rafmacalaba/datause-encoder-data huggingface dataset:
correlated with sales in the other dataset, implying that excluding it from the regression could bias the difference in productivity between female-owned and male-owned firms upwards.

**<u>Table 3a: OLS Productivity Regressions</u>**

||D|ependent var|iable:Log sal|es<br>Enterrise|
|---|---|---|---|---|
||Mexico|Bolivia|Peru|p<br>Surveys|
|Female dummy|-0.089***|-0.013|-0.155**|-0.022|
||(0.034)|(0.152)|(0.073)|(0.032)|
|Ln employees|0.170***|0.502***|0.657***|0.383***|
||(0.038)|(0.072)|(0.068)|(0.021)|
|Ln property value|0.017|0.076||0.042***|
||(0.014)|(0.047)||(0.009)|
|Ln machinery value|0.032***|0.138***||0.076***|
||(0.008)|(0.040)||(0.010)|
|Ln material costs|0.120***|0.452***|0.443***|0.574***|
||(0.012)|(0.052)|(0.037)|(0.016)|
|Adopted new technology during past 3<br>years||||-0.005<br>(0.024)|
|Ln hours worked|0.029<br>(0.019)|0.003<br>(0.100)|0.038<br>(0.063)||
|Owner's years of schooling|0.009***<br>(0.003)|0.021*<br>(0.012)|0.015<br>(0.010)||
|R-squared|0.086|0.757|0.695|0.947|
|Numberof firms|8,293|265|582|2,175|

_Note_ : These regressions are based on a Cobb-Douglas production function of the form Y = (AL)<sup>1-α-β</sup> H<sup>α</sup> K<sup>β</sup> , where A stands for technology, L stands for labor, H for human capital, and K for physical capital. Ln property, ln machinery and ln material cost are replaced with zero when their non-log values are zero and the regressions include dummies indicating whether the non-log values are zero. All monetary values are in US Dollars. All regressions include industry fixed effects, as well as city, region, or country fixed effects. Robust standard errors in parentheses. Significance levels: *** 1 percent, ** 5 percent, * 10 percent.

_Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

Table 3b displays the differences in productivity across female-owned and male-owned firms obtained by running quantile regressions instead of OLS regressions. The results show that the differences in productivity are largest for firms that are at the 25<sup>th</sup> percentile of sales and smallest for firms that are at the 75<sup>th</sup> percentile of sales. At the 25<sup>th</sup> percentile of sales, female-owned firms are between 6.4 percent and 20.3 percent less productive than male-owned firms<sup>8</sup> . At the median, the differences in productivity range only from 1.8 percent to 16.9 percent. At the 75<sup>th</sup> percentile of sales, the differences in productivity turn positive in the Enterprise Surveys and the

> 8 Although the difference of 20.3 percent that comes from the Bolivian data is not statistically significant, possibly due to the small sample size.

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cost of providing real services will depend on scenario-specific input prices and efficiency in government service production.

For all the scenarios that it simulates, MAMS provides wage and employment by activity and labor category (with labor split between three educational levels) as well as non-labor incomes. In the microsimulation model, this information is used, in turn, to generate a full picture of the impact of changes in the labor market variables on the level and distribution of micro-level household incomes.

As the database for our microsimulation model, we use the Yemen 2005/06 HBS; it covers 98,941 individuals in 13,136 households. The information from the household survey is used to estimate the poverty and inequality impacts of different counterfactual scenarios, linking the microsimulation model to the results generated by MAMS. MAMS communicates with the microsimulation model in a ―top-down‖ fashion (i.e., without feedback) via the following aggregate linking variables: wages, sectoral structure of employment, unemployment, and non-labor income. That is to say, results from MAMS scenarios are taken and applied to the full distribution of income as given by the household survey and assume there are no further feedback effects that would affect MAMS. The microsimulation model follows the non-parametric methodology described in Vos and Sanchez (2010) but extended to consider changes in non-labor income. The labor-market structure is defined in terms of the following parameters: unemployment among different groups of the population at working age defined according to skill, sectoral structure of employment, relative and overall level of wages, and skill composition of the employed population. The effect of altering each of those six parameters on poverty and inequality is analyzed. In addition, we consider changes in non-labor income. Specifically, the (per capita) non-labor income is increased or decreased in order to match the change in per capita household income estimated using MAMS. The change in per capita non-labor income depends on the household initial endowment of non-labor income. As a consequence, the absolute change in non-labor income is larger for those households that, according to the HBS, have more non-labor income in the base year.

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Recent experience during the COVID-19 market gyrations of early 2020 have shown the importance of maintaining a significant portion of their portfolio in overseas investments. As markets seized up in March, in some cases the international holdings provided the only reasonable source of liquidity on short notice. Additionally, despite the dramatic volatility, non-Colombian equity investments performed relatively better in 2020 on a Colombian peso (COP) basis; although the MSCI All Country World Index (ACWI) declined about 1% in the first half of the year in USD terms, in Colombian peso terms it was up by about 10%. Meanwhile the MSCI Colombia index was down by roughly 30% in the same period in local currency terms. Given this reality, it is clear that AFP portfolios with relatively higher investments overseas performed relatively better than those with only domestic equity investments.
**Table 10. Robustness Checks: High/Low Tech Industries and Regions of the World. Estimation: Fixed Effects. Dependent Variable: Credit.**

|Sample|**HighTech**|**LowTech**|**EAP**|**ECA**|**LAC**|**MENA**|**SSA**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Formality|0.058**|0.050***|0.008|0.041|0.048***|0.119*|0.052**|
||(2.103)|(3.207)|(0.165)|(0.917)|(1.884)|(2.975)|(2.571)|
|Fowned|-0.020|-0.033**|-0.036|-0.020|-0.057**|-0.009|-0.013|
||(-0.855)|(-2.207)|(-0.859)|(-0.230)|(-2.263)|(-0.294)|(-0.708)|
|Age|-0.000|0.001**|0.000|0.001|-0.000|0.001**|0.001***|
||(-0.060)|(2.026)|(0.132)|(1.156)|(-0.380)|(2.239)|(1.765)|
|Exporter|0.034*|0.027**|0.011|-0.010|0.024|0.039***|0.043**|
||(1.665)|(2.183)|(0.286)|(-0.272)|(1.306)|(1.684)|(2.148)|
|Manager's education|0.025|0.069***|0.010|0.052|0.059*|0.092*|0.049*|
||(1.270)|(6.546)|(0.294)|(1.461)|(3.706)|(3.925)|(3.212)|
|Size dummies: Small|0.122***|0.098***|-0.010|0.153*|0.112*|0.108**|0.102*|
||(4.830)|(6.890)|(-0.187)|(3.199)|(5.302)|(2.169)|(6.022)|
|Medium|0.211***|0.204***|0.133**|0.299*|0.220*|0.176*|0.191*|
||(6.554)|(11.007)|(2.072)|(5.170)|(8.164)|(3.137)|(7.189)|
|Large|0.240***|0.248***|0.271*|0.326*|0.226*|0.198*|0.263*|
||(7.254)|(12.844)|(4.045)|(5.594)|(7.800)|(3.608)|(9.252)|
|Very Large|0.311***|0.261***|0.277*|0.299*|0.258*|0.288*|0.244*|
||(8.255)|(12.290)|(4.013)|(4.617)|(7.738)|(5.028)|(7.483)|
|Location dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Cons|0.175|0.213***|0.185**|0.129***|0.370*|0.006|0.168*|
||(1.349)|(9.255)|(1.989)|(1.873)|(8.554)|(0.088)|(4.356)|
|Number of observations|2,957|8,966|1070|1066|4418|1884|3264|
|Number of countries|46|49|4|1|10|4|28|
|Adjusted R2|0.082|0.108|0.135|0.064|0.105|0.053|0.294|

Notes: t-statistics in parenthesis. Significance: 01 - ***; .05 - **; .1 - *; Size dummies: reference category: micro firms. EAP: Cambodia, Mongolia, Philippines and Vietnam; ECA: Turkey. LAC: Brazil, Chile, Costa Rica, Dominica Republic, Ecuador, , El Salvador, Guatemala, Guyana, Honduras, Nicaragua; MENA: Egypt, Lebanon, Morocco, Oman; SSA: Angola, Burundi, Benin, Burkina Faso, Botswana, Cameroon, Cape Verde, Eritrea, Guinea, Guinea-Bissau, Gambia, Kenya, Lesotho, Madagascar, Mali, Mozambique, Mauritania, Mauritius, Malawi, Namibia, Rwanda, Swaziland, Tanzania, Uganda, South Africa, Congo, Zambia.

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# **2. Background**

Despite the growing importance of FAFH in household expenditures, most national household surveys in developing countries largely focus on measuring consumption of food consumed at home. Furthermore, even when surveys attempt to better capture FAFH, little is known about how this information should be captured. This lack of attention and knowledge is the result of the difficulties in including FAFH within current food consumption modules, as well as the lack of rigorous evidence around best practices for collecting information on FAFH. With household surveys not well-designed to adequately capture these different types of consumption, FAFH consumption is often poorly measured.

A number of methodological challenges and various sources of measurement error arise when including FAFH. To begin with, simply distinguishing between ‘food consumed at home’ and ‘food consumed outside’ is not enough. Under this classification it is not clear where take-away meals belong (i.e. those ready-to-eat meals that are produced outside, for example at a restaurant, but brought to eat at home). Careful definition, classification of types of foods, and an organized framework is therefore needed to prevent double counting or omission.

In addition to clear classification of consumption types, there are myriad other potential sources of measurement error when collecting FAFH consumption. We focus in this study on three sources: omission, telescoping, and the level of disaggregation. Measurement of FAFH is particularly vulnerable to these issues, and as such, the need to address them formed the basis for our design of the different treatment arms.

Omission of consumption can occur due to accidental omission, purposeful deception, noncompliance, or lack of knowledge. Omission of consumption is likely to happen in any recall survey, but is expected to be more common when a single household member provides information for the entire household as a result of purposeful and unintentional misinformation as well as separate spending accounts for different members of the household (Dillman and House, 2012).<sup>2</sup> Reporting on food consumed away from home poses additional challenges since it is quite possible that the informant will not be fully aware of the outside consumption of all other household members. While he or she can often directly observe food consumed at home, the same level of observation is much less likely for FAFH. Therefore, the informant is often unable to accurately report the FAFH consumption of other household members.

> 2 While asking every member of the household for data (versus one representative respondent) could lead to higher noncompliance, individual-level (rather than household-level) diaries often lead to higher total reporting than those using one proxy respondent’s diary, implying that single representative diaries (or surveys) can miss out on expenditures (Browning et al., 2014).

4
districts, and year fixed effects _γ_ to account for the overall age profile of wages. Standard errors are clustered conservatively at the district level, clustering all individuals in a district regardless of their year of birth or within-district primary sampling unit. We include district fixed effects using within estimation, rather than least-squares dummy variables, to properly correct standard errors for the finite sample (Cameron and Miller, 2013).

To demonstrate the stability of our coefficient estimate and the robustness of our strategy, we add controls _Xidt_ in stages:

- state-specific linear time trends: identifies the disease environment from the extent to which the district time trend in disease differs from the state-wide disease time trend, to rule out any spurious state-level omitted variables;

- state _×_ urban fixed effects: controls for a separate rural-urban difference in each state;

- state _×_ social group indicators: state-specific indicators for eight caste and religion groups;

- female literacy: district-level female literacy in the district and year of the man’s birth, matched from census data in the same way as are sanitation and IMR.

The control for female literacy – another indicator of human development and an important – determinant of early-life human capital helps ensure that we are identifying effects of the disease environment, and also verifies that no spurious coefficient is mechanically introduced by our district-level matching process.

We will additionally add two further sets of covariates which may entail _overcontrolling_ , relative to a properly specified model, but which will allow us to further rule out omitted variable bias while investigating possible mechanisms of the effect we document. First, we will add indicators for the worker’s membership in seven job categories,<sup>4</sup> which rules out spurious structural differences in district labor markets. Finally, we will add detailed indicators for education-by-grade interacted with literacy. These additions would be overcontrolling if education or job categories were partially caused by early-life disease.

> 4The seven categories are family (own) farm work, animal care, agricultural wage labor, non-agricultural wage labor, a salaried job, work at a family business, and any other work (the omitted category).

6
We also separated the kickbacks variable into two parts: whether or not firms report that an informal payment is needed, and (conditional on one being needed) its size as a share of contract value. Results from the probit regression found that bribes are significantly less likely to be needed where COMPLAINT-AB or INDEX scores are higher. Among the approximately 1,050 firms reporting that a bribe is needed, the OLS regression found a highly significant inverse relationship between the size of bribes and COMPLAINT-AB or INDEX scores.

Conditional on a bribe being needed, one might expect the average size of kickbacks to increase with AUDIT: as the risk of detection and punishment rises, a larger bribe may be required to justify the risk from an official’s standpoint (e.g., Kenny, 2009: 29). If so, the coefficient on AUDIT should be negative for the probit equation but positive in the OLS equation (for observations with kickbacks > 0). However, the coefficient estimates were negative (and significant) in both regressions: a stronger external audit mechanism is associated with a lower likelihood an informal payment is needed, and with a smaller bribe conditional on one being needed.

Kickback regressions could suffer from sample selection bias, if firms that report bidding on contracts, and that respond to the survey question on informal payments needed to obtain contracts, are systematically different from other firms. We therefore tested a Heckman selection model, using as selection variables in the first stage the firm’s share of sales made in the domestic market, and the dummies for international certification and city size. Inverse Mills ratios were not significant, consistent with the null hypothesis of no selection effects. Coefficients for COMPLAINT-AB and INDEX remain significant, and differ little in magnitude from OLS tests.

# **8. Endogeneity of Procurement Systems**

Reverse causation is often a potential concern when governance outcomes are found to be associated with policies or institutions. For example, perceptions of corruption levels across countries may influence subjective assessments of public institutions. In our case, PEFA assessments are implemented by specialists in public financial management, and procurement (question 19) is merely one of 28 topics assessed. Bidding and kickbacks are measured by objective experiential questions in enterprise surveys, and it is highly unlikely that country-level results from these surveys are known by the PFM specialists conducting PEFA assessments.

Governance outcomes can also motivate policy or institutional reform. For example, procurement reforms may be triggered by low levels of competition or by corruption scandals in 25
10 children were thinking, some combination of loss of self-confidence and expectation of prejudicial treatment likely explains the result.

**Figure 5 Children’s performance differs when their caste is made public**

<!-- Start of picture text --> Average number of mazes solved, by caste,<br>8 in five experimental treatments<br>High caste<br>7 Low caste<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Piece rate, Piece rate, Tournament, Tournament, Tournament,<br>caste not caste caste not caste caste announced<br>announced announced announced announced and segregated<br><!-- End of picture text --> _Source:_ Hoff and Pandey (2004). _Note:_ A vertical line in the figure illustrates the statistically significant caste gaps.

The expectation by the low-caste subjects of prejudicial treatment may be rational given the discrimination in their villages. But the discrimination itself may not be fully rational. Cognitive limitations may prevent others from judging stigmatized individuals fairly. The fact that people are bounded in their ability to process information creates broad scope for belief systems—in which some social groups are viewed as innately inferior to others—to influence economic behavior. If such beliefs persist, it will generally be rational for those discriminated against to under-invest (with respect to others) in the accumulation of skills for which the return is likely to be lower for them.

The three examples above illustrate a growing body of microeconomic evidence of the inefficiency of inequality.<sup>10</sup> One case – that of Ghanaian farmers unable to switch from cassava and maize to pineapple – exemplifies the classic interaction between fixed costs, poverty and a missing credit market. Farmers were too poor to be able to pay the fixed costs required for making the switch. If a perfect credit market existed, they would have been able to borrow against the large expected returns of switching, to finance the investment. Market imperfections and a mass of poor people at the bottom of the distribution lead to missed opportunities and X-inefficiency.

But the other two examples are different. The Indian children who solve fewer mazes when they are explicitly reminded of their inferior social status do not require any markets that might be missing or imperfect. Yet, in a convincing experimental setting, their productivity is reduced by the mere existence of the social hierarchy. If similar declines in productivity occur in real work situations, the private and social losses would 10 To paraphrase the title of Glaeser, Scheinkman and Shleifer (2003): “The Injustice of Inequality”.
**Table 6: Regional Comparison on the Scope of Secured Transactions**

||**East Asia**<br>**& Pacific**|**Europe &**<br>**Central**<br>**Asia**|**High**<br>**income:**<br>**OECD**|**Latin America**<br>**& Caribbean**|**Middle East**<br>**& North**<br>**Africa**|**South**<br>**Asia**|**Sub-**<br>**Saharan**<br>**Africa**|
|---|---|---|---|---|---|---|---|
|**Legal Rights Index (0-**<br>**10)**|**5.7**|**6.6**|**6.8**|**5.5**|**3.3**|**5.3**|**4.6**|
|**Non-possessory**<br>**security interests**|100.0|100.0|100.0|90.5|83.3|85.7|97.4|
|**Security interests in**<br>**revolving assets with**<br>**general description**|66.7|57.1|63.6|28.6|0.0|71.4|41.0|
|**Security interests in all**<br>**assets with general**<br>**description**|75.0|57.1|63.6|42.9|38.9|85.7|92.3|
|**Future assets, products**<br>**and proceeds**|50.0|39.3|72.7|61.9|11.1|71.4|41.0|
|**General description of**<br>**debts and obligations**|83.3|75.0|100.0|81.0|55.6|57.1|87.2|

**Types of contractual agreements** _._ In addition to the restriction on the types of assets, none of the countries in MENA have adopted the ―functional approach‖ to secured transactions, which should allow equal treatment to all transactions secured by movable property no matter what the nature of them is (financial leases, consignments, assignment of receivables, secured sales contracts, loans secured with movable property, retention of title, etc.) with regard to publicity and priority vis a vis third parties. This is a critical concept to avoid having a system with hidden liens due to the lack of publicity of some of them.

# _2.3 Creation of Security Interests_

Formal requirements for creating security interests should be kept minimal. Modern secured transactions laws recognize parties' ability to create a security interest as a real right in movable collateral by agreement. A written agreement signed by both debtor and creditor, identifying the collateral and the secured obligation should be sufficient to create a security interest in movable property. Registration of that security interest in the collateral registry establishes the priority and effectiveness against third parties, but should not be a requirement for a security interest to be valid and enforceable. No special terminology, forms, or notarization should be required, as these elements create no value but add additional costs to the loan transaction, and these costs end up being passed on to the borrowers, increasing the overall cost of credit. Parties should have the freedom, within the limitation of the law, to address through the security agreement all matters relating to their relationship, including defining warranties and covenants, events of default, and remedies.

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# **Human Capital Growth, 2000-10**

<!-- Start of picture text --> South Asia, excl. India<br>India<br>Sub-Saharan Africa<br>Middle East and North Africa<br>Latin America and the Caribbean<br>Europe and Central Asia<br>East Asia and the Pacific<br>0 0.0050.010.0150.020.0250.030.035<br><!-- End of picture text --> Source Data: Barro & Lee and World Bank staff calculations Note: The variable is defined as secondary and tertiary schooling

**Control of Corruption Changes, 2000-10**

# **Political Stability Changes, 2000-10**

Source Data: Worldwide Governance Indicators , the World Bank

**ICT Infrastructure Growth, 2000-10**

Source Data: WDI and World Bank staff calculations

**Road Infrastructure Growth, 2000-10**

<!-- Start of picture text --> Source Data: WDI and World Bank staff calculations<br><!-- End of picture text -->

**Investment Policy Openness Growth, 2000-10**

**Trade Liberalization Growth, 2000-10**

Source Data: UNCTAD statistics and World Bank staff calculations Note: The variable is defined as effective tariif rate reduction

**Real Wage Growth, 2000-10**

- South Asian countries in Figure 12 do not include Afghanistan due to its lack of data.

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