rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. Methodological approach**

The main challenge in fulfilling the objectives stated above is to find a way of translating these economic and demographic shocks into the consumption distribution. The traditional solution is to extrapolate poverty measures by estimating an “elasticity” of poverty to output or GDP. The elasticity approach uses historical trends of output and poverty to determine the responsiveness of poverty rates to growth in output (and consumption), which is then combined with macroeconomic projections to estimate the impact of future growth on poverty. Although this method is easy to implement, it is limited in its predictive capability since it yields only aggregate poverty impacts, with no indication of the broader distributional effects. It may also prove deficient in predicting poverty impacts during a macroeconomic event that affects output growth in a way that is not entirely consistent with the recent growth experience in Iraq. In addition, measured poverty has in recent times been very inelastic with respect to increases in GDP growth (as measured), so that elasticity approaches would imply little poverty impact of such a massive economic shock.<sup>8</sup> The estimates and analysis presented in this paper uses a micro‐simulation model to evaluate the welfare and distributional impacts of the IDPs and macro (oil price and non‐IDP IS‐related) crisis in Iraq during 2014. The micro‐simulation model superimposes macroeconomic projections on behavioral models built on last available household survey (Iraq Household and Socio‐ Economic Survey, or IHSES 2012). The model is loosely based on previous approaches to micro‐simulation described in Bourguignon, Bussolo and Pereira da Silva (2008) and Ferreira, et al. (2008) – with an important simplification of omitting the computable general equilibrium (CGE) component, which is difficult to employ in most developing countries. Instead the approach described here links the behavioral model to sectoral and aggregate macroeconomic data for Iraq, and extrapolates the microeconomic snapshot of a future scenario from this projection.<sup>9</sup>
ratios. Demirgüç-Kunt and Detragiache (2002) likewise find that the probability of having a banking crisis increases in the coverage limit and in having a funded scheme. Hovakimian, Kane and Laeven (2003) show that risk-shifting to the government or subsidization of risk taking is stronger in poor institutional environments but can be reduced with an incentive-compatible design.

Our paper also falls within the broader literature on the optimal design of financial systems and financial sector regulation. Levine (2003) shows that financial systems tend to be dominated either by banks or by markets but that this has no significant impact on economic growth. Jayaratne and Strahan (1995) show that economic growth in U.S. states increased substantially following bank branch deregulation. Barth, Caprio and Levine (2004) collect crosscountry data on an array of regulatory and supervisory practices, including regulations on bank entry, activity restrictions, capital adequacy, loan classification, and provisioning, as well as information about the official power, independence, and resources of bank supervisors and the regulations fostering information disclosure and private-sector monitoring of banks. They find that banking systems with excessive supervision and regulation of banks are less developed and stable than banking systems where markets play an important role in monitoring banks. Similarly, Beck, Demirguc-Kunt and Levine (2006) find that firms face higher obstacles due to corruption in lending in countries with more powerful bank supervisors and less private-sector monitoring. Often political economy factors affect the design of financial systems. Kroszner and Strahan (1999) show that lobbying by interest groups affected the decision to relax bank branching restrictions in the United States, while Kroszner and Stratmann (1998), Kane and Wilson (1998), and Laeven (2002) show that political interest groups affect the design of deposit insurance.

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>(.011)<sup>_∗∗∗_</sup>|.049<br>(.013)<sup>_∗∗∗_</sup>|-.019<br>(.008)<sup>_∗∗_</sup>|
|Delicensed|-.008|.002|-.010|
||(.016)|(.016)|(.009)|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.119|.11|.016|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. The top and bottom 1% of TFP have been winsorized.

53
|0.3913|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Table A3. Descriptive statistics by** **<u>gender and age group</u>**

||**Young te**|**enagers**|||**Young ad**|**ults**|||**Prime ag**|**ed**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Men||Women||Men||Women||Men||Women||
|**Variable**|Obs|Mean|Obs|Mean|Obs|Mean|Obs|Mean|Obs|Mean|Obs|Mean|
|**wage**|187|5.924|129|5.868|463|5.963|240|5.850|588|5.992|289|5.886|
|**women**|878|0|830|1|913|0|802|1|859|0|690|1|
|**lhours**|187|3.620|129|3.667|463|3.675|240|3.608|588|3.659|289|3.580|
|**gensec**|878|0.300|830|0.304|913|0.060|802|0.087|859|0.052|690|0.074
Source: based on Kremer et al (2021) Note: The horizontal axis shows the natural log of real per capita GDP, and the vertical axis shows average growth of GDP per capita.

As can be seen in Figure 22, the reversal of fortune is far more evident using the end of the commodity price boom – the end of the Golden Decade – as the final era. From a steeply negative slope indicating strong convergence, and slope now is flat and may even flip to be upward sloping.

Would such a post-1995 trend continue, then countries of the bottom billion would never catch up with the rest of the world. This is because the post-Golden Decade undercuts the impressive result during the Golden Decade. For policy, this result matters. Business-as-usual will not get us there, even with a positive result for Beta Convergence found by Kremer et al. (2021).

# Country data coverage and sample selection

Availability of data is a recurring issue, especially in countries with limited local statistical capacity, which unfortunately often correlates to the world’s lowest income countries.

Macroeconomic indicators such as GDP are widely available for most countries although with some exceptions. For example, Somalia – a country that has largely suffered from extreme poverty and poor growth – has limited GDP data. While the World Bank has been tracking it since 2013, GDP data is unavailable before that year, and therefore it is hard to observe its economic performance in previous years. São Tomé and Príncipe is another African country that while the PWT data goes back to 1970, the World Bank has only tracked it after 2001, and therefore, we are unable to run comparable analysis with this country. Other countries are 29
independence’ and ‘child imagination’ are non-significant. Whereas the finding on trust is not new, the finding on tolerance of homosexuality is rather surprising. The proportion of homosexuals in the population has been used in some studies as a proxy for bridging attitudes in the composite ‘creativity index’, which, in its turn, has been

> 7For example, Woolcock (2001) considers ‘linking social capital’, besides bonding and bridging social capital.

> 8The choice of world regions rather than single countries is dictated by the number of observations available given the type of equation. Table 2 in annex provides the full list of countries and number of observations for each world region.

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period was Hindus (38 percent). 22 Landholding is the sum of agricultural and non-agricultural land measured in cents, where 100 cents is equivalent to an acre.

> 23 I confined the indicators used in the construction of the overall asset index to a common set found in both survey rounds, in order to examine changes over time. The 1998 KMS data provided a larger set of assets indicators (21 indicators of durable assets and 15 indicators of housing quality). The pair-wise correlation between the composite index created using both sets of indicators was high ( _r=0.93_ ).

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country manages to reduce poverty (Goal 1) but also increases inequality (Goal 10) at the same time? Or what is the trade-off between strong economic growth (Goal 8) and deteriorating environmental quality (Goal 11)? The next two examples illustrate such situations.

Table 2 presents recent estimates on the dynamic changes with poverty and vulnerability for 21 African countries, drawing with some modifications on Dang and Dabalen (2019), who analyzed household consumption surveys mostly implemented in the late 2000s. These countries together account for two-thirds of the population in Sub-Saharan Africa. We rank the countries in a decreasing order of reduction in the headcount poverty rate (column 3), and show the changes to the population shares of the vulnerable group and the middle class (columns 4 and 5). We also show the growth in the mean consumption levels for the bottom 40 percent of the consumption distribution (column 6) versus the growth for the whole distribution (column 7), which represents the World Bank’s definition of shared prosperity (see, e.g., Basu, 2013; Jolliffe et al., 2015). This measure of shared prosperity is the first SDG indicator for tracking inequality reduction (Indicator 10.1.1). However, since concerns have been raised that this measure tracks only the (anonymous) growth of the bottom 40 percent, we also show for comparison another measure of shared prosperity that tracks dynamic changes of the population shares of the poor and the vulnerable groups. This latter measure is based on a simple typology of pro-poor growth scenarios recently proposed by Dang and Lanjouw (2016).<sup>7</sup>

> 7 In particular, we consider three welfare categories that correspond respectively to the poor, the vulnerable, and the middle class groups. There are in total six possible growth scenarios depending on whether (the population share for) each of the three categories is expanding or shrinking. The most positive pro-poor growth scenario is one where both the poor
data used for GNI per capita and the Gini index are presented in table A3 in the appendix. We also control for country size using the total population of the country. These data are available from the World Bank’s World Development Indicators. Data source and description of the variable can be found in table A1, with summary statistics in table A2.

# **3. Estimation**

We estimate following equation using OLS.

- (1) _crimelossij_   1 _Policelag j_   2 _GDPgrj_   3 _Femij_   4 _GNIcap j_   5 _GINI j_   6 _Population j_   7 _Smallij_   8 _Mediumij_   9LargeCityij   10 _Manfij_   _ij_

Where _crimeloss_ is the losses due to crime as a % of sales for firm i and country j, _Policelag_ is the lagged number of police per 100,000 population, _GDPgr_ is the real GDP per capita growth, _Fem_ is a dummy representing female ownership, _GNIcap_ is the real GNI per capita, _GINI_ is the gini coefficient, _Population_ is the total population of the economy, _Small_ and _Medium_ are firm size dummies, LargeCity is a dummy for cities with population of 250,000 and greater, or capital cities, and finally _Manf_ is a dummy for manufacturing firms.

All estimates are based on standard errors clustered at the country level. In the later sections we add additional variables and interact them with the variable of interest to elucidate several relationships. The usual econometric issues of endogeneity and omitted variable bias are of a concern in the estimation. Concerns about simultaneity bias are alleviated in our use of aggregate police variables because crime experienced at the firm level should not affect country-level variables such as our measure of police size. However, endogeneity may arise because the mean 11
Policy Research Working Paper 10425

# **Abstract**

Liberian women make significant economic contributions yet are constrained from contributing even more due to their exclusion from productive opportunities. This study develops a macrosimulation model of the Gender Dividend that estimates the economic contributions of women and the societal costs incurred by excluding them. Using macroeconomic, demographic, and survey data from Liberia, the analysis finds that women were responsible for 39 percent of market-based output produced annually in 2020, equal to US$1.08 billion, and contributed another US$530 million in non-tradable sources of production, namely, housework and domestic chores. Using the macrosimulation model, the study estimates that if the gender gaps in labor force participation, intra-sectoral wages, and sector of employment were closed, gross domestic product would be 11.5 percent higher. If further reforms were undertaken to equalize education and reduce fertility rates to a net-reproduction rate, gross domestic product would be 23.7 percent higher. Finally, if the model also accounts for the value of non-tradable production, gross domestic product would be US$5.89 billion, or 45.3 percent higher than today’s estimates, with women being responsible for 53 percent of the labor market output. These estimates reinforce the need for a unified policy agenda that actively invests in women’s human capital and work-related opportunities simultaneously.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wcunningham@worldbank.org, sgupta@worldbank.org, and mdelbono@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out
focus on all years that were five years after conflict and use our explanatory variables to distinguish the years that are followed by a relapse to conflict from those that do not. All variables have the expected sign and are significantly correlated with conflict relapse. The fitted values from this risk model vary between 0.12 and 0.88 and have a mean of 0.25.

In columns (2) to (5) of Table 10 we bring these numbers to the various foreign investment data. We use the fitted values instead of the actual fragility dummy and set all years outside of the first five years after conflict equal to zero. Expected fragility, estimated with refugees and political exclusion, explains foreign direct investment extremely well. An increase by 10 percentage points in the estimated fragility is associated with a reduction of flows by about 50 percentage points. In Table 11 we use FDI data from OECD dataset. We show that this result is robust to adding a large set of controls like democracy scores, log-GDP and growth of GDP. What is more, we can even add 55
To further examine the relationship between central bank independence (CBI) and the strength of the interest rate pass-through, we replace our baseline measure of independence by Garriga (2016)— which enables us to maximize the estimation sample—with that of Dincer and Eichengreen (D&E; 2014) that can be complemented with a commensurate metric of central bank transparency. However, using the variables from D&E reduces our sample size by about 22 percent, eliminating all estimates with the median year before 1998 (175 estimations). Countries such as Chile, Ghana, Nigeria, and the Slovak Republic drop out of the sample as a result. The estimation results for this subsample using the alternative measure of CBI and the additional variable measuring central bank transparency (CBT) are reported in Table A5 in the Appendix (column 1). Due to increased collinearity among variables, only the GETS results are reported—that is, results after the general to specific variable selection procedure has been applied.

The results show a higher estimated precision of the pass-through compared with the full sample. One explanation might be lower credibility and transparency of monetary policy before 1998 (Crowe and Meade 2008; Blattner et al., 2008), which might have weakened the transmission of interest rates. Hence, the meta-analysis without study estimates from before 1998 shows a higher precision, implying a complete interest rate pass-through. This supplementary meta-regression supports our results of the baseline meta-regression. Namely, we confirm the statistical significance of the credit risk dummy, average lending rates, rates on loans for other purposes, and market capitalization. In addition, other factors turn out to be statistically significant. From the study-specific factors, we find that studies using panel data estimate a lower pass-through than studies using single dimensional data. This result highlights the importance of data structures for estimating the pass-through. From the macro-financial factors, we also find a statistically significant effect of trade openness—similar to our baseline model, in which the estimated negative coefficient did not reach the common
# **_Do Migrants Really Foster Trade?_**

# **The Trade-Migration Nexus, a Panel Approach 1960-2000**

# **Christopher R. Parsons**<sup>**1**</sup>

## **Key Words: International Trade, Panel Data, Immigration, Emigration**

## **JEL Codes: [F22], [015], [J11], [F16]**

> 1The Leverhulme Centre for Research on Globalisation and Economic Policy (GEP). The correspondence address is: Nottingham School of Economics, Clive Granger building, University Park, Nottingham, NG7 2RD, United Kingdom. Christopher Parsons may be contacted by e-mail at lexcrp1@nottingham.ac.uk, by phone on: +44 (0)1159 951 5620 or by fax: +44 (0)1159 951 4159. The author gratefully acknowledges funding from the Economic and Social Research Council and support from the World Bank Knowledge for Change Program.. He is appreciative to Alison Ma for the clarifications received with regards the trade data and also wishes to thank James Anderson, Michel Beine, Richard Disney, Gabriel Felbermayr, Richard Kneller, Çağlar Özden, Maurice Schiff and Richard Upward for very useful and timely comments.
skill and task data via online job vacancy data, which has become common in recent years. In developing country contexts, online vacancy data tends to be biased towards higher-skilled digital-intensive occupations and jobs at firms that use the internet (CSC 2019; World Bank 2021). The SDS avoids this bias by selecting the occupations of interest, including low-skilled jobs, first and using a sampling frame based on Vietnam’s Labor Force Survey. Still, the SDS has some weaknesses. The SDS requires outlays on administering a survey. Initial data collection covered only 30 of the hundreds of occupations in Vietnam. Inclusion of all occupations with regular updating would require ongoing investment.

2
affecting the child’s current health status via Ht-1. Therefore, the coefficient cannot be signed a priori since it reflects the net effect of two opposite effects: the income and the disease environment effects. To the extent that the positive income effects are greater than the short-lived negative effects of the disease environment, it is expected that In addition, we include the square of past rainfall deviation to examine how rainfall variability may affect weight-for-height. While higher rainfall should have a positive effect on income, we expect that excessive rainfall may both be harmful to crops and increase diseases.

For children surveyed during the rainy season, we also examine the impact of the current rainfall deviation on weight-for-height Z-scores, summarized by the coefficient . It is expected that since higher than normal rainfall is likely to increase the incidence of water-borne diseases and the benefit from the current higher rainfall on crop yields and income has yet to be realized.

# Figure 7

Figure 7 illustrates the way in which rainfall shocks are constructed. For the past season we compute the deviation of total rainfall from the normal. A contemporaneous shock is constructed based on the cumulative rainfall from the beginning of the growing season up to the month of interview (for both the realization in that particular year and the normal). However, for children interviewed during the dry season we set the normal rainfall and its deviation to 0. Recall from the previous section that the length of the growing season varies geographically, with a gradually shorter season in the more northern areas of the country. Depending of the date a household was surveyed, this also affects which calendar year we used to compute the past and current shocks. Figure 8 shows the distribution of rainfall shocks as percentage deviations from normal for the last completed rainy season and the current season shock (in this last case only for those children born during the rainy season).

# Figure 8

# **Height-for-Age**

We also use the pooled child-level DHS sample to examine the effect of weather shocks on height-for-age; an indicator of
<br>-0.9<br>-1.2<br>0 -1.2 -1.5<br>5 years before Year turned 5 years after 5 years before Year turned 5 years after 5 years before Year turned 5 years after<br>MIC MIC MIC<br><!-- End of picture text --> Source: Worldwide Governance Indicators (WGI), World Bank staff calculations, World Development Indicators. A. New LICs include countries that were not LICs in 2001: Syria and South Sudan. South Sudan gained independence in 2011. C. Sample includes 59 2001 LICs. GNI per capita according to the World Bank Atlas method. GNI per capita for 1999 was published in August 2000 and reflects the original data used for country income classification in the 2001 World Bank fiscal year, while GNI per capita for 2018 was published in July 2019 and reflects the data used for the 2020 World Bank fiscal year. Exceptions are Liberia and Myanmar, for which GNI per capita in 2002 is used as a proxy for 1999. Rapid-growing LICs with low starting points are defined as LICs that had per capita incomes below one-third of the $755 LIC threshold in 2001, and these incomes have increased to above two-thirds of the $1025 LIC threshold in 2020. D. Sample includes 13 LICs that became MICs due to data limitations. Year turned MIC reflects the World Bank fiscal year. E.-F. WGI index scores are standard normal units that range between -2.5 and 2.5, with zero mean. A negative score implies government effectiveness or rule-of-law below the global average. Sample includes all 32 LICs that became MICs. Year turned MIC reflects the World Bank fiscal year.

# **_New LICs: 2016-20_**

Countries that have reached MIC status often face structural challenges that constrain their prospects for continued strong growth, while those MICs with incomes near the LIC threshold risk falling back into the LIC bracket (EBRD 2019).