rafmacalaba/datause-encoder-data huggingface dataset:
We use mirror data for FDI in the same way as for bank loans and deposits. CDIS and UNCTAD report two values for FDI for a given country pair: (i) the value of FDI assets country _i_ holds in country _j_ , and (ii) the value of FDI liabilities country _j_ owes to country _i_ . Hence, FDI positions appear twice in each database whenever source and destination are both reporting countries (as FDI assets of country _i_ in country _j_ and as liabilities of country _j_ with respect to country _i_ ) _._ To maximize data coverage, we use the mirror data for each database separately. Whenever the value of FDI assets (liabilities) of country _i_ vis-à-vis country _j_ is available but its mirror value obtained from liabilities (assets) data is not, we use the value of assets (liabilities). Whenever both assets and liabilities are available and the corresponding values do not coincide, we use the maximum of the two. This methodology yields aggregate FDI assets, for the CDIS and the UNCTAD data, that are closer to the values reported in Lane and Milesi-Ferretti (2018).<sup>27</sup> Combining the two databases is not straightforward because each source can report different FDI values for the same bilateral connection, starting from different baseline values. We deal with these differences by adjusting the UNCTAD data so that they match the CDIS data when both are available. Specifically, we calculate the ratio of the CDIS’s FDI value to the UNCTAD’s FDI value for each country pair for 2009–12 (for the earliest year with data on both data sets).<sup>28</sup> We then use this ratio to multiply the UNCTAD data backward for 2001–08, bringing it to the same level as the CDIS data. For 2009–12, when the CDIS and UNCTAD data are available, we use the CDIS data when the value for a given bilateral link for a given year is positive or when the CDIS value is zero and the

> 27 As a robustness check, we analyzed alternative methodologies: using only the data on assets, using primarily the data on assets and filling in the missing values with the data on liabilities if available, and using primarily data on assets and filling in the missing values by assuming that those assets in missing years grew at the same rate as liabilities (when this growth rate was available). The regression estimates using these alternative criteria were quantitatively similar to the ones we report using the maximum between assets and liabilities.

> 28 To calculate the ratio, we need the IMF and UNCTAD data for at least one year during 2009–12 for each country pair in the databases. If this is unavailable, the UNCTAD data are not leveled up and the FDI values not covered by the CDIS data are coded as missing observations.

35
|Socio-emotional<br>skills training|There is positive<br>evidence of the<br>impact of<br>personal initiative<br>training on<br>micro/small<br>women-led<br>business,<br>although not in all<br>contexts|McKenzie et al.<br>2023 (review),<br>Campos et al. 2017<br>(RCT), Alibhai et al.<br>2017 (RCT), Ubfal et<br>al. 2022 (RCT),<br>LACGIL 2021<br>(review)|Large and positive<br>effects have been<br>observed in one<br>RCT for both male-<br>and female-led<br>businesses, which<br>has spurred<br>interest. However,<br>these effects have<br>not yet been widely<br>confirmed. More<br>evidence is needed<br>to understand why<br>it works in some<br>contexts and not<br>others, and to<br>assess the impacts<br>of combining it with<br>other interventions.|
|---|---|---|---|
|Gender-oriented<br>training|Positive effects<br>are observed<br>when combined<br>with traditional<br>training; further<br>evidence is<br>required on the<br>additional impact<br>of gender-specific<br>components|Bulte et al. 2016<br>(RCT), McKenzie<br>and Puerto 2021<br>(RCT)|More evidence is<br>needed to<br>understand the<br>cost-benefit<br>analysis of adding<br>gender-oriented<br>components to<br>traditional<br>programs.|
|Mentorship and<br>consulting|Mixed evidence<br>indicates low or<br>non-persistent<br>effects for<br>micro/small<br>women-led<br>businesses but<br>more positive<br>effects for new<br>approaches; a lack<br>of causal evidence<br>exists for larger<br>women-led firms|Brooks et al. 2018<br>(RCT), McKenzie<br>and Puerto 2021<br>(RCT), Bakhtiar et<br>al. 2022 (RCT),<br>Anderson et al.<br>2023 (RCT),<br>Germann et al.<br>2023 (RCT) Bloom<br>et at al 2013, 2020<br>(RCT), Iacovone et<br>al. (2022), Otis et al.<br>2024 (RCT)|The literature<br>points to low<br>average effects, but<br>more evidence is<br>needed on the<br>impact on women-<br>led businesses of<br>novel approaches<br>using technology<br>and female<br>mentors, which<br>show more positive<br>effects, and on<br>consulting for larger<br>women-led firms.|
|Networking|There is a need<br>for more causal<br>evidence on the<br>impacts on<br>women-led<br>businesses|Asiedu et al. 2023<br>(RCT), Cai and Szeidl<br>2018 (RCT), Vega<br>Redondo et al. 2019<br>(RCT), Gonzalez-<br>Uribe and Hmaddi<br>2022(review)|There is a growing<br>consensus on<br>positive effects, but<br>only one RCT has<br>studied the impact<br>on women-led<br>businesses.|

31
# **References:**

- Ades, A. and R. Di Tella (1995), "Competition and Corruption", Oxford Applied Economics Discussion Paper, Series. 169 April: 18.

- Ades, A. and R. Di Tella (1996), "The Causes and Consequences of Competition: A Review of Recent Empirical Contributions". In B Harris-White and G White (Ed), _Liberalization and the new corruption,_ Institute of Development Studies Bulletin 27(2):6-11.

- Ades, A and R. Di Tella (1997), "The New Economics of Corruption: a Survey and Some New Results" in _Political Corruption,_ (ed) Paul Heywood, Blackwell Publishers.

- Arvind K. Jain (1998), "Economics of Corruption", Kluwer Academic Publishers, Boston/Dordrecht/London.

- Brunetti, A.G. Kisunko, and B. Welder (1997), "Credibility of Rules and Economic Growth: Evidence from World Wide Private Sector Survey", _Background paper for the World Development Report 1997,_ Washington: The World Bank.

- Brunetti, A and B. Weder (1998a), "A Free Press is Bad News for Corruption", _Wirtschaftswissenschaftliches Zentrum der Universitat Basel Discussion Paper,_ No 9809

- Brunetti, A and B. Weder (1 998b), "Explaining Corruption", Draft version, University of Saarland and University of Basel.

- Clements, B., R. Hugouneng and G. Schwartz (1995), "Government Subsidies: Concepts, International Trends and Reform Options", _IMF Working Paper, 95/91,_ Washington, International Monetary Fund.

- _Elliott, KA. (1997), "Corruption as an International Policy Problem: Overview and Recommendations ",_ Corruption and Global Economy, ed. by K.A. Elliott, Institute for International Economics, Washington D.C.

- Evans, P.B. and J.E. Rauch (1996), " Bureaucratic Structures and Economic Performances in Less Developed Countries", _Mimeograph,_ San Diego: University of California, San Diego.

- Gaultung, F (1997), "The Social and Economic Implications of Corruption", _Transparency International) Report 1997,_ Berlin Transparency International, 76-8.

- Goel, R.K., and M.A. Nelson (1998), "Corruption and Government Size: A Disaggregated Analysis", _Public Choice,_ XCVII, 107-20.

14
- 15 -

By Ra - Ca - Dep - Int.

The difference between this tax base and BRet is that dividends are no longer deductible. In fact, retentions of foreign subsidiaries are often exempt from taxation when multinational firms are taxed on their foreign-source income. When this occurs, one could argue that the appropriate tax base should be dividends and other repatriated income, assuming that the capital exporting country is willing to recognize this as a creditable tax. In some instances this has happened, such as in the case of withholding taxes on income accruing to foreigners. These withholding taxes are often credited against foreign taxes and can be used as a substitute for the comlany tax as a withholding device. However, the withholding tax rates are usually set by treaty and difficult to adjust. Thus the company tax is used for withholding instead.

None of the above motivations for company taxation suggest that the tax base should be equity income (i.e. including both retentions and dividends). The argument for an equity income tax is that withholding on both retentions and dividends is desirable, for personal tax compliance and for international reasons. Indeed, this was the view taken by the Carter Report in its recommendations for corporate taxation in Canada.

There has been considerable disagreement, however, regarding the appropriate base that should be used for company taxation. Much of the debate is related to whether the return accruing to savings should be taxed at both the personal and company levels. Those who have advocated a consumption-based tax at the personal level sometimes argue in favor of a similar tax at the company level to withhold rents and labor income. Those
# **3. The Capture of the Capital in the Republic of Yemen**

We investigate the ability of subjective questions on welfare to track more traditional measures of deprivation during a crisis using the capture of the Republic of Yemen’s capital at the end of September 2014 by Houthi forces. The assault began on September 21 and ended on September 24, with Houthi forces in charge of key government ministries (e.g., BBC 2018). The capture was a surprise, and was in part driven by Houthi forces being upset with a new constitution that was being finalized by the transition government following the Arab Spring (e.g., Baron 2015). The significant capture of territory by Houthi forces, that continued in early 2015 after the president fled from Sana’a, led to a coalition of nine countries in the region to join the conflict and support the internationally recognized government (e.g., World Bank 2017a).

The capture of Sana’a resulted in a number of shocks that affected households. The political uncertainty negatively impacted economic activity and investment (e.g., BBC 2015), and also negatively impacted safety (e.g., Human Rights Watch 2016). Additionally, the capture disrupted supply chains and led to immediate increases in prices of particular types of food items (e.g., Tandon 2019). Overall, the increase in prices and the decline in economic activity had unambiguously negative impacts on the population of Sana’a. Almost immediately after the capture, there were large declines in expenditure, food consumption, and stable employment, and a worsening in other welfare dimensions, all of which were not evident at the same time elsewhere in the Republic of Yemen (Tandon 2019).

Importantly, the government that was effectively overthrown was not popular among people in the Republic of Yemen, both in Sana’a and in the rest of the country. The president was not elected, but rather temporarily assumed the position following the Arab Spring and the resignation of the elected president (e.g., Baron 2015). Furthermore, there were large demonstrations against the government shortly after it took power.<sup>13</sup> Thus, although there was evidence of a decline in safety following the capture by Houthi forces (e.g., Human Rights Watch 2016), some portion of the population might have not been

> 13One example was protests over an elimination of a fuel subsidy earlier in the year that the government had to reinstate (e.g., World Bank 2017a).

9
Some papers have found that a marginal increase in land can make a tremendous difference for smallholders; however, the difference varies depending on the season (and country). Previous work in Malawi documented an overall labor scarcity at peak of the cropping season and excess labor after the harvest leading to underemployment (Kamanga, 2002; Wodon and Beegle, 2006). A paper using panel data from Mexico found that an additional hectare of land significantly increases expenditures by 880 pesos, which is 1.3 times the earnings of the average agricultural worker (Finan, Sadoulet, and de Janvry, 2004). However, evaluations of land reform projects intended to increase the size of land cultivated show mixed results.

More specifically, one study found that the 1980 Zimbabwe land reform had dramatically positive impacts in terms of assets, household consumption, agricultural income, and reduced inequality (Gunning, Hoddinott, Kinsey, and Owens, 2000; Kinsey, 1999; Hoogoveen and Kinsey, 2001). However, another paper that utilized propensity score matching only found modest, although positive impacts on per capita expenditure, largely due to a marked increase in household size (Deininger, Hoogeveen, and Kinsey, 2004). Reforms in Japan, Korea, and Taiwan were shown to improve productivity (Jeon and Kim, 2000), while some reforms in Latin America appear to not have such an impact (de Janvry and Sadoulet, 1989). A 20 year panel studying Indian land reforms demonstrates significantly positive impacts on income and consumption, especially for those who are relatively poor, that tend to decline over time (Deininger, Jin, and Nagarajan, 2007).

The impact of land titling is likewise controversial. Some papers find that ownership security increases capital formation, land-improving investments, and land-specific investments (Alston and Libecap, 1996; Feder and Onchan, 1987), while others find no significant impact on such investments, use of inputs, access to credit, productivity, or land value (Jacoby and Min4
We also asked the principals: Do you believe that the agent will send you back 100 rupees if you invest? The right panel of Figure 7 reports the proportion that said yes. There are no significant differences in principals’ beliefs by the caste status of the principal or the agent. Finally, we asked the agents: Do you believe the principal will invest the 50 rupees? In a probit regression (not reported here), all coefficients are close to zero and insignificant, and the results are robust to controls for the agents’ wealth.

In sum, the investment game experiment provides no evidence that high-caste men are less trusting than low-caste men. The results suggest that trust differences do not account for the caste differences in behavior in the Stag Hunt.

# **6.2 A Difference in Self-efficacy?**

Another alternative explanation of the results in the Stag Hunt would be that high- and lowcaste cultures have the same mental models for interpreting others’ actions and the same rules of thumb for retaliation, but that low-caste men feel unable to respond to transgressions. The belief in the ability to have an effect on one’s environment has been variously referred to as _locus of control_ or _self-efficacy_ in the psychology literature. If low-caste individuals systematically had a lower sense of self-efficacy, then that would explain the results of the Stag Hunt experiment and the vignette-based survey.

To investigate this hypothesis, we conducted an additional survey in 2013 involving 62 men—30 high-caste and 32 low-caste—to test for caste differences in self-efficacy. We employed a survey design from the psychology literature (Paulhus, 1983): The men were presented with four statements that take positions on different aspects of one’s locus of control, e.g., “I have no trouble making and keeping friends.” The respondents were asked whether or not they felt that the statements described them.

Table 5 reports the statements and the proportions of men who responded yes or no to whether or not the statements described them. The remaining respondents gave equivocal answers or were unable to respond. Not only did the responses not vary significantly across castes, but the point estimates indicate that if anything, low-caste men have greater locus of control than their high-caste counterparts. For example, the second row shows that 84 percent of low-caste men disagreed that they had a hard time finding others to help them, compared to 77 percent of high-caste men. The results suggest that a caste difference in self-efficacy does not explain the caste difference in responses in the punishment survey.

26
<!-- Start of picture text --> Survey Year<br>-0.030<br>2012 —e—<br>-0.097<br>2007 4 —*—<br>Region<br>o}o03<br>Other urban<br>0.042<br>Rural —_—+—<br>Individual characteristics<br>0.024<br>Higher education —_<br>-0.015<br>Secondary Education<br>0.029<br>Primary Education —_——<br>0.096<br>Male —+—<br>-0.100 -0.050 0.000 0.050 0.100<br>Point estimate and 95% Cl<br><!-- End of picture text -->
One paper that stands out in the recent literature and is closely linked to our paper is Jotikasthira et al. (2012). Their paper shows that movements in investor flows force significant reallocations in equity fund portfolios related to emerging markets, which in turn affect equity returns, correlations among emerging markets, and the developed market betas of emerging markets. These effects are particularly acute during periods of financial distress. Their paper provides important evidence on the role that mutual funds play in emerging markets and how they transmit shocks across international markets through their impact on returns. Two other earlier exceptions that are also good complements to our paper are Kaminsky et al. (2004) and Hau and Rey (2008). Kaminsky et al. (2004) study momentum trading by investors and managers. Hau and Rey (2008) use data on equity funds to analyze whether foreign exchange and equity risk measures trigger rebalancing behavior at the fund and stock level.<sup>5</sup> In this paper, we use a micro-level dataset on international mutual funds to shed new light on how investors and managers react to shocks and crises and how they impact capital flows through their investment reallocations across developed and emerging countries. International mutual funds are especially useful because they enable us to analyze separately: (i) injections/redemptions driven by the underlying investors; (ii) actual portfolios across countries that are allocated and rebalanced at the sole discretion of managers (and do not need to be inferred from other data); (iii) their interactions (how investors monitor managers); and (iv) the relative contribution of investors and managers to capital flows.<sup>6</sup> The main data consist of portfolio weights and assets invested in each country around the world for 1,076 equity and bond mutual funds on a monthly basis during 15 years, from January 1996 to November 2010. The data cover portfolio allocations to 124 developed and emerging countries and cash, plus fund returns that allow us to obtain injections and redemptions into each fund.

We explore several questions of interest. How volatile is the mutual fund investment across countries? How can mutual funds help transmit crises? What was their specific behavior during the global crisis of 2008? What is the role of investors and managers? How volatile are injections? To what degree do weights remain constant over time? To the extent that weights change, how much are they the cause of valuation effects versus actual buying/selling in

> 5 A much larger finance literature studies other aspects of the behavior of mutual funds at the domestic and international levels. See, for example, Grinblatt et al. (1995), Wermers (1999), and Gompers and Metrick (2001) for U.S. domestic funds, and Kang and Stulz (1997), Dahlquist and Robertsson (2001), Kim and Wei (2002), Chan et al. (2005), Gelos and Wei (2005), and Didier et al. (2010) for international funds.

> 6 Henceforth, we sometimes use the term “injections” to refer to both injections and redemptions (negative injections).

3
**Table 4. OLS Estimation Results for the effect of JOHAR irrigation on Area under Irrigation**

|**Area under Irrigation**|**(1)**|**(2)**|
|---|---|---|
|**across allthree seasons**|**Gross Area Irrigated**|**Area Irrigated/Acre land**|
|**JOHAR Farmer (vs non-beneficiary)**|0.0220<br>|0.0751**<br>|
||(0.0709)|(0.0290)|
|**Months of Operation of JOHAR System (**Base: <=18 months**)**|||
|_>18 months_|0.260***|0.0700*|
||(0.0921)|(0.0401)|
|Total Land Holdings (Acre)|0.190***<br>||
||(0.0261)||
|**Pump Access**(Base: No source)|||
|_Diesel (own)_|0.547***|0.268***|
||(0.0791)|(0.0390)|
|_Diesel (rented)_|0.401***|0.272***|
||(0.1000)|(0.0465)|
|_Electric_|0.418***|0.214***|
||(0.0702)|(0.0292)|
|_Pond/Canal_|0.449***|0.205***|
||(0.112)|(0.0351)|
|JOHAR farmer used a supplementary source of<br>|-0.0339|-0.0291|
|irrigation|(0.119)|(0.0441)|
|Household Size|0.00873|-0.00217|
||(0.0163)|(0.00490)|
|At least one member migrates for work|-0.0732|-0.0116|
||(0.0521)|(0.0251)|
|Type of JOHAR pump=Diesel (vs Solar)|-0.119|0.0940*|
||(0.109)|(0.0479)|
|The proportion of land that is upland|0.166**<br>|0.0508<br>|
||(0.0833)|(0.0367)|
||(0.0534)|(0.0171)|
|**Caste Category**(Base = General)|||
|_Scheduled Caste (SC)_|-0.00478|-0.0391|
||(0.225)|(0.0673)|
|_Scheduled Tribe (ST)_|-0.0547|-0.0182|
||(0.120)|(0.0406)|
|_Other Backward Classes (OBC)_|-0.118|-0.00546|
||(0.136)|(0.0442)|
|Gender of Respondent = Male|0.0772<br>|0.00693<br>|
||(0.0920)|(0.0326)|
|Age of Respondent (In years)|-0.00564*|-0.000730|
||(0.00295)|(0.00136)|
|Literacy of Respondent (Can read and write)|-0.00245|-0.00278|
||(0.0659)|(0.0233)|
|Experience of farming in family (in years)|<br>0.00433|<br>0.000913|
||(0.00430)|(0.00173)|
|Farming is the main source of family income|0.0940|-0.00996|
||(0.0590)|(0.0211)|
|Block FE|Yes|Yes|
|Observations|757|757|
|R-squared|0.484|0.353|

Note: Robust standard errors in parentheses clustered at village level *** p<0.01, ** p<0.05, * p<0.1 The coefficients in the table above are based on an OLS estimation that analyses the difference in area irrigated between JOHAR beneficiary 16
**Table 1.** The Base Year (2007) Aggregated SAM for the Brazilian Economy<sup>13</sup> Unit: billion Brazilian Real (R$)

||4274|-333||-3941||||||
|---|---|---|---|---|---|---|---|---|---|
||||204|-204||||||
||-29|||||29||||
||-1100|||||||1100||
||-1127|||||||1127||
|||||||3|-3|||
|||||||-32||32||
||-1968|||4296|-405||-1385||-538|
|_tax or transfer_|-51||-6|-151||||-318|526|
|||333|-197|||||-11|-125|
||||||405||-541||136|
||||||||1929|-1929||

**Table 2.** Industrial Sectors in the Model

|#|Industry|#|Industry|
|---|---|---|---|
|i01|Other agriculture|i17|Metal industry|
|i02|Sugarcane industry|i18|Machinery equipment|
|i03|Soy beans industry|i19|Other manufacturing|
|i04|Forest sector|i20|Electricity generation|
|i05|Livestock sector||ei01 electricity: hydro|
|i06|Food and beverage||ei02 electricity: nuclear|
|i07|Crude oil & natural gas||ei03 electricity: coal|
|i08|Metal & mineral mining||ei04 electricity: gas|
|i09|Coal mining||ei05 electricity: oil|
|i10|Textile & leather||ei06 electricity: wind|
|i11|Wood production||ei07 electricity: biomass|
|i12|Pulp paper & furniture|i21|Processed gas|
|i13|Petro refinery|i22|Construction sector|
|i14|Biofuels sector|i23|Commercial sector|
|i15|Chemical industry|i24|Transportation sector|
|i16|Non metallic industry|i25|Other service sector|

> 13 This study presents the SAM in the form of a “Micro Consistent Matrix (MCM)” mentioned in Markusen (2006) and first presented in Rutherford (1999).  Each row correspond to a market clearing condition with the positive and negative numbers representing supply and demand, respectively.  The first 8 columns correspond to the zero-profit conditions for different production technologies with positive and negative numbers representing output and input, respectively.  The last 2 columns are income balance conditions for household and government, respectively. Within an income balance condition, positive and negative numbers are income and expenditure, respectively.

15
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