rafmacalaba/datause-encoder-data huggingface dataset:
resulting in omitted variable bias. Controlling for ideology and as many controls as possible reduces a part of this bias.

# **5.1 Explaining perceptions of (in)equality**

We start with regressing equation (3) in which equality perception is regressed against a set of individual variables that aim at proxying individuals’ perception of objective inequality and ideology as well as individual characteristics. The analysis is based on individual data and therefore exploits individual variation within a country, while clustering standard errors at the country level to account for clustered data. Using the perceived Gini index as the dependent variable, Table 2a shows the importance of the economic context, and not only of the objective Gini index, in explaining individuals’ perceptions about inequality. The same analysis using the categorical version of the variable representing perceptions is presented in Appendix Table A.3.

The four columns of Table 2a present different specifications all of which include the four main macroeconomic variables at the country level: the unemployment rate, the poverty headcount rate, the Gini index of income inequality, and the government expenditures in education. The sign of the correlations is as expected, and the coefficients are precisely estimated. The higher the unemployment rate, the Gini index, and the poverty rate, the more unequal society is perceived. This is not surprising given the cross-country correlations found before. This might mean that the Gini index is more difficult to ‘observe’, while poverty and unemployment, also correlated with the actual Gini index, are easier to understand and directly observe or experience. The fact that a fairly abstract measure such as the Gini index is correlated with equality perceptions may reflect that such index does indeed capture income inequality that can be perceived by individuals. We also introduce government expenditures in education, as in contrast with social expenditures these are not mechanically correlated with current inequality, but rather with future income inequalities to the extent that education expenditures might increase equal opportunities. The coefficient for government expenditures in education has the expected sign: more government expenditure is translated into lower perceived Gini index. Finally, specifications 2 to 4 introduce different proxy variables of intergenerational mobility. Intergenerational mobility is measured in three different ways, which represent two fairly different concepts. All our variables are drawn from Narayan and Van der Weide (2018) and represent the intergenerational elasticity of education. The first variable (specification 2) is the average country intergenerational elasticity of education, while the other two variables (specifications 3 and 4) assign to each individual their own cohort (and gender) intergenerational elasticity of education. The coefficients of these variables are very imprecisely estimated so that it is difficult to draw conclusions. The sign of the point estimates suggests a counter-intuitive association, as reducing intergenerational mobility 25
WPS3945 migrant households and there are no discernible differences in per capita consumption expenditures.

A simple comparison of proportions indicates no difference in enrollment rates among migrant households by female headship. However, girls in female headed households are signi fi cantly more likely to dropout of school (with dropout rates rising from .25 to .41, p-val .02). Girls in female headed households are also signi fi cantly behind in terms of completed grades (by .58 grades, p-val .02) and conditional on current enrollment they fall a whole grade behind (p-val .01). There is, in contrast, no effect on boys.

These results indicate that female headship, in the context we study, might serve to dampen rather than accentuate, the potential positive gains from migration and, moreover, that girls are likely to bear the greatest burden. Of course, as descriptive statistics, these are only suggestive. They do, however, anticipate some key results.

# 3 Econometric Speci fi cation and Identi fi cation

In order to assess the effect of migration on schooling and child labor, we need to estimate a regression function of the form Where Sijv is a measure of school attainment for child i, in household j and village v. Mjv is an indicator of whether the household has a migrant, Bijv is the child’s gender, and Cijv and Xjv are vectors of exogenous child and household characteristics. The mean zero error term ηjv captures the effects of unobserved factors common to a given village and household. The child-speci fi c error term εijv re fl ects measurement error in our schooling variables and, potentially, unobserved attributes of the child, including innate ability, or parental preferences which vary by child gender. The key difference between ηjv and εijv is of course that while the latter is not likely to be correlated with the migration decision, the former could in fl uence both the decision to migrate and investments in human capital formation. At the village level, ηjv may, for example, include unobserved variation in local labor market conditions or in school quality, while at the household level, it could include preferences over human capital accumulation, access to credit or insurance markets or costs that affect schooling but are not observed in the data.

Regardless, since Mjv is likely to depend on at least one of the factors captured by ηjv, we need to contend with a potential endogeneity problem. We tackle the problem in two 11
**Annex 1. Summary of DeMPA**

|**Perform**|**ance Indicator **|
|---|---|
|**Govern**|**ance and Strategy Development**|
|DPI-1|1. Legal Framework|
|DPI-2|1. Managerial Structure: Borrowingand Debt-Related Transactions|
||2. Managerial Structure: Loan Guarantees|
|DPI-3|1. Debt Management Strategy:Qualityof Content|
||2. Debt Management Strategy: Decision-MakingProcess|
|DPI-4|1. Evaluation of Debt Management Operations|
|DPI-5|1. Audit: Frequency|
||2. Audit: Appropriate Response|
|**Coordin**|**ation with Macroeconomic Policies**|
|DPI-6|1. Fiscal Policy: Provision andQualityof Debt-Service Forecasts|
||2. Fiscal Policy: AvailabilityandQualityof Information on KeyMacro Variables and DSA|
||1. MonetaryPolicy: Clarityof Separation between DeM and MonetaryPolicyOperations|
|DPI-7|2. MonetaryPolicy: Regularityof Information Sharing|
||3. MonetaryPolicy: Limited Access to Central Bank Financing|
|**Borrow**|**ing and Related Financing Activities**|
||1. Domestic Borrowing: Market-Based Mechanisms and Preparation of a BorrowingPlan|
|DPI-8|2. Domestic Borrowing: AvailabilityandQualityof Documented Procedures|
||1. External Borrowing: BorrowingPlan and Assessment of Costs and Terms|
|DPI-9|2. External Borrowing: Availabilityof Documented Procedures|
||3. External Borrowing: Involvement of Legal Advisers|
||1. Loan Guarantees: AvailabilityandQualityof Documented Policies and Procedures|
|DPI-10|2. On-lending: AvailabilityandQualityof Documented Policies and Procedures|
||3. Derivatives: AvailabilityandQualityof Documented Policies and Procedures|
|**Cash Fl**|**ow Forecasting and Cash Balance Management**|
||1. Effective Cash Flow Forecasting|
|DPI-11|2. Effective Cash Balance Management|
|**Operati**|**onal Risk Management**|
||1. Debt Administration: AvailabilityandQualityof Documented Procedures for Debt Service|
|DPI12|2. Debt Administration: Availability and Quality of Documented Procedures for Data<br>Recordingand Storage|
|-|3. Data Security: Availability and Quality of Documented Procedures for Data Recording and<br>System and Access Control|
||4. Data Security: Frequencyof Back-Ups and Securityof Storage|
||1. Segregation of Duties|
|DPI-13|2. Staff Capacityand Human Resource Management|
||3. Operational Risk Management,Business Continuity,and Disaster RecoveryPlans|
|**Debt Re**|**cords and Reporting**|
||1. Debt Records: Completeness and Timeliness|
|DPI-14|<br>2. Debt Records: RegistrySystem|
||1. Central Government Debt Data: Statutoryand MandatoryReportingRequirements|
|DPI-15|2. Public Sector Debt Data: Statutoryand MandatoryReportingRequirements|
||3. Debt Statistical Bulletin:Qualityand Timeliness|

29
<!-- Start of picture text --> Percent of GDP Percent<br>230 —Debt —GDP (RHS) 5<br>220 4<br>210 3<br>200 2<br>2010 2012 2014 2016 2018<br><!-- End of picture text --> <!-- Start of picture text --> fetcent of GDP Percent<br>160 —Debt —Growth (RHS) 7<br>6<br>140 5<br>4<br>120<br>3<br>100 2<br>2010 2012 2014 2016 2018<br><!-- End of picture text -->
under the “disability” category reflects a growing recognition of sickness and disability as a major labormarket issue (Immervoll, 2011) **.** ECA countries spend significantly less than the OECD average, but spending is particularly low in Georgia, FYR Macedonia, Armenia, Moldova, Serbia and Albania (See Figure 15). In ECA, ALMPs remain very small in most countries, so it is difficult to expect them to have a large impact if not expanded significantly (as exemplified by Nordic countries). There is need for more rigorous evaluations of what works and under which conditions; how to design activation policies for different population groups; what should be priority groups. The comprehensive labor market reforms in 2003 and 2005 in Germany including changes to ALMPs can present important lessons for policy makers (See Box 4).

# **Figure 15 – ALMP Spending in Many ECA Countries Remains Low**

ALMP spending per unemployed, 2007 <!-- Start of picture text --> 2,000<br>1,800<br>1,600<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>USD Spent per Unemployed<br><!-- End of picture text --> Source: Authors’ calculation based on IZA and UN population data

# **<mark>Box 4 - Comprehensive Active Labor Market Policy Reform in Germany</mark>**

<mark>Having faced high unemployment rates for more than a decade, Germany introduced far-reaching reforms of its unemployment and social assistance benefits between 2003 and 2005 (the so-called “Hartz” reforms), adhering to a three-part reform strategy: (1) improving employment services and active labor market programs (ALMPs); (2) activating the unemployed and social assistance beneficiaries; and (3) fostering employment demand by deregulating the labor market. To this end, the reform radically modernized the organizational structure of public employment services and social welfare centers, modified many of the already existing ALMPs, and introduced a set of new ones. The reform fundamentally changed the institutional and legal framework that determines the rights and duties of the unemployed and social assistance beneficiaries. Furthermore, employment protection was reduced in some segments of the labor market.</mark> <mark>Public employment services and social welfare centers were modernized along the lines of New Public Management. This includes results-based accountability of local employment agencies, outsourcing of many services and open competition between</mark> <u><mark>public and private service providers. The former employment offices were (partially) merged</mark></u> 32
Policy reforms aimed at improving sector regulation are not costly, as it requires changes in laws and other norms and regulations. However, even if the monetary costs are low, political costs could be quite high and a _de facto_ barrier to change. The challenge then is to find out how countries in Latin America can generate the political consensus needed to adopt the optimal policies to reduce logistics costs.

Table 14 shows the results of an exercise conducted by Guasch [2004] that estimates the effect of reducing logistics costs from 34 percent of product value to 20 percent in Peru. The results are impressive. To achieve the reduction of logistics costs presented by Guasch, it is necessary to combine investments in infrastructure and changes in regulations. Thus, the monetary cost is difficult to estimate, but the potential benefit is huge. Assuming the increase in demand and employment presented in Table 14 fosters the rate of growth just 1 percent, the Peruvian GDP would grow US$800 million per year (the nominal 2005 GDP was US$79 billion). This growth would generate additional fiscal space to fund the investment required to address infrastructure bottlenecks.

**TABLE 14: IMPACT OF THE REDUCTION IN LOGISTICS COSTS FROM 34% TO 20%**

|**Sector**|**Increase in Demand**|**Increase in Employment**|
|---|---|---|
|**Agro-industry**|**12 %**|**6 %**|
|**Wood and furniture**|**14 %**|**16 %**|
|**Textiles**|**8 %**|**9 %**|
|**Leather / shoes**|**18 %**|**15 %**|
|**Mining industry**|**10 %**|**2.5 %**|
|_Source: Guasch, 2004. Mimeo._|||

**<u>Recommendation 3: Latin American countries need to adopt policies to improve trade facilitation. In particular, they need to (i) strengthen the institutions that promote trade facilitation; (ii) support the internationalization of national firms, which implies helping national firms open new markets and adopt the technologies and quality standards required to compete in the global economy; (iii) implement effective multimodality laws, providing in particular insurance across modes and the use of a single bill of landing; (iv) improve access areas to ports and connections to the other transport modes; and (v) facilitate the development and supply of associated services such as transport services, testing, cooling services, and logistic terminals/dry ports.</u>**

In Latin America, recent studies [Soloaga et al.,2007] show that main reforms on trade facilitation are of relatively of low costs, as they relate to regulatory and policy reforms, and not to hard infrastructure. Thus, it is expected that benefits from reform will most likely outweigh the costs.

The areas that would benefit from a better policy design and implementation and a capacity building program to strengthen trade promotion institutions are:

- _Country Strategy for capacity building in trade facilitation_ includes the creation (or strengthening) of an export development corporation to promote international trade.

39
expenditures and revenues, and the resulting budget deficit to each of the structural shocks using the variance-covariance matrices of reduced form errors derived from these coefficients.<sup>5</sup> The confidence bands for the IRF come from parametric bootstrapping on the model assuming normally distributed reduced form errors.

# **3. Data**

To conduct the analysis we collected data on the incidence of disasters and several measures of macroeconomic and fiscal performance for middle and high-income countries. Low-income countries are not included because their fiscal expenditures, revenues, and overall debt are typically related to official and multilateral aid support. Therefore, the fiscal responses to shocks are likely to differ qualitatively from those of other countries and depend on exogenous aid allocation.

Data for natural disasters were obtained from the Emergency Disasters Database (EMDAT) maintained by the Center for Research on the Epidemiology of Disasters (2008) (CRED). This is a comprehensive database that includes data on the occurrence and effects of over 12,800 mass-disasters in the world since 1900, and is compiled from a diversity of sources. As a general principle, to enter into the database an event has to meet any of the following conditions: there are ten or more people reported killed; there are 100 or more people reported affected; a state of emergency is declared; or there is a call for international assistance.

The data contain information on various types of disasters that following Skidmore and Toya (2002), we classify in three broad categories. Geological disasters include earthquakes, landslides, volcano eruptions, and tidal waves. An important characteristic of this type of events is their unpredictability and relatively fast onset. The second category is climatic disasters. This category includes floods, droughts, extreme temperatures, and windstorms (e.g. hurricanes).

> 5 The use of SURE is standard for the estimation of the reduced form equation. It is equivalent to estimating the model equation by equation by OLS, but is more efficient because it takes into account contemporaneous correlations among variables. It also directly estimates the variance-covariance matrix of reduced-form residuals. We use only the two-step version of the estimator for reasons of speed, but when iterated until convergence the SURE estimators are equivalent to the maximum likelihood estimators.

10
worker in the region as a whole (0.3 percent per year). We argue that the contribution of natural capital is important in countries of the region that are abundant in energy commodities (for instance, Chad, Republic of Congo, Gabon, and Nigeria) and metals and mineral ores (say, Botswana, Democratic Republic of Congo, and Zambia).

**Figure 4 Source of growth in Sub-Saharan Africa, 1996-2014: The role of natural resource wealth**

_(Annual average percentage change, population-weighted)_ <!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Sub-Saharan Africa SSA Oil Rich Countries SSA Metal Rich SSA Non-Resource Rich<br>(SSA) Countries Countries<br>Physical Capital Natural Capital Human Capital TFP Output<br><!-- End of picture text --> _Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._ Figure 4 plots the growth decomposition that includes the role of natural capital for different country groups in the region. The contribution of natural capital, as expected, is greater among resource rich countries than among non-resource rich countries. During the period 1996-2014, oil rich countries grew at an annual average rate of 3.9 percent and natural capital explains about 10 percent of the observed growth (contributing with 0.4 percent per year). The contribution of natural capital to output per worker growth is significantly larger among countries in the region that are abundant in minerals and metals (with a contribution of about 1.2 percent per year). However, the contribution of TFP growth for this group becomes detrimental, thus indicating inefficiencies in the use of inputs. In sum, Figure 4 shows that if we do not account for the role of natural resources in the production function, the contribution of TFP to economic growth in resource abundant countries/regions might be over-estimated.

We have argued that total factor productivity (TFP) can be misstated if we do not account for the role of natural resources in the economy. The comparison of growth accounting exercises with and without natural capital as another input of production is depicted in Figure 5 —while holding constant the sample of countries remains constant across the different methodologies. This figure presents the contribution of a _composite_ capital good that includes physical and natural capital: (i) it represents only the contribution of the physical capital stock from PWT 9.0 in the conventional _page 9_
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# **8 Appendix**

Table 1: Factor Loadings Corresponding to the Productivity Index

|Variable|Factor 1-Loadings|
|---|---|
|dummy exporter|0.7812|
|dummy RD|0.1659|
|dummy Holding of licenses|0.1437|
|dummy Foreign ownership|0.6312|
|Log (workers)|0.8040|

Note: This table displays the factor loadings corresponding to each of the variables included in the principal component analysis.

24
Regressions 2 and 4 provide a structural framework for the relation between the Public Sector Deficit and inflation. A 1% point of Primary Deficit is financed with 0.7% of revenue from money creation (the rest with debt), and the effect of collecting this revenue from money creation is around 67.9% of additional inflation( 97*0.7).

REGRESSION **III.4** INF = -84.98 + 0.151 INF(-1) + 29.34 ITAX (1.76) (5.89) (3.60) R2 Adj.:0.63 D-W.: 1.898 SAMPLE: 1964-1987 IMPLIED LONG RUN RELATION:

**INF** - -340 + 97.2 PD

23