rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Insurance markets are growing rapidly in the developing world. As part of this growth, innovative new products allow individual smallholder farmers to hedge against agricultural risks, such as drought, disease and commodity price fluctuations. For example, a recent World Bank volume (World Bank, 2005a) discusses ten case studies in countries as diverse as Nicaragua, Ukraine, Malawi and India. Each is a study of “index insurance an insurance product whose payouts are linked to a publicly observable index such as rainfall recorded on a local rain gauge. Advocates argue that index insurance is transparent, inexpensive to administer, enables quick payouts, and minimizes moral hazard and adverse selection problems associated with other risk-coping mechanisms and insurance programs.

These financial innovations hold significant promise for rural households. Shocks to agricultural income, such as a drought-induced harvest failure, generate movements in consumption for households that are not perfectly insured, and at the extreme, may lead to famine or death. Available evidence suggests households in developing countries are partially although not fully insured against income shocks (e.g., Townsend 1994, Morduch 1995, Lim and Townsend 1998). Moreover, weather events tend to affect all households in a local geographic area, making other risk-sharing mechanisms like interhousehold transfers and local credit and asset markets less effective at ameliorating the impact of the shock. Other evidence suggests that households engage in costly ex ante risk-mitigation strategies to reduce fluctuations in agricultural income. Morduch (1995) summarizes a range of evidence of this kind of household “income smoothing”; for example, Indian farmers near subsistence level spatially diversify their plots, and devote a larger share of land to lower-yielding, traditional varieties of rice and castor. These activities reduce the variability of agricultural revenues, but at the expense of lower average income.

This paper studies a particular rainfall insurance product offered in recent years to smallholder farmers in the Andhra Pradesh state of southern India. The product provides a payout 1
more violence, as women had to step in as household heads and in local political roles. Another study finds reduced prevalence of risky sexual transactions among female sex workers as a result of violence, presumably in response to increased insecurity (Manian, 2021).

Regarding the indirect effects of conflict, the literature to date has focused on the impacts across generations (e.g., Phadera, 2021). Much less is known about the impacts on distant areas. A few cross-country panel studies show that conflict can have spillover effects on economic growth in neighboring countries (Murdoch and Todd, 2002, 2004).<sup>4</sup> We add to the literature by focusing on spatial spillover effects of conflict on both individual economic and non-economic outcomes within the same country. Also, to the best of our knowledge, we are the first to examine the indirect effects of conflict on women’s empowerment.

This study also relates to previous evidence on the impact of the arrival of IDPs or refugees on host communities. Most of the literature focuses on how the arrival of refugees affects labor market outcomes of the local population (see, e.g., Ruiz and Vargas-Silva, 2013; Ruiz, Isabel and Vargas-Silva, Carlos, 2015; Verme and Schuettler, 2021). In general, studies find small impacts on employment and wages and some studies find short-term adverse effects on young women working in the informal sector.

More recent studies analyze the impact of the displaced population on other outcomes beyond labor market outcomes. These studies find that the arrival of displaced people has adverse effects on health outcomes (Soazic Wang Sonne and Verme, 2019) and consumption by host-community members (Ayenew, 2021). Other studies find no impact on overall poverty indicators for the host population (Azevedo _et al._ , 2016) but an improvement in access to services and infrastructure due to investments being directed to refugee-hosting areas (Zhou _et al._ , 2023). Still other studies find that hosting refugees can lead to price changes favorable for local producers due to the increase in demand (Alix-Garcia _et al._ , 2018). To the best of our knowledge, causal estimates of the effects of forced migration

> 4Bendavid _et al._ (2021) estimate the effects on the health of women and children living within 50 km of conflict, which they interpret as indirect effects because the subjects are not directly involved in violent events. In our paper, we consider these individuals to be directly affected.

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Table 5: The Impact of HSR on Firms’ DVAR: By Distance

|Distance Range (km)|(1)<br>150-800|(2)<br>150-1200|(3)<br>150-1600|(4)<br>150-2300|
|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|
|CI|-0.014|-0.000|0.024***|0.028***|
||(0.009)|(0.008)|(0.006)|(0.006)|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|229,526|229,526|229,526|229,526|
|Adjusted R<sup>2</sup>|0.722|0.722|0.722|0.722|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

**Firm Ownership Type, Trade Regime, and Industry** Tables 6 and 7 report the regression results trade and based on our The by firm ownership, regime, industry affiliation, preferred specification. findings show that HSR has the largest impact on the DVAR of FDI firms, processing firms, and electronics firms. Given that China’s aggregate exports are dominated by processing exporters in the electronics sector with FDI, these results suggest that the overall increase in China’s DVAR in Figure 1 is largely due to these firms gain access to local suppliers through HSR expansion.

**Substitutability of Imported Inputs** The impact of HSR expansion on firms’ input sourcing decisions also hinges on the substitutability between domestic and imported inputs. To capture this, we interact CI with _Diffi,t_ 0—a firm-level, import-weighted differentiation index that reflects the degree of product differentiation in the firm’s import bundle. This interaction term examines the different impacts of HSR on firm DVAR, based on the complexity of the imported inputs of the exporters:

where the variable _Diffi,t_ 0 _≡_<sup>�</sup> _j_<sup>_sjit_</sup> 0<sup>_×Rauchj_measuresthedegreeofdifferentiationinthe</sup> products imported by firm _i_ at the initial period. Here, _sjit_ 0 represents the share of product _j_ (classified at the HS 6-digit level) in the total imports of firm _i_ during its first observed year, _t_ 0. The term _Rauchj_ refers to the product differentiation indicator from Rauch (1999), which assigns a value of 1 for differentiated products and 0 for homogeneous products.18 Firms importing a larger proportion of differentiated products will have a higher value of _Diffi,t_ 0.

> 18Rauch (1999) classifies products at the 4-digit level of the Standard International Trade Classification (SITC, version 2). Homogeneous goods include those traded on organized exchanges and reference-priced goods, while the remaining are categorized as differentiated products. The correspondence between the HS 2007, HS 2012, and SITC 4 classifications is provided by the UN Trade Statistics (UN Trade Statistics Correspondence Tables).

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between 5 and 14 (incl.) years of age. However, we will later have to modify the lower bound to nine years due to econometric considerations.

The incidence of child labor and schooling across age is shown in table 3.1. The figures for the full sample seem to indicate that child labor is not a substantial issue in Ghana, since the vast majority attend school. However, when breaking the sample up by age it is revealed that there is a substantial amount of harnful child labor (defined as child labor directly conflicting with education, i.e. obtaining whenever the child works only) taking place for the older children. This may reflect that earnings foregone raises with age. As children grow older and their potential earnings increase, they are pulled out of school.

**Table 3.1 Incidence of Child Labor and Primary Education Across Age**

|Age|School|Work|Both|Neither|
|---|---|---|---|---|
|_5_|99.0|0.0|0.4|0.0|
|6|98.7|0.1|0.3|0.9|
|7|98.0|_0.5_|_0.5_|1.0|
|8|98.3|_0.5_|0.3|0.9|
|9|97.2|1.2|0.4|1.3|
|10|96.6|1.7|0.3|1.4|
|1 1|95.3|2.7|0.4|1.7|
|12|95.3|2.5|0.5|1.8|
|13|93.2|3.2|0.5|3.1|
|14|87.0|**5.9**|0.3|6.8|
|Total|96.0|1.8|0.4|1.9|

_Source:_ Core Welfare Indicators Questionnaire 1997 (Ghana).

Turning to the incidence of child labor and school attendance across rural/urban location, table 3.2 reveals that the incidence of harmful child labor is more than twice as high in rural areas than in urban (although still low), possibly reflecting the usage of children in agriculture.

Tentatively investigating the incidence of child labor and school attendance across gender, reveals the existence of gender related differences, see table 3.2. While boys are more likely to attend school, girls are more likely to engage in harmful child labor activities.

The second main focus of our study, investigating the possible link between poverty and child labor, is initially touched upon in table 3.2, also. The table reveals a 7
9 nearest available population Census, which was used to generate the small-area welfare estimates, is also included.<sup>8</sup> Finally, the table also lists the poverty line used in each country, both in national currency (in survey year prices) and US dollars (in 2006 prices and at PPP exchange rates)<sup>9</sup> . As noted in the introduction, we have opted to use national poverty lines, which better capture the meaning of poverty in each specific country. This has the drawback that poverty measures are not defined with reference to comparable standards of living across countries. The alternative of imposing a constant poverty line across countries, however, would have an even greater disadvantage. Had we selected a low internationally comparable poverty line, such as $1-a-day, we would be comparing traces of poverty driven largely by measurement error and transitory shocks in the richer countries (such as Albania and Brazil) with real poverty in Kenya and Sri Lanka. Had we instead selected a higher line, like those used in Albania, Brazil or Morocco, we would be comparing “reasonable” poverty incidences in the richer countries, with the bulk of the population in the poorer countries. Since this paper is largely about the _relative_ extent of poverty in larger and smaller towns, the absolute level of the poverty line is of limited importance. We do, nevertheless, examine the sensitivity of our results to varying the poverty line in some of the countries, in Section 4.

A more serious caveat is that we have made no attempt at systematically correcting for differences in the cost-of-living across different urban categories. In some settings, these differences may be substantial, and future research should attempt to take them into account.<sup>10</sup> Similarly, with the exception of Kenya, we have used consumption expenditure (or income) per capita as the individual welfare indicator throughout. If there are substantive differences in family size or composition across different urban categories, one might like to investigate the robustness of the results with respect to

> 8 Further details about the poverty maps analyzed here can be found in respectively (INSTAT, 2004) for Albania, IBGE (2003) for Brazil, Kenya Central Bureau of Statistics (2003) for Kenya, López-Calva et al (2005) for Mexico, Haut Commissariat au Plan (2005) for Morocco, Healy and Jitsuchon (2007) for Thailand, Department of Census and Statistics (2005) for Sri Lanka. In the case of Kazakhstan, the poverty map for that country was produced on a pilot basis in collaboration with the Agency of Statistics of the Republic of Kazakhstan. The results of this exercise have not been placed in the public domain. 9 Each poverty line is per capita per month.

> 10  Section 7 reports on a robustness check indicating that our findings for Brazil are not overturned after correcting by cost of living differences across city-size categories.
individual-level unobservables (as well as location and classroom effects, and the “main effect” of completion) and _γt_ controls for common (monthly) shocks to the labor market. We then correct for within-class correlations in the error term (i.e., across students taking the same course in the same place for the same period; _>_ 15,000 classes/clusters) and for potential aggregate correlations by month _t_ (36 clusters). We limit the sample to 18 months before and after the start of the course, and estimate equation 1 via OLS.

As a complement to the above approach, we then estimate program effects in an event study framework to better gauge the evolution of effects over varying horizons relative to training. We choose this to accommodate the amount of time it may take for effects to materialize from vocational/technical training (Card et al., 2015), as it allows for an unrestricted examination of the differences in labor market outcomes in the months before and after a worker begins the training course. From the event study analysis, we are then better able to characterize the likely pattern of effects we expect to see during a slightly longer time horizon.

In the event study specification, each month relative to the start of the course is given its own differential effect for completers and is estimated by the following equation: In equation 2, the vector _β_ measures differences in outcomes for completers relative to noncompleters at each period relative to course start, capturing the effect of program completion. Similar to the above, individual fixed effects in _λ_ and month fixed effects in _γ_ control for individual selection on time-invariant unobservables and common aggregate shocks. In the estimation, the course start month (indexed to zero) is the omitted group.

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of the relevant variation in income across cities. However, within countries, this assumption may underestimate GDP in major metropolitan areas that are richer than the national average, or alternatively overestimate income in more remote cities. Therefore, in a second specification we replace our baseline estimate of income where possible with an estimate of city GDP from Oxford Economics, a research service that prepares forecasts. As expected, these estimates in some cases are lower than the national average, and in some cases are higher, with the average city GDP being 2.3 times larger using the alternative measure. Compared to variation across countries in national GDP per capita, this alternative specification does not appear to have a large effect on city income. In a third specification, we allocate all national income to cities on the network, in proportion to their relative populations (i.e., so a city with 20 percent of the population has 20 percent of GDP). This specification is comparable to what would be obtained by a country-level analysis that treats projects as a shock to international trade costs, while assuming a country’s entire land and population is affected similarly by reductions in trade costs (see, e.g., De Soyres, Mulabdic and Ruta 2019).

# 4. RESULTS

# 4.1 Trade on the Eurasian transportation network

Several cross-country results will be familiar to readers in international trade, and provide assurance that the gravity model (the second of our model’s three key assumptions) is appropriate in our context when using the specification of trade costs described above. In Figure 2, Panel (a) shows China’s 2017 exports to all countries in the sample, and (b) shows imports, both as reported by COMTRADE since comprehensive data on trade and traffic flows between cities are not available. In the top chart of each panel, the horizontal axes of both figures show the GDP of the trading partner. Trade flows and GDP are in log terms and normalized by dividing by the corresponding value for Portugal. Both have a positive slope and show substantial fit: R<sup>2</sup> = 0.64 for exports and R<sup>2</sup> = 0.65 for imports. The bottom chart in each panel displays 20
<!-- Start of picture text --> London Barcelona<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>1 3 5 7 9 11 13 15 17 19 21 23 25 1 3 5 7 9 11 13 15 17 19 21 23 25<br>DISTANCE FROM CBD (KM) DISTANCE FROM CBD (KM)<br>i) j)<br>Washington DC Seoul<br>250 300<br>200 250<br>200<br>150<br>150<br>100<br>100<br>50<br>50<br>0 0<br>1 3 5 7 9 11 13 15 17 19 21 23 25 1 3 5 7 9 11 13 15 17 19 21 23 25<br>DISTANCE FROM CBD DISTANCE FROM CBD (KM)<br>Source: Authors calculations using 2012 Landscan data. Landscan is a global dataset that models<br>population densities on roughly 1x1 km grid. The city center was identified using the location of<br>oldest building as a proxy, or a government building if necessary. This location was then visually<br>verified using google maps and night time lights.<br>PERSON/HECTARE PERSON/HECTARE<br>PERSON/HECTARE PERSON/HECTARE<br><!-- End of picture text --> Figure 2 above show the density profiles for Nairobi, Kigali, Addis Ababa, Dar es Salaam, Dakar and other major international cities. In the African cities of Nairobi, Kigali, Addis Ababa, Dar es Salaam, there appears to be a high concentration of population near the CBD and a rapid decline as one moves towards the periphery, suggesting a strong monocentric pattern in all cases. Dakar’s is a bit of an anomaly, with its unique physical geography strongly influencing its gradient. Located on a narrow peninsula, data averages are greatly influenced by land restrictions.

In Dar es Salaam, there is a slight dip in density between 1 and 3 km of the city center, due to a large industrial park that is only a few kilometers from the CBD. In Nairobi, a similar drop is seen slightly further out, and can be attributed to a portion of land taken up by an airfield base and railroad infrastructure. In Addis Ababa’s gradient, a slight immediate rise and then fall is consistent with the existence of a large number of parks and government buildings right in the heart of the CBD. Lastly, while Kigali is a considerably smaller and less dense city, it shares a similar monocentric pattern as the other three cities analyzed.

These gradients also reveal that, despite the variation in the size of the city footprints, a quick decline in population density is observed for all African cities roughly 6‐8 kilometers from the CBD. This could be an indicator that many people are forced to live within walking distance from their jobs. According to a 2013 UN report, “the share of walking trips in Sub‐Saharan Africa is higher than in any other region of 7
cluding parent representatives. Despite measures to prevent graft inspired in part by the Ugandan experience (Ritva Reinikka & Jakob Svensson 2004), an external audit of the FPE funds commissioned by the Ministry of Finance in 2009 found enormous shortfalls in actual disbursements. Actual funds received fell short of the legislated amount in each year audited by approximately 5 to 15% (Andrew Teyie & Henry Wanyama 2010).<sup>7</sup> If, in line with the most pessimistic media reports, schools received an average of $11.90 per pupil in FPE grants ($14 minus 15%), the net effect would have been an increase in per pupil financial resources for four of the five groups in Figure 2a. But for the fifth group, i.e., the richest households, the effect of FPE on per pupil funding in public schools was likely negative. This is true even when ignoring changes in class size, to which we now turn.

# **Human resources**

In addition to the capitation grants, the key resource provided to public schools by the central government is teaching staff. In this section we examine what happened to pupil-teacher ratios under FPE, what these changes mean in monetary terms, and how they were distributed across different socio-economic groups.

We have access to administrative data on pupil-teacher ratios at the school level from the Ministry’s Education Monitoring Information System (EMIS) for 2002 to 2005, spanning the introduction of FPE in 2003. This data covers all public primary schools with a unique identifier to link schools across years. Exploiting the longitudinal structure of the data, Figure 2b shows the change in the pupil-teacher ratios from 2001 to 2005, relative to the starting level in 2001. The graph depicts a locally-weighted Fan regression (Jianqing Fan 1992) with a 95% confidence interval based on 50 replications of a bootstrap routine, clustered at the level of the enumeration area. The graph shows a clear pattern of convergence, or reversion to the mean. Schools with small class sizes in 2001 saw significant gains, and schools with large classes saw significant declines.

> 7For the sake of the calculations here, we assume that leakage of FPE grants was uniform across schools. We know of no evidence on the distribution of missing grants across schools, but should acknowledge the possibility that heterogeneous leakage could exacerbate or attenuate the unequal changes in school funding induced by FPE.

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