rafmacalaba/data-use-mentions-extended huggingface dataset:
chemical applications took place prior to its introduction. In some respects, these two scenarios can be mapped to developed and developing countries where the input intensity may roughly correspond to the numbers used in this hypothetical experiment. Thus, the difference in incremental profit under the two scenarios ($100 _versus_ $600), which reflects productivity increases, can be seen as the driving force behind China’s and India’s adoption of biotechnology and subsequent increase in cotton production.

The pros and cons of biotech cotton (and biotechnology in general) have been discussed extensively in a broad context and in terms of specific costs and benefits, the latter mostly from survey-based research. Despite early signs regarding the benefits of biotechnology, institutions were at first reluctant to engage in the debate (or take an ‚official position‛), not only in terms of policy or financial assistance but also in terms of a general policy discussion. Such reluctance reflected, most likely, the controversial nature of the subject.

Perhaps, the first institutional study to discuss and explicitly acknowledge the broader benefits of biotechnology in developing countries was FAO’s 2004 _The State of Food and Agriculture_ report, which showed that on balance, biotech cotton growers were better off than growers of conventional seed varieties. Individual authors followed suit. Baffes (2005) argued that in addition to subsidy elimination and domestic policy reforms, adoption of biotech varieties should have been a priority among policymakers in low-income cotton producing countries. Falck-Zepeda, Horna, and Smale (2007) and Anderson, Valenzuela, and Jackson (2008) warned that the downward pressure on world cotton prices caused by the large-scale adoption of biotech cotton is likely to force other countries to adopt the technology in order to compete in the global market.

Numerous survey-based country-specific papers have evaluated the costs and benefits of biotech cotton. An earlier review by Smale, Zambrano, and Cartel (2006) surveyed 47 peer-reviewed articles published between 1996 and mid-2006. While they concluded that the evidence is promising in the sense that biotechnology is beneficial
The countries in our sample are drawn from all World Bank regions. While the coverage reflects heavily the availability of surveys which include detailed information on the income sources—as well as the expenditure patterns—of the poor, we sought to improve the coverage of our country sample in the countries that contain the largest numbers of poor people: for example, in terms of population our sample represents 98 percent of the South Asia region, 32 percent of Sub-Saharan Africa, 41.8 percent of middle income countries and 34.5 percent of the population of lowincome countries.

Where available, we used country-level data on actual changes in domestic food prices from _Food Price Watch_ (World Bank, 2011) presented in Table 2. Comparison of Table 1 and Table 2 suggests that the transmission of global price increases to domestic prices has been high in many countries. For instance, between June 2010 and December 2010, the 68% increase in the international price of wheat was associated with large price increases in Kyrgyzstan (54%), Bangladesh (45%), Tajikistan (37%), Sri Lanka (31%), Azerbaijan (24%), and Pakistan (16%). The domestic price of rice rose broadly in line with the 21% increase in global prices in Indonesia (19%), Bangladesh (19%), and Pakistan (19%) during this six month period. Higher sugar prices have fed through to domestic prices quickly in many countries—for instance, sugar prices doubled in Cambodia between June and December 2010 and edible oil prices increased by 15% between September and December in Afghanistan. Several countries have intervened to temper this pass-through. In Algeria, taxes and import duties on sugar and edible oil were sharply reduced in January 2011 due to double-digit price rises. In Indonesia, the government reduced taxes on sugar and increased subsidies to local cooking oil producers.

In cases, where no domestic price data were available, we used import shares reported in version 7 of the GTAP
,” Washington, D.C. : World Bank Group, 2020, Vol. 1 of 2.

Deaton, Angus, “The analysis of household surveys: a microeconometric approach to development policy,” The World Bank, 1997.

Deaton, Angus, “Saving and liquidity constraints,” Econometrica, 1991, 59 (5), 221–248.

___ and Christina Paxson, “Saving, growth, and aging in Taiwan,” in “Studies in the Economics of Aging,” University of Chicago Press, 1994, 331–362.

___ and Christina Paxson, “Growth and saving among individuals and households,” Review of Economics and Statistics, 2000, 82(2), 212-225.

20
technologies. To illustrate, increased erosion from deforestation may increase silt levels in irrigation water, thereby decreasing the efficiency of irrigation canals. Likewise, removal of mangroves and wetlands along coastal areas can increase saltwater incursions into groundwater sources, increasing salt levels in irrigation water and decreasing agricultural yields.

In many cases these ecosystem goods and services may be quite sensitive to climate change. Under most climate change scenarios, changes in rainfall and temperature are predicted to alter the mix of local plant species. Using 23 different climate change scenarios, Battisti and Naylor (2009) found a greater than 90% probability that, by 2100, the average summer temperatures in the tropics and subtropics will exceed the recorded high temperatures from 1900 – 2006 for those areas. As average and maximum temperatures increase, the productivity and viability of plant species change, particularly above the threshold temperature of 35 C (Schlenker and Lobell 2009; Schlenker and Roberts 2006). Thus climate change is likely to cause species loss as well as altering the types of fauna supported by an ecosystem (Walther et al. 2002; McCarty 2001). Climate change is likely to introduce new pest and predator species and reduce beneficial species – factors which are particularly critical for agriculture (Kevan 1999).

Reductions in natural resource goods and services are likely to have a significant impact on the poor. Empirical evidence from household surveys in Zimbabwe estimate that poor households derive as much as 40% or more of their incomes from environmental goods (Cavendish 2000), and 24% of incomes in Peru (Takasaki, Barham, and Coomes 2004). An estimated 31% of household production income derives from bush meat in the Democratic Republic of Congo (de Merode, Homewood, and Cowlishaw 2004) and 75% of surveyed households in Brazil devoted time to collecting non-timber forest products (Pattanayak and Sills 2001). Natural resource goods also represent a significant fraction of cash incomes (Cavendish 23
|Classifcation|Defnition|
|---|---|
|Towns|Sets of contiguous grid cells with population density > 95<sup>th </sup>percentile|
||of the counterfactual with no core.|
|Other rural|Areas not classifed as cities or towns.|
|areas||

Note: See Combes et al. (2023) for details. Towns are classified as urban in the stricter version of definition and as rural in the looser version of definition in Combes et al. (2023)

# **3 Data**

For this study, we have prepared a new dataset called the International Urban Poverty Database by integrating two types of data: high-resolution gridded population data for constructing urban classifications, and detailed household-level data for constructing poverty measures.

## **3.1 Gridded population layers**

We have utilized two gridded population datasets for the year 2015: GHSPOP and WorldPop, with a resolution of 1km and 250m, respectively. Both GHSPOP, created by the European Commission, and WorldPop, produced by the University of Southampton, are open-source datasets that cover most countries globally for multiple years.<sup>15</sup> The preparation of both datasets involves allocating each country’s census-based population across gridded cells within a given administrative area. For GHSPOP, the population is evenly distributed across grid cells that contain built-up areas (Florczyk et al., 2019). In contrast, WorldPop utilizes a machine learning approach, specifically the random forest method, to calculate weights for each grid cell. These weights are based on various spatial input layers, including land cover, nighttime lights, and (social) infrastructure data. The population is then distributed across grid cells using these weights. Constrained WorldPop further constrains the distribution of the population to grid cells that contain built-up areas (Stevens et al., 2015).

In this study, our primary choice of input population layer is WorldPop, while GHSPOP serves as an alternative data source.
Table 19: in foreign invested companies the minimum wage is high

||Monthly<br>Average wage<br>(in Dongs)|Ratio minimum wage<br>To average wage|
|---|---|---|
|Total|522,610|0.93 - 1.06|
|Total urban|625,420|0.89 - 1.00|
|Total rural|445,090|1.09|
|Private enterprises|623,430|0.78- 0.89|
|100% FDI|979,550|0.49 - 0.64|
|Joint ventures|524,180|0.93 - 1.19|
|_Source:_VLSS 2 (GSO|, 1999)||

**Table 20: The minimum wage is higher than wages of skilled workers**

||Monthly<br>Average wage<br>(in Dongs)|Ratio minimum wage<br>To average wage|
|---|---|---|
|No diploma|486,230|1.00- 1.28|
|Primary diploma|509,430|0.95-1.23|
|Lower secondary diploma|497,050|0.98-1.26|
|Upper secondary diploma|567,620|0.85 - 1.10|
|College and university|747,840|0.69-0.88|

_Source:_ VLSS 2 (GSO, 1999) To what extent is this minimum wage enforced? According to Vietnam's Ministry of Labor, Invalids, and Social Affairs (MOLISA) minimum salary breaches are widespread in foreign invested companies, mainly in garments, leather tanning, and paper production, as well as in agriculture. Interviews in Haiphong showed that some managers of foreign invested companies or joint ventures have indeed difficulties recalling the level of the minimum wage. Others openly admit paying below mandatory minimum
# **Figure 15: Percentage with at least some secondary education, Cambodia Labor Force Survey 2001**

<!-- Start of picture text --> 0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 to 3 4 to 8 9 to 13 14 to 18 19 to 23 24 to 28 29 to 33 34 to 38 39 to 43 44 to 48 49 to 53<br>Age in 1975<br>Males Females<br>Percentage with at least some secondary education<br><!-- End of picture text --> 35
changes and make informed decisions regarding the design and implementation of social policies.

_Policy Scenario 1: GMI increase and constant take-up._

**In this policy scenario, we undertake a simulation consistent with the reform of the Monthly Social Allowance (MSA) program.** This implies an increase in the GMI base from its previous value of 75 Bulgarian Leva (BGN) to 30% of the at-risk-of-poverty (AROP) poverty line. This adjustment assumes that the estimated take-up rate from survey data remains constant. This scenario is not limited to the MSA alone. It also considers its interaction with the heating allowance, which is instrumental in addressing seasonal affordability challenges, particularly those related to heating expenses. With the higher threshold, more households would now meet the criteria to access these benefits, based on data from the 2021 Survey on Income and Living Conditions (SILC). We assumed that the take-up rates would remain consistent with our earlier estimates of 56% for MSA and 99% for the heating allowance.

**Under this government reform, the increase in the MSA's base value results in a tangible expansion of its coverage.** This coverage now extends up to the second decile, encompassing a more significant proportion of the population in need (Figure 9, panel a). Additionally, the financial support provided through these programs witnessed a notable uptick. According to our estimates, the number of households benefiting from these schemes would rise from 24,000 to 84,000, and the total benefits disbursed would increase substantially, reaching nearly BGN 230 million.

**However, it is important to note that while these adjustments notably impact the number of beneficiaries and the total resources allocated, the effects on poverty and income inequality remain modest** . The primary influence is seen in a more pronounced reduction in the poverty gap, highlighting the challenges of crafting policies that meaningfully alleviate poverty and reduce inequality (Figure 9, panel d). This scenario demonstrates the intricate balance between increasing program coverage and
not comparing countries in East Asia to countries in Africa. Large trade volumes have surely contributed to the development of sophisticated port facilities in Singapore and other East Asian countries. If trade facilitation influences trade in large discrete steps, as investing in ports tends to be lumpy, our estimation is robust.<sup>17</sup> The cost of this strategy is that it reduces the variation in the time delays in exporting. This is because countries within a preferential trade agreement group are similar in terms of tariff and procedural barriers to trade.

Endogeneity may still be a problem since relatively high export volumes within regions may lead to better or worse trade facilitation. To control for the potential effect of export volumes on export time, we also report the results using instrumental variables. The instruments we use are the required number of signatures for exports the required number of signatures for imports. The intuition is that the number of signatures is a measure of excessive bureaucracy that slows down trade facilitation, but is not a result of shipping volumes, such as congestion or having containerized ports is likely to be.<sup>18</sup> For example with congestion effects, more trade may extend the waiting time for a required signature, but it would not affect the number of signatures required.

# **IV. Results**

We start by estimating a gravity equation with importer fixed effects, Equation (1) above, using aggregate bilateral data. The results are shown in Table 3. Column 1 reports the

> 17 There may, however, still be an endogeneity concern if even marginally higher trade volumes encourage relatively better trade facilitation or if higher trade volumes lead to congestion effects, extending the time to process goods.

> 18 A potential problem is that political interests may lobby more extensively for more transparent export procedures if exports are large.  In addition, the number of signatures may be related to the overall business climate, which may affect exports.

15
iterative method results on Susenas February 1999, what was the poverty incidence in February 1996 and, hence, what is the change in headcount poverty during this period.

Crucial in these comparisons over time is the choice of deflator to convert the February 1999 regional poverty lines to those of February 1996. Suryahadi _et al_ (2000) recommend a price deflator where the share of food in the deflator uses the share of food in the poverty basket (0.8). They admit that this deflator overstates poverty changes because it does not allow substitution and does not use the actual consumption bundle of the poor. But they argue that this deflator is defensible as the price index shares represent the actual consumption pattern of some group in poverty, although the group is considerably below the poverty line.

The regional headcount poverty rates in February 1996 and the percentage changes between February 1996 and February 1999 are shown in table 4 using this deflator and food price inflation from Susenas unit prices and non-food price inflation from CPI. Meanwhile, the associated poverty lines and number of poor people in February 1996 are shown respectively in tables A4 and A5 in the appendix.

17
To the best of my knowledge, this is first study to disentangle nature and nurture effects in the intergenerational transmission of human capital in the context of a developing country. Family structures and the average schooling attainment of parents and children differ widely between developed and developing countries and therefore the transmission of human capital from one generation to the next might also work differently. Developing countries, in particular because of the large number of orphans produced by the HIV/AIDS epidemic and conflicts, are also in need of policy recommendations for organizing orphan care. The results from this paper suggest that maintaining an orphan in his extended family is beneficial and favors the transmission of human capital.

Section 2 of this paper describes the data and the Rwandan context. Section 3 presents the results of the empirical analysis together with several robustness tests. This section also discusses the external validity of the estimates. Section 4 concludes.

# **2. Recomposition in Rwandan Families after the Genocide and Data Description**

This study uses data from the Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) 1999-2001. This is a fairly standard survey that measures household living conditions. In urban areas the survey took place between October 1999 and December 2000, while rural areas were surveyed between July 2000 and July 2001 (Siaens, Subbarao and Wodon, 2003). Using this survey data Siaens, Subbarao and Wodon (2003) assess the vulnerability of orphans and conclude that they are more at risk in terms of school enrollment,<sup>3</sup> child labor, and malnutrition.

Figure 1 suggests how large the orphan population is in Rwanda. It plots, by age, the proportion of children living in households from which _both_ biological parents are absent. For children over age 6, this proportion is consistently over 15 percent and reaches 22 percent for children aged 15. Not all of these children are orphans; the survey only indicates that their biological parents are not present in the household in which they live. However, Siaens,
# **References**

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- Bernal, C., M. Prem, F. Vargas, and M
017|0.265***|0.201***|0.165***|0.061***|
||(0.034)|(0.014)|(0.030)|(0.025)|(0.027)|(0.011)|
|Observations|10,618|10,618|10,618|10,618|10,618|22,664|
|R-Squared<br>_Note_: Each column rep|0.021<br>orts results from|0.022<br>a single regres|0.204<br>sion of a binary va|0.014<br>riable equal to 1|0.042<br>if the respondent|0.022<br>stated that the|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent stated that the household relied on that strategy to cope with the loss of income and 0 if they did not. The binary variable is regressed on indicators for each country, with Malawi as the base case, and round, along with an indicator equal to 1 if the household head is female, as opposed to male. Data come from all rounds of phone survey data in which FIES questions exist. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

52
However, on average, women were not successful in finding a job as the increase in labor force participation translated into increases in unemployment, with no statistically significant changes in the employment rate (Column 1 in Panels B and C). The only exception are older and skilled women who did, in fact, witness a significant increase in their employment probability. Women with high school education or more experienced an increase in their employment rate of about 0.49 percentage points for each one percentage point increase in the internet access rate. Nevertheless, given that the impact on labor force participation is higher than on employment, their unemployment rate rises. 6 The absence of significant average employment effects is in line with evidence showing limited job opportunities in the labor market of Jordan (Winkler & Gonzalez (2019)).<sup>7</sup>

# **5.1 Inspecting the mechanisms**

## **5.1.1 Changes in social norms**

In this section we investigate what drives the increase in labor force participation of women. One potential explanatory factor of the increase in female labor force participation as a result of internet adoption is the change in social norms regarding gender roles. The internet, as other communication technologies, may improve access to information contributing to change social norms directly (Arias (2018)), and to shift the bargaining power within the household.

We evaluate this mechanism using information from the JLMPS and constructing a wide array of variables capturing different dimensions of social norms including women’s decision making power, women’s access to money for home expenses, women’s saving and ownership of valuables such as jewelry and land, violent behavior of husbands against their wives, women’s being afraid of disagreeing with their husbands or other males in the households, and women’s agreement with statements about women’s empowerment.<sup>8</sup> We estimate model (1) using as

> 6We also run these models for the
**Table S13:** Program impacts on marriage market outcomes (husband attitudes towards women's empowerment)

|**Panel A: Baseline Dropouts**|Attitudes<br>Towards<br>Women's<br>Empowerment<br>Super Index<br>(Standardized)|Index of<br>Preferences for<br>Children's<br>Education<br>(Standardized)|Index of<br>Attitudes<br>Towards Wife's<br>Autonomy<br>(Standardized)<br>Two Years A|Index of<br>Attitudes<br>Towards (non)-<br>Abuse<br>(Standardized)<br>fter Program|Index of Wife's<br>Divorce<br>Prospects<br>(Standardized)|Desired<br>Fertility|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|=1 if Conditional Schoolgirl|0.145|-0.000|0.189|0.162*|-0.162|-0.161|
||(0.100)|(0.117)|(0.129)|(0.091)|(0.129)|(0.138)|
|Mean in Control Group|0.000|0.000|0.000|0.000|0.000|3.649|
|Sample Size|326|326|326|325|325|324|
|**Panel B: Baseline Schoolgirls**|||||||
|=1 if Conditional Schoolgirl|0.069|0.013|0.125|0.123|