rafmacalaba/data-use-mentions-extended huggingface dataset:
entrepreneurship program for high school students in Uganda. Data from a follow-up survey, conducted 3.5 years after the program ended, shows that the program increased the probability of having a business by about 6 percentage points, relative to 33.6 percent of control students who reported owning a business. The percentage of students with a business is much smaller in our sample (6.9 percent), in part because we use administrative data and thus focus on businesses that are registered with the government. The percentage increase in students with a business found by Chioda et al. (2021), about 18 percent, is larger than the 10 percent increase in formal business ownership we observe in our data. A reason for the smaller effect in our study could be that the financial education program in Brazil dedicated two out of nine modules to work and entrepreneurship, while the program in Uganda focused entirely on entrepreneurship.

We also examine a proxy for informal employment derived from data on a COVID-19 pandemic government transfer distribution in 2020. This proxy provides weak evidence that the financial education intervention increased the share of students in the informal sector. We cannot disentangle whether these students are owners of informal firms or employees who are not registered with the government. However, we suspect that the effect is driven by informal business owners, not employees, since our other results show that the financial education program increased formal microenterprise ownership. Some students who started their own business likely started informal businesses since nearly two-thirds of businesses in Brazil are informal (Ulyssea 2018).

Tying these results together, we find that the high-school financial education program had significant and lasting effects on long-run economic behavior related to credit use and employment outcomes. The program shifted employment from formal sector jobs to entrepreneurship, which 4
We also find that the effect on impact is entirely driven by non-collateralized credit: the effect on non-collateralized and collateralized credit is around 8 and 0 percentage points, respectively, when _h_ = 0 (see panels (b) and (c) of Figure 1). Finally, we find that, although absent on impact, the effect of procurement on collateralized credit gets bigger over time. For example, the 5 years ( _h_ = 4) cumulative effect is around 6 percentage points. This final result is consistent with the idea that firms use their extra profits earned in procurement to increase their net worth and therefore increase their borrowing capacity in the future.

An alternative explanation for the estimated “persistent” effect of procurement on credit is that obtaining a contract in a given year may increase the probability of obtaining more contracts in the subsequent periods. While it is true that there is persistence in procurement, in Appendix B.1 we show that the persistent effect of procurement on credit in credit is very similar for a subsample of firms that only obtain one contract during our sample period. Finally, a limitation of our procurement data is that it does not contain information about the duration of each contract. One potential concern is that the estimated “persistent” effect of procurement on credit results from long contracts. To have a sense of the duration of contracts in Spain, we use a different dataset, Tenders Electronic Daily (TED), which provides information on the estimated length of contracts.<sup>11</sup> For the years 2018-2019, for example, around 75% of the contracts have a duration of one year or less, 89% have a duration of two years or less, and 93% have a duration of three years or less (see Appendix B.5 for details). These numbers likely represent an upper bound, given that

> 11TED is the online version of the supplement to the Official Journal of the EU dedicated to European public procurement. See Garc ́ıa-Santana and
of the curve, we find a group of Latin American and Caribbean (LAC) countries with high inequality and average female participation rates, and another group of Euro-Mediterranean countries with similar participation rates but much lower inequality. Other groups of countries are more spread out along the curve, as is the case for sub-Saharan African (SSA) countries.

Secondly, we test the U-shaped hypothesis at the micro level; notably, we expect that FLFP can both have a pro-equality or pro-inequality impact when regressed on household income inequality, depending on which regional blocs of countries, e.g. MENA, SA and LAC, a country belongs to. In these countries, the increase in FLFP will almost always have an inegalitarian impact (as they are on the left-hand side of the curve); in continental Europe the impact is mixed, but strongly egalitarian in Euro-Mediterranean and Scandinavian countries, although with different FLFP rates.

As it is important to examine this issue from a broader perspective, we attempt to put together different data sources to come up with a more integrated approach to the problem. Unfortunately, in the micro-analysis, a trade-off needed to be made between the homogeneity of data sources and completeness of information. On the one hand, the choice of the Luxembourg Income Study (LIS) database enabled us to use a very reliable and widely used set of variables; on the other hand, the LIS’s usefulness was limited because it lacked information on developing countries. Whereas LAC countries are broadly represented, MENA countries are only partially included, and there are severe data limitations. Therefore, in order to provide a micro-snapshot of MENA countries, the World Bank’s PovcalNet data set was used, with the Arab Republic of Egypt, Morocco and Jordan subsequently included. However, in addition to the fact that these countries use consumption rather than income data as a welfare variable, the other main limitation is that the main instrument we use for correcting endogeneity – the average women’s wage of the past 10 to 30 years
) uses firm level data for the manufacturing industry in the 1986-1998 period to analyze how the reduction of inward trade barriers between 1990 and 1993 affected productivity and finds evidence that foreign competition pressures firms to raise productivity markedly, whereas the use of foreign inputs plays a minor role for productivity change. Schor (2004) runs a firm level analysis also for manufacturing industry and for the same period and finds evidence that along with higher competition pressure exerted by output tariff reduction, new access to better inputs also contributes to enhance productivity after trade liberalization. Lisboa, Menezes Filho and Schor (2010) use 1988-1998 data and confirm that the trade liberalization episode that took place in Brazil in the late 1980s and early 1990s brought positive impacts for productivity in the manufacturing industry and that the main driver of productivity growth was the reduction of input tariffs.

Productivity dividends accruing from trade liberalization can be maximized when domestic markets are integrated in a way that resources are allocated to their most efficient uses. Easing barriers to trade is expected to trigger an intensive reallocation and churning process where resources are expected to move to more productive uses, within and between firms, sectors and regions, therefore boosting productivity growth. In this context, resource reallocation can be dampened by policy interventions. For example, Restuccia and Rogerson (2017) provide a

> 2 See for instance Bustos (2011) and Aw, Roberts and Xu (2011) as examples of studies covering this type of effect. 3 See Fernandes (2007), Topalova and Khandelwal (2011), and Halpern, Koren and Szeidl (2015) as examples of studies assessing the relevance of the input channel, while Bernard, Redding and Schott (2010) provide empirical evidence for the within-firm reallocation process.

> 4 See Melitz (2003) and Melitz and Ottaviano (2008) for a theoretical and empirical investigation on this aspect, and Pavcnik (2002) for empirical evidence on the productivity dividends coming from the reallocation effects caused by trade liberalization in Chile.

2
from the nationally representative India Human Development Survey (IHDS) for 2005 and 2012. Poverty rates estimated from this survey using a similar welfare standard (consumption levels below official poverty lines) as in the NSS (National Sample Survey, on which official poverty estimates are based) show a large decline in poverty between the two years, which is roughly comparable to the trend estimated from NSS. This allows us to take advantage of the rich information on income sources of households available from the IHDS and its panel nature to decompose the sources of poverty change among households between 2005 and 2012. More specifically, we look at the extent to which shifts in household composition and income sources have contributed to changes in household income; and with that, to changes in the real value of household consumption. In our analysis, these household level factors can be grouped into broad categories of interest such as the demographic composition of households, extent of engagement in the labor market and labor earnings, and non-labor earnings such as remittances and social benefits.

To conduct this analysis, we employ a nonparametric decomposition method based on the counterfactual simulations done previously by Inchauste et al. (2012) and Azevedo et al. (2013), following Barros et al. (2006)—see also Inchauste et al. (2014). Our results should not be interpreted in causal terms, since the method we use adopts a simple “accounting” approach toward decomposing incomes, with counterfactual simulations that do not take into account behavioral responses of individuals and households to changes in different components of household income. In spite of these limitations, our results are useful for assessing the relative importance of different forces that

> 2 Dang and Lanjouw (2015) find that the remarkable decline in poverty between 2009/2010 and 2012 is not driven by statistical noise or changes in survey design.

2
total population in 1980, the working age population share grew by more than 13 percentage points in the last 35 years. The growth received further tailwind from increasing participation rates. The aggregate participation rate rose from approximately 45 percent in the mid‐1970s to over 60 percent in the 2010s, largely due to a massive jump in the female labor force participation rate, which grew from a low base of 12 percent in 1989 to 30 percent in 2013. What has been most encouraging is that not only the quantity but also the quality of labor resources has been rising over time. The Penn World Table (PWT) human capital index for <!-- Start of picture text --> 35 Population ages 15 ‐ 64 (% of Total Population)<br>70<br>30<br>Total<br>25 65<br>Private<br>20<br>60<br>15<br>10<br>55<br>5<br>0 50<br>Source: World Development Indicators Source: World Development Indicators<br>Investment Share of GDP (%)<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Bangladesh has grown by more than 1.5 percent per year on average during 1990 – 2010.

Apart from growth in underlying factors of production – capital and labor – economic growth can also be derived from advances in technology or through an efficient use of existing resources. The growth in the productivity of resources, or Total Factor Productivity (TFP), has been an important driver of growth for many countries.<sup>3</sup> The TFP growth rate for Bangladesh during 2001 – 2011 has just been above zero implying that the strong growth seen during the period was not aided by
to final income.

Quintiles one and two are net cash beneficiaries of the fiscal system, with a cash gain equivalent to 169.8 and 1.4 percent of their market income plus pensions, respectively. In contrast, the rest of the households are net payers of the fiscal system. The cash loss ranges from -10.1 percent of MIPP in quintile 3 to -22.2 percent of MIPP in quintile 5. The net cash position for the first quintile seems high due to a sizable population whose livelihood heavily depends on direct transfers and that have a very low level of market income plus pensions. 29F<sup>30</sup> While households in quintile 2 are net beneficiaries of the fiscal system, their net benefits are low. Finally, when looking at the total fiscal position of households (blue dashed line), which includes the in-kind benefits from health and education, the results show that quintiles 1-3 are net receivers, while quintiles 4-5 are net payers of the fiscal system.

To illustrate how important the transfers are for disposable income (i.e., market income plus pensions minus direct taxes plus direct transfers), Figure 3 panel b illustrates the patterns of the net cash and fiscal position using households’ disposable income as a benchmark. Similar to the incidence using the MIPP as reference, the first two quintiles are net cash beneficiaries, while the first three quintiles are fiscal beneficiaries.

> 30 According to market income plus pensions estimates made using PNAD-C and POF, around of 2.7% of the individuals of the first quintile report a market income plus pensions equals to zero. Such households rely on receiving rural pensions, BPC pensions, unemployment benefits, and other direct transfers.

19
Gini coefficient to country and year fixed effects may relate to various factors. It may be for example that there is moderate within countries variation of the Gini over time or, if there is variation, time trends are similar across countries. The World Values Survey is characterized by many countries, few years for each country (from one to four years depending on the country) and several years in between any two consecutive observations within countries. Changes in the Gini can be significant but, among the countries with more than one observation, about half have decreasing Ginis, one has a Gini that goes up and down and the rest of the countries have increasing Ginis. Therefore, the Ginis do not move together over time across countries whereas the within countries variation is limited by the number of years available for each country.

Having more data points for the Gini may help to better capture the relation between happiness and inequality but this is not always the case. For example, Senik (2004) finds non significant coefficients for the Gini when she estimates the Gini at the national, regional or PSU levels. Also, in a previous version of the paper, we estimated the Gini WVS at the regional level and compared the coefficient of this variable with that of the Gini estimated at the country level. We found that the Gini region was even more sensitive than the Gini country to the use of country and year fixed effects.

In substance, there is a trade-off between the inclusion of country and year dummies, which allows to control for unobserved factors but generates collinearity, and the exclusion of these dummies, which fixes collinearity but increases the problem of unobserved heterogeneity. Moreover, with smaller samples, increased standard errors can be generated by 14
today. Rwanda’s 2019–20 Demographic and Health Survey (DHS) reports that 40% of women report experiencing physical and/or sexual IPV in their lifetime, with 24% reporting incidents in the previous year.<sup>6</sup> Supporting norms remain widespread, particularly in rural areas, with 41% of women and 18% of men believing husband-to-wife violence can be justified in some circumstances (Mannell et al., 2018). This context is further shaped by the 1994 genocide’s legacy of sexual violence, where rape was used as a weapon of war.

In parallel, over the past three decades, Rwanda has become a global leader in gender equality reforms. Following the genocide, when women comprised 70% of the population, women’s organizations successfully advocated for female representation in the interim government (Hansén, 2017). Since then, Rwanda has enacted comprehensive reforms including equal land ownership rights (1999), a 30% gender quota for civil servants and parliamentarians (2003, now exceeded), and equal pay legislation (2009). The government also passed a Gender-Based Violence Bill requiring all citizens to report violence, and penalties of eight months to two years imprisonment for perpetrating IPV including ‘harassing one’s spouse’ and marital rape (Mannell et al., 2018). At compulsory monthly community meetings, gender-based violence is regularly discussed. Recent reforms have overturned men’s legal status as household heads (Abbott and Malunda, 2016), established IPV survivor support centers, and expanded women’s rights in marriage, divorce, and inheritance — including protections for unofficially married women. These progressive legal reforms have been mirrored in health and education policies for women and girls, resulting in improved maternal mortality, contraceptive access, and girls’ educational attainment (DHS, 2016).

However, a significant gap persists between national policy and local attitudes, particularly in rural areas. Traditional gender norms about women’s subservience to men remain influential, and
**|**Loss of**<br>**crops**|**Loss of**<br>**livestock**|**Dwelling,**<br>**crops and**<br>**livestock**|
|---|---|---|---|---|---|---|---|---|
|1|29.0|10.8|11.5|6.1|1.6|5.7|2.3|8.0|
|2|24.9|8.8|12.0|6.5|1.3|3.7|1.0|5.4|
|3|25.8|8.7|12.2|7.4|0.4|1.9|0.5|2.5|
|4|24.2|8.2|12.1|7.1|0.5|1.2|0.7|2.2|
|5|21.5|7.4|11.0|6.0|0.5|1.5|1.5|3.1|
|**Total**|**25.1**|**8.8**|**11.8**|**6.6**|**0.9**|**2.8**|**1.2**|**4.2**|

_Source_ : Author‘s calculations based on data from MxFLS panel database.

In the cases of Chile and Peru the data shows only the occurrence of health shocks, so that a cross-country comparison is only possible for those shocks requiring hospitalization
0.1|41.3|12.2|67.7|
|**Uganda (2016)**|**22.44**|**3.41**|**43.4**|**22.84**|**92.47**|
|Zambia (2015)|36.0|2.8|55.0|52.6|84.3|

_Note: using the $1.25 PPP [2005] poverty line, and with respect to market income plus pensions._ _Sources: Uganda, 2016/17: authors’ calculations based on UNHS 2016/17; Uganda, 2012/13: Haas and others (2016); Zambia, 2015: de la Fuente and others (2017); all others: Higgins and Lustig (2016)._

# 4.4. Pensions as Government Transfers scenario

In the “Public Contributory Pensions as a Government Transfer” (PGT) scenario, we define our pre-fiscal income as “Market Income”. Pensions are considered to be a government transfer, rather than market income, and therefore included with Disposable Income, when the direct transfers are added. Figure 6 and 7 above shows that at Market Income the poverty headcount is slightly higher than when pensions are included (as at Market Income plus Pensions), but that the Gini coefficient is practically unchanged (0.01 Gini points decline). This means that the Public Contributory Pension system is poverty reducing<sup>21</sup> but does not significantly affect inequality. Thus, when Market Income is taken as pre-fiscal income, the combined effect of taxes and transfers in Uganda also lowers inequality by 3.2 Gini points. In terms of the effect on poverty, under the PGT the increase in poverty is slighter smaller: 2.21 percentage points compared to the 2.31 increase in the PDI scenario.

> 20 In 2012/13, fiscal
settings to account for endogeneity. Duranton and Turner (2012) use a historical highway plan of the US highway system to estimate a structural model of city growth and transportation, Baum-Snow et al. (2012) use Chinese rail and road networks from 1962 as a source of identifying variation in rail and road networks after 2000, Volpe Martincus et al. (2017) use historical Inca routes for Peru, Garcia-L ́opez et al. (2015) and Holl (2016) use Roman roads and the 1760 Bourbon postal routes as sources of exogenous variation of highway extension in Spain, and M ̈oller and Zierer (2018) rely on an 1890 plan of railroad network in Germany and 1937 map of planned autobahns to study regional employment.

> 1For an overview on the impacts of infrastructure improvements on various economic outcomes, please see Redding and Turner (2015).

4
The odds ratios or factor change coefficients (table 8) illustrate the dynamics among MDG performance outcomes. These coefficients depict the expected change in the probability of a country being on target vs. far from target and on target vs. close to target, following a one standard deviation increase in development drivers and holding all other variables constant.

Economic growth can jump-start countries particularly far from the goals. For countries that are far from the target (starting from a low base), the effects of a one-standard-deviation simulated increase in per capita growth on the probabilities of reaching some MDGs tend to be distinct and large. It would raise 12-fold the probability of reaching the targets for primary completion and gender parity in secondary education, more than double it for under-five child mortality and sanitation, almost double it for extreme poverty and hunger, and increase it by more than half for access to safe drinking water.

**Table 8. Effects of a one-standard-deviation increase in selected development drivers from the multinomial logit estimates (baseline representation)**

||**Due to increas**<br>**capita growt**|**e in GDP per**<br>**h (percent)**|**Due to increase**<br>**(perc**|**in CPIA index**<br>**ent)**|
|---|---|---|---|---|
||_on target vs._<br>_far from target_|_on target vs._<br>_close to target_|_on target vs._<br>_far from target_|_on target vs._<br>_close to target_|
|**MDG 1.a extreme poverty**|**93**|19|12|-19|
|**MDG 1.c hunger**|**88**|15|*
# **1 Introduction**

The two largest trading economies in the world, the United States and China, increasingly see each other as geopolitical rivals. For both the US administration and the Chinese government, this great-power rivalry is central to their foreign and national security strategy (Wolff, 2023). While other reasons contributed to the surge in trade policy activism on both sides of the Pacific in recent years, geopolitical considerations are increasingly cited as a driver of new trade and industrial policy. For instance, data from the New Industrial Policy Observatory show that a significant number of measures imposed by China and the United States in 2023 and the first half of 2024 are motivated by geopolitical and national security concerns (Evenett et al., 2024). Irrespective of the validity of these concerns and the efficacy of new trade measures to achieve the intended goals, an increasingly commonly held view is that US-China rivalry, and the change in policies that it generates, could reshape trade flows and fragment the world trading system and the global economy more generally (Aiyar et al., 2023 and Gopinath et al., 2024).

In this paper, we study the implications of geopolitical rivalry for trade cooperation and for the purpose and design of the rules-based multilateral trading system. Our work builds from an understanding of the logic of the multilateral trading system that comes from the literature on the economics of trade agreements (see Bagwell and Staiger, 1999, for an initial statement of the framework that provides this logic, and Bagwell et al., 2016, for a review of this literature). The key findings of this body of work can be simply stated. The first is that the fundamental source of inefficiency associated with the unilateral trade policy choices of governments is the presence of cross border spillover effects in the form of a terms-of-trade externality. Second, the primary purpose of a trade agreement is to mitigate these externalities, meaning that countries agree to limit their protectionist policies thus preventing them from manipulating trade prices to their advantage at