rafmacalaba/datause-encoder-data huggingface dataset:
Between 1996 and 2006, Nepal experienced violent civil conflict as a consequence of a Maoist insurgency. This paper investigates the impact of being exposed to this insurgency at a young age on education outcomes.

This study makes both an empirical and a methodological contribution to the growing literature on the impact of civil conflict on human capital formation. First, this paper extends our understanding of the impact of civil conflict on education to include a conflict of moderate intensity. With just over 13,000 casualties and less than 1 percent of the population forcibly displaced, the level of violence considered in this study was much lower than in conflict episodes considered in previous studies.<sup>1</sup> Second, two alternative identification strategies are employed to increase confidence in the reliability of the estimates: the first relies on variation in conflict exposure across birth cohorts and geographic areas in a single survey, as is standard in the literature (e.g., Akresh and de Walque 2008; Shemyakina 2011; Chamarbagwala and Morán 2011), and the second relies on variation in exposure to conflict among school-aged individuals between household surveys and geographic areas.

Educational attainment is generally expected to be adversely affected by exposure to armed civil conflict. Direct youth enrollment in the military, limited mobility, and the destruction of schools may all negatively affect the ability of children to attend school. Increased poverty may drive parents to remove children from school (to avoid direct costs) and put them to work (to avoid opportunity costs). Political instability and reduced life expectancy may decrease expected returns to education and, in turn, reduce investments in human capital. Moreover, the schooling of girls is often more sensitive to worsening economic conditions than that of boys. A conflict environment may also hinder the functioning of education programs by weakening government institutions and imposing logistical and staff security challenges on local and international NGOs.

However, the general expectation that schooling is disrupted in conflict areas may not be well founded in the particular case of Nepal. National trends do not indicate an increase in poverty coinciding with the conflict but rather a steady decline in poverty (World Bank 2005). Despite 2
growth in year t+2 (2020). The educational attainment, specifically the percentage of adults with secondary education, represents human capital accumulation and is an indirect measure of technological progress. Its projection from 2010 to 2050 is imputed using a simple projection equation capturing stable correlation with GDP per capita.<sup>2</sup> Other national level exogenous variables, such as projections of national population growth and urbanization rate, are from the UN Population Division projections. In this way, we estimate city population projections of 2,175 core cities of more than 100,000 population.

In the second stage, we extend the city population projections to 8,301 cities of more than 50,000 population. The data are from the CIESIN (2010), but they only contain estimates for a single point in time, for the year 2000. We also note that data for these 8,301 cities have been collected from different sources and city definitions are not completely harmonized with the aforementioned city profiles of 2,175 core cities with more than 100,000 population.<sup>3</sup> These data limitations do not allow a full-fledged panel data analysis as in the first stage core city modeling, and we cannot identify city-specific growth dynamics. In order to circumvent this problem, we extrapolate city growth dynamics from the first stage core city growth modeling results. A key assumption, derived from the _systems of cities_ literature, is that a city’s growth dynamics crucially depend on its rank in the urban hierarchy.<sup>4</sup> In other words, the relative city population size in a competing region determines its future growth when other exogenous variables are controlled for.

> 2 The percentage of adults with secondary education of country _j_ in year _t_ is predicted using GDP per capita projections of Hawksworth (2006).

Robust standard errors are in parentheses. All coefficients are significant at 1%. R<sup>2</sup> = 0.835.

> 3 In contrast to the previous analysis we include cities in industrialized countries in these projections.

> 4 A basic model of multiple types of cities involves different types of urban specialization, where different types of cities are specialized in different products and resulting in different city sizes. See Henderson (1974), and Duranton and Puga (2002) for a review.

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Figure 5 **.** Cumulative Distribution Function of Price Volatility for Goods Available for the Whole Period <!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 1 2 3<br>standard deviations<br>primary commodities all manufactured goods others<br>cumulative distribution functions<br><!-- End of picture text --> Note: The figure shows the cumulative distribution functions of the standard deviations of Hodrick-Prescott filtered series of individual goods prices. The goods represented are those which prices are available for the whole sample period. Data are from the Foreign Trade Division of the U.S. Census Bureau.

21
Two recent papers deal more specifically with road infrastructure. The first of them, Coulibaly and Fontagné (2006), focuses on West Africa. The authors find that a composite measure of road quality in the importing and exporting countries has a positive and statistically significant effect on trade. Transit effects are also important, with the authors using a count of the number of borders crossed as a proxy. By contrast, Buys et al. (2006) examine road network quality across the whole of Sub-Saharan Africa (SSA). They use detailed road transport data to construct measures of international distance on an overland basis. They then build up a multi-dimensional measure of road quality, which is aggregated taking proper account of transit effects. Results from their gravity model show that network quality has a significant impact on intra-regional trade, while simulations suggest that the benefits of a road upgrade are very substantial: around $250bn over 15 years. The trade benefits far outweigh the costs, which include an initial investment of the order of $20bn and yearly maintenance of $1bn.

Our paper builds on and extends this literature in five main ways. In terms of substance, our analysis incorporates a new ECA road distance database similar to the one used by Buys et al. (2006) in the SSA context (Section 2). It allows us to identify road transit routes in detail, and to construct bilateral road quality indicators based on actual distances traveled in the exporting, importing and transit countries (Section 3). Secondly, our data and gravity model (Sections 4-5) allow us to simulate the trade impacts of road upgrades in different countries, taking account of cross-border spillovers due to overland transit. Thirdly, our model includes policy variables covering applied tariffs and trade facilitation, in addition to road network quality. This enables a comparison of the relative impacts of different reform scenarios (Section 6). On the methodological front, we use the “theoretical” gravity model specification due to Anderson and Van Wincoop (2003, 2004). Fixed effects are used to take account of unobservable country- and

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world imports.’ By the same token, the measure used to evaluate the level of non-tariff restrictions is the weighted percent of tariff-code lines covered by various types of non-tariff barriers (licenses, quotas, prohibitions) as a percentage ofall tariffcode lines, using as weights the respective shares in world trade. The dispersion of tariff and para-tariff rates is measured by their simple standard deviation. Table 1 presents data on each of these indicators for the periods 1984-87, 1988-90, and 1991-93. Unfortunately, only ten countries could be included in the table; they are the largest countries in South America and Mexico. For most of the smaller countries in the region, UNCTAD (1994), the main data source, does not report historical figures for the indicators on trade restrictions.

A summary index was computed to measure the degree of trade liberalization .* This index was constructed in such a way that comparisons over time and across countries are possible. This policy-based index of trade liberalization is presented in Chart 1. It shows that Chile, the pioneer of trade liberalization in the 1970s, has also led the process in the late 1980s and early 1990s. Bolivia, Mexico, Venezuela and, to a lesser extent, Brazil, made considerable progress in trade reform in the late 1980s; all of the remaining countries conducted major trade reforms in the early 1990s. Whereas Brazil and Venezuela liberalized their tariff regime over a few years, Argentina, Colombia, and Peru conducted faster trade liberalizations. (Although not included in the index for lack of comparable data, countries such as Uruguay and Costa Rica in the late 1980s, and El Salvador, Guatemala, Nicaragua, and the members of the Caribbean Community in the early 1990s also conducted important trade liberalizations.”) Closer examination of the components of the index (see Table 1) reveals that the process of trade liberalizationwas accomplished through a balanced reduction of all trade restrictions, that is, a decrease in the level and dispersion of tariffs and para-tariffs and sharp reductions (even elimination, as in Chile and Peru) of non-tariff barriers. To appreciate the forcefulness of trade liberalization in Latin America, it is illustrative to compare the regional average of both the rate of tariff and para-tariff charges and the incidence of non-tariff measures with those of the Asian NICs. Chart 2 shows that the enormous differences in trade barriers between the two groups of countries that existed in the 1980s were considerably reduced in the early 1990s.

> ” See Edwards (1995).

.

4
# Tables A.13. Explaining conservative social norms, by gender **Table 13.1. Explaining Social Norms – Females only sample**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||PNB Index -<br>HH Roles|PNB Index -<br>Public Space|SNE Index -<br>HH Roles|SNE Index -<br>Public Space|Sanctions<br>Index|
|Respondent’s age in years,<br>squared|0.00<br>(0.00)|-0.00<br>(0.00)|-0.00<br>(0.00)|-0.00*** (0.00)|-0.00<br>(0.00)|
|Years of education, derived from|-0.01**|-0.02***|-0.01|-0.02*** (0.01)|-0.11***|
|education level|(0.01)|(0.01)|(0.01)||(0.03)|
|Indicator for respondent having|-0.02|0.00|-0.05|-0.02|0.23|
|childyounger than 5years|(0.02)|(0.03)|(0.03)|(0.04)|(0.17)|
|Respondent lives in urban area|0.01<br>(0.05)|0.04<br>(0.15)|-0.07<br>(0.05)|-0.11<br>(0.14)|-0.92*<br>(0.48)|
|Log of household's total income|0.02***<br>|0.01|0.01|-0.00|0.12*<br>|
|minus working female’s earned<br>income(NPR)|(0.00)|(0.01)|(0.01)|(0.01)|(0.07)|
|Madhesh Province|1.07***<br>(0.08)|0.64***<br>(0.11)|0.96***<br>(0.07)|0.78*** (0.10)|-0.30<br>(0.62)|
|Gandaki Province|-0.14**<br>(0.07)|-0.29***<br>(0.08)|-0.22***<br>(0.06)|-0.25** (0.09)|-1.42***<br>(0.49)|
|Sudurpashchim Province|0.43***|0.54***|0.50***|0.34**|-1.54**|
||(0.08)|(0.12)|(0.13)|(0.15)|(0.55)|
|Respondent belongs to Madheshi|0.16***|-0.04|0.22***|0.09|0.96**|
|(non-Dalit) ethnic<br>group|(0.05)|(0.2)|(0.06)|(0.15)|(0.40)|
|Migrant HH|-0.01<br>(0.03)|-0.03<br>(0.05)|-0.02<br>(0.03)|-0.00<br>(0.04)|-0.57*<br>(0.31)|
|At least one among MIL and FIL|-0.00|-0.02|0.00|-0.02|0.04|
|<br>live in the HH|(0.02)|(0.06)|(0.03)|(0.04)|(0.19)|
|Constant|0.30**<br>(0.11)|0.61***<br>(0.19)|0.65***<br>(0.12)|1.18***<br>(0.20)|17.33***<br>(0.81)|
|Observations|1,150|1,150|1,150|1,150|1,150|
|Adjusted R-squared|0.67|0.28|0.65|0.34|0.08|

_Notes_ : (1) Reference group for province is Bagmati Province. (2) SEs clustered at the ward level. (3) Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. SEs clustered at the ward level.

47
systems are geared for following government instructions on credit allocation, a sudden move to a laissez-faire system would most likely result in large losses. Similarly, when there are severe information asymmetries, banks main source of information will arise from longstanding relationships with their clients. Destruction of this information capital, through devaluation, reduction in protection, or cutbacks in public investment spending, can lead the banking sector to retreat from lending and thereby deter investment. This consideration does not imply that such changes should not be made, only that their impact on banks' willingness to lend should be taken into account.

The argument that post-reform developments likely will depend greatly on initial conditions highlights the key role played by banks' portfolios and the stock of information capital. Not just aggregates of assets and liabilities, but the division between different categories of each can have an impact on the response of individual institutions and the entire - financial system to reform. For example, in many countries both among those reviewed here and more widely, including the industrialized economies -- problems with nonperforming loans in the real estate sector followed on the heels of attempts to deregulate finance. In addition to the possibility of interest rate mismatching, in some markets it has been argued that there was a run-up of property prices spurred by bank lending."<sup>0</sup> In certain cases it appears that, prior to the onset of financial reforms, real estate loans had been "crowded out" of banks' portfolios by other priority sectors, so that as intervention was lessened, banks began to adjust their portfolios in favor of this sector. However, a widespread portfolio reallocation always entails some dangers: it can lead to higher asset prices in the favored sector, and persuade bankers that their initial portfolio reshuffling was so profitable that they should invest even more in the growing sector, thereby contributing to real resource shifts that later may well be reversed." Malaysia appears to fit this case, with a dramatic rise in the real estate exposure of banks (from 12% to 36% of bank assets), and Indonesian data are suggestive as well. More generally, the point is that financial reform usually entails a portfolio shift, away from forced holding of government securities and directed credit. Allowing the shift to occur suddenly
# _Disentangling Input and Output trade liberalization_

A further question worthwhile exploring is related to the types of goods: which are the French exported products that are affected the most by Asian liberalization? To test the relationship between market access and types of traded products, we estimate equation (3) by splitting the sample into firms producing intermediate and final goods.

To classify HS6 products into intermediate and final products, we use BEC (Broad Economic Categories) classification from United Nations.<sup>21</sup> In line with the firm-level tariff built in section III, we construct construct a similar measure for both intermediate and final goods using information at the HS6 to classify products.

Table 5 reports the estimate results for our two subsamples of firms: firms exporting intermediate and final products respectively. When controlling for observable country characteristics and foreign competitive pressures, we find that Chinese tariff cuts are mainly significant for French exports of intermediate products (columns (1) and (3)). Our point estimate implies that a 10 percentage points fall in Chinese applied tariff, increases by almost 17 percent the number of intermediate products exported and by almost 28 percent French export sales to China relative to the other Asian destinations. When we restrict the sample to firms exporting only final goods, interestingly we do not find any significant effect of Asian or Chinese liberalization on French extensive margin (column (2)), while the effect on the intensive margin is only significant at the 10 percent level (column (4)).

These findings confirm the results found by a number of recent works on the increasing role of intermediate inputs in international trade and the effects of input liberalization on firm performance. Amiti and Konings (2007), using firm-level data for Indonesia, show that the effect of reductions on input tariffs on firm total factor productivity improvements is much important than the effect of reductions on final good tariffs. Goldberg et al. (2010), using firm level data for India, demonstrate that input tariff liberalization allows firms to expand their product scope for the domestic market. Bas and Strauss-Khan 23
US$4.7 billion a year. Improving the reliability of electricity supply would add to these gains. According to IHDS 2012 data, the average duration of power outages of households who have less than 24 hours of power supply is about 12.5 hours a day. Increasing the supply of electricity to 24 hours a day would lead to an estimated income gain for the rural population of US$6.5 billion annually.

The Indian government plans to achieve 100 percent electrification in rural areas by 2017, at a total estimated cost of US$4.4 billion between 2015 and 2017. Its plans call for both connecting the villages that remain without electricity and improving the reliability of power supply in villages that are already connected. Assuming a 20‐year project lifespan, the annual average investment cost is about US$200 million. Even without accounting for other benefits associated with better health and education outcomes, our estimates suggest that the net income gain from providing reliable electricity to rural households would reach almost US$11 billion a year.

Based on quantile regression analysis, we also find that while every income group benefits from electricity, the benefits are larger for households in lower‐income quantiles. This suggests that the current rural electrification policy in India is progressive.

Overall, the findings from our analysis suggest that merely ensuring connectivity is not enough; increasing the reliability of service is essential to realizing greater benefits from electrification. While the availability of electricity is important, it is only one of several factors that matter to the impact of electrification. Others include the frequency of power disruptions, fluctuations in voltage, and the ability to simultaneously use multiple appliances, including high‐capacity ones. Ongoing initiatives are adding to our knowledge on these issues. One set centers on a multidimensional energy access matrix recently developed by the Sustainable Energy for All (SE4ALL) program, i.e. the Multi‐tier Framework (MTF) of energy access. The Energy Sector Management Assistance Program (ESMAP) at the World Bank is using this framework to measure all the essential attributes of quality in electricity access around the world. And the World Bank is conducting a number of household surveys based on this framework to evaluate energy access in developing countries. Once such data become available, further analysis can be carried out to estimate how different aspects of the quality of service affect the impact of rural electrification.

18
This approach to constructing a counterfactual never requires that households have access to additional cash in order to buy in bulk (although they might need the cash a little sooner, once in the lifetime of the household—see the extensive analysis in the first subection of Section 5). Because we use observed total two-week quantities in constructing the counterfactual—not large, hypothetical purchases off of the observed support—buying in bulk as defined here can only increase, not decrease, the household’s cash reserves over the total two-week period.

Because we are estimating price schedules, not demand curves, the approach is not threatened by censoring concerns related to the two-week observation window. The most likely form of data censoring is that very large purchases, e.g., of wholesale bags of grain, are too infrequent to appear as focal quantities, even though they may be widely available at markets. The absence of these large-quantity purchasing opportunities means that our calculations are likely to be lower bounds on actual losses.

# **3 Data and descriptive patterns**

The data for this paper are from the Survey of Household Welfare and Labor in Tanzania (SHWALITA). The survey was part of an experiment to test the impact of questionnaire design on consumption measures (see Beegle et al. (2012) for details). In one arm of the study, 9 households per village were randomly assigned to complete a consumption diary. Three of these households completed a single, household-level diary, with no monitoring by project staff. Three completed a single household diary but received multiple follow-up visits from field staff. For the last three households each adult member kept their own diary, with children placed on the diary of the most knowledgeable adult. Households in the third group received multiple follow-up visits, similar to those in the second group. The differences between module arms are small but not zero, and they have no impact on the findings in this paper. We control for diary type whenever relevant.

The SHWALITA survey was conducted in 24 villages per district, in 7 districts. The resulting data set includes responses for 1,512 diary households. After dropping households that did not purchase any of the items that we study or that did not complete the end-line 10
# **1 Introduction**

Despite its potential to lift incomes and increase welfare, intra-regional trade in Sub-Saharan Africa remains low relative to other regions. Intra-African trade accounted for 17 percent of the region’s total exports in 2017, compared to 10 percent in 1995. By contrast, in Asia and Europe, the corresponding shares are 59 and 69 percent, respectively (Songwe, 2019).

Yet the lack of regional integration likely masks a deeper level of informal trade which is not typically captured with official customs data (Brenton and Isik, 2012). While measurement issues around the volume of cross-border trade flows abound (Mitaritonna and Traor ́e, 2017), concerted efforts to measure them hint at the scale of these informal flows. For example, a recent national survey at 171 unofficial border crossings in Benin found that informal trade made up a very large share of flows with its neighboring countries, with the ratio of informal to formal trade flows at 5.1 to 1 with Nigeria and 2 to 1 with Togo (Bensassi et al., 2018). Similarly, a comprehensive 2006 survey of Uganda’s informal cross-border trade flows into the Democratic Republic of Congo and four other neighboring countries found informal export levels that were 86 percent that of official export flows (Lesser and Mois ́e-Leeman, 2009).

Supporting informal cross-border trade is consequently an important element of the regional trade integration agenda. Cross-border trade can enhance welfare through increased income for producers and traders, reduced price dispersion, and cheaper food in areas where production is scarce (Brenton and Isik, 2012; Golub, 2015). Yet small-scale traders in the Great Lakes region of Africa, most of whom are women, face many obstacles to doing business, including harassment and requests for bribes at border crossings. The fragile institutional setting also constrains their ability to get their goods to market, as traders confront a maze of shifting formal and informal rules and procedures.

In this experimental study, we draw on the insights of Hirschman (1970)’s Exit, Voice, and Loyalty framework to shed light on the strategic behaviors of cross-border traders in a setting where border officials frequently extract rents and harass traders. By developing a randomized evaluation of an NGO program which sought to give traders new information about the legal framework governing cross-border trade, we examine whether reducing informational asymmetries between women traders and border officials could lower corruption and harassment at the border via the voice mechanism.

2