rafmacalaba/datause-encoder-data huggingface dataset:
to reflect administrative targets rather than actual enrollment levels. For this reason, we have created two additional instruments from the ENCV data. The first attempts to replicate the level of program rollout at the municipal level. It consists of a weighted average at the municipal level of all age- and Sisben-eligible individuals who actually receive the benefit in each of the four years. This provides us with an estimate of the effective year-specific level of rollout in municipalities. A second instrument can be constructed in similar fashion at a lower level of aggregation. In addition to the location in specific municipalities, we also observe whether a household resides in a municipality seat ( _cabecera_ ), some other urban setting (defined as anything that resembles a town), or in a dispersed rural environment. We then construct the same weighted instrument for each of these subareas across all four years. More formally, our instruments are as follows: where the _g_ subscript represents the specific geographical area, and _D_ is a specific dummy variable indicating program participation. The two populations denoted by _N_ include all age- and Sisbeneligible individuals within the respective subgroups.

Neither of the two instruments is perfect on its own, and there are differences between the two. The first is municipality and year specific. Given that we control for municipality and year fixed effects, we are concerned with potential endogeneity because of unobserved time variant factors in the municipalities. In terms of the empirical model presented above, the instrument may be correlated with an additional error term, say,  _tm_ . The second instrument is constant across all four years (if it were also year specific it would be highly collinear with the first instrument), but specific to an area at the submunicipal level. Any endogeneity would thus derive from unobserved time invariant characteristics that are idiosyncratic to the specific geographical group within a municipality once we have controlled for time invariant municipal characteristics. We can think of this as an additional error term,  _mg_ . The advantage of having two instruments available for a single potentially endogenous variable lies in the possibility of testing for validity through an overidentification restrictions (OIR) test, which gives us additional confidence in our strategy.

22
Table 4. The <u>effects of COVID-19 exposure on educational outcomes by year</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_A. Dropout_|||||
|Warning index x 2020|0.009|0.009|0.009|0.009|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|0.005|0.005|0.005|0.005|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2020=2021)|0.006|0.005|0.007|0.009|
|Mean outcome|0.086|0.086|0.086|0.086|
|Adjusted R<sup>2</sup>|0.007|0.079|0.090|0.128|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|_B. Effective promotion_|||||
|Warning index x 2020|-0.029|-0.028|-0.027|-0.027|
||(0.003)***|(0.003)***|(0.003)***|(0.003)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|-0.006|-0.006|-0.007|-0.006|
||(0.003)**|(0.003)**|(0.003)**|(0.003)**|
||[0.035]|[0.054]|[0.027]|[0.035]|
|Pr(2020=2021)|0.000|0.000|0.000|0.000|
|Mean outcome|0.802|0.802|0.802|0.802|
|Adjusted R<sup>2</sup>|0.022|0.069|0.095|0.119|
|Observations|13,337,281|13,301,355|13,337,055|13,301,145|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

(Continues on next page) 20
Policy Research Working Paper 10418

# **Abstract**

From 1965 to 1985, the number of schools doubled in developing countries, but little is known about their impacts on intergenerational educational mobility. This paper studies the effects of 61,000 public primary schools constructed in the 1970s in Indonesia on intergenerational educational mobility, using full-count census data and a difference-in-differences design. The educational mobility curve is concave in most cases, and school expansion reduced the degree of concavity. Evidence on primary completion suggests contrasting effects across the distribution: relative mobility improved irrespective of gender in the uneducated households, but it worsened in the highly educated households. For completed years of schooling, there are striking gender differences, with strong effects on sons, but no significant effects on girls. This surprising finding reflects an unintended bottleneck at the secondary schooling level which created fierce competition among the Inpres primary graduates. The girls suffered an 8.5 percentage points decline in the probability of completing senior secondary schooling, while the boys reaped a 7.7 percentage points gain. The gender-based crowding out occurred across the board, suggesting mechanisms unrelated to family background such as low labor market returns for girls and gender norms in a patrilineal society. Available evidence on returns to education of girls rejects a labor market-based explanation. The authors test and find evidence consistent with gender norms as a mechanism by exploiting data from the “Matrilineal island” West Sumatra. In West Sumatra, girls are not crowded out at the secondary level; instead, boys face significant crowding out.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fshilpi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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The magnitude of the observed associations could be a function of selection into highpaying occupations that require advanced computer use. It could also be capturing some of the education effect, since education and computer use are highly correlated. However, we find that even after controlling for schooling, the magnitude of these associations, although slightly smaller, is still substantial (see Table 6).

Another explanation for the large magnitude of these effects could be the relatively smaller share of jobs requiring complex computer use across these countries, making these jobs highly remunerated--even after controlling for type of employment and occupation. Unfortunately, the data used in this study do not allow for examining whether this hypothesis is supported. Additional research is needed to disentangle the effects of the use of computer skills as well as the complexity of computer skills.

**Table 6. Estimated effect of computer complexity at work on hourly earnings, workers ages 25 to 64**

|Panel A||||||
|---|---|---|---|---|---|
||Browser-|||||
||based tasks|MS Office tasks|Basic program<br>tasks|Advanced<br>program tasks|N|
|Armenia|0.116|0.242***|0.400***|0.388***|1,557|
||(0.12)|(0.06)|(0.10)|(0.10)||
|Bolivia|0.030|0.240|0.121|0.403***|847|
||(0.14)|(0.15)|(0.14)|(0.15)||
|Colombia|0.053|0.357***|0.5693***|0.603***|1,190|
||(0.07)|(0.10)|(0.10)|(0.11)||
|Georgia|0.307*|0.562***|0.5781***|0.653***|1,464|
||(0.16)|(0.10)|(0.11)|(0.14)||
|Ghana|0.878**|0.361|1.0244***|0.831***|1,174|
||(0.37)|(0.27)|(0.21)|(0.26)||
|Kenya|0.853***|0.881***|0.9474***|1.072***|1,454|
||(0.15)|(0.12)|(0.12)|(0.13)||
|Ukraine|0.160|0.255***|0.3344***|0.243*|1,370|
||(0.10)|(0.09)|(0.08)|(0.14)||
|Vietnam|0.451***|0.404***|0.555***|0.472***|1,948|
||(0.16)|(0.08)|(0.09)|(0.09)||

24
**Table 5 Costa Rica: Interest Payments, ratios to GDP** percentages, domestic and foreign debt

|Year|Int/GDP|
|---|---|
|1993|3.1|
|1994|4.1|
|1995|5.5|
|1996|6.4|

Source: IMF

**Table 6 Costa Rica: Financing Operational Deficits**

Year Operational Deficits %FFinancing %DDFinancing

|1991|9.0|16|47|
|---|---|---|---|
|1994|63.1|10|70|
|1995|33.8|-22|146|

Source: IMF Operational deficit is in Bn Colones; 1992 and 1993 experienced operational surpluses Table 6 shows that Costa Rica has been financing a large share of its operational deficit with domestic debt. Particularly crucial was 1995 in which a share of the increment in domestic debt was used to reduce foreign borrowing.

Applying some experimental values to equation (9), **Costa Rica** shows a sustainable debt of 59 percent of GDP<sup>22</sup> .

The following chart depicts the ratio of total debt to GDP and the ratio of Domestic Debt to GDP.

> 2 2 gP=.20; gy=.03; k.16; r=.15

25
**Figure 4:** Exchange rate and price developments, 2001-2008

<!-- Start of picture text --> 120 30<br>CPI (12 month percent change)<br>(right scale)<br>110 25<br>REER<br>(left scale)<br>100 20<br>90 15<br>80 10<br>70 5<br>60 0<br>50 -5<br>NEER<br>(left scale)<br>40 -10<br>Source : Authors’ calculation based on IMF data.<br>Index<br>percent<br>Jan... Ma... Sep... Jan... Ma... Sep... Jan... Ma... Sep... Jan... Ma... Sep... Jan... Ma... Sep... Jan... Ma... Sep... Jan... Ma... Sep... Jan... Ma... Sep...<br><!-- End of picture text --> In addition, goods exports, dominated by coffee and tea, do not seem to have suffered, likely due to their dependence on non-price factors (such as infrastructure and supply constraints) rather than prices and exchange rates.<sup>11</sup> In fact, Burundi’s export performance improved from an average of 6.1 percent of GDP during 2000-2004 to 6.4 percent of GDP during 2005-2008, indicating that the appreciation of the real effective exchange rate has not been detrimental to exports.<sup>12</sup> Dutch disease does not seem to be a significant problem in Burundi so far. This is confirmed by the IMF’s assessment of Burundi’s exchange rate, which concluded that the real exchange rate has been broadly in line with economic fundamentals (IMF, 2008).

# **4. Allocation of increased aid and impact on growth**

Burundi has not experienced the expected post-conflict growth spike seen in other post-conflict countries. This is despite the high levels of aid Burundi has been receiving, most of which has been spent, as established in the previous section. Real GDP growth averaged only 3.0 percent between 2001 and 2008 and real GDP per capita declined on average by 0.3 percent. This section analyses the allocation of spending and tries to determine the linkages between aid, spending in different sectors, and economic growth. Of course, aid is only one of many factors affecting growth as shown by an extensive literature on determinants and factors of growth. We also examine how the composition of the domestically financed budget has contributed to Burundi’s growth performance as well as what the impact the predictability of aid was.

# 4.1. <u>The effect of aid on growth – theoretical background</u>

The effect of aid on growth has been widely discussed in the literature without a consensus. A large literature on aid and growth has typically examined the impact of aggregate aid on growth,

> 11 See also Foster and Killick (2006).

> 12 The share of other, non-traditional exports remained fairly constant.

12
# **III.Data used for the analysis**

<u>Mixed-methods approach</u> The multi-component design of this project—coupled with the diversity in methods of implementation, beneficiary types, timing, and geography—provides for a complex evaluation context; this is compounded by limited program data collected and the non-random assignment of the intervention. The mixed-methods approach seeks to lessen the limitations from one data source alone; qualitative information can shed light on how the work was implemented and help contextualize the information into the social and cultural systems of the coastal region, and elucidate the realities of program participants (Ravallion 2003, Rao and Woolcock 2004) that may have influenced the impact (or lack thereof) of the infrastructure rehabilitation component.

# <u>Data</u>

The survey data available are very rich in terms of coverage and themes covered, but they was not designed specifically to evaluate PSI, thereby restricting the analysis to measure impacts on treated localities rather than treated households. In other words, while some households may have been in areas that benefited from rehabilitated infrastructure, they may not have used water from this infrastructure. This issue is reduced by limiting the analysis to only farm households. Other limitations from the data can be attenuated by using qualitative information collected specifically to evaluate PSI, particularly to assess non-quantifiable aspects of the program that may have influenced the impacts.

7
<!-- Start of picture text --> 2001<br>2%<br>S><br>11%<br>mw Com Jet<br>m Widebody<br>mw Eastern - Undetermined<br><!-- End of picture text --> <!-- Start of picture text --> 2007<br>0%<br>5% \ 8% 0%<br>)<br>m Com Jet<br>mw Widebody<br>mw Eastern - Undetermined<br><!-- End of picture text -->
**Table 3. Decomposition of Employment Ratio between Changes due to Employment Rate and Labor Force Participation**

||(1)|Precrisis (2006|-2008)|(|2) Post (2008-|2009)||(3) Differen|ce|
|---|---|---|---|---|---|---|---|---|---|
||employment|changes<br>|due to|employment|change<br>|s due to|employment|change<br>|s due to|
|Group|ratio change|employment<br>rate|Participation|ratio change|employment<br>rate|Participation|ratio change|employment<br>rate|Participation|
|Men|0.67|0.38|0.29|-1.87|-1.62|-0.25|-2.54|-2.00|-0.54|
||[100%]|[56.8%]|[43.2%]|[100%]|[86.4%]|[13.6%]|[100%]|[78.6%]|[21.4%]|
|Women|1.02|0.45|0.56|-0.36|-0.73|0.36|-1.38|-1.18|-0.20|
||[100%]|[44.1%]|[54.9%]|[100%]|[202.8%]|[-100.0%]|[100%]|[85.5%]|[14.5%]|
|Young|0.72|0.50|0.21|-1.91|-1.46|-0.45|-2.62|-1.96|-0.66|
||[100%]|[69.9%]|[30.1%]|[100% ]|[76.5%]|[23.5%]|[100%]|[74.7%]|[25.3%]|
|Old|0.78|0.34|0.45|-0.96|-1.13|0.17|-1.74|-1.47|-0.27|
||[100%]|[43.0%]|[57.0%]|[100%]|[118.1%]|[-18.1%]|[100%]|[84.4%]|[15.6%]|
|Low Edu|0.39|0.24|0.15|-1.13|-1.07|-0.06|-1.52|-1.31|-0.21|
||[100%]|[60.5%]|[39.5%]|[100%]|[95.1%]|[4.9%]|[100%]|[86.2%]|[13.8%]|
|High Edu|0.82|0.52|0.30|-1.28|-1.22|-0.06|-2.10|-1.74|-0.36|
||[100%]|[63.5%]|[36.5%]|[100%]|[95.4%]|[4.6%]|[100%]|[82.9%]|[17.1%]|

Note: Numbers are for average over all 17 countries. Each proportion of change explained by employment rate and labor force participation is presented in the brackets.

28
8 begins by computing the minimum cost of food baskets required to attain the FAO-recommended caloric requirements. Because of substantial differences across the country's regions - and within these regions, from metropolitan to other urban areas and then to rural areas - in both consumption patterns and prices, a food basket was calculated for each area specifically.<sup>13</sup> The food costs for each area therefore respect not only price differences, but also differences in tastes and local food availability.<sup>14</sup> Rather than using the inverse of an Engel coefficient to obtain the poverty line, Rocha estimated non-food expenditure amongst the poor directly for each separate metropolitan area<sup>15</sup> . The sum of non-food expenditures amongst the poor and the cost of the food basket gives the set of regional poverty lines. The values of the region-specific poverty lines, in 2004 Reais, for the relevant PNAD regions are reported in Appendix 2, which is converted from table XIII in Rocha (1993).

Recently, however, an ad-hoc poverty line set at R$100 per capita per month (in 2004 values) has gained currency, largely because it corresponds to the means-test in Brazil’s main new cash assistance program, _Bolsa Família_ . Its increased usage in the press and in policy discussions is analogous to the use of “administrative” or “policy-based poverty lines”, derived from benefit means-test income levels, in European countries. In what follows, we present the poverty series for both of these lines.

Table 2 reports the three standard FGT poverty measures: the headcount index; the normalized poverty deficit; and the FGT(2) measure.<sup>16</sup> The corresponding time series are plotted in Figure 2. Over the period as a whole, all three poverty measures fell for both lines, although the declines were quantitatively modest for such a long period. The proportional decline in poverty incidence (according to the Administrative Poverty Line) from 0.296 to 0.222 is of exactly 25%. This contrasts, for instance, with a poverty reduction of 62% (from 0.418 in 1975 to 0.157 in 1992) in

> 13 In fact, this was done for the nine metropolitan areas (Belém, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, São Paulo, Curitiba and Porto Alegre), as well as Brasília and Goiânia, using the 1987 expenditure survey - Pesquisa de Orçamentos Familiares (POF). For the other urban and rural areas, conversion factors were borrowed from an earlier work by Fava (1984), which was based on the most recent available data for these areas, namely the 1975 Estudo Nacional da Despesa Familiar (ENDEF). These were updated to 1990 prices using the INPC price index.

> 14 For an alternative approach to dealing with regional differences in the cost of living, using a regional price index defined for a fixed basket, see Ferreira et. al. (2003).

> 15 'The poor' amongst whom she computes non-food expenditures are those who, according to information recorded in the POF, were unable to meet _minimum_ caloric requirements as specified by FAO.
to 2007 and abolished before 2017; 2,367 (38.1%) regulations were implemented before 2007 and we still in place in 2017; 84 (1.4%) regulations were introduced and then abolished between 2007 and 2017; and 3,743 (60.3%) regulations were introduced between 2007 and 2017 and remained active in 2017. Table 6 reports the corresponding figures for each LAC member. Ecuador and Panama stand out in the region as they introduced more than 83% of regulations between 2007 and 2017, far above the region’s average. In sum, this means that the majority of applicable regulations in 2017 were implemented after 2007. This provides us with sufficient variation to estimate the impact of NTMs on Colombian exports using an identification strategy based on the introduction and withdrawal of NTMs.

<u>Table 6: NTM turnover</u>

||Entry before<br>|2007<br>|Entry after<br>|2007<br>|
|---|---|---|---|---|
||Abolished before 2017|Active in 2017|Abolished before 2017|Active in 2017|
|ARG|1.3|65.5|3.4|29.8|
|BOL|0.0|74.4|0.0|25.6|
|BRA|0.3|39.2|3.0|57.6|
|CHL|0.2|52.9|0.6|46.2|
|CRI|0.0|27.3|0.3|72.4|
|CUB|0.0|66.7|0.0|33.3|
|ECU|0.1|10.9|2.0|87.0|
|GTM|0.0|62.9|0.0|37.1|
|HND|0.0|49.3|0.0|50.7|
|MEX|1.7|40.5|2.7|55.1|
|NIC|0.0|39.9|0.0|60.1|
|PAN|0.0|15.1|1.0|83.9|
|PER|0.0|38.6|0.0|61.4|
|PRY|0.0|56.3|0.0|43.7|
|SLV|0.0|61.6|0.0|38.4|
|URY|0.0|43.2|0.3|56.5|
|VEN|0.0|59.7|1.1|39.2|
|Total|0.3|38.1|1.4|60.3|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: This table reports the turnover of NTMs for each of the 17 LAC countries. The row “Total” reports the average of the total NTM turnover in all 17 LAC countries.

Next, we study the margin with the greatest importance: regulations introduced between 2007 and 2017 and active in 2017. There the composition of NTMs is heavily skewed toward technical measures: SPS and TBTs make up 40% and 52%, respectively, of all new regulations introduced be13
-30-

<!-- Start of picture text --> GHANA<br>0.5 1.4<br>12<br>40.4-<br>-1<br>0.3- -0.8<br>0.2 -0.6<br>0.4<br>0.1-<br>0.2<br>0 1 I a -A II " 0<br>71 727374 75767778798081 82 8 84 85868<br>- intl seig . r_v2<br><!-- End of picture text --> FIGURE 3a
# **Table 11. The Effect of Titling Firms’ Ownership on preferential treatments**

The sample consists of all firms in the CSMAR database that has Allocated land before the land titling program starting from 2012. This table presents parameter estimates from OLS regression of title firms’ ownership on the preferential treatment received by the firm. In Column (1), the dependent variable is a dummy variable that is 1 if the firm has disputes either with the government or with other firms and 0 if otherwise. In Column (2), the dependent variable is a dummy variable that is 1 if the firm has disputes with the government and 0 if otherwise. In Column (3), the dependent variable is a dummy variable that is 1 if the firm has disputes with other firms and 0 otherwise. All variables are winsorized at the upper and lower one percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. *,**,*** indicate significance at the 1%, 5% and 10% level respectively.

||All Disputes|Disputes with government|Disputes with private firms|
|---|---|---|---|
||(1)|(2)|(3)|
|SOE dummy|-0.367**|-0.432**|-0.388**|
||(-2.087)|(-2.542)|(-2.205)|
|Log(Total Asset)|0.002***|-0.000|-0.001|
||(3.293)|(-1.000)|(-1.262)|
|ROA|-0.006|0.003|-0.033*|
||(-0.274)|(0.166)|(-1.753)|
|Tobin's Q|0.012|0.018|-0.057**|
||(0.419)|(0.557)|(-2.300)|
|Constant|0.480|0.405|0.781**|
||(1.628)|(1.460)|(2.324)|
|Industry FE|Yes|Yes|Yes|
|Exchange FE|Yes|Yes|Yes|
|Adj. R<sup>2</sup>|0.133|0.133|0.264|
|# of Obs.|43|43|43|

57