rafmacalaba/data-use-mentions-extended huggingface dataset:
border delays related to border compliance come from the “trading across borders” section in the World Bank’s Doing Business Database.<sup>15</sup> Border compliance data capture the time associated with compliance with the customs clearance and mandatory inspections regulations. To account for the very high share of maritime shipping in international trade due to price differentials, the algorithm opts for maritime if the shipping time is lower than four times the shipping time incurred using rail links. In the improved scenario of the network simulation, when a project involves building a new port or upgrading an old port, the associated “processing time” is assumed to decrease to 50 percent of the port delay in the region or to the lowest worldwide processing time, whichever is higher.

Finally, the population weighted time distance between country-pairs is transformed into ad-valorem equivalents using estimates from Hummels and Schaur (2013) on the “daily value of time” at the sector level. These estimates are added to transport costs and data on tariffs from GTAP to obtain country pair-sector values of trade costs. Table 2 presents the results for two scenarios, referred to as the “lower-bound” and the “upper-bound”. The “upper-bound” scenario allows for changes in transportation mode due to the new infrastructure while the “lower-bound” scenario assumes that switching mode of transportation is difficult -allowing for modal changes lower than 5 percent with respect to the pre-BRI modes of transport. The decrease in total trade costs associated with the new BRI projects ranges between 1.05 and 2.19 percent. For some country‐pairs this decline is zero, while the maximum change ranges between 61.52 and 65.16 percent.

_Table 2:Percentage decrease in trade costs due to the BRI_

|% decrease in<br>trade cost<br>|Min<br>|Max<br>**Wo**<br>|Mean<br>**rld**<br>|Std. Dev<br>
Policy Research Working Paper 5415

# **Abstract**

A potential concern with survey-based data on corruption is that respondents may not be fully candid in their responses to sensitive questions. If reticent respondents are less likely to admit to involvement in corrupt acts, and if the proportion of reticent respondents varies across groups of interest, comparisons of reported corruption across those groups can be misleading. This paper implements a variant on random response techniques that allows for identification of reticent respondents in the World Bank’s Enterprise Survey for Nigeria fielded in 2008 and 2009. The authors find that 13.1 percent of respondents are highly likely to be reticent, and that these reticent respondents admit to sensitive acts at a significantly lower rate than possibly candid respondents when survey questions are worded in a way that implies personal wrongdoing on the part of the respondent.

This paper—a product of the Macroeconomics and Growth Team, Development Research Group—is part of a larger effort in the department to study the causes and consequences of good governance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akraay@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
.

We use granular data on international syndicated lending to evaluate whether MDBs can mobilize private capital flows. Our results indicate that once an MDB enters a country-sector pair through the participation in a loan syndication, the number of total syndicated loans and the associated bank lending flows increase. In addition, access to credit improves, since the average number of lending banks per loan and the average loan maturity also increase. These effects last over time and are economically sizable: for each dollar that MDBs invest in a country-sector pair over a three-year period, commercial banks lend almost 7 dollars.

These results are robust to a large set of tests controlling for the potential role of several con-

> 16As for the size of syndicated loans, we winsorize the bond size variable at 1%.

> 17Since the sample used to analyze effects on bonds is smaller, we test whether the absence of effects is driven by the sample construction, and re-estimate mobilization effects on syndicated loans in this same sample. Results are robust—see Table A8.

17
previous 12 months||(0.0267)||||||
|Had wage income during previous||0.0425||||||
|12 months||(0.0265)||||||
|Had domestic remittances during||0.178***||||||
|previous 12 months||(0.0315)||||||
|Had remittances from abroad<br>during previous 12 months||-0.0399<br>(0.0654)||||||
|Constant|0.403***|0.264***|0.323***|0.398***|0.317***|0.598***|0.286|
||(0.0531)|(0.0644)|(0.0553)|(0.0610)|(0.0847)|(0.0974)|(0.396)|
|Observations|2,888|2,888|2,227|1,752|827|599|65|
|R-squared|0.011|0.041|0.007|0.008|0.019|0.011|0.090|

Note: OLS estimation results, standard errors clustered at EA level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All estimations control for country fixed effects. Samples in col. 3-7 include only households receiving the relevant type of income before the pandemic. _Source_ : HFPS Nigeria R9 (Jan 2021) and Uganda R5 (Feb 2021).

19
- Susenas February 1999 (consumption module of 65,000 HH),

- 100 Village Survey May 1999 (12,000 HH), and Mini Susenas August

1999 (10,000 HH).

All these databases are collected by BPS.

We start by using the poverty rates for February 1996 and February 1999 from Susenas as estimated in Table 4. We choose method II with CPI prices for inflating poverty line during the period. Hence, we have a poverty rate of 9.75 percent for February 1996 and 16.27 percent for February 1999. We apply the samne method to other databases, but the inflation rates used are based on the national level only. Table Al in the appendix describes the steps in estimations for these data sets, with the results presented in table 7. In the first step, we calculate the poverty line which produces a poverty rate of 9.75 percent in the Susenas February 1996. In step II we update this poverty line for Mini Susenas December 1998 and August 1999 using the appropriate price indices and then estimate the respective poverty rates.

The 100 Village Survey data needed to be treated differently for two reasons. First, it was not a nationally representative sample. Second, its consumption expenditures questions were not identical to Susenas consumption module. So, in this case we calibrated the 100 Village Survey poverty rate to match the other surveys at one point in time. Therefore, in step III we calibrate the poverty line in the 100 Village Survey December 1998 so that it produces the same poverty rate as the Mini Susenas December 1998 (12.33 percent). In the final step, we then update the resulted poverty line backward to May 1997 and August 1998 and forward to May 1999 and then estimate the respective poverty rates.

24
- Guimbard, Houssein, Sébastien Jean and Mondher Mimouni (2011), "GTAP 8.1 Data Base Documentation - Chapter 10.D: MAcMap-HS6 2007, an exhaustive and consistent measure of applied protection in 2007."

- Harris, Richard (1984), "Applied General Equilibrium Analysis of Small Open Economies with Scale Economies and Imperfect Competition," _American Economic Review_ , Vol. 74(5), pages 1016-32.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (2002), “Trade Policy Options for Chile: The Importance of Market Access,” _World Bank Economic Review_ , Vol. 16, Number 1.

- Harrison, Glenn W., Thomas F. Rutherford and David G. Tarr (1997), "Quantifying the Uruguay Round," _Economic Journal_ , Vol. 107, No. 444, September, 1405-1430.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), “Economic Implications for Turkey of a Customs Union with the European Union,” _European Economic Review_ , Vol. 41(3-5), 861870.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr. (1996), “Increased Competition and Completion of the Market in the European Community: Static and Steady-State Effects,” _Journal of Economic Integration_ , 11(3), September 1996, 332-365.

- Harrison, Glenn W., Thomas F. Rutherford and David G. Tarr (1995), "Quantifying the Outcome of the Uruguay Round," _Finance and Development_ , December.

- Harrison, Glenn H., Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (2004) “Trade Policy and Poverty Reduction in Brazil,” _The World Bank Economic Review_ , Vol. 18, 289-317, 2004.

- Helpman, Elhanen and Paul Krugman (1985), _Market Structure and Foreign Trade_ , Cambridge MA: MIT Press.

- Helpman, Elhanen, Mark
tradition of senior community members providing advice to village heads as a potential mechanism of reform.

way through surveying, so we do not have data on this question for one-third of respondents. Fortunately, because the survey was proceeding geographically and we randomized by geographic blocks, the sample is still well balanced across experimental arms. In table 8 of the appendix, we consider how the villages for which we have political data differ from the villages for which we do not.

> 12Interestingly, these results are not simply the result of the village head’s personal biases being overwhelmed by biases against opposition supporters. There is even less evidence that supporters of the ruling party are favored in the distribution of food aid in our study villages, as indicated in the results in table 2 of the appendix. Much of the food aid in these villages comes from international organizations, which may explain the limited bias.

13
can see that the average wages from the dataset are close to that from the statistical yearbook, although there are some differences. We also compute in column (6) of Table A-3 the ratio of both wages and non-wage costs to total output, and the average is not much different than 10 percent. While labor‘s share could be too low and the share of intermediate inputs too high, we feel confident that the factor shares implied by the OLS and OP coefficient estimates are broadly consistent with the factor shares in our data as well as external evidence.

# **B.  The Effects of Different Sources of Foreign Investment**

In many FDI spillover studies, all domestic firms are assumed to benefit equally from FDI. However, different indigenous firms have varying absorptive capacities and the effectiveness of technology diffusion depends on technological capacities of indigenous firms as well as the characteristics of the foreign investors. To provide insights into the effect of this externality of FDI spillovers, we divide sector-level FDI variables into two groups based on their sources. The results are reported in Table 5.

The results point to significant and large differences in vertical as well as horizontal linkages which depend on the origin of the foreign investors. While horizontal linkages, which are not differentiated by country of ownership of the foreign investors, are sometimes insignificant, this average hides significant and contrasting effects. Horizontal linkages are negative but not significant for sectors with large shares of foreign investors originating in Hong Kong-Taiwan-Macau, suggesting that these firms act as competitors for domestically-owned firms. In contrast, horizontal linkages are positive and significant for foreign investment originating in other countries, suggesting that there are positive linkages within the same sector for foreign investment coming from further afield. The coefficient estimate, at .35, indicates that a 1 percentage point increase in horizontal FDI from sources other than Hong Kong-Taiwan-Macau is associated with an increase in output of .35 percent.

20
shock. Overall, the evidence suggests that households are likely to pool some resources, along with some retention of own income by individual members. In this case, access to one‘s own income sources as well as the strength of one‘s bargaining power within the household becomes crucial to welfare outcomes.

Important gender differences also exist in the access and control over resources in Malian society. Women in Mali are highly dependent on men ─ the responsibility first of fathers, and then husbands and if widowed adult sons (with luck). They marry very young ─ legally as young as 11 with parental consent ─ and typically marry much older husbands. The 2006 DHS reveals that 46, 70 and 90 percent of women were married by ages 15, 17 and 20, respectively. More than 43 percent of Malian rural families are polygamous (DHS 2006). The groom‘s family commonly pays bride wealth and brides move to their husband‘s village and extended family structure. A woman comes to a marriage with her own limited wealth—a trousseau that typically includes bedding, cooking pots, kitchen utensils and a small wardrobe that marks her new identity as a housewife.

In rural Mali, she often has few opportunities to build on those meager belongings. Traditional inheritance rules of descent favor men through whom houses and land are passed on. Women‘s access to property is limited and achieved primarily through their husbands. For example, land use rights can only be obtained though marriage and remain contingent on marital status. In the case of divorce or widowhood, the rights are typically lost.<sup>8</sup> Using the 2006 DHS, Peterman (2010) finds that only 40 percent of widows received any assets following a husband‘s death. The majority of the assets went to the widowed wife or her children in only 29 percent of cases.

In this culture, men and their wives do not share incomes and they keep separate budgets. In many ethnic groups such as the Bambara
Finally, the OLS results show that children in communities that have either a feeding posts or a Partage program or both are taller; they are almost 0.3 SD higher compared to the height of the children who live in communities without these programs.<sup>4</sup> OLS estimates have also been performed on the underweight z score of the child. In this case, the presence of a nutrition intervention in the community no longer has a significant impact on the (standardized) weight of the child (it does in the preferred specification discussed in the next section). Expenditures on the other hand, continue to have a positive and significant effect. A doubling of household per capita consumption would generate an increase of the weight z-score of about 0.15 SD. Nutritional status still worsens with age, though this effect is more tenuous than in the case of stunting. Finally in this regression maternal education has a positive effect. So whereas stunting responded positively to additional education of the father, the weight z-score is responsive to additional maternal education. An additional year of maternal education increases the weight for age z- score of the child by 0.02 SD.

# **4.2 Instrumenting Program Placement and Observed Consumption.**

In seeking instruments to address the possibility of endogenous program placement it is necessary to find variables that have predictive power on the endogenous placement variables and, at the same time, are not correlated with the child’s health output and the measurement errors present in the explanatory variables. The set of instruments used to model Partage and feeding posts include the average per capita household consumption in the village and the average height of adults aged 25-32 in the village as proxies for the average nutritional status of the village before Partage or other programs were started. The set of instruments also includes the main shocks each community experienced in the previous ten years, the main causes of adult and children death in the community where the child resides and whether the village has a daily
, they do not show any significant effect over perceived economic mobility.

**Countries with weak growth have on average more negative perceptions, particularly for those countries faring better pre-transition.** A 10-percent increase in GDP per capita between 1990 and 2006 translates into 3 percentage points higher probabilities of perceiving a movement up. However, the starting point matters for perceptions of mobility: for countries with similar growth, a higher GDP per capita in 1990 (by US$1,000) translates into a 3 percentage point lower probability of reporting improvements.

**A stronger economic contraction during the transition period is associated with more positive perceptions.** After controlling for the effect of initial GDP per capita level and the change between 1990 and 2006, a more severe contraction of the GDP per capita during the nineties is associated with more probabilities of self-reported upward economic mobility. This result suggests that those in countries that experienced tough economic times in the nineties place more value in the country’s or own relatively improved economic situation by 2006. The lowest point during the transition period is likely to be a reference point for countries that had severe economic contractions.

**Bigger public sectors in 1989 are associated with more negative perceptions.** With the exception of Turkey, in all the countries of the region the State controlled considerable productive resources in the economy. This translated in massive public employment which ranged from 57% for Czech Republic to 91% in Belarus (own estimation based on EBRD 2006). The regression analysis shows that an increment of 10 percentage points in the share of public employment in 1989 translates in 9 percentage points less probabilities of perceiving upward mobility. This result hints that populations in countries that use to have more certainty about future employment may have been hit worse by the changes introduced with the liberalization of the economy.

**Accordingly, countries that reduced more intensely their share of public employment have more negative perceptions.** Between 1989 and 2006 all countries in the region reduced the participation of the
humanitarian organizations such as the UNHCR should work together with development organizations such as the World Bank from the very beginning of a forced displacement crisis to devise solutions that are sustainable for the livelihoods of the forcibly displaced and their hosts (Verme and Macleod, 2016; Devictor, 2016).

The growth of the forced displacement phenomenon and the new consensus on the humanitarian‐ development nexus encouraged development organizations to start looking at refugees, IDPs and their hosts. These populations were largely excluded from the development discourse until a few years ago. For example, the global count of poverty excluded for the most part refugees and IDPs because national household budget surveys would normally exclude these populations. Humanitarian organizations that conducted surveys on these populations did so mostly for humanitarian protection purposes and did not have special expertise on the measurement of welfare and poverty. Data remained scarce and economists did not pay much attention to forced displacement. This changed when development organizations started to engage with refugees, IDPs and their hosts, with this phenomenon contributing to raise economists’ interest.

Until these recent events, the economics profession approached the question of displacement from the perspective of internal (within nation) or external (across nations) migration. The economics of migration finds its theoretical and empirical roots in labor and development economics and is now an established discipline on its own with thousands of contributions in international journals. More recently, some contributions emerged on forced migration (Ruiz and Vargas‐Silva, 2013) but these contributions remained empirical in nature and scarce in number.

A search by keywords of the major economic and social science research repositories shows a vast difference in the number of hits between the terms “migration” and “migrants” on the one hand and the terms “refugees” and “internally displaced” on the other hand (Figure 4). For example, a research in titles and keywords among published journal articles in the main economics repository ( _Econpapers_ ) shows 6,350 hits for the word migration, 1,586 for the word migrants, 383
WPS3487

# **Child Labor, School Attendance, and Indigenous Households:**

## **Evidence from Mexico**<sup>**++**</sup>

### **Rosangela Bando G.**

Universidad de las Américas, Puebla

### **Luis F. Lopez-Calva**

Universidad de las Américas, Puebla

**Harry Anthony Patrinos** The World Bank

**Abstract** :  This paper uses panel data for Mexico for 1997 to 1999 in order to test several assumptions regarding the impact of a conditional cash transfer program on child labor, emphasizing the differential impact on indigenous households.  Using data from the conditional cash transfer program, PROGRESA (OPORTUNIDADES), in Mexico, we investigate the interaction between child labor and indigenous households.  While indigenous children had a greater probability of working in 1997, this probability is reversed after treatment in the program.  Indigenous children also had lower school attainment compared with Spanish-speaking or bilingual children.  After the program, school attainment among indigenous children increased, reducing the gap.

#### **JEL classification code** : I21, I32, J13, J24

**Keywords** : Child labor, Mexico, Indigenous, Conditional cash transfers

World Bank Policy Research Working Paper 3487, January 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ++ The authors gratefully acknowledge support from the UNICEF/ILO/World Bank
# **Table A12: Effects of temperature on agriculture by share of agriculture in GDP**

|Crop yield|Rice|Maize|Soybean|Wheat|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Share of agriculture in GD_|_P (Reference gr_|_oup: Low sha_|_re)_||
|Temperature*High share|-0.127***|-0.005|-0.119***|-0.035***|
||(0.015)|(0.016)|(0.012)|(0.012)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Number of countries|42|70|16|68|
|Number of regions|641|915|178|634|
|Observations|9,967|14,259|2,778|9,635|
|R-squared|0.648|0.619|0.682|0.726|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Crop yield data is provided by Iizumi and Sakai (2020). *** p<0.01, ** p<0.05, * p<0.1 60