rafmacalaba/datause-encoder-data huggingface dataset:
receive regular transfers for consumption support, which contrasts with most graduation programs studied in the literature.

The RCT took place in 147 localities in the western regions of post-conflict Cˆote d’Ivoire, covering both urban and rural areas. Localities were randomly assigned to one of the three interventions (“VSLAs”, “cash grants with repayment” or “cash grants”) or a control group. Within localities, program participants were selected based on a baseline survey designed to calculate a vulnerability proxy measure. We collected follow-up data approximately 22 months after the program started, or 15 months after the injection of cash grants or redistribution of savings. In both treatment and control localities, we surveyed individuals with a baseline proxy score below the program selection cut-off (potentially selected individuals), but also above the cut-off (non-selected individuals). We thus measure direct impacts (for beneficiaries) as well as local spillovers (for nonselected individuals) on economic and social outcomes. The analysis of local spillovers is particularly relevant in a post-conflict setting given concerns over lingering tensions.

Our primary contribution is to compare cash grants with savings groups to assess whether an external capital injection is necessary to generate investments in income-generating activities, or whether savings mobilization alone can trigger investments by increasing savings efficiency. Our second contribution is to compare two different types of capital injection modalities, cash grants with or without repayment, to test whether adding a repayment condition alters investment behaviors relative to unconditional capital.

Poor households can struggle to access capital for income-generating activities, for example facing imperfect capital markets, unattractive credit terms or collateral conditions. In credit scarce environments, cash grants can directly address capital constraints and high returns have been documented for micro-entrepreneurs (e.g., de Mel et al. (2008), De Mel et al. (2012) and Blattman et al. (2014)). However, compared with asset transfers, cash is more fungible, raising questions on how much will be used for productive investments. While there is evidence that poor households consume part of grants (e.g. Berge et al. (2015), Haushofer and Shapiro (2016)), unconditional cash grants have shown positive effects on economic diversification and earnings (Blattman et al. (2014), Macours et al. (2022)).<sup>5</sup> Cash grants with repayment conditions are akin to highly subsidized credit

> 5There is a large literature on the optimal design of small, regular cash transfers, but we focus here on one-off, lumpy cash grants for investments.

2
Figure 1-A

# CPI Inflation and Official Devaluation

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- Has a business website

- Uses Facebook/Whatsapp/Other social media for marketing

- Accepts mobile money payments

- <u>Maximum loan size possible: the maximum amount in real September 2021 USD that the firm</u> owner thinks they could borrow within 2 weeks if they needed to, winsorized at the 99<sup>th</sup> percentile. Asked only in the in-person survey.

# **C. Appendix Tables and Figures**

Tables A1-A5 provide more detail on response rates, and robustness to attrition Table A6 shows robustness of impacts on profits and sales to other measures Table A7 provides the traditional training impacts by gender.

Tables A8-A10 and Figure A1 examine gender differences in more detail.

27
release of the dataset was used. A brief description of each variable is provided, taken verbatim from that dataset’s codebook. The original source, which the codebook identifies, is indicated. “Variable name” indicates how the variable is tagged in the codebook.

_Pop_ : population. Source: Heston, Summers and Haten 2012. Variable name: pwt_pop).

R_g/gdp: the share of public expenditure over GDP (Source: Heston, Summers and Haten 2012. Variable name: pwt_gsg) _Emp_right_ : empowerment rights index (Source: Cingranelli and Richard 2010. Variable name: ciri_empinx_new). It is an “additive index constructed from the Foreign Movement, Domestic Movement, Freedom of Speech, Freedom of Assembly & Association, Workers’ Rights, Electoral Self-Determination, and Freedom of Religion indicators. It ranges from 0 (no government respect for these seven rights) to 14 (full government respect for these seven rights).” _Free_press_ : freedom of the press index (Source: Freedom House. Variable name: fh_fotpc3). “The press freedom index is computed by adding four component ratings: Laws and regulations, Political pressures and controls, Economic Influences and Repressive actions. The scale ranges from 0 (most free) to 100 (least free).” _Free_speech_ : freedom of speech (Source: Cingranelli and Richard 2010. See Teorell et al. for details. Variable name: ciri_speech). “This variable indicates the extent to which freedoms of speech and press are affected by government censorship, including ownership of media outlets”.

_Voice_acc_ : Voice and Accountability (The World Bank. Variable name: wbgi_vae). “‘Voice and Accountability’ includes a number of indicators measuring various aspects of the political process, civil liberties and political rights. These indicators measure the extent to which citizens of a country are able to participate in the selection of governments. This category also includes indicators measuring the independence of the media [...].” _Checks_ : a measure of “checks and balances” (Source: Database of Political Intitutions Variable name: dpi_checks). “Equals 1 if the Legislative Index of Political Competitiveness 36
The overall effect of a tariff includes both its direct effect, which is captured by the second term in (6), and the indirect effect resulting from its impact on the quantity of foreign investment. Note that the direct effect of the tariff includes the terms of trade effect on capital, which provides an incentive for the government to impose a tariff to drive down the payment to foreign capital. However, (6) indicates that the tariff will also drive out foreign capital, which has a negative effect on host country welfare. We first examine this question in the case in which the host country can commit itself to a period 1 tariff rate prior to the entry of investors in period 0.

# C. Optimal Policy with Host Country Commitment to Tariff Rates

If the host country can commit to the tariff it will charge in period 2, then the tariffs are chosen to at time 0 to maximize host country welfare. The necessary conditions for a maximization of domestic welfare are obtained by setting _awraj_ = 0 in (6), making use of aZ7/aTj from (4). This yields the following result: = Proposition 1: For a host country that can commit to its tariffs, the optimal policy is free trade (ro _-r1_ = 0)To verify this result, note that at _;r_ = 0 in (6) the trade volume effects of the tariff are zero. The only effect of the tariff in period i results from its impact on the return to foreign capitalists _(ZjRhzdri),_ and the resulting effect of the tariff on the volume of foreign investment (-4ZtRzz). However, it can be seen from (4) that these two effects will exactly cancel out. This result holds for both the case in which there is positive investment in period 1 and the case where rxi < r;. Therefore, awMaT = 0 at _To= T1_ = 0.

10
# **Table 2: On  firms’ reporting demand as the main factor**

|||Repor|ting Reduc|ed demand|as the main|factor|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Demand Sensitivity|1.157***||||0.974**|1.029**|0.877*|0.897*|
||(0.39)||||(0.44)|(0.48)|(0.45)|(0.51)|
|Dependence on External Finance||0.172|||0.371|||0.463|
|||(0.13)|||(0.31)|||(0.38)|
|Cash Conversion Cycle|||0.122|||0.020||-0.283|
||||(0.10)|||(0.12)||(0.19)|
|% of investment from own fund 2007||||0.001|||0.001|0.000|
|||||(0.00)|||(0.00)|(0.00)|
|Firm age||||||||0.002<br>(0.00)|
|Small firm||||||||0.061<br>(0.08)|
|Foreign owned||||||||0.191***<br>(0.07)|
|Small*DemandSensitivity|||||||||
|Constant|0.720***|0.696***|0.707***|0.709***|0.649***|0.695***|0.705***|0.874***|
||(0.06)|(0.08)|(0.12)|(0.07)|(0.10)|(0.13)|(0.10)|(0.19)|
|Country fixed effects|√|√|√|√|√|√|√|√|
|Observations|964|803|580|910|406|425|598|211|
|R-squared|0.02|0.02|0.03|0.02|0.06|0.05|0.02|0.14|

Similarly we run the regression with the dependent variable “reporting reduced access to credit as the main factor”. Table 3 shows that none of the credit variables is alone significantly correlated with the dummy variable (dependence on External Finance is an exception when it becomes significant if we include demand sensitivity). This implies two possibilities: it could be that the credit proxies do not capture actual credit needs. We discount this possibility because the proxies are well-established and used extensively in the literature. Another possibility is that many credit dependent firms were also hit harder by demand shocks, and hence did not report reduced access to credit as the main factor. We lean towards this possibility, because indeed when controlled for demand sensitivity, dependence on external finance becomes significant.

13
# _6.2.2 Labor Regulations_

Table 8 presents survival models that control for provincial level real minimum wages and allow their impact to vary over time (the baseline models presented in columns 1 and 3) as well as to interact with productivity (the extended specifications presented in columns 2 and 4). Since our models include province dummies, the impact of minimum wages is essentially identified off within-province temporal variation in real minimum wage levels.

The baseline models demonstrate suggest that firm survival was not on average strongly correlated with minimum wage levels before and during the crisis, though post-crisis firms facing higher minimum wages were less likely to exit. Controlling for minimum wages does not reduce the attenuation effect. Yet, the extended specifications suggests that the baseline specifications hide significant heterogeneity; firms in provinces with higher minimum wages are on average less likely to exit yet the odds ratio associated with the interaction between value added per worker and demeaned province level minimum wage levels is significantly larger than 1, indicating that highly productive firms are more likely to exit when minimum wages are relatively high. Labor regulation thus appears to interfere with market selection. Importantly, this distortionary effect is especially pronounced during the crisis; the crisis interaction term between minimum wages and productivity is strongly statistically significant using both log value added per worker and the Solow residual, indicating that relatively productive firms confronted with relatively low reductions in real minimum wages were proportionately more likely to exit. Note also that including this interaction reduces the coefficient on the crisis-productivity interaction term. In other words, labor regulations appear to provide a partial explanation for the attenuation effect.

# _6.2.3 Reduced Entry_

Table 9 presents regressions that include a dummy for whether or not a firm is an entrant, interacted with period dummies and productivity. The results suggest that while entrants‟ survival is typically less strongly correlated with productivity, this result was not different during 26
things being equal, this will raise the combined weight of education deprivation indicators. Without any correction, the approach will ceteris paribus tend to favor dimensions represented by a larger number of indicators. To guard against this, we thus use a nested version of the inverse incidence approach, which in this respect is similar to the nested approach to uniform weights used in the global MPI.

The nested inverse incidence weights are thus determined in two steps. First, we begin with the three main dimensions relating to education, health and the standard of living and livelihood, and establish their weights based on inverse incidence. In the second step, we establish inverse incidence weights for individual indicators within any main dimension such that their sum equals the weight established for that main dimension in the first step. These weights are determined for 2004 using APIS data for that year, and then kept fixed over time.

# **iii) Subjective Welfare Weights**

This approach generates weights from an estimated relationship between a measure of subjective welfare (obtained through a survey) and the indicators that are sought to be included in a multidimensional welfare index.<sup>10</sup> For constructing these weights, we exploit data from the 2004 APIS survey relating to the following question in the survey: “Imagine a ladder with ten steps. The first represents the poorest in society and the tenth represents the richest. On what step of the ladder would you be?” The percentage of the population at different steps of the ladder turned out to be as below:

|_Step (poorest to richest)_|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|_% of population_|6.3|15.8|21.9|17.9|25.6|7.7|3.3|1.2|0.2|0.2|

Based on this, we define a binary subjective poverty variable which takes a unit value (and zero otherwise) if a household perceives themselves to be on the second rung of the ladder or lower.<sup>11</sup> We then estimate a logit regression of subjective poverty on the 9 (or 10) multidimensional poverty indicators in Table 1. The marginal effects of indicators estimated from this regression are shown in

> 10 See Bellani (2013), Bellani et al. (2013), Decanq et al. (2013) and Mitra et al. (2013) for related applications of this approach.

> 11 This is arbitrary but yields a subjective poverty incidence of about 22% that is not very different to the official income poverty incidence of 25% (for 2003 based on FIES).

9
# **1. Introduction**

Developing _South_ countries have been gaining space in the global economy and in global finance, both as senders and as receivers, as is documented in the recent Flagship Report (de la Torre et al., 2015).<sup>1</sup> In particular, since the 2000s, South countries have become a significant source and target of foreign direct investment (FDI), bank lending, and portfolio investments. For example, while South countries received about 26% of global capital inflows during the 1990s, by the end of the 2000s they received almost 55% of the total flows. As sources of capital flows, South countries sent about 53% of global capital outflows by the end of the 2000s, up from 14% in 1990. Moreover, before the 2000s, most of the capital exported from South countries had a developed _North_ country as a target.<sup>2</sup> In more recent years, however, South countries have become an important source of capital flows for other South countries. In fact, the number of active South-South financial connections as a ratio of all active connections in the world increased faster than the North-North, North-South, and South-North connections, reflecting a higher degree of connectivity within the South. Moreover, current projections suggest that these patterns are not temporary and the South will continue to gain space in the years to come. Although the growth in South-South capital flows reflects developing countries’ increasing integration into global financial markets, countries from the North still stand alone at the center of the global financial network. A significant number of South countries still needs to be connected with a wide set of countries in the world.

To better understand the South’s integration in global finance, this paper expands upon the existing evidence by exploring how Latin American and Caribbean (LAC) countries have been integrating financially with both North and South countries. In particular, the paper addresses the following main questions about LAC’s financial integration. How are LAC countries connecting financially with countries in the North, countries in other regions of the South, and other LAC countries? How have these connections evolved during the 2000s? What role have new connections played in the evolution of these flows?

The paper also investigates if trade is an important factor behind the dynamics of LAC’s financial flows. In recent years, there has been growing interest in understanding the link between international trade and financial flows. The classical Heckscher-Ohlin-Mundell paradigm predicts that exports are based on endowments, the North exports capital and countries invest in economies where

> 1 Other studies make similar points. See, for example, Aykut and Ratha (2003) and World Bank (2006, 2011, and 2013). 2 In this paper, the North comprises the G-7 and Western European countries. The South includes all other economies, though in the empirical analysis in this paper, we separate the South into LAC and non-LAC countries.

2
Blaikie, P., T. Cannon, I. Davis and B. Wisner (1994). _At Risk: Natural Hazards, People’s Vulnerability and Disasters_ . London: Routledge.

- Bouwer, L. M. (2011). Have disaster losses increased due to anthropogenic climate change?. _Bull. Amer. Meteor. Soc_ ., 92: 39–46.

- Brecht, H., S. Dasgupta, B. Laplante, S. Murray, and D. Wheeler (2012). Sea-level rise and storm surges: High stakes for a small number of developing countries. _The Journal of Environment and Development_ , 21(1): 102-138.

Cardona, O. (2005). Indicators of Disaster Risk and Risk Management: Program for Latin America and the Carribean. Main Technical Report. Washington, DC: Inter-American Development Bank (IDB).

- CIESIN (2010). Global Rural Urban Mapping Project (GRUMP), population data including city location and population estimates, http://sedac.ciesin.columbia.edu/data/collection/gpw-v3 .

- Cutter, S. (ed.) (2001). _American Hazardscapes: the Regionalization of Hazards and Disasters_ . Washington, DC: Joseph Henry Press.

Cutter, S.L. and C. Finch (2008). Temporal and spatial changes in social vulnerability to natural hazards. _Proceedings of the National Academy of Sciences_ , 105(7): 2301-2306.

- Davidson, R. (1997). A multidisciplinary urban earthquake disaster risk index. _Earthquake Spectra_ , 13(2): 211–223.

Dilley, M., R. Chen, U. Deichmann, A. Lerner-Lam, M. Arnold, J. Agwe, P. Buys, O. Kjekstad, B. Lyon and G. Yetman (2005). _Natural Disaster Hotspots: A Global Analysis_ . World Bank Disaster Risk Management Series, No. 5. Washington, DC: World Bank.

- Duranton, G. and D. Puga (2001), "Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products," American Economic Review, 91, 1454-1477.

Elvidge, C.D., P.C. Sutton, B.T. Tuttle, T. Ghosh and K.E. Baugh, Global urban mapping based on nighttime lights, in P. Gamba and M. Herold (eds), _Global Mapping of Human Settlements_ , Taylor and Francis, London, 129-144.

Füssel, H.-M. (2007). Vulnerability: A generally applicable conceptual framework for climate change research. _Global Environmental Change_ , 17: 155-167.

- Gill, I. and H. Kharas (2007). _An East Asian Renaissance: Ideas for Economic Growth_ . Washington, DC: World Bank.

39
farther past. Together, these findings suggest that, in the short term, older individuals in households with adult death may actually be better off, due to a decreased burden of care for the deceased individual before time of death. However, in the longer term, farm work increases significantly, and we find some evidence for declines in health as well. Even in settings where HIV prevalence is low today, high rates in the past (in this context, the early 1990s) have a lingering impact of the situation of older individuals. Policies which help ensure complete markets for livestock and other forms of assets, provide asset accumulation, and preserve women’s rights to property may help mitigate the long-run negative impact of prime-age deaths.

The death of a non-resident biological adult child is not associated with more work or poorer health among older adults. We do not have direct measures of the extent to which adult children support their parents who live in another household, either through cash or in-kind assistance. These results suggest that either this support is replaced by other family members or the support is much lower than is currently speculated. In their cross-country study of income sources, Carletto _et al._ (2007) find that transfers (including remittances from relatives) constitute less than 10% of total income in the set of African countries they examine. This is a much lower share than for other regions (East Asia, East/Central Europe and Latin America). Of course, it is possible that the share of income from remittances is much higher for particular subgroups of households, and non-income support (labor-sharing) may be important too. We also note, though, that most of the older adults in the sample reside with at least one prime-age adult (Table 2). This decreases slightly by 2004, but still fully three-quarters reside with a working-age adult. We do not examine the sub-sample of older adults with no prime-age adult in the household, since the sample is too small, but this may be the group for whom the death of an adult child does impact on working hours and health.

While this study contributes to a more complete picture of the impact of primeage mortality on older surviving adults by focusing on work burden and health, the findings should be caveated by concerns of endogeneity. The main driver of prime-age adult mortality, HIV/AIDS, is associated with behaviors that call into question a causal interpretation of our findings. Short of experimental options to study the impact of adult 24
Chart 2. Asia: Emerging Markets and the Mexican Crisis Stock Prices in US dollars, November 21, 1994 - April 30, 1995 <!-- Start of picture text --> Sri Lanka<br>180 ~~~~~~~~~~~~~~~~~~~<br>170<br>140<br>130 -3<br>110<br>Nov21 Dec19 Jan16 Apr24<br>-<br>:<<br>Indonesia<br>100<br>100 -<br>N ~~~~~~~~~~~~~~~~~~~~o-<br>Nov21 Dec19 Jan 16 Apr 24<br>Philippines<br>3NO ~ -~ ~ ~~~~08<br>3700 -<br>_____<br>340<br>3100<br>2700~~~~~~~~~~~~~~~~~~~~~~~2<br>Nov 21 Dec 19 Jan 16 Apr 24<br><!-- End of picture text --> <!-- Start of picture text --> India<br>.--<br>440<br>340 -<br>Nov21 Dec19 Jan16 Apr24<br>Malaysia<br>3<br>no -~~~~~~~~~~~~~~~~~~~0<br>300-<br>Nov21 Dec 19 Jan 16 Apr 24<br>Thailand<br>~~~~~~~~~~~~~~~~~~~~1060<br>1040-<br>Nov21 Dec19 Jan 16 Apr 24<br><!-- End of picture text --> Source: International Finance Corporation.