rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Exposure to Coffee Mills and School Attendance

||Dep. Var:|Child atte|nded schoo|l during th|e current sc|hool year (0/1)|
|---|---|---|---|---|---|---|
||A|ll|Ru|ral|U|rban|
||(1)|(2)|(3)|(4)|(5)|(6)|
|CatchmentArea X Post|0.0167**|0.0176**|0.0176**|0.0169**|0.0033|0.0035|
||(0.0075)|(0.0076)|(0.0085)|(0.0086)|(0.0203)|(0.0214)|
|CatchmentArea|0.0036|0.0040|0.0084|0.0099|-0.0259|-0.0265|
||(0.0083)|(0.0084)|(0.0094)|(0.0095)|(0.0228)|(0.0236)|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|YOB FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Province X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.0847|0.0852|0.0844|0.0847|0.1240|0.1260|
|Observations|42640|42346|35229|34996|7411|7350|

Notes: CatchmentArea X Post is an indicator variable equal to 1 for a child living in a community within the vicinity of an operational coffee mill and 0 if otherwise. CatchmentArea is an indicator variable equal to 1 for a child living in a community within the vicinity of a coffee mill. Controls include gender of the child, rural/urban, and time trends interacted with coffee suitability and road density. Standard errors are clustered at the enumeration area level. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 39
**Figure 2: Entrepreneurship trends**

<!-- Start of picture text --> Total Business Trend New Business Trend<br>140%<br>118%<br>120% 103%<br>100% 88% 83%<br>80% 72% 70% 65%<br>55%<br>60% 45% 48%47%<br>39%<br>40%<br>20%<br>0%<br>Africa & Asia Easter EuropeLatin America Developing Industrialized<br>Middle East &<br> Central Asia &<br> Caribbean<br><!-- End of picture text --> Source: 2008 World Bank Group Entrepreneurship Survey.

# **_3.1 The Impact of the Regulatory and Legal Environment_**

The WBGES data were merged with other variables measuring governance, political stability, the business environment, and the ease of registering a business. The data reveal interesting correlations, providing further evidence of the relevance of the regulatory and political environment in fostering entrepreneurship.

# _3.1.1 Governance_

The WBGES 2008 data suggest that good governance and political stability are the first prerequisite to setting up a favorable business environment. The information collected reveals an inverse relationship between political risk and business creation, with countries with lower political risk having significant higher business entry rates.

For instance, using the average of the six Kaufmann Governance Indicators (Voice and accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption) to measure good governance, the data also shows a strong and significant relationship between higher business entry rates and better governance (Figure 3).

8
down, falling to 4 percent in 2017. Meanwhile, education expenditures as a share of the budget almost doubled, rising from 15 percent in 2000 to 27 percent in 2013. In other cases, reductions in fuel subsidies (e.g., Ghana and Indonesia) have allowed governments to allocate more to education (Van Der Burg and Whitley 2016).

Overall, the share of government budgets allocated to education has remained relatively stable over the last 20 years across all regions and income groups (Table 1). Only in upper-middle-income countries has the share decreased. However, looking at regional averages, education expenditures as a percentage of government budgets have increased in Africa but declined in the Middle East and North Africa and in East Asia.

Low-income and lower-middle-income countries tend to devote a greater share of government spending to **–** the education sector than richer countries (Table 1). For example, in 2014 17, low-income countries spent an average of 16.4 percent of government funds on education compared to 12.6 percent for high-income countries. Regional differences tend to follow a similar pattern, with lower-income regions such as Africa devoting a greater share of government spending to education than higher income regions like Europe and Central Asia.

|**Table 1: Share of Government Budget Used for Education, by Income Group and Region**|
|---|

||1998**–**2001|2002**–**05|2006**–**09|2010**–**13|2014**–**17|
|---|---|---|---|---|---|
|**Income Group**||||||
|LIC|15.0|15.8|16.0|15.9|16.4|
|LMIC|15.7|16.7|16.3|16.0|15.7|
|UMIC|16.0|15.5|14.9|14.9|14.5|
|HIC|13.1|13.2|13.1|12.7|12.6|
|**Region**||||||
|AFR|14.8|16.2|16.6|16.7|16.1|
|ECA|11.9|12.2|12.2|12.4|12.2|
|LCR|16.2|15.7|16.1|17.3|17.4|
|SAR|15.5|16.3|14.1|13.5|15.0|
|EAP|16.1|17.2|15.4|14.5|14.0|
|MNA|17.3|16.5|15.4|12.9|13.6|

_Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ Income groups are defined by country income group classification in 2017. LIC = low-income country, LMIC = lower-middle-income country, UMIC = upper-middle-income country, and HIC = high-income country. AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, SAR = South Asia, EAP = East Asia and the Pacific, and MNA = Middle East and North Africa.

# **_Low-Income Countries Have Spent More on Primary Education, Richer Ones on Post-Primary_**

Wealthier countries have devoted a greater share of government spending to post-primary education than less well-off countries. In 2014 **–** 17, less than one-third of public education spending in high-income countries went to primary education compared to about one-half for low-income countries (Table 2). This suggests that as countries become wealthier and their education systems expand and develop, additional public funding tends to be spent on post-primary education (Box 4).<sup>5</sup>

> 5 The correlation coefficient between per capita GDP and the share of public education spending going to secondary education is 0.4 and for tertiary education it is 0.3. This is statistically significant at the 1 percent level.

11
# **Introduction**

This paper estimates the effect of pre-primary education on school stay-on rates and levels of completed education among individuals aged 7-15. We exploit a rather unique feature of the Uruguayan _Encuesta Continua de Hogares_ (ECH) for the years 2001-2005 that collects retrospective information on the number of years of preschool attended. In order to control for unobserved household characteristics that are common to all children in the household and that might affect simultaneously exposure to pre-primary education and school progression we rely on a within household estimator that only exploits variability in the outcome and treatment variables across siblings. In order to account for the possibility of siblings' systematic differences in treatment and outcomes we complement this strategy by instrumenting preschool attendance with average attendance rates by locality of residence and birth cohort. A major expansion in the provision of public pre-primary education in Uruguay over the last decade that led to an acceleration in preschool attendance among subsequent birth cohorts and that mainly affected children from more disadvantaged backgrounds generates sufficient variation in exposure to preschool education to warrant identification.

We find a significant positive effect of preschool attendance on completed years of primary and secondary education. This works both through a fall in retention rates since the early school years (from age 11 onwards) and a reduction in drop out rates among teenagers (from age 13 onwards). The gains from attending preschool increase as children grow older, so that exposure to pre-primary education leads to gradually diverging paths in school attainment between treated and untreated children. We speculate that early grade retention increases the incentives for early drop out and raises the probability of grade failure later in the school life and hence, pre-primary education appears as a successful policy to prevent early school failure and its long lasting consequences.

In poor countries, a large share of the population is excluded from the education system already at an early age and well before completion of the compulsory schooling cycle. Exclusion from the 1
# **1 INTRODUCTION**

Poor and low-income societies are exposed to a variety of daily shocks and risks. These often strain household budgets and can lead to poverty traps through extreme fluctuations in household incomes and consumption, especially when financial and/or insurance markets are incomplete (Townsend 1994; Morduch 1995; Dercon & Christiaensen 2011; references therein).

Social protection programs —which serve as safety nets— have sparked much interest among development agencies and practitioners (e.g., including governments) as a prominent way to help these societies manage their risk and vulnerabilities (Ravallion 1999). Social protection programs are common (Grosh et al. 2008) and can be wide ranging. Notable examples include India’s National Rural Employment Guarantee Act (NREGA) (Dutta et al. 2014), Ethiopia’s Productive Safety Net Project (PNSP) (Hoddinott et al. 2012), and Malawi’s Social Action Fund (SAF) ( <mark>Kathleen, Galasso and Goldberg 2015</mark> ). These are broadly in the form of either cash transfers or in-kind transfers to eligible households.

Estimates and conversations with relevant practitioners suggest social protection expenditures of over US$170 million to $US190 million per annum (translating to about 2.5-3.0% of GDP).<sup>2</sup> This expenditure estimate excludes other societal transfers among informal households. In contrast to the usual objective of escaping poverty, Niger is extremely poor with recent evidence suggesting that some households and regions have become poorer. For instance, the fraction of food-poor households in the Dosso region increased from 30% in 2011 to 50% in 2014; while the median household food and non-food consumption expenditure in the Maradi region decreased from 1,126,152 CFA in 2011 to 1,101,629 CFA in 2014 (approximately 2.2% decline rate).

In this paper, we devise tractable empirical frameworks to explore several aspects and hypotheses within social protection schemes using newly available rich panel data sets. We begin by examining the distribution of both economic and non-economic shocks faced by Nigerien societies. The distribution of the frequency and severity of household shocks is globally characterized. Next, we discuss potential leakages and reachability of the current social protection schemes in Niger. Effective design and targeting of social assistance occurs at

> 2 For Niger, data on spending or expenditure on social assistance programs are largely unavailable. From the World Bank’s ASPIRE database: the share of Nigerien population participating in social assistance is about 2.7%, in 2011. See http://datatopics.worldbank.org/aspire/country/niger.

‐ 2 ‐
provided childcare, however, would increase the rate of women‟s labor force participation by as much as 12-15 percent. The elasticity of mothers‟ labor supply with respect to childcare cost are found to be -0.17, which is in line with estimated elasticities reported in the related literature based on data from the U.S. and Canada.<sup>21</sup> Fong and Lokshin (2000) conclude that government subsidies for childcare are an effective means of increasing the number of mothers who work, increasing the incomes of poor households and lifting some families out of poverty, but that the effects of such policies are less significant for the poorest households.

A similar study by Lockshin (1999) studies mothers‟ participation in the labor force, working hours and demand for childcare in Russia. In the 1980s, most women in Russia worked and the government heavily subsidized childcare programs that were widely available. A decline in GDP in the 1990s led to a sharp decrease in the availability of state-run child care facilities and an increase in the cost of sending children to these facilities. As described in Lockshin (1999), Russia moved from a country in which childcare was provided by the government and almost all households with children had access to affordable or free childcare to one in which few households have access and the cost of day care significantly affects labor force participation decisions. Lockshin (1999) builds a static utility maximizing model of households‟ decisions about labor force participation, working hours, and choice of childcare mode to motivate an econometric model that he uses to assess the effects of three different kinds of policy interventions: family allowances, childcare cost subsidies and wage subsidies. The model is estimated using panel data from the Russian Longitudinal Monitoring Survey (RLMS).

The simulations show that childcare subsidies increase maternal employment by almost twice as much as comparable wage subsidies. Also, childcare subsidies are more effective than wage subsidies or family allowance transfers (transfers to families with children) in increasing family income. Childcare subsidies increase the amount of time working women spend at work and increase the proportion of mothers who choose to work.

A limitation of the analysis is that it does not examine changes in the utility of households under the alternative types of policies. Increasing a mother‟s work would likely reduce her time spent in leisure, which leads to a loss in utility that offsets the benefit of higher income. It would be useful to compare the alternative policies on a utility basis. Also, in both the Lockshin (1999) and Fong and Lockshin (2000) studies, it is not clear how the quality of formal childcare

> 21 Cleveland, Gunderson, and Hyatt (1996) for Canada, Connelly (1992) and Blau and Robins (1988) for the U.S.

45
|`judi`|`0.092`|`0.112`|`0.092`|`0.004`|
|---|---|---|---|---|
||`(0.37)`|`(0.55)`|`(0.96)`|`(0.04)`|
|`judirate`|`0.108`|`0.159`|`-0.080`|`-0.006`|
||`(0.33)`|`(0.59)`|`(0.71)`|`(0.05)`|
|`centgov`|`0.084`|`-0.579`|`-0.110`|`-0.136`|
||`(0.22)`|`(1.77)`|`(0.81)`|`(0.97)`|
|`parl`|`-0.067`|`0.801`|`-0.038`|`0.112`|
||`(0.18)`|`(2.65)**`|`(0.30)`|`(0.86)`|
|`cbank`|`-0.041`|`-0.181`|`-0.096`|`-0.026`|
||`(0.12)`|`(0.66)`|`(0.68)`|`(0.18)`|
|`police`|`-0.165`|`-0.266`|`0.060`|`0.067`|
||`(0.59)`|`(1.14)`|`(0.53)`|`(0.58)`|
|`govine`|`-0.083`|`-0.415`|`-0.075`|`-0.122`|
||`(0.33)`|`(1.96)`|`(0.74)`|`(1.15)`|
|`ginter`|`0.186`|`0.175`|`0.185`|`0.048`|
||`(0.63)`|`(0.74)`|`(1.76)`|`(0.46)`|
|`corobs`|`-0.085`|`-0.390`|`0.031`|`-0.028`|
||`(0.27)`|`(1.50)`|`(0.23)`|`(0.20)`|
|`corry`|`0.595`|`0.314`|`-0.044`|`-0.010`|
||`(2.17)* `|`(1.31)`|`(0.33)`|`(0.07)`|
|`corqb `|`-0.058`|`0.511`|`-0.228`|`-0.228`|
||`(0.18)`|`(1.80)`|`(1.21)`|`(1.09)`|
|`infr`|`-0.210`|`0.020`|`0.015`|`-0.037`|
||`(0.62)`|`(0.07)`|`(0.10)`|`(0.24)`|
|`roads`|`0.245`|`0.388`|`0.279`|`0.065`|
||`(0.99)`|`(1.87)`|`(2.75)**`|`(0.61)`|
|`post`|`-0.011`|`-0.297`|`0.312`|`0.049`|
||`(0.03)`|`(1.05)`|`(1.61)`|`(0.23)`|
|`elec`|`-0.063`|`-0.225`|`-0.379`|`-0.206`|
||`(0.21)`|`(0.88)`|`(2.25)* `|`(1.18)`|
|`water`|`-0.218`|`-0.073`|`-0.082`|`-0.082`|
||`(0.73)`|`(0.30)`|`(0.56)`|`(0.54)`|
|`crimest`|`-0.263`|`-0.389`|`0.062`|`-0.093`|
||`(0.71)`|`(1.33)`|`(0.44)`|`(0.65)`|
|`crimeorg`|`0.301`|`0.143`|`-0.149`|`0.174`|
||`(0.84)`|`(0.48)`|`(0.99)`|`(1.14)`|
|`sale1`|`-0.328`|`0.675`|`-0.337`|`0.086`|
||`(0.74)`|`(1.72)`|`(2.17)* `|`(0.53)`|
|`sale2`|`0.766`|`0.824`|`0.336`|`0.514`|
||`(1.59)`|`(1.90)`|`(2.18)* `|`(3.17)**`|
|`owind`|`-0.322`|`-0.946`|`0.039`|`-0.640`|
||`(0.90)`|`(3.17)**`|`(0.30)`|`(4.87)**`|
|`owfam`|`-0.451`|`-1.463`|`0.181`|`-0.843`|
||`(1.09)`|`(4.19)**`|`(1.11)`|`(4.64)**`|
|`owork`|`-0.311`|`0.563`|`-0.199`|`0.198`|
||`(0.61)`|`(1.50)`|`(0.99)`|`(1.11)`|
|`owsta`|`-0.510`|`-0.251`|`-0.435`|`0.298`|
||`(1.18)`|`(0.73)`|`(2.52)* `|`(1.93)`|
|`Constant`|`-0.038`|`-0.581`|`0.171`|`-0.226`|
||`(0.05)`|`(0.87)`|`(0.73)`|`(0.93)`|
|`Observations`|`788`|`788`|`3316`|`3316`|

``` Source: BEEPS. Absolute value of z statistics in parentheses; * significant at 5%; ** significant at 1% ``` 48
(monthly). Results show that the program incidence is associated with a reduction in crime rates except for rape and robbery. On average, in periods where the program operated at the municipal level, the total crime rate is on average 8.6 per 100,000 people lower than in municipalities without the program. By types of crime, the program seemed to have a larger effect in reducing theft and extortion, followed by injury and homicide. These results support the idea that PATI helped supplement lower incomes, and thus reduce mostly economically motivated crimes. Reduction in homicide rates, and in not-economically-motivated crimes, could be due to social capital factors such as the community participation nature of the program, as well as its intense work requirements.

**Table 2: Effects of PATI on serval types of crimes**

|VARIABLES|**Total**|**Extortion**|**Homicide**|**Vehicle**<br>**theft**|**Theft**|**Injury**|**Other**|**Robbery**|<br>**Rape**|
|---|---|---|---|---|---|---|---|---|---|
|PATI|–8.603**|–0.768**|–0.608***|–0.500**|–0.826***|–0.687**|–4.614**|–0.567|–0.032|
||(–5.113)|(–2.552)|(–1.669)|(–2.970)|(–1.850)|(–2.502)|(–4.946)|(–1.427)|(–0.343)|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.037|0.017|0.015|0.008|0.018|0.009|0.036|0.009|0.008|
|Observations|24,960|24,960|24,960|24,960|24,960|24,960|24,960|24,960|24,960|

_Note:_ Robust standard errors in parentheses. FE: Fixed Effects. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1 Our second specification allowed us to test the effects of the program on crime rates during different months of exposure. We also test for temporal spillovers considering that crime exhibits high degrees of persistence (Chioda 2017). Results can be seen in Table 3. The effects of PATI on crime hold several months after the program is introduced in a selected municipality. The effects are again particularly driven by economically motivated crimes such as extortion and theft. No significant effect on the homicide rate is found.

**Table 3: Effects of PATI on several types of crimes on time**

|**Variables**|**Total**|**Extortion**|**Homicide**|**Vehicle**<br>**theft**|**Theft**|**Injury**|**Other**|**Robbery**|**Rape**|
|---|---|---|---|---|---|---|---|---|---|
|PATI|–3.965**|–0.605**|0.025|–0.418**|–0.061|–0.562|–2.055**|–0.158|–0.132***|
||(–2.793)|(–2.096)|(0.072)|(–2.080)|(–0.117)|(–1.507)|(–2.207)|(–0.370)|(–1.773)|
|PATI t+1|–0.295|–0.102|0.031|0.355|–0.287|0.392|–0.706|0.084|–0.062|
||(–0.207)|(–0.368)|(0.095)|(1.561)|(–0.570)|(1.063)|(–0.652)|(0.176)|(–0.499)|
|PATI t+2|–2.585***|-0.499|–0.612|–0.438***|0.434|–0.097|–1.312|–0.304|0.242***|
||(–1.890)|(-1.533)|(–1.613)|(–1.816)|(0.878)|(–0.294)|(–1.049)|(–0.792)|(1.838)|

11
important than the level of investment incentives<sup>in determiningthe volumeof private</sup> investment.

Recent econometric work by Scott Wallsten<sup>(2001 a, 2002) on telecommunications</sup> reform in developing countries is very relevant<sup>tothe analysisdescribed in thispaper.Wallsten</sup> (2001a) finds that country level telecommunications<sup>performanceis positively relatedto</sup> regulation, as measured by a dummy indicating<sup>whether the countryhad establisheda separate</sup> telecommunications agency not directly under the<sup>controlof a ministry.That study usesa fixed</sup> effects approach to explore the impact of privatization,<sup>competitionand regulationon</sup> telecommunications performance in Africa and<sup>LatinAmericabetween1984and1997.</sup> Wallsten (2002) shows that countries that established<sup>separate regulatoryauthoritiesprior to</sup> privatization saw increased telecommunications<sup>investmentcomparedto countries that did not,</sup> and, also, that investors are willing to pay more<sup>for telecommunicationsfirmsin suchcountries.</sup> These findings are consistent with the hypothesis<sup>that investorsrequirea risk premium to invest</sup> where regulatory rules remain unclear. Our analysis<sup>contributesto thisliteratureon the role of</sup> regulation in private sector development. We<sup>use detaileddata on measuresof regulatory</sup> independence in addition to sectoral data on both<sup>private and publicinvestmentacross the major</sup> economies of Latin America. This allows us<sup>to more thoroughlyinvestigatethe importanceof</sup> independent regulatory institutions for the climate<sup>for private investment.</sup> The effectiveness of regulatory institutions depends<sup>on thestructure andprocessof</sup> regulation, key aspects of which are the independence,<sup>competenceand clarityof mandateof the</sup> regulatory agency, the transparency and openness<sup>of the regulatory processand the existenceof</sup> formal oversight and timely judicial review.<sup>2Smith(1997a,b) discussing the "desirable"</sup> attributes of utility regulators considers independence<sup>fromthe regulatedfirm,customers and</sup> 2 See Noll (2000) for an exposition.

8
- [12] Maskus, K.E., 2000. Parallel imports. World economy 23, 1269-1284.

- [13] Maskus, K.E., Chen, Y., 2002. Parallel imports in a model of vertical distribution: theory, evidence, and policy. Pacific Economic Review 7, 319-334.

- [14] Maskus, K.E., Chen, Y., 2004. Vertical price control and parallel imports: theory and evidence. Review of International Economics 12, 551-570.

- [15] Pecorino, P. , 2002. Should the US allow prescription drug reimports from Canada? Journal of Health Economics 21, 699-708.

- [16] Raff, H., Schmitt, N., 2007. Why parallel trade may raise producers’ profits. Journal of International Economics 71, 434-447.

- [17] Richardson, M., 2002. An elementary proposition concerning parallel imports. Journal of International Economics 56, 233-245.

- [18] Scherer, F., Watal, J., 2002. Post-TRIPS options for access to patented medicines in developing nations. Journal of International Economic Law 5, 913-939.

- [19] Shaked, A., Sutton, J., 1983. Natural oligopolies. Econometrica 51, 1469-84.

- [20] Smith, A., Venables, A.J., 1988. Completing the internal market in the European Community: Some industry simulations. European Economic Review 32, 1501-1525.

- [21] Valleti, T.M., 2006. Differential pricing, parallel trade, and the incentive to invest. Journal of International Economics 70, 314-324.

- [22] Valletti, T.M., Szymanski, S., 2006. Parallel trade, international exhaustion and intellectual property rights: a welfare analysis. Journal of Industrial Economics 54, 499-526.

- [23] Venables, A.J., 1990. The economic integration of oligopolistic markets. European Economic Review 34, 753-73.

41
12 (11) _8aLnE_ I _1_<sup>_aoCC_</sup> _as ~b+ 2 as_ The coefficient of schooling b,' is a downward biased estimate of the partial effect of schooling on earnings. It merely measures the average effect of schooling on earnings within occupational strata. The coefficient b2 ' is the effect of a higher occupational strata on earnings and _aocC_ is the effect of schooling on occupational status. It is not the _as_ coefficient of schooling, but rather _aLnE = b,_ **_+b2_ ..** that is the estimated value of _as as_ rK. Similarly, stratifying the data by measures of labor market success (e.g., within occupational categories) and computing regression equations within strata also biases downward the coefficient of schooling.

When interpreted correctly, the HCEF can be an invaluable tool for estimating the rate of return from schooling.