in terms of a growing debt burden. External debt as a share of GDP, for example, grew from 37% in 1981 to 50% in 1992. Domestic debt (excluding transactions with the Central Bank) expanded from 13% in 1981 to 31% in 1992.
# **Abstract**

There is a growing belief in development policy circles that participation by local communities in basic service delivery can promote development outcomes. A central plank of public policy for improving primary education services in India is the participation of Village Education Committees (VECs), consisting of village government leaders, parents, and teachers. This paper reports findings from a survey in the state of Uttar Pradesh, of public schools, households, and VEC members, on the status of education services and the extent of community participation in the public delivery of education services. We find that parents do not know that a VEC exists, sometimes even when they are supposed to be members of it; VEC members are unaware of even key roles they are empowered to play in education services; public participation in improving education is negligible, and correspondingly, people’s ranking of education on a list of village priorities is low. Large numbers of children in the villages have not acquired basic competency in reading, writing, and arithmetic. Yet, parents, teachers, and VEC members seem not to be fully aware of the scale of the problem, and seem not to have given much thought to the role of public agencies in improving outcomes. That is, learning failures coexist with public apathy to improving it through public action. Can local participation be sparked through grassroots campaigns that inform communities about the VEC and its role in local service delivery? Can such local participation actually impact learning outcomes, and can any impact be sustained? We describe information and advocacy campaigns that have been experimentally implemented to address some of the problems with local participation, and future research plans to evaluate their impact.

2
# **Table A.4: Relation between expenditure growth, poverty, and inequality, , with additional control variables**

|||Dependent va<br>|riable is the growth of per|capita expenditure (Log|Yt – Log Yt-1)<br>||
|---|---|---|---|---|---|---|
|Explanatory variables||FE|||GMM||
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged log of per capita expenditure|-0.195***|-0.819***|-0.863***|-0.149***|-0.187***|-0.816***|
||(0.033)|(0.058)|(0.065)|(0.044)|(0.049)|(0.110)|
|Lagged Gini index|-0.631*|0.100|0.115|-0.581***|-0.853***|-0.233|
||(0.362)|(0.146)|(0.160)|(0.203)|(0.161)|(0.305)|
|Lagged poverty rate|-0.170**|-0.070|-0.073|-0.055|0.055|-0.203|
||(0.073)|(0.068)|(0.077)|(0.102)|(0.100)|(0.127)|
|Lagged log of other State spending|||0.002*|||-0.001|
||||(0.001)|||(0.002)|
|Lagged log of investment spending|||0.001|||-0.012|
||||(0.005)|||(0.012)|
|Lagged log of population density|||-0.064|||0.058***|
||||(0.072)|||(0.021)|
|Lagged share of urban population|||-0.001|||0.005***|
||||(0.001)|||(0.001)|
|Lagged share of population with high-school diploma|||0.096|||-0.388|
||||(0.291)|||(0.386)|
|Lagged share of ethnic minority population|||0.001***|||0.001|
||||(0.000)|||(0.000)|
|Lagged share of wage income|||0.244**|||-0.050|
||||(0.096)|||(0.192)|
|Lagged share of non-farm income|||0.156|||-0.237|
||||(0.106)|||(0.193)|
|Share of village with year-round passable road|||-0.052|||0.680***|
||||(0.038)|||(0.215)|
|Share of village with daily market|||0.036|||0.180|
||||(0.032)|||(0.194)|
|Distance from village to the nearest town|||-0.015|||-0.094|

41
# **Table A3.2: Transition matrices excluding East European countries**

||[0-0.15)<br>**_Panel I:_**|[0.15-0.3) <br>**_Total numbe_**|[0.3-0.45) <br>**_r of transit_**|[0.45-0.6)<br>**_ions_**|≥0.6||[0-0.15)<br>**_Panel I_**|[0.15-0.3) <br>**_II: Mean firs_**|[0.3-0.45) <br>**_t passage ti_**|[0.45-0.6)<br>**_me_**|≥0.6|
|---|---|---|---|---|---|---|---|---|---|---|---|
|[0-0.15)|351|17|0|0|0|[0-0.15)|44.6|189.3|66.1|71.8|66.7|
|[0.15-0.3)|20|84|19|1|0|[0.15-0.3)|944.9|59.9|44.5|50.1|45.1|
|[0.3-0.45)|0|6|17|14|2|[0.3-0.45)|1866.9|167.6|148.7|23.4|17.8|
|[0.45-0.6)|0|0|3|15|11|[0.45-0.6)|2199.3|332.3|332.3|36.8|5.9|
|≥0.6|0|0|0|1|87|≥0.6|2287.3|420.3|420.3|88.0|1.1|
||**_Pan_**|**_el II: Transit_**|**_ion matrix_**|||||||||
|p(i,j)|[0-0.15)|[0.15-0.3)|[0.3-0.45)|[0.45-0.6)|≥0.6|**_Ergodic distrib_**|**_ution for t_**|**_ransition m_**|**_atrix_**|||
|[0-0.15)|0.95|0.05|0.00|0.00|0.00||0.02|0.01|0.01|0.03|0.94|
|[0.15-0.3)|0.16|0.68|0.15|0.01|0.00|**_Eigenvalues fo_**|**_r transitio_**|**_n matrix_**||||
|[0.3-0.45)|0.00|0.15|0.44|0.36|0.05||0.24|0.58|0.76|0.98|1.00|
|[0.45-0.6)|0.00|0.00|0.10|0.52|0.38|**_Half-life for tr_**|**_ansition ma_**|**_trix_**||||
|≥0.6|0.00|0.00|0.00|0.01|0.99||39.3|||||
||**_Panel I:_**|**_Total numbe_**|**_r of transit_**|**_ions_**|||**_Panel I_**|**_II: Mean firs_**|**_t passage ti_**|**_me_**||
||[0-1/16)|[1/16-1/8)|[1/8-1/4)|[1/4-1/2)|≥1/2||[0-1/16)|[1/16-1/8)|[1/8-1/4)|[1/4-1/2)|≥1/2|
|[0-1/16)|177|14|0|0|0|[0-1/16)|8.5|43.0|43.0|77.1|109.6|
|[1/16-1/8)|31|84|19|0|0|[1/16-1/8)|102.0|15.1|29.3|63.4|96.0|
|[1/8-1/4)|0|21|92|22|0|[1/8-1/4)|261.4|29.4|29.4|34.1|66.7|
|[1/4-1/2)|0|0|10|59|13|[1/4-1/2)|407.4|146.0|146.0|22.0|32.6|
|≥1/2|0|0|0|1|105|≥1/2|513.4|252.0|252.0|106.0|1.3|
||**_Pan_**|**_el II: Transit_**|**_ion matrix_**|||||||||
|p(i,j)|[0-1/16)|[1/16-1/8)|[1/8-1/4)|[1/4-1/2)|≥1/2|**_Ergodic distrib_**|**_ution for t_**|**_ransition m_**|**_atrix_**|||
|[0-1/16)|0.93|0.07|0.00|0.00|0.00||0.12|0.04|0.03|0.05|0.77|
|[1/16-1/8)|0.23|0.63|0.14|0.00|0.00|**_Eigenvalues fo_**|**_r transitio_**|**_n matrix_**||||
|[1/8-1/4)|0.00|0.16|0.68|0.16|0.00||0.45|0.65|0.86|0.99|1.00|
|[1/4-1/2)|0.00|0.00|0.12|0.72|0.16|**_Half-life for tr_**|**_ansition ma_**|**_trix_**||||
|≥1/2|0.00|0.00|0.00|0.01|0.99||53.4|||||

32
where violence increased (471 totals) represent 24.9% of our sample while counties with stable violence levels (945 totals) represent 51% of our sample. The fitted values correspond to the estimated time trend for the 2005-2007 period (using an extrapolation of a linear trend). We can see that while the enrollment for the counties less affected by the increase in violence stayed at levels close to the previous trend, for those counties most affected by the upsurge in violence, the number of enrolled students seems to divert, though only slightly, from the previous trend. Figure 4 Panel B below aims to make this clearer by showing the residuals between actual enrollment and the enrollment as predicted by the 2000-2007 trend. These graphs show the residuals for counties with a low change in homicide rates, oscillating around zero for the whole period, while the residuals for counties with a high violence increase descending after 2007. After 2007, in all but one 2008 quarter, the actual number of upper secondary students remains below the trend.

# **Figure 4: Total Enrollment in Upper Secondary School in Municipalities with Low and High Changes in Violence**

**Panel A Trends for Upper Secondary School Enrollment (index 2000=100)**

<!-- Start of picture text --> Non-Violent Counties Violent Counties<br>2000 2005 20102000 2005 2010<br>Year<br>Students Upper Sec. (index) Fitted values<br>120<br>100<br>80<br>60<br><!-- End of picture text --> Source: INEGI (2014).

Note: Municipalities were divided into “high violence increase” and “low change in violence” according to their homicide rate trend percentile from 2007 to 2011. Municipalities classified as violent (471 total) represent 24.9% of our sample while non-violent municipalities (945 totals) represent 51% of our sample.

17
(Volume 2). Oxford: Elsevier.

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17