rafmacalaba/data-use-mentions-extended huggingface dataset:
0.42|0.46|
|_Note:_|||_∗_p_<_0.1|; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Marginal effects of a one unit increase in each variable on the probability of an EMDE joining convergence ‘Club 1’ relative to other EMDEs. Derived from a logit model, with standard errors calculated using the delta-method. Average years of schooling for males and females from Barro and Lee (2015). Economic complexity index of Hidalgo and Hausmann (2009). Exports and imports as a percent of GDP. Government effectiveness survey from the World Bank’s Worldwide Governance indicators, defined as: perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Gross fixed capital formation and FDI are measured in percent of GDP.

# **F Productivity measurement: PPP versus market exchange rates**

In the analysis of productivity growth and differentials in this chapter, cross-country comparisons are made using productivity measured in dollars at 2010 prices and exchange rates. Often, studies of convergence have used cross-country comparisons of income per capita calculated at purchasingpower parity (PPP). This box addresses the question of how PPP and market-exchange rate measurement of productivity differs conceptually, and how the different measurements affect statistical tests of convergence.

Purchasing power parity calculates the rate at which the currency of one country would have to be converted into another to buy the same assortment of goods and services. Market exchange rates are the rates at which goods and services are actually traded in international markets. Since PPP reflects that goods and services that are not traded internationally tend to be cheaper in lowerincome countries, the purchasing power of lower-income country currencies tends to be higher at PPP exchange rates than at market exchange rates.

Transactions in global trade, financial markets
47

# **Table 6 Measures of Financial Infrastructure, MENA and other Regions**

|**Region**|**Legal Rights**<br>**Index**|**Coverage**<br>**Public Credit**<br>**Registry**|**Coverage**<br>**Private Credit**<br>**Bureau**|**Credit**<br>**Information**<br>**Index**|
|---|---|---|---|---|
|Middle East & North Africa|3.5|3.5|2.6|2.6|
|GCC|3.8|4.2|6.4|2.9|
|Non-GCC|3.2|3.7|0.0|2.5|
|East Asia|5.3|8.9|14.6|3.7|
|Eastern Europe|6.8|2.4|14.1|3.5|
|Former Soviet Union|5.7|0.5|1.1|1.7|
|High income: Non-OECD|6.5|0.0|46.3|4.5|
|High income: OECD|6.8|8.1|58.1|4.9|
|Latin America|4.4|10.6|36.2|4.6|
|South Asia|5.7|0.5|2.0|2.9|
|Sub-Saharan Africa|5.2|1.0|6.9|1.5|

Source: The Doing Business Database

**Table 7**

**Outstanding Guarantees as a % of GDP**

|**Country**|
Figure IX: Returns to Schooling by Sector <!-- Start of picture text --> .2<br>.1<br>0<br>Non Agriculture<br>-.1 Agr i culture<br>6 7 8 9 10<br>Log GDP p.w.<br>Returns to Schooling<br><!-- End of picture text --> _Notes_ : The figure plots the estimated sector-specific coefficient on years of schooling from a regression with log wage income as dependent variable against log GDP per worker (in PPP terms). Each dot corresponds to a different country ˆ year sample. The estimation is performed on the World Bank I2D2/GLD database, while GDP per worker is taken from version 9.0 of the Penn World Tables (Feenstra et al., 2015).

capital. This would result in skills mismatch, workers’ frustration, and lower returns to human capital investments. In addition, given that agriculture is the sector displaying the largest productivity differences between poor and rich countries (Caselli, 2005), facilitating the reallocation of workers away from this sector through human capital accumulation might support economic growth in the developing world (Figure VII.i).

# **3.3 Technology Adoption**

**Human capital is a key driver of both the invention and the adoption of new technologies (Figure VII.c)** . One reason for this is that understanding new technology developments and adapting to changes in the production process require advanced skills, which are typically acquired through education and experience (Nelson and Phelps, 1965). The literature has offered several pieces of evidence in support of this idea. For example, Ciccone and Papaioannou (2009) show that countries with higher levels of education in 1980 saw faster growth in human-capital-intensive industries (relative to other industries) in the following decades, a period characterized by the ICT revolution and the rapid introduction of skilled labor augmenting technologies. Consistently with this idea
1995 and 2012. The authors use income, health insurance, retirement benefits, tenure, job contract, job stability, and job satisfaction components to create a composite job quality index. One of their key findings is that Argentina, Brazil, Chile, Costa Rica, and Uruguay exhibit higher job quality, primarily due to a lower share of their employed population working in the agriculture sector compared to other countries. Additionally, they found that the average job quality in the region increased by approximately 25 percent between 2004 and 2012.

On the other hand, Senhbruch et al. (2020) developed a synthetic indicator of the quality of employment for 10 countries in Latin America using household and labor force surveys from 2015.<sup>4</sup> The authors select three dimensions in their study, each operationalized

> 4The list of countries includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, and Uruguay.

4
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labor-relevant domains to be completely closed and by accounting also for the remaining domains modeled.

We continue by parametrizing the additive sub-module for which convergence is not necessarily relevant or accounted for, namely the non-tradable production. We parametrize the former by drawing statistical measures from the HIES 2014 and the UN Women (2019). According to these sources, we calibrate the time spent on non-tradable production activities to be different between men and women and therefore modeling the higher allocation of female time to unremunerated activities. The female population aged 15+ is estimated to allocate 8.1 percent of their time to non-tradable activities (or 1.95 hours a day) compared to 3.9 percent (or 0.93 hours a day) allocated by men. Note that under a more equitable redistribution between men and women, the total value of non-tradable production would still be higher for women than men since the female population aged 15+ is larger than the male population aged 15+.

Our approach to modeling closures in the gender gap for a given factor assumes that convergence occurs instantaneously in the starting period, and the effects of this convergence are therefore immediately reflected in subsequent periods. To some extent, this modeling feature reflects the upper bound of the potential impact of closing gender gaps since the effects of an instantaneous convergence are able to accrue over the longest time horizon.

# **5 Results**

We present several of estimates, including macroeconomic measures as well as estimated labor-market and earning-relevant statistics. We begin by depicting the current economic contribution of Liberian women, which we refer to as the “status quo.” This is followed by the estimates of the gender dividend for each sub-module or characteristic studied. For each, we describe the initial condition of the sub-module and provide the macro-economic estimate, depicted as a share of GDP, GDP per capita, GDP per worker and GDP per sector-gender output. In relevant cases, estimated differences in the labor-market composition or wages are given. Finally, we present the estimates given by the simultaneous
level outcomes and do not find any statistically significant effects of the two placebo cutoff points.

In the third test, we assess the program impact on exogenous variables including individuals' age, gender, ethnicity, and education completion. For the education variables, we restrict the sample to individuals ages 30 or above to ensure that their education was not affected by the program. We use the same model as described in equation (3), utilizing both the pre-program and post-program data from the VHLSSs from 2004 to 2020. We do not find significant impact of the program on these exogenous variables, suggesting that there is good balance in the exogenous variables around the cutoff point (Appendix A, Table 16).

Second, we examine the sensitivity of the estimated program effects when using different bandwidths. Initially, we expand the bandwidth to include districts with the 2006 poverty rate above 35%. Subsequently, we use narrower bandwidths by limiting the sample to households in districts where the poverty rate ranged from 40% to 60% in 2006. There are no available estimators for combining nonparametric RDD with fixed effects. However, we can apply nonparametric RDD to the post-treatment data to find the optimal bandwidth. We select optimal bandwidths using data-driven mean-squared errors (Calonico _et al._ , 2017), which yields an average optimal bandwidth of approximately 7%.<sup>8</sup> Using this bandwidth, we estimate the program impact and present the results in Tables A.17 to A.19 in the Appendix. Due to space constraints, we only provide robustness analysis for several key variables of individual and household outcomes. The estimated program effects on individual outcomes, using different bandwidths, closely align with the main results presented in Tables 2 and 3. As another check, Table A.20 in the Appendix illustrates the 'donut' RDD, wherein we exclude districts that are very close to the cutoff point (2006 poverty rate of 50%) to assess

> 8 To find these optimal bandwidths, we use
- **Bau, Natalie, Gaurav Khanna, Corinne Low, Manisha Shah, Sreyashi Sharmin, and Alessandra Voena.** 2022. “Women’s Well-being During a Pandemic and its Containment.” _Journal of Development Economics_ , 156: 102839.

- **Carranza, Eliana, and David McKenzie.** 2024. “Job training and job search assistance policies in developing countries.” _Journal of Economic Perspectives_ , 38(1): 221–244.

- **Chakravorty, Bhaskar, Apurav Yash Bhatiya, Clément Imbert, Maximilian Lohnert, Poonam Panda, and Roland Rathelot.** 2023. “Impact of the COVID-19 crisis on India’s rural youth: Evidence from a panel survey and an experiment.” _World Development_ , 168: 106242.

- **Deshpande, Ashwini.** 2022. “The Covid-19 pandemic and gendered division of paid work, domestic chores and leisure: evidence from India’s first wave.” _Economia Politica_ , 39(1): 75– 100.

- **Hogan, Vincent.** 2004. “Wage aspirations and unemployment persistence.” _Journal of Monetary Economics_ , 51(8): 1623–1643.

- **Imbert, Clément.** 2020. “Covid-19: Expected migrant movement as lockdown eases.” _Ideas for India_ , 1.

- **Kelley, Erin M, Christopher Ksoll, and Jeremy Magruder.** 2024. “How do digital platforms affect employment and job search? Evidence from India.” _Journal of Development Economics_ , 166.

- **Ministry of Skill Development & Entrepreneurship, Government of India.** 2018. “Tracer Study of ITI Graduates.”

- **Ray, Debraj.** 2006. “Aspirations, poverty, and economic change.” _Understanding poverty_ , 1: 409–421.

11
Additionally, the recent IPCC report finds that approximately 3.3 to 3.6 billion people live in contexts that are highly vulnerable to climate change (IPCC, 2022).

There are also a number of global indicators that address the dimensions of climate risk and vulnerability (e.g., ND GAIN (Notre Dame Global Adaptation Initiative), INFORM (Index for Risk Management, UNDP), <u>Global Climate Risk Index (German Watch)). The method proposed in this paper adds to this global</u> indicator work by taking a measurement approach that is much more focused on household-level vulnerability and risk. Unlike other indices which use data aggregated to the national level for each of their dimensions of climate risk and vulnerability, which makes it challenging to aggregate across dimensions, we use the household as our unit of analysis, considering exposure and vulnerability at the household level. We overlay data on exposure to extreme weather events with household data at the subnational level, thereby providing a more granular and spatial assessment of vulnerable populations globally.

Finally, there are modeling efforts that measure global exposure to multiple climate-related or natural hazards, such as UN-ISDR (2015), Pesaresi et al. (2017), Maes et al. (2022). These estimates often focus on asset losses (i.e., the repair or replacement value of damaged or lost assets). Based on estimates of asset losses from UN-ISDR (2015), Hallegatte et al. (2017) estimated the number of people falling in poverty every year due to storms, floods, droughts, and earthquakes, the socioeconomic resilience (as the ability of households to cope with and recover from disaster losses), and the risk to well-being (as the expected loss of welfare due to natural hazards). As highlighted above, this paper follows a different approach by estimating the number of people with high exposure and using proxies to measure vulnerability rather than metrics based on monetary losses. However, the key drivers of vulnerability
catch-up‖ patterns.<sup>17</sup> For example, Figure 2.7.3 shows the intra-industrial and inter-industrial dimension of ―Flying Geese catch-up‖ patterns. Along the value chain to high value added products, Korea moved from exports of clothing to those of textiles and to production of synthetic fibers (Lim, 2011). In the electronics industry, a comparative advantage recorded by the net trade index reveals industrial upgrading from simple goods to more sophisticated goods (Lin & Chang, 2009). While Korea had a dominant comparative advantage in the home appliance electronics industry which started with the _assembly of radios_ from imported components (World Bank, 1987), it started to _gain_ a comparative advantage in electronic parts (i.e. transistor and semiconductor) in the mid of the 1980s, and in information communication & industrial electronics in the 1990s (Figure 2.7.4).

**Figure 2.7.4 Trade Specialization Index of Korea’s electronics**

**Figure 2.7.3 Revealed Comparative Advantage of Korea’s industries**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>Clothing<br>8<br>6<br>Footwear<br>4<br>Textiles<br>2<br>Electronics<br>0<br>62 67 72 77 82 87 92 97 02 07<br>Tran sport<br><!-- End of picture text --> <!-- Start of picture text -->
1.00<br>0.80 Home ap pliance<br>0.60<br>0.40<br>Comm. & Industrial<br>0.20<br>Parts & Components<br>0.00<br>74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06<br
0.72|0.81|New York, NY|Chicago, IL|
|Indonesia|3|735|2.0E+04|0.12|5.4E+10|1.10|0.45|New York, NY|Los Angeles, LA|

Source: Author's analysis based on data described in text. Notes: Table provides descriptive statistics on intensive-margin sample. An included observation is the first-difference of values for an exporter-importer-industry-period from the prior period. For the differencing, the exporter's labor productivity at the industry level must be reported in the UNIDO database in both periods and the export volumes in the World Trade Flows database must exceed $100k for both periods. All trade with the United States is excluded. Column 2 reports the number of periods after first differencing that a country is included in, and the third column provides route-level observation counts after first differencing. Columns 4 and 5 provide labor productivity estimates from the UNIDO database. Columns 6-8 report statistics from the World Trade Flows database, with volumes expressed in US dollars. The last two columns document the two largest ethnic heritage cities used in the instrument design. Cities are defined at the consolidated metropolitan area level, with abbreviated names provided.
6.48|5.47|3.28|5.59|7.39|4.34|2.04|5.26|
|Other|7.45|5.25|4.92|6.87|6.69|2.88|1.51|4.66|
|HH without children|4.10|3.59|2.46|3.44|5.44|1.98|1.30|2.80|
|Retiree HH|1.61|0.90|0.81|0.87|1.54|0.83|0.64|0.36|
|HH with children|7.65|5.53|4.38|6.95|6.43|4.02|2.52|5.14|
|1 child|7.50|5.81|3.90|7.54|6.11|4.37|2.55|4.75|
|2 children|6.48|4.92|5.40|6.60|7.17|3.77|2.58|5.73|
|3 and more children|12.55|5.84|3.89|3.66|5.73|2.35|2.12|5.74|
|singleparent|3.27|3.00|4.71|2.48|6.82|3.18|2.19|3.12|

Source: Authors’ estimates based on HSS data.

34
of a futures market. Of course, not all of the risk can be shifted as there is never a perfect relationship between cash and futures markets. There is always some _residual risk_ associated with the hedging process. This residual risk (the price risk that can not be shifted to futures markets) is commonly referred to as the basis risk. Put another way, the basis risk is the price variation in the physical cash market that can not be explained by futures price variation. A low level of variation in the basis implies that there is a strong price relationship between cash and futures markets and viable risk management strategies can be devised. A high level of basis variation indicates that the relationship between the physical cash market and the futures market is not strong and that not enough risk can be shifted to make the hedging operation worthwhile. This section examines the basis relationship between Polish wheat prices and prices of wheat on the CBOT and LIFFE.

# **3.1. Relationships between Polish Wheat Cash Prices and** CBOT **and** LIFFE **Wheat Futures Prices**

Ordinary least square regression was used to assess the correlation between cash prices in Poland and the corresponding futures prices on the CBOT and the LIFFE. The equation used is of the following form: s(t) = a + b* f(t) where: s(t) is the cash price for wheat in Poland at time t, and f(t) is the futures price for wheat in CBOT or LIFFE at time t. We used three forms of the variables. First, the variables were expressed in levels. Second, the variables were deseasonalized to assess the impact of seasonality on the relationship between futures and cash prices. The deseasonalized prices were created by regressing each of the prices on twelve monthly dummies plus a time trend. The estimated residuals from that regression are the deseasonalized prices. Third, the variables were differenced.

The adcdusted R squared can be viewed as one way of measuring the
# **2.2 Data and Data Sources**

We use the World Bank’s HFPS data for Ethiopia, conducted between April 2020 and May 2021 to monitor the impacts of the COVID-19 pandemic (World Bank, 2020).<sup>12</sup> The phone survey sample is a subsample of households drawn from both urban and rural areas in all regions of Ethiopia surveyed face-to-face in the Living Standards Measurement Study - Integrated Survey on Agriculture (LSMS-ISA) in 2019. The 2019 LSMS-ISA Ethiopia data are nationally representative sample of 6,770 households. Of these, 5,374 (79.3 percent) reported phone ownership and were used as sampling frame for the phone survey in 2020. The actual sample size for the phone survey was set at 3,300 households of which 3,247 of them were successfully interviewed in the first phone survey in April 2020 (Wieser et al., 2020). These households were re-interviewed every 3- 4 weeks, for eleven rounds, until May 2021, allowing high-frequency monitoring of changes in key outcomes of interest including labor market participation and food security. We combine the 2019 face-to-face survey data with these HFPS data.

The 2019 baseline data provide detailed characteristics of households, including GPS coordinates of household residence, which we used to merge these data with the ACLED database, which records conflict events at specific locations and which has been widely used to study the consequences of conflicts in different settings. The ACLED database provides event-based information for different types of conflicts, including battles, attacks against civilians, remote violence, and protests and riots. For our purpose, we focus on number of battles because the Ethiopian civil war that broke out on November 4, 2020, involved intensive battles. More than half of the incidents recorded by ACLED during our sample period (August 2019-May 2021) represent battles. We construct household-level measure of exposure to battles by counting the number of battles within a 10, 20, 30, 40 and 50 km radius around each
level of education in the Labor Force Survey in 2012. In each case we estimate the regression equation (9) using employment in a given subgroup (job or occupation) and firm type (all firms in column 1, below-median TFP firms in column 2, and above-median TFP firms in column 3) as the outcome variable. In curly brackets we report the subgroup share within each panel. In angle brackets we report the mean of the outcome variable in May 2012 – the probability of being employed in a given subgroup and firm type. Columns (1)-(3) report the difference-in-differences estimate that compares the change in employment among the 55 to 57 age group that was affected by the payroll tax cut with the change in employment among the 52 to 54 age group that was not affected by the tax cut. In Columns (4)-(6) we calculate the employment elasticity with respect to the wage for all firms (column 4) and separately for below-median (column 5) and above-median (column 6) TFP firms. We obtain percent change in employment by dividing the estimated change in employment with the mean outcome variable reported in angle brackets. Then we divide the percent change in employment with the percent change in labor cost (estimates are not reported, but available on request). Standard errors are reported in brackets, clustered at the age _×_ period level. ( _N_ = 9 _,_ 003 _,_ 984 individual-months)
terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in TA 1. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 2 use the estimated parameters based on the TA 1. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.7.

36