rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Data description**

# **_Inequality measures_**

The first of the two large databases we use to derive inequality measures is that of Occupational Wages around the World (OWW). As mentioned before, the data cover the period from 1983 to 1999 and more than 150 countries. The coverage in all its dimensions, however, is problematic and fragmentary. Although there are 156 countries in total, each country does not provide data (occupational wages) for every year. The yearly country coverage varies between 48 and 76. Occupations included also vary from country to country. Moreover for a given country even when it does provide the annual data, the occupational coverage is not necessarily uniform for each year.<sup>20</sup> Furthermore, it should be noticed that each observation is an observation on “habitual” country-wide wages for a given occupation. Thus some averaging is already built into the data. That however need not be a problem since, for example, the differences in earnings by skill levels are also based on averaging. There is however a difference in that the latter are obtained through a statistical analysis that covers a well defined spectrum of wage earners (labor force survey) and controls for other relevant factors (gender, experience), while the ILO data represent a mish-mash of average “habitual” wages for different underlying populations: some countries—for some years— report monthly wage rates, others report collectively bargained wages, yet others report hourly wages. At times men and women are combined, and at times only wages for men are reported. Freeman and Oostendorp overcome the problems of data comparability by “calibration” which is essentially a process of finding the adjustment coefficients (based on a regression analysis) for the data given in a “non-standard” form, where the standard form is defined as the most common form being used in the dataset, _viz_ . monthly wages for male workers.<sup>21</sup>

> 20 For example, the US gives the data on 11 occupations in 1983 and 150 occupations in 1999. 21 They do several such calibration and show (in an  Annex) that the results (inequality statistics) do not depend on a particular calibration. For our calculations, we have used their suggested base-wage calibration denoted _x1wu_ in the oww.dta database.

24
5. _School enrollment._ Whether a school-age child (ages 6-18) is enrolled in school.

# **4.3 Design Integrity**

## **4.3.1 Baseline Balance**

Table A4-A in the Online Appendix presents the test for baseline balance for the households surveyed in any of the four endline survey rounds, while Table A4-B presents the balance test for households surveyed in all four endline survey rounds. We follow the same specification used for impact analyses, comparing baseline treatment and control households, controlling for randomization strata. In both tables, Panel A presents these comparisons for primary outcomes, and Panel B for secondary outcomes and other relevant indicators. We find no statistically significant or economically meaningful differences across most outcomes and indicators, except for the primary woman’s psychological well-being and the value of livestock. Treatment households interviewed in any of the four survey rounds have 36% lower (p-value = 0.033) value of livestock at baseline (this difference is similar in magnitude but not significant for households surveyed in all four endline survey rounds), compared to control, while primary women in treatment households have a lower psychological well-being measure by 0.23 SD (p-value = 0.007) (this difference is similar in magnitude and level of significance for households surveyed in all four endline survey rounds), compared to their counterparts in the control group. These differences in livestock value and psychological well-being at baseline may result in underestimates of treatmentment effects. To assess whether these imbalances influence any of the main results, we present robustness checks including these variables as controls (Table A6 in the Online Appendix).

## **4.3.2 Compliance**

As described in Bedoya et al. (2019), the program was highly successful at delivering the components, with 97% of the treatment households reporting being aware of the TUP program, and of those, the majority (99.5%), reporting having received program assets or services, compared to 3% in the control group. 96% of treatment households and 1.5% of control reported receiving livestock assets.

## **4.3.3 Attrition**

The lottery assigned 1,219 households into treatment (491) and control (728). Of these, 1,173 (96%) were surveyed at baseline.<sup>17</sup> The endline phone survey was successfully completed among 839 households, which implies an attrition of 31% with respect to baseline, and of 27% with respect to midline. While 17The difference is because the implementer conducted an ultra-poor re-verification for a sample of households when the survey firm already had started the baseline. For households located in villages where the baseline survey firm had already conducted data collection, the baseline was missed because they could not go back.

16
# **2.  A First Look at the Data**

We illustrate the challenge of identifying relevant determinants of governance outcomes based on very disaggregated governance indicators using two leading datasets. The first is the Global Integrity Index (GII), compiled by Global Integrity, a Washington‐based advocacy organization. Quoting from its mission statement, _“Global Integrity generates, synthesizes, and disseminates credible, comprehensive and timely information on governance and corruption trends around the world. As an independent information provider employing on‐the‐ground expertise, we produce original reporting and quantitative analysis in the global public interest regarding accountable and democratic governance. Our information is meant to serve simultaneously as a roadmap for engaged citizens, a reform checklist for policymakers, and a guide to the business climate for investors.”_ The GII reports over 300 individual variables that score countries on various highly‐detailed dimensions of institutions that matter for public sector integrity and accountability. The individual questions on which the GII is based are scored by locally‐recruited experts (typically one per country), and are then vetted by an anonymous peer‐review process involving 3‐5 reviewers per country.

The GII can be disaggregated at three levels. The overall index is organized into six main categories (Civil Society, Public Information and the Media, Elections, Government Accountability, Administration and Civil Service, Regulation and Oversight, and Anticorruption and Rule of Law). These six are further disaggregated into 23 sub‐categories (e.g. Elections is further decomposed into Voting and Citizen Participation, Election Integrity, and Political Financing). And finally these 23 subcategories are built up from the 303 individual variables. For example, Election Integrity is an average of 15 separate questions relating to the existence and effectiveness of electoral monitoring bodies. A key and extremely valuable feature of GII is that it consistently matches up questions about _de jure_ rules and the _de facto_ implementation of these rules. For example, within Government Accountability, question 12a assesses whether “In Law, citizens have a right of access to government information and basic government records (Yes/No)”. This is followed by question 13a which asks “In practice, citizens receive responses to access to information requests within a reasonable time (0‐100 scale)”. In our use of the GII data we systematically distinguish between the “In Law” and “In Practice” questions in GII. In particular, we decompose each of the six GII main categories into separate averages of all the “In Law” and “In Practice” questions, resulting in 12 high‐level aggregates. Similarly, we disaggregate each of the 7
# _4.1. Effects of Changes in Market Conditions_

Amid concerns about increased indebtedness since the GFC, several studies analyze how different shocks could impact the balance sheet of emerging market firms. One of the first studies on this issue was performed by the IMF in 2014 (IMF, 2014). The IMF conducted a scenario analysis to test the resilience of emerging market firms to shocks to earnings, borrowing costs, and exchange rate depreciations. Debt-at-risk is used as the outcome variable. Using data for 2012, the analysis concludes that under an adverse scenario debt-at-risk in emerging economies would almost double, from 19 to 35 percent. This scenario analysis was re-run in 2015 and in 2016 with similar results (Chow, 2015; IMF, 2016).

In 2017, the United States Board of Governors of the Federal Reserve (henceforth, Federal Reserve) conducted a similar analysis with two improvements (Beltrán et al., 2017). First, the study qualifies the levels of risk by comparing resulting values of debt-at-risk with those during the AFC. Second, the study assesses the relative importance of different shocks. This study models the effect of shocks to earnings, interest rates, and the exchange rate on debt-at-risk of nonfinancial corporates assuming large but plausible shocks, based on previous crisis episodes. In particular, the authors assume a fall in earnings of 20 percent, a rise of 100 basis points in the interest rate paid by firms, and a 20 percent depreciation of the exchange rate. This study finds that if the three shocks would occur simultaneously, the debt-at-risk in emerging economies in 2016 would rise from 25 to 41 percent. Excluding China, debt-at-risk would increase from 15 to 34 percent. Although high, these values would still be below those on the eve of the AFC when debt-at-risk stood at 48 percent. Nevertheless, in several large emerging economies the share of debt-at-risk would get near or, even, surpass 48 percent. This would be the case of Indonesia (62 percent), Mexico (53 percent), China (48 percent), and Brazil (45 percent). In other words, in major emerging economies about half of nonfinancial corporate debt would correspond to firms with weak debt servicing capacity.

20
Turning to spatial inequality, the estimates for the spatial Gini are weaker and less precise than those for night-light density. The reduced-form estimates in Panel C are positive and marginally significant at the 10 percent level, suggesting that forced displacement may have widened within-municipality spatial inequality. However, the 2SLS estimates in Panel B are not statistically significant at conventional levels, and we therefore interpret the spatial inequality results as suggestive rather than conclusive.

Table 1: Forced Displacement and Development

||Night|Light|Log(Nigh|t Light)|Spatia|l Gini|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: OLS**|||||||
|Migration Outfows|-0.033**|-0.044***|_−_0.032***|_−_0.025***|0.001**|0.001**|
||(0.013)|(0.014)|(0.006)|(0.005)|(0.001)|(0.001)|
|**Panel B: 2SLS**|||||||
|_Second Stage_|||||||
|Migration Outfows|-0.177***|-0.173***|_−_0.073***|_−_0.075***|0.005|0.005|
||(0.061)|(0.057)|(0.023)|(0.020)|(0.004)|(0.003)|
|_First Stage_|||||||
|Imputed Outfows|0.205***|0.209***|0.205***|0.209***|0.205***|0.209***|
||(0.054)|(0.052)|(0.054)|(0.052)|(0.054)|(0.052)|
|F Statistic (excl. instrument)|664.48|786.67|664.48|786.67|664.48|786.67|
|**Panel C: Reduced Form**|||||||
|Imputed Outfows|_−_0.037***|_−_0.036***|_−_0.015***|_−_0.016***|0.001*|0.001*|
||(0.009)|(0.009)|(0.004)|(0.003)|(0.001)|(0.001)|
|Observations|10,020|10,020|9,974|9,974|10,020|10,020|
|Dep. Var. Mean 1992|3.77|3.77|0.034|0.034|0.27|0.27|
|Municipality & Year FE|✓|✓|✓|✓|✓|✓|
|Controls _×_ Time Trends||✓||✓||✓|

_Notes:_ The table illustrates the estimates of specifications (1), (3), and (2). Panel A presents the OLS estimates of equation (2). Panel B represent the 2SLS estimates of equations (3) and (2). Panel C presents the reduced-form estimates of the outcomes in the instrumental variable. Odd-numbered columns exclude baseline controls and even-numbered columns include baseline municipal characteristics interacted with year fixed effects. The F-statistic is the Kleibergen-Paap Wald F-statistic. Controls in the baseline are interacted with time trends and include urban coverage, water bodies, and tree-cover loss for 2001; night-light density for 1992; and political repression records for 2004. Robust standard errors clustered at the municipality level are in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1.

Figure 6 examines the timing of these effects through an event study. Before 2013, municipalities with high and low foreign settlement shares followed statistically indistinguishable 28
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# **References:**

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- Campa, J. M. and L. S. Goldberg. 2002 “Exchange Rate Pass-Through into Import Prices: A Macro or Micro Phenomenon?” _NBER Working Papers_ 8934.

- CEPAL (Comisión Económica par América Latina y el Caribe). 2001. _Panorama Social de América Latina 2000–2001._ Publicación de las Naciones Unidas, Santiago de Chile.

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- Nicita, A. 2004. “Who Benefited from Trade Liberalization in Mexico? Measuring the Effects on Household Welfare.” _World Bank Working Papers_ 3265.

- Singh, I. L. Squire and J. Strauss. 1986. _Agricultural Household Models, Extensions, Applications and Policy_ . The World Bank and The Johns Hopkins University Press.

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- Winters, L. A. .2002. “Trade liberalisation and poverty: what are the links?” _World Economy (U.K.)_ 25:1339-67.

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- .

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TABLE 12 CONSOLIDATED FINANCIAL POSITION OF THE MOSHAV SECTOR (in millions of US dollars, September 1986)

# A. LIABILITIES

SuDPlV Cooperatives Long-term bank loans 190 --Short-term bank loans 120 Suppliers' credit 130 Other sources 40 Moshav associations - Bank loans 230 Other debtors (incl. gray market and intermoshav debt) 130 Total liabilities of the moshav sector 840

# B. RESOURCES AND USES-

## Resources:

Uses:

|Farm income (farm gate<br>value)|870|Farm expenses (incl. hired<br>labor)|490|
|---|---|---|---|
|Non-farm incomeb|140|Living expensesb|230|
|||Depreciation of production<br>assets|70|
|||Financing expense on working<br>capital|30|
|||Income tax expensec|70|
|Total resources|1,010|Total uses|890|
|||Available for debt repayment<br>and retention as equity|120|
|||= - = = =|A~~~<br>= =|
|Source: Gibton Committee|(1986).||0|

Source: Gibton Committee (1986).

- ,Notes: a) Consolidated estimates including all moshav farmers. b) Non-farm income: it was estimated that out of 48,000

participants in the labor force in the moshavim, 26,000 hold non-farr occupations with an average monthly income of $450; living expenses estimated as 27,500 families X $700 average a month.

- c) Including tax liability on non-farm income.

41
owned banks and banks specialized in lending to small- and medium-sized firms (e.g. the Small Industry Development Bank of India, or SIDBI, and State Financial Corporations, or SFCs). However, credit availability is not uniform across the surveyed firms, and the market for bank credit is clearly relationship-driven. Over 70% of the respondents said that their firms had to meet operating/profitability criterion to obtain their largest loans, while the median “monitoring” frequency of the banks (bank staff contacting borrower about the loan) is once per quarter.

We also find that informal governance mechanisms based on trust, reputation and relationships are much more important than legal remedies in resolving disputes and enforcing contracts. For example, when asked about the consequences of delay of (or non-)payments and breach of contracts, the respondents ranked loss of future business opportunities, reputation and personal assets as main concerns, while fear of legal remedies was the least important. When asked who would be the best mediator for disputes (multiple choices allowed), 46% of the respondents specify “mutual friends and business partners” and 26% specify a non-government organization like a trade association as their choice, and only 20% of respondents choose “going to courts.” When asked how a firm ensures payments, 53% of the respondents screen their business partners carefully so that such issues do not occur, while 59% said they would go to courts but would leave negotiation possibilities open. Finally, when asked about government regulatory authorities (e.g., obtaining a license to start a business), our survey indicate corruption is part of doing business. The two most common methods to overcome corruption are bribes and using friends of government officials.

Our paper extends the literature on law, institutions, finance and growth. Many crosscountry studies focus on one or two dimensions of a country’s legal and financial systems, and treat each country in their sample on an equal-weight basis. We might expect that, compared to large and diverse countries (e.g., India and China), small homogeneous economies (e.g., Hong Kong and Singapore) could have more effective legal and financial institutions because they can be tailored to 5
are common in the New Keynesian model literature applied to developing countries, including Dutch disease effects, investment inefficiencies and weak tax systems. The model is parameterized following various empirical works and data from Uganda, and is used to simulate the effects of a one standard deviation oil production shock, interpreted as a boom in the energy sector.

Our results show that: ( _i_ ) a better fiscal management is to save the resource income in a sovereign wealth fund for future generation when public capital is almost unproductive; ( _ii_ ) a gradual scaling-up of public investment ( _The Sustainable-Investing Approach_ ) yields the best outcomes as it minimizes macroeconomic volatility; e.g. the real exchange rate appreciation is 30 percent lower than in the all-investing approach, which might be viewed as an attractive fiscal policy to accelerate economic development in public capital-scarce economies; the trade balance improves substantially and impulse response functions suggest that output, non-tradable and tradable goods production, employment and wages rebound faster.

We also define criteria to determine the optimal value of the oil share to invest in public infrastructures by minimizing a social loss function; we use four loss functions that include a households’ welfare indicator and a macroeconomic/fiscal stabilization indicator. We find that the loss functions have convex shapes with optimal values of _φ_<sup>_oil_</sup> varying between 0.55 and 0.85, depending on the policy maker’s preference for macroeconomic stability. Furthermore, our results show that the optimal share of oil revenues to be used for public investment is very robust to the various parameter calibrations; only parameters that directly affect these revenues (such as interest rates on savings and the persistence of oil price shocks) play an important role. In comparison with the recent literature, our optimal share to invest domestically in public infrastructures is slightly higher than those estimated in Ag ́enor (2014), which ranges from 30 percent to 60 percent using oil price shocks.

A number of extensions could usefully be considered. First, the paper assumes an exogenous oil sector, and the analysis can be extended to explore labor movements between non-oil and oil sectors. In practice, oil sectors in developing countries use domestic labor and Dutch disease effects can be optimally analyzed using an endogenous oil production sector. Second, the paper does not consider the effect of foreign direct investments (FDI). Indeed, it’s well-known that in low-income countries, FDI account substantially and may affect macroeconomic dynamics.

49