rafmacalaba/datause-encoder-data huggingface dataset:
2 Maduro administration. The interaction of these two sources of variation corresponds to a plausibly exogenous _predicted migrant shock_ measure.<sup>3</sup> Our identification strategy exploits the fact that crisis-induced migrants tend to move disproportionately to municipalities where they have preexisting networks, formed before the beginning of the migration wave (Bartel, 1989; Greenwood and McDowell, 1986). Our identification assumption is that predicted migration shock is correlated with actual migration but do not affect educational outcomes independently or through any other channel. This is plausible after controlling for municipality-specific trends parametrized by key pre-determined municipality characteristics, which we choose using machine learning techniques.

We find that the migration shock increased the enrollment of both male and female foreign students. This effect is mainly driven by public schools, and it is stronger for younger kids, who enroll in primary school grades. We also find, however, negative externalities of the increased enrollment in terms of promotion rates and dropout rates. While falling promotion rates and increasing dropout occurs for both native and foreign students, the effects are larger for the former. Migrants, on the other hand, seem to exert more effort as suggested by their performance in standardized tests. Finally, we document that the mechanisms that explain these negative effects of the migration shock have to do with the deterioration of key school inputs in public schools. For instance, we show that the migration shock reduces the number of teachers and increases the ratio of pupils to teachers (and thus class size). Our results highlight important policy implications, which we discuss in the conclusion.

> 3Importantly, given the absence of longitudinal data on the places where Venezuelan migrants have settled over time, we use our predicted migration measure in a reduced-form specification instead of as an instrument for the endogenous actual settlement of Venezuelans. However, a visual comparison of Figures 2 **B.** and 2 **D.** suggests that the spatial distribution of the census-observed and the predicted number of cumulative Venezuelans in 2018 is very similar. Indeed, the correlation is 0.67.
and 2010 (World Bank 2011a). Roads contracts procured through the World Bank are usually awarded in a one stage sealed bid auction, with the lowest bidder winning the auction. Alexeeva et al. (2011) find that in about 20% of the auctions in their sample of two hundred contracts in Europe and Central Asia, at least 50% of firms who acquired bidding documents do not bid, the winning bid is not selected for detailed examination, or there is a time overrun of more than 30% of the contracted period. The estimates of costs of collusion and cartels in the road sector are large and range between 8% and 60% of the contract value (World Bank 2011a). Considering that substantial resources are allocated to road construction and maintenance (US$56 billion between 2000 and 2010 by the World Bank alone), this represents a massive waste of funds. Further evidence from investigations discussed in World Bank (2011a) is striking: in Bangladesh, companies paid officials up to 15% of the contract value in exchange for award of the contract; evidence from Africa indicates that fraudulent claims such as cement contents and thinner layers than specified accounted for 15-20% of the bid price.<sup>4</sup> To our knowledge, the only study quantitatively investigating the link between conflict and the cost of transport infrastructure is Benamghar and Iimi (2011) who use data on 157 rural road projects in Nepal and show that the number of security incidents is significantly and positively correlated with the value of submitted bids, cost overruns, and project delays. Considering corruption in transport infrastructure, Olken (2007) finds that missing expenditures amounted to on average 24% of the total cost of the road in his experiment in Indonesia. Burgess et al. (2015) show that road expenditures are substantially higher in districts that share the ethnicity of the president than what would be predicted by their population share. See Blattman and Miguel (2010) for a recent review on the literature on conflict and Olken and Pande (2012), Zitzewitz (2012) and Banerjee et al. (2012) on corruption in developing countries.

Finally, the paper relates to a fairly recent literature on government procurement processes and waste associated with it (Bandiera et al. 2009; Di Tella and Schargrodsky 2003;

> 4The use of substandard materials imposes costs ex-post through higher maintenance costs and costs on vehicle drivers due to worse road conditions and might eventually lead to even negative rates of return of a particular project (Kenny 2009).

5
NASA’s Tropical Rainfall Measuring Mission (TRMM) provides a detailed and comprehensive data set on the four dimensional distributions of rainfall and latent heating over vastly under sampled oceanic and tropical continental regimes. This study links the DHS cluster-level and GHS Enumeration Areas (EA) GPS-coordinates and provinces, with data from TRMM satellites that measure rainfall at a 0.25x0.25 latitude/longitude grid. The data are available every month from January 1998 to December 2012 and the unit of observation used here is average rainfall (in mm/hour).

For each DHS cluster/ GHS (EA), monthly rainfall is estimated using the four nearest data points available in the TRMM data. The estimated rainfall in the cluster is calculated as the weighted average of the measured rainfalls in these four points, which represent the four corners of the world of the gridded cell to which the cluster belongs. The weights of the equation are the inverse distances between the two points, so that the nearer they are to the cluster/ EA the larger the weight assigned to the data points. For household survey data, we aggregate the average of the rainfall observations falling within the geographical limits of the province of a country. Similarly, in the case of households’ living conditions data, the rainfall value for households located in each province is simply comprised of the average of the rainfall measured in the TRMM points located in a given province.

Figure 2 shows the amount of rainfall precipitation during the growing period (between June and September/October) over the last 10 years. This period accounts for about 80% to 90% of the total precipitation over the year and it is crucial for crop growth. There is a lot of variation among the countries presented. Northern Nigeria, Northern Ghana, Burkina and Senegal receive cumulatively between 600 and 800 mm of rainfall during the growing period; Mali and Niger on the other hand, see much lower levels of precipitation. Niger cumulatively receives less than 200 mm in the growing period.

For the purpose of our analysis, we selected rainfall or NDVI corresponding to the year before the survey was conducted. In some cases this implied we could observe the impact of particularly negative climatic conditions. For example, Burkina Faso’s 2003 EBCVM and DHS were conducted after a severe drop in precipitation in 2002. Likewise, GHS 2012-2013 data have been collected after a particularly dry year for Nigeria (2011). In both cases, nonetheless, the other survey rounds (2010 and 2010-2011) were collected in a relatively favorable period. Overall, this makes the surveys particularly suitable for the resilience analysis we undertake since these surveys contain a negative as well as a positive ‘state of the world’.

# **5.4 NDVI**

14
Miguel, E., S. Satyanath, and E. Sergenti (2004): "Economic Shocks and Civil Conflict: An Instrumental Variables Approach." Journal of Political Economy, 112 (4), pp. 725—53.

Ministry of Social Development of Chile (2015): Guia del cidadano: validacion y actualizacion de datos del hogar atraves de la pagina web del municipio, Chile: Govierno de Chile.

Noussair, C., S. Robin, and B. Ruffi eux (2004): "Revealing Consumers’Willingness-toPay: A Comparison of the BDM Mechanism and the Vickrey Auction." Journal of Economic Psychology, 25 (6), pp. 725—41.

Parra, J. (2011): "The Effects of Religion on Social Cooperation: Results from a Field Experiment in Ghana." Mimeo, Georgetown University, Washington, DC.

Ringold, D., A. Holla, M. Koziol, and S. Srinivasan (2011): Citizens and Service Delivery: Assessing the Use of Social Accountability Approaches in Human Development Sectors, Directions in Development Series, Washington, DC: World Bank.

Silva, J., V. Levin, and M. Morgandi (2013): Inclusion and Resilience: The Way Forward for Social Safety Nets in the Middle East and North Africa. MENA Development Report, Washington, DC: World Bank.

Silverman, D., J. Slemrod, and N. Uler (2014): "Distinguishing the Role of Authority ’in’ and Authority ’to.”’Journal of Public Economics, 113, pp. 32-42.

World Bank (2009): Conditional Cash Transfers: Reducing Present and Future Poverty, A. Fiszbein and N. Schady (Eds.), Washington, DC: World Bank.

World Bank. (2015): The State of Social Safety Nets 2015, Washington DC: World Bank.

22
# **Figure 12. Transition risks (carbon intensive production) and macro‐financial risks**

## A. CO2 intensity vs. macroeconomic risks, 2017

<!-- Start of picture text --> 4<br>LBN IRQ<br>3.5<br>CHN<br>AUS BIH LBY<br>3 ISR SYR<br>JAM<br>2.5 LUX TUN<br>AGO<br>KOR<br>UKR<br>2 SGP<br>1.5 NORNZL GHA<br>CHE<br>1<br>SWE<br>0.5<br>0<br>10 30 50 70 90<br>EIU economic risk index (100=High), 2017<br>C. CO2 intensity vs. banking risks, 2017<br>4<br>3.5 LBN IRQ<br>HKG LBY<br>3 AUS ISR TUNCUB<br>SYR<br>IRN<br>2.5 LUX<br>CHL AGO<br>UKR<br>2 SGP<br>NZL<br>NOR<br>1.5<br>CHE<br>1 SDN<br>SWE<br>0.5<br>0<br>10 30 50 70 90<br>EIU banking risk index (100=High), 2017<br>E. CO2 intensity vs. public debt risk, 2017<br>4<br>3.5 JOR LBN IRQ<br>HKG LBY<br>3 AUS ISR GRC CUB<br>SYR<br>TUN<br>2.5 LUX<br>CHL AGO<br>UKR<br>2 SGP<br>NZL LTU<br>1.5<br>CHE<br>1 SDN<br>SWE<br>0.5<br>0<br>10 30 50 70 90<br>EIU debt index (100=High), 2017<br>CO2 intensity (kg per kg oil eq energy use), 2017<br>CO2 intensity (kg per kg oil eq energy use), 2017<br>CO2 intensity (kg per kg oil eq energy use), 2017<br><!-- End of picture text -->

## B. CO2 intensity and macroeconomic risks by region

<!-- Start of picture text --> 100% 0 0<br>90% 2 2<br>5<br>5 1<br>80%<br>70% 10<br>3<br>60% 5<br>4<br>50% 1<br>6 7<br>40% 1<br>30% 3<br>20% 5<br>6 6 1<br>10% 3 0<br>1<br>0% 0<br>EAP ECA LAC MENA SA SSA<br>low both high climate high macro high both<br><!-- End of picture text -->

## D. CO2 intensity and banking risks by region

<!-- Start of picture text --> 100% 0<br>1 1<br>90%<br>5 4<br>2<br>80%<br>70% 1 1<br>11 2<br>60% 4<br>6 4 7<br>50%<br>40%<br>30% 1<br>20% 5 8 6<br>5 1 1<br>10%<br>1<br>0% 0 0<br>EAP ECA LAC MENA SA SSA<br>low both high climate high banking high both<br><!-- End of picture text -->

## F. CO2 intensity and public debt risks by region

<!-- Start of picture text --> 100% 0 0<br>1<br>90% 2<br>6 4 1<br>80%<br>70% 10<br>1<br>60% 5 3 6<br>50% 5 1<br>40% 4 1<br>30%<br>1<br>20% 5 8<br>6 1<br>4<br>10% 2<br>0% 0<br>EAP ECA LAC MENA SA SSA<br>low both high climate high debt high both<br><!-- End of picture text --> Note: Panels A, C, and E: Countries above (below) country median in Notre Dame Readiness indicator in green (red). Panel A: average losses (% GDP) between 2005 and 2017.

Panels B, D, and F: columns show proportion of countries in each category (i.e., low climate and macro‐financial risks, high climate‐low macro‐ financial risks, low climate‐high macro‐financial risks, high climate and macro‐financial risks) by region. Thresholds are country median values of EIU indicator or climate variable as of 2017 (historical country median for the case of fuel exports). Numbers in columns are the number of countries in each category. EIU data for Saudi Arabia as of 2016.

Source: EIU Credit Risk Model, GermanWatch, Notre Dame GAIN, WDI. Staff calculations.

38
sells overcoats and that other East Asian exporters sell mainly suits. At the 3-digit level, these products will appear to be competing, but it is unlikely that an increase in overcoat exports from China will displace suit exports from other East Asian exporters.

18 In our empirical analysis, we use bilateral trade data at the 4-digit standard international trade classification (SITC) level from 1985 to 2004. We test whether Chinese exports to a particular country in a given category are affecting exports from East Asia to a greater extent than exports from other countries and areas. We also control for overall exporter supply growth. The advantage of this specification is that we are exploiting both cross-sectional and time series variation to estimate how the exports of East Asia are affected by China. If Chinese export growth is primarily displacing domestic producers or is not competing with East Asia for some other reason, we will not pick it up. While Chinese exports might not be pushing out the exports of East Asia (it may be that China is entering because East Asia is exiting), this is less likely since we are controlling for export supply growth.

We estimate the following equation: where _i_ is the exporter, _j_ is the importer, _k_ is the industry, _t_ is the time, _dif lnexp_ is export growth, _diflnimpnonch_ is the growth in non-China exports to _j_ in product _k_ at time _t_ , and _diflnimpCHN_ is the growth in China’s exports, multiplied by China’s lagged market share in the specific sector and market. Weighting China’s export growth by the lagged market share ensures that only sectors are picked in which China is a significant supplier. A negative coefficient on China ( _β1_ ) indicates that Chinese export growth is correlated with a decline in East Asian export growth in a given industry. 19 See Klinger and Lederman (2006).

33
21 found to have, on average, a higher per capita consumption when neighbour provides access to a private well (instead of providing access to a tap connection). The latter is significant at the 20 percent level. For the first time, we find a significant effect of the variables indicating ethnicity. Sinhalese households are found to have a significantly lower per capita consumption from private wells, all other things equal. Mill’s ratio is not significant in this model.

# **5. Conclusions and policy implications**

Using data from a survey of 1,800 households in Southwest Sri Lanka in 2003-2004, we estimate water demand functions of piped and non-piped households.

The analysis leads us to five findings. First, the (marginal) price elasticity of households exclusively relying on piped water is -0.74. Even though these households only use piped water, their access to alternative water sources tends to be high. On average, households who only use piped water have access to 1.8 alternative sources of water (the most frequent alternative source being access through neighbors), while piped households who combine piped and non-piped water have access to 2.4 alternative sources of water (private well being the most prevalent non-piped source). The value of the price perception parameter in the models that captures the effect of the tariff structure on the demand for piped water is close to zero. This means that households tend to respond to the marginal price, and suggests that consumers tend to be well-informed about the price of piped water despite the relative complexity of the tariff schedule. Widespread metering enables consumers to exercise control over their consumption. As a result, the majority of households using piped water consume less than 20 cubic meter per month – this is a level of consumption at which water tariffs are highly subsidized (see footnote 4).

Secondly, the price elasticity of households using piped water but supplementing their supply with other water sources shows a price elasticity of -0.69. The cross elasticity of alternative water sources (as measured in time costs) is 0.08. Alternative water sources are hence considered substitutes for piped water. Due to insufficient data to translate time costs into monetary cost of water, it is not possible to determine the precise level of substitution between piped and non-piped water. It seems that piped water supply is valued in terms of
<!-- Start of picture text --> Model Utility = 10 Counterfactual Utility = 11.7661, Reallocation = 0.49823<br>0 oe meyers 0 imine OS my tcc eer e cece cece ces<br>&::::4::::<br>2: : : : 2 : : . :<br>=::::=::::<br>>1Actual ss On $1 Actual ae 0<br>=——= Modeled : : : === Efficiency: U.S. Dispersion : :<br>10 12 14 16 18 10 12 14 16 18<br>In(population) In(population)<br>Counterfactual Utility = 12.2581, Reallocation = 0.35155 Counterfactual Utility = 9.9826, Reallocation = 0.015624<br>0 eee . Wa ryt rrr ccccss es 0 ports erste met ays rrr teres<br>3 : : : : sg : : : :<br>a::::a::::<br>A : : : : A : : : :<br>2 2<br>: : : : 2 : : : :<br>° =——— Actual Ty N “ =——— Actual : pS<br>===—=—=—= Amenities: U.S. Dispersion : : seem Exc. Frictions: U.S. Dispersion] : :<br>-610 12 14 16i 18i -610 12 14 16i 18i<br>In(population) In(population)<br><!-- End of picture text -->
22 The ultimate equation in the structural model for recent fertility is the number of births in the last five years. Here, we find that the predicted use of various kinds of family planning in the last five years is only significant for modern reversible methods and here it is strongly significant. Several of the background variables are significant as well. The number of births in the last five years decreases monotonically with age and with urban residence. It increases with location in remote areas and rather surprisingly with female education of four to six years. Again, this latter result may arise from the fact that age of marriage has not been controlled as discussed above.

In general, this structural model does not prove very satisfying. In part this probably arises from the fact that fertility intentions five years ago are very poorly approximated by our measurement procedure and poorly explained by the current variables at hand. There is no way around such problems except to use panel data sets. These are rarely available in developing countries. In the US the National Fertility Survey has had such data and this has been used effectively for these purposes by Rosenzweig and Schultz (198-).

Given our disappointment with the structural model, we estimated reduced form equations for the proportion of time covered by contraception (modem reversible or traditional) in the last five years and the number of births in the last five years. These are shown in Table 6. Age has significant effects on the expected sign in all three cases, unlike the structural equations for use. Education has a more significant effect as well: 1) wife's education beyond seven years increases the proportion of time protected by modern reversible methods, 2) husband's education increases usage of traditional methods, and 3) wife's education of four to six years increases fertility. The first effect was not observed in the structural equations and is the only expected effect of wife's education on contraceptive use or fertility in either the current or the five year model. Rural residence at age 12 also plays a larger role in this formulation, reducing the use of modern reversible or traditional methods and increasing fertility. The effect of access variables remains the same between the two equations. Higher community mortality significantly reduces the use of modern reversible methods and surprisingly increases the use of traditional methods<sup>21</sup> . It also has a significant effect of increasing fertility in the last five years. Thus, with the exception of its effect on the proportion of time covered by use of traditional methods, community mortality plays perhaps the most consistent role in fertility transition of any community variable between both the current and the five year analysis, but the analysis of current behavior indicates that access to specific methods as opposed to access to health facilities per se is extremely important in determining use of modern contraception.

2' This is the only unexpected effect of community mortality in the entire analysis and is not easy to explain.
gender gaps persist when comparing firms within the same sector and of similar size and capital intensity. On these grounds, several authors have challenged the ‘female underperformance hypothesis’ (Du Rietz and Henrekson 2000; Zolin and Watson 2013).<sup>1</sup> In the development economics literature, research on gender differences in entrepreneurship gained momentum only more recently. Over the last decade, an increasing policy focus on female entrepreneurship and improved data availability have prompted a growing literature to assess gender-specific constraints to entrepreneurship and evaluate the effectiveness of various interventions in tackling these constraints. In a recent review for Sub-Saharan Africa, Campos and Gassier (2017) develop a conceptual framework that illustrates how gender-specific constraints – including contextual factors (legal discrimination, social norms, etc.), endowments (skills, capital and assets, etc.) and preferences (risk, time, etc.) – affect strategic choices (capital and labor inputs, etc.) of male and female entrepreneurs and ultimately outcomes (e.g. gender differences in firm performance).

More closely related to this paper is a strand of the development economics literature that quantifies gender gaps in firm productivity at the country or regional level using household and firm survey data. These studies, which are summarized in Table 1, focus on differences in labor or total factor productivity between firms owned or managed by men vs. women. This literature reveals several interesting patterns. First, in most countries, unconditional gender differences in labor productivity favor men (e.g. Bardasi et al. 2011 for Europe and Central Asia and Latin America; Rijkers and Costa 2012 for Bangladesh, Ethiopia and Sri Lanka; Hallward-Driemeier 2013 for Sub-Saharan Africa; Gui-Diby et al. 2017 for Asia). Second, similar to industrialized countries, gender gaps in productivity typically narrow – and sometimes even close – if the analysis controls for key enterprise characteristics, most notably sector and firm size (e.g. HallwardDriemeier 2013; Rijkers and Costa 2012; Gui-Diby et al. 2017). Third, though comparison across studies is hampered by differences in the underlying methodology, there is some evidence that gender gaps are

> 1 More recently, fueled by legislative strides to improve gender equality in executive positions, several studies have investigated the relationship between the share of women in top management (such as CEOs and boards of directors) and firm performance. These studies have often find that _gender diversity_ in top management positively impacts firm performance (Smith et al 2006; Martín-Ugedo and Minguez-Vera 2014; Christansen et al 2016; Flabbi et al 2016).

5