rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The world of official (bilateral and multilateral) lending remains largely unexplored in the academic literature, despite the scale and relevance of this type of cross-border flows. Most studies focus on a single government or institution, such as US lending as part of the Marshall Plan or IMF bailouts during the Asian Crisis. Compared to the vast literature on private cross-border flows, research on official international financial flows has been limited by a lack of systematic data. Existing datasets on official debt and inter-governmental lending typically have a much narrower focus (i.e. on development aid, concessionary lending) or they start only in the 1970s.

We collect data on the lending transactions of 134 creditor countries and 56 international and regional financial organizations across 200 years, building on hundreds of sources, including international treaties, budget accounts, proprietary lending data by the World Bank, and archival material. In total, we identify more than 230,000 official loans, grants and guarantees from 1790 to 2015, with total commitments amounting to more than 15 trillion US Dollars (in constant 2015 terms). Using this data, we document the characteristics of official capital flows, link it to the occurrence of disasters (wars, financial crises and natural disasters), and study its determinants with a gravity model of bilateral lending since the 18<sup>th</sup> century. Our main findings can be summarized as follows.

Official lending by governments, central banks, and multilateral institutions is larger than commonly known. Indeed, over the past two centuries, official creditors have played a major role in international finance. Since 1800, official capital flows have repeatedly exceeded private cross-border flows. This is especially true during times of war, financial crises, or other disasters, when private flows decline and official actors become the chief international lenders.

The biggest surges in official lending occurred during major wars, in particular in WW1, WW2 and the Napoleonic wars. The amounts lent during these global conflicts were a
--|---|---|---|---|
||Rural|Secondary<br>Urban|Dar es<br>Salaam|Total|Rural|Secondary<br>Urban|Dar es<br>Salaam|Total|
|Business created within 12 months<br>|21.3<br>|25.1|28.7<br>|24.0|17.9|17.3|19.9|17.9<br>|
|Limited market|16.6|27.0|28.5|22.4|13.8|24.1|17.1|15.0|
|Lack of inputs|9.4|8.8|6.5|8.7|11.8|5.5|18.1|11.2|
|Seasonal / temp activity|18.8|9.3|7.7|13.4|20.0|19.4|11.1|19.7|
|Other work (agric.)|18.5|8.2|1.3|11.8|31.5|28.2|28.3|31.1|
|HH demands|1.8|0.7|1.7|1.4|0.4|0.0|1.3|0.4|
|Personal,other reasons|13.7|20.9|25.5|18.3|4.7|5.4|4.0|4.7|
|Total|100|100|100|100|100|100|100|100|

_Source: Calculations based on the ILFS 2006 data._ 44. Regardless of causal factors, it is worth noting that seasonality sometimes affects the earning capacity of HEs (Table 3
EU countries. Currently, the monthly minimum wage in Kosovo is 130 euros for employed individuals aged 15-34, and 170 euros for employed individuals aged 35-64. This minimum wage applies for full-time employment, defined as 174 hours per month.<sup>3</sup> This amount has not changed since 2011 and has not grown with the inflation rate. Also, there is no variation in minimum wage across regions, despite geographic price differences. According to the official statistics of EUROSTAT, Kosovo and Albania report the lowest minimum wages in Eastern Europe.<sup>4</sup> Figure 1 Panel a shows the evolution of the minimum to the median wage ratio in Kosovo between 2012 and 2018.  The ratio of the minimum wage to the median wage in Kosovo is slightly lower than the level observed in most EU countries (Figure 1 Panel b) and constituted 0.38 in 2018 (based on Labor Force Survey estimates). The ratio of the minimum wage to the median wage is a common proxy used to measure the restrictiveness of minimum wages (ILO, 2012). The review of practices across countries by the ILO indicates that countries that use this indicator typically calculate the minimum wage as 50-60 percent of the median wage. However, according to Rutkowski (2003), this ratio should not surpass one-third in countries with high unemployment among young and low-skilled workers.

> 2 Source: <mark>International Monetary Fund, 2016.</mark>

> 3 For part-time employment (fewer than 174 hours per month), a minimum hourly wage should be applied. This is 0.75 euro per hour for employed individuals aged 15-34, and 0.98 for employed individuals aged 35-64.

> 4 In 2019, average monthly minimum wages in Eastern Europe were: 886 euros in Slovenia, 505 euros in Croatia, 331 euros in Montenegro, 343 euros in Serbia, and 210 euros in Albania.

4
10 using LPG as the primary cooking fuel were retained for the analysis. If the 20 percent mark was exceeded nationally, the national data were used. Burkina Faso, Kenya, and Nigeria did not meet the 20 percent mark nationally but met it in urban areas, and hence in these three countries only urban households were analyzed. Although use of gasoline or diesel for back-up power generation is polluting, most household survey questionnaires did not distinguish between automotive and electricity-generation uses of these two fuels, and hence they were not included among the polluting household fuels.

LPG users are categorized into three types: (1) those who use LPG as the primary cooking fuel and have abandoned polluting fuels altogether; (2) those who use LPG as the primary cooking fuel and continue to use other fuels, including polluting fuels; and (3) those who use LPG but not as the primary cooking fuel. The households in the last category were identified by searching for those who reported non-zero expenditures on LPG and did not name it as the primary cooking fuel.

# **Patterns of Primary Household Energy Choice for Cooking**

Comparison of primary energy sources for cooking showed significant differences from country to country. The share of people living in households citing clean sources of energy ranged from 99 percent in Mauritius to less than 1 percent in The Gambia, Liberia, and Uganda. Only five countries had more than half of the entire population living in households using clean cooking energy, whereas 16 countries had more than half using wood as the primary cooking fuel. When wood, charcoal, and coal were combined into a single category, the country count increased to 19 (Figure 2). There was a broad correlation between income (represented by national average annual expenditures per capita) and the share of people living in households citing clean energy as their primary sources for cooking.

**Figure 2  Percentage of people living in households citing different primary energy sources for cooking and national annual expenditures per capita**

<!-- Start
rather than the impact of AD protection in the home market. Prusa (2001) shows that exporters react to a tentative AD duty imposition immediately after an AD investigation is filed and that AD filings reduce the imports of the countries named in a filing even if the complaints are not upheld. From this point of view, beginning an investigation triggers retaliatory incentives for the target country.<sup>3</sup> Additionally, our analysis is based on the trading effects of AD activity in a particular export destination, namely, the country targeted by the domestic industries rather than the entire international market. In this regard, the four-dimensional information on firms, products, and export destinations in the Brazilian firm-level export data fits our research question perfectly.

In addition to its rich firm-level export data, Brazil is a suitable country for our analysis in terms of AD. Brazil implemented an AD law in 1987, and it ranks in the top 10 of all World Trade Organization (WTO) members in terms of both the number of AD petitions filed and the number of AD duties targeted against it in the post-WTO period. Brazil was the defendant country in 26 AD cases during our sample period, which corresponds to 208 unique six-digit Harmonized System (HS) products from 11 different industries.<sup>4</sup> Furthermore, Brazil filed 71 AD cases for 96 six-digit HS products, which is far above the mean and median of worldwide AD activity.

As shown in table 2, Brazil named its major trading partners, which are also very active AD users. Brazil seems to be a member of the “AD club”; its major trading partners also targeted Brazil in several cases after the creation of the WTO. Therefore, the threat of AD retaliation is an important consideration for exporters, not only because of the dramatic impact of AD duties on trade flows, but also because of the target countries’ past AD activities against Brazil. In tables 3 and 4, we document the AD cases filed by and
>_λ_istheso-calledregularizationparameterthatcontrolsthepenaltyonthe</sup><sup>_l_1-</sup> norm of **_β_** and the corresponding shrinkage. In principle, _λ_ is chosen by the researcher, however we adopt an automated approach to _λ_ selection. Our preferred _λ_ minimizes the mean cross-validation error which provides a good balance of model parsimony (from the LASSO procedure) and model

fit (necessary for subsequent propensity score matching).<sup>8</sup> We also conduct sensitivity analyses on

> 7Our biodiversity index comes from UNEP (2007). Furthermore, many countries are already members of common markets over our sample period and hence always sign the same agreements. We drop these perfectly multicollinear country indicators, only keeping one indicator per trading bloc.

> 8Table A2 presents the covariates selected (and the coefficient sign in the logit model) across a range of values for _λ_ . This table includes only covariates that are ever chosen in the Lasso procedure in any of the penalty values and does not provide an exhaustive list of all candidate covariates. Selected covariates notably include the (maximum) biodiversity index among signatories, the percent of signatory land area that is forested, whether any signatories have previously signed an RTA with environmental provisions, whether any signatory is from Latin America, the number of North American signatories, the date that the agreement is signed, and finally, a variety of country indicators. Our analysis proceeds with _λ_ = 0 _._ 26—the value that minimizes the mean cross-validation error.

12
allocating resources and monitoring and evaluating social programs. Through this Directive, the index has been used to modify the allocation of resources, which helped accelerate poverty reduction during austerity without an increase in the budget (Multidimensional Poverty Peer Network 2017). To effectively use indices like the Multidimensional Poverty Index as a management tool, it is vital that it is updated frequently and that the index use data sources that can be disaggregated to the level at which the budget is allocated (see, also, discussion of granularity). This allows the index to become a tool to inform policy decisions to reduce poverty and deprivation, and not just an occasional point of information.

Similarly, indices like Subnational Doing Business – an index created by the World Bank that measures business regulations at the subnational level -- can be used to track performance if they are reported frequently. The Subnational Doing Business in Nigeria series includes four rounds of studies analyzing business regulations and their implementation across all 36 states and the capital in 2008, 2010, 2014 and most recently 2018. This has inspired several reforms and improved the overall business climate in the country. Authorities in the state of Kaduna, for example, set in motion several reform initiatives after being ranked among one of the last states in 2014 in the area of registering property. The state responded by restructuring and digitizing the land registry, replacing percentage-based registration fees with flat fees, and making the issuance of the governor’s consent—previously the biggest bottleneck in the process—much more efficient. This overhaul resulted in one of the most significant improvements recorded in the Doing Business in Nigeria series since 2008. It made one-third of procedural requirements redundant, reduced the time to register property by more than two months and cut the cost by almost one-third (World Bank 2018d).

# **Timeliness**

When data are timely, they can lead to better emergency response when disasters hit, whether environmental, financial, health, or conflict related. For example, weather data, especially weather forecasts
of the budget data we calculated that additional survey costs per household are around 20% higher for self-reporting than that for proxy-reporting.

Next, we proceed to make a valuation of the trade-off between power analysis and survey costs. For girls, power analysis shows that one would need hundreds of additional observations to detect meaningful impacts if one uses the proxy reporting results rather than self-reports as the baseline, control-group statistic. Thus, given the high costs of field surveys in rural areas of underdeveloped countries, one would incur higher costs if planning a hypothetical RCT intervention by using proxy-reporting rather than self-reporting particularly when one expects small or moderate impacts on child labor. Put differently, the extra costs that would be incurred by surveying 1781 (5% impact) or 389 (10% impact) additional households under proxy reporting exceed the extra costs of having a self-reporting survey with the same fewer number of household units. For boys, on the other hand, self/proxy reporting does not affect child labor statistics, and thus there seem to be no benefits for carrying out self-reporting interviews given the somewhat higher costs associated with it. Self-reporting appears to yield a good compromise between survey precision and costs for national surveys on child labor, particularly in settings where gender stratification of work and social lives is the norm. Further research is necessary to generalize and validate this preliminary claim.

# **6. Conclusions**

Over the past decades, the increasing availability of household surveys has helped to create a large body of research on the main determinants of child labor (e.g., Edmonds 2009) and the effects of social protection policies on children’s allocation of time (e.g., Dammert et al 2018). However, 24
officials at least once) and bribery depth (percentage of public transactions for which a gift or informal payment was requested). Regulation is measured with two indicators: percent of management time spent on complying with regulatory processes and number of visits made to the tax office to meet with tax officials. Productivity refers to labor productivity computed as sales of firms per worker.

We estimate the model using sample weighted OLS (Ordinary Least Squares) regression analysis. To mitigate the endogeneity problem, we control for country, industry, sector, region and year fixed effects, and extensive numbers of productivity determinants cited in the literature. To further mitigate the endogeneity between productivity and corruption and regulation, we use the averages of the latter two over all other firms in the cell, where cell is defined as the group of firms in the same size category, industry and city within a country.

We find that the negative relationship between corruption and productivity is amplified at high levels of regulation. In fact, at low levels of regulation, the relationship between corruption and productivity is insignificant. To fix ideas, we find that a 1 percent increase in bribes that firms pay to get things done, expressed as the share of annual sales, is significantly associated with about a 0.9 percent decrease in productivity of firms at the 75<sup>th</sup> percentile value of regulation (high regulation). In contrast, at the 25<sup>th</sup> percentile value of regulation (low regulation), the corresponding change is very small and statistically insignificant, though it is still negative.

This paper is the first using global firm-level data to investigate the interlinkages between corruption, regulation and productivity and it is among the handful of firm-level studies on the subject. To the best of our knowledge, De Rosa et al. (2010) is the only other study using international firm-level data to analyze the corruption-regulation-productivity nexus. However, their data cover only 28 countries in Central and Eastern Europe and the Commonwealth
standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 9: Effects on intra-household dynamics

||(1)<br>Brigadiere takes care<br>of children alone|(2)<br>Brigadiere often takes care<br>of children|(3)<br>Brigadiere takes care of children<br>as well as her partner|(4)<br>Brigadieres partner often takes<br>care of children|
|---|---|---|---|---|
|**Full sample**|||||
|ITT estimate|-0.031|-0.035|0.063|-0.001|
||(0.048)|(0.060)|(0.041)|(0.026)|
|Observations|1788|1788|1788|1788|
|_R_<sup>2</sup>|0.088|0.055|0.072|0.092|
|Control mean|0.307|0.505|0.153|0.034|
|**Have child aged 0-2**|||||
|ITT estimate|-0.055|-0.020|0.077*|-0.003|
||(0.043)|(0.058)|(0.038)|(0.027)|
|Observations|1035|1035|1035|1035|
|_R_<sup>2</sup>|0.100|0.079|0.102|0.093|
|Control mean|0.325|0.495|0.145|0.034|

_Notes:_ Robust standard errors in parentheses **
Caselli, F., Esquivel, G. and Lefort, F. (1996), Reopening the convergence debate: A new look at cross-country growth empirics, _Journal of Economic Growth,_ **1** (3) **,** 363-390.

- Castello, A. and Domenech, R. (2002), Human capital inequality and economic growth: Some new evidence, _Economic Journal,_ **112** (478) **,** C187-C200.

- Deininger, K. and Okidi, J. (2003), Growth and poverty reduction in uganda, 19992000: Panel data evidence, _Development Policy Review,_ **21** (4) **,** 481-509.

- Deininger, K. and Squire, L. (1996), A new data set measuring income inequality, _World Bank Economic Review,_ **10** (3) **,** 565-91.

- Deininger, K. and Squire, L. (1998), New ways of looking at old issues: Inequality and growth, _Journal of Development Economics,_ **57** (2) **,** 259-287.

- Easterly, W. and Levine, R. (1997), Africa's growth tragedy: Policies and ethnic divisions, _Quarterly Journal of Economics,_ **112** (4) **,** 1203-1250.

- Elbers, C. and Gunning, J. W. (2004) "Transitional growth and income inequality: Anything goes", mimeo, Vrije Universiteit, Amsterdam.

- Elbers, C., Lanjouw, J. O. and Lanjouw, P. (2003), Micro-level estimation of poverty and inequality, _Econometrica,_ **71** (1) **,** 355-364.

- Elbers, C., Lanjouw, J. O. and Lanjouw, P. (2005), Imputed welfare estimates in regression
poverty headcount rate under the international line of $1.25 PPP 2005 a day. Under this scenario, the fiscal system in 2016/17 increased poverty by approximately 2.3 percentage points; the analogous increase in poverty due to 2012/13-era Ugandan fiscal policy was approximately 0.1 percentage points. The impact of fiscal policy (excluding the government-cost value of publicly-provided in-kind health and education services) on poverty in Uganda is less poverty inducing than the impact of fiscal policy in Kenya and Tanzania, but far more poverty inducing than the impact of fiscal policy in South Africa.

_Figure 7. Poverty Before and After Fiscal Policy a. Poverty Headcount Rate (under the national poverty line)_ <!-- Start of picture text --> 24.07%<br>23.70%<br>21.49% 21.39% 21.44% 21.42%<br>21.13%<br>Market Income Market Income Net Market Disposable Disposable Disposable Consumable<br>(PGT) plus Pensions Income Income Income + All Income - All Income<br>(PDI) indirect indirect Taxes<br>Subsidies<br>Percentage of the population<br><!-- End of picture text -->

- _b. Poverty Reduction Effect, Sub-Saharan African countries_

<!-- Start of picture text --> 15.80<br>-0.12<br>-1.33 -0.75<br>-2.31<br>-4.40<br>-7.78<br>Tanzania Kenya (2015) Uganda (2016) Ethiopia (2010) Ghana (2012) Uganda (2012) South Africa<br>(2011) (2010)<br>Percentage points<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17; CEQ Institute database; and World Bank (2018a)
In establishing whether _Mahila Samakhya_ has a significant impact on female empowerment, we account for two potential sources of endogeneity: (1) the program’s choice of communities in which to operate, and (2) the individual’s choice of whether to participate. We conduct this analysis in five stages. First, we estimate an intent-to-treat (ITT) effect of the program by comparing the bargaining power outcomes of treated and untreated women. Second, we consider the potential of differences among women in communities that receive the program and those in communities without. We match non-participants in treated districts (“non-participants”) and women in untreated districts (“the untreated”), to examine whether they are significantly different from each other. We also compare census data on indicators of female bargaining power in the blocks where the program was placed to the blocks where it was not placed from before the _Mahila Samakhya_ intervention to find no significant differences between treated and untreated blocks. All of our results suggest that the program was not targeted in its placement.

Third, we test whether program participants are significantly more empowered than similar women from untreated districts to determine whether the program has a significant treatment effect. Using instrumental variables and matching, we compare pre-determined levels of empowerment and empowerment outcomes (similar to a differences-in-differences approach) to find that even after accounting for the pre-determined level of empowerment, the program significantly improves access to employment, physical mobility, and political participation. Our instrument relies on the roll-out of the program to control for selection in the participation decision. Using both approaches, we find a positive, significant treatment effect of the program on women’s empowerment outcomes; participants are more likely to have access to outside employment, are able to leave the house without permission and are more likely to attend village council meetings, although this last effect is not significant in all specifications. The marginal effects from the IV approach emphasize the potential for large numbers of women to benefit from interventions like _Mahila Samakhya_ . These results are qualitatively
local firms to develop and benefit from linkages with FDI activities due to the prevalence of market-seeking motivation and their high labor intensity.

The Enterprise Surveys confirm that domestic linkages of foreign multinationals tend on average to be less strong than those of domestic firms, with only very few exceptions (see Fig. 12). Part of this variation could be due to industrial activities where multinational firms are concentrated. Yet, in countries with large market size, such as China, India, Nigeria, and Indonesia, both multinationals and domestic firms exhibit strong local sourcing. This is due to the availability of local inputs for a variety of activities and also because of the intensity of market-seeking FDI in addition to other types. A number of countries in South East Asia upgrading their GVC participation exhibit very high foreign input use, as well as Vietnam and Poland.

Implications of these differences for domestic economy are vast, as this is one of the main channels for productivity spillovers between foreign firms and their domestic suppliers (Javorcik, 2004).