rafmacalaba/data-use-mentions-extended huggingface dataset:
these deviations. Empirical analyses in different settings have found the elasticity of substitution in consumption across sectoral goods to be less than unity. This complementary association implies that the sector with relatively higher productivity growth loses its share as its price relative to other sector goods falls. Figure 6 plots the change in employment shares against the excess of labor productivity growth in the sector over the aggregate labor productivity growth.<sup>7</sup> For example, the industrial labor productivity growth during the period in Costa Rica outpaced the aggregate labor productivity growth by roughly 40 percent. A compelling inverse relationship between the two variables is evident with regards to the aggregate industrial sector. Changes in consumption shares imply substantial reductions in employment shares in Chile and Costa Rica where industrial productivity growth has far exceeded the growth in aggregate productivity. Concurrently, the consumption shares have shrunk in Argentina and Mexico in line with the industrial productivity growth lagging behind the aggregate. However, Peru and to some extent Brazil deviate from the broad trend when seen

> 6The analysis in this paper focuses on changes in final consumption shares which is different from changes in value-added shares. In essence, the two effects are applicable when specifying either final goods or value-added production functions at the sectoral level. Nonetheless, the relative importance of the two effects is not necessarily independent of the specification. For instance, Herrendorf _et al._ (2013) explores the relative influence of the two effects in accounting for the transformation of the post-war US economy.

> 7Labor productivity is measured as real value-added per worker using data from the GGDC 10-Sector database.

16
the migrants and fight exploitation and trafficking (UNDP 2009, p. 103). Providing knowledge about the migration process and language of the destination

> wide discrepancies were discovered between the true estimated remittance flows and the figures reported in the balance of payment statistics to the International Monetary Fund (IMF).

> 29 A recent report by the Commission on International Migration Data for Development Research and Policy (2009) suggest five concrete steps towards improved availability and quality of migrant and remittance data. Further guidance can be found in Black & Skeldon (2009).

20
**A1.1 1 Political Economic Risk Consultancy (PERC)**

The Political and Economic Risk Consultancy specializing in strategic information and analysis for companies doing business in the countries in East and Southeast Asia. PERC was founded in 1976 and is headquartered in Hong Kong. As of May 1999, PERC can be reached at http:l/www.asiarisk.com/perc.htr **il.** PERC has conducted three surveys of expatriate business managers in the East Asia region. The results of these surveys were published under the tiles "Corruption in Asia in 1998" (from Asian Intelligence Issue #507' April 1, 1998), 'Transparency Problems In Asia" (from Asian Intelligence Issue #498, November 19, 1997), and "Shortcomings of the Media in Asia" ([PUBLICATION INFO]). Based on the average responses in these surveys, PERC has produced country ratings. In this paper, we use data from each of these three surveys.

# **Corruption in Asia in 1998**

In this survey, foreign managers working within the East Asia region were questioned about their perception of corruption, the quality of the legal system, and the professionalism and reliability of the police and judiciary. We have obtained their data on corruption for 12 countries, based on a total of 427 responses. With respect to corruption, respondents were asked "To what extent does corruption exist in a way that detracts from the business environment for foreign companies?".

# **Transparency Problems in Asia**

In this survey, foreign managers in the East Asia region were asked about their perceptions of transparency in the business environment. A precise definition of transparency was not provided to respondents.

# **Quality of the Media**

In this survey, businessmen (including foreign and local mangers) in the East Asia region were asked their perceptions regarding the degree of censorship practiced, the quality of local media, access to foreign print media, and access to
Government of India, Planning Commission. (no date). Program Evaluation Office Study No.147 (http://planningcommission.nic.in/reports/peoreport/cmpdmpeo/volume1/147.pdf)

- Government of India. circa 2000. _Child Development_ . Ministry of Human Resource Development, Department of Women and Child Development (http://wcd.nic.in/)

- Government of India. 2002. _The Indian Child: a profile_ . Ministry of Human Resource Development, Department of Women and Child Development (http://wcd.nic.in/)

- Government of India, Department of Women and Child Development. 2003. _Empirical Analysis of Integrated Child Development, National Family Health Survey._

Government of India, Ministry of Finance Economic Survey, 2003-04, <u>http://indiabudget.nic.in/es2002-03/chapt2003/tab18.pdf.</u>

- ⎯

- Greiner, Theodore, and David F. Pyle.2000. “Nutrition Assessment India”, paper presented at the World Bank-UNICEF Joint Nutrition Assessment Workshop, Oct 11-12, 2000.

- Heckman, J., Ichimura, H., and P. Todd, (1998), “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program,” _Review of Economic Studies_ Vol. 64(4): 605-654

- Heckman, J., Ichimura, H., J., Smith and P. Todd, (1998), “Characterizing Selection Bias using Experimental Data,” _Econometrica_ , Vol. 66: 1017-1099

- International Institute for Population Sciences (IIPS). 1995. _National Family Health Survey – India 1992-93_ , Bombay: IIPS. (http://www.nfhsindia.org/india1.html)

- International Institute for Population Sciences (IIPS) and ORC Macro. 2000. _National Family Health Survey (NFHS-2), 1998-99: India_ , Mumbai: IIPS.

<u>(http://www
familiar from our other studies in the 1980s in the manufacturing sectors of the vast majority of countries surveyed, as reported in Table 2.

In diagnosing changes in demand for skills, shifts in supply are a confounding factor. One approach to neutralizing the effects of supply shifts is to assume an elasticity of substitution of unity between skilled and unskilled labor, so that the wage-bill is invariant to movement along the relative demand curve as in Figure 3 above. That approach recommends the wage-bill share of non-manual workers as a measure of demand for skills, since it shifts only with demand under the assumption. Figure 2 illustrates that approach for the ASI data. The non-manual share of wage-bill drops between 1984 and 89 and then rises fairly steadily through the 1990s. Compared to the two series in Figure 1, relative wages and proportion of non-manual workers in employment, the wage-bill share series is smoother, suggesting that indeed supply shifts are being muted in this series by motion along a demand curve.

India is a country of diverse manufacturing technologies spread across vast distances with poor transport between States and between interior States and foreign markets. Consequently, one might suspect that shifts in demand for skill would differ across States. Table 4 investigates cross-state differences in demand for skills in manufacturing, reporting for each state in both periods the change in wage-bill shares of non-manual workers, the proportion of that change that occurs within industries and the change in relative wages. Note that registered manufacturing employment varies widely across states (as does population) from a high of 1.5 million in Maharashtra to just 11 thousand in Chandigarh. Six states are not reported as they have so little employment that reclassification across 3 digit industries (NIC) may be suspect.

Table 4 reports three clear findings about demand for skills in the 1990s. First, in fully 19 of the 21 States the wage-bill share of non-manual workers increased in the post-reform sample 18
15 provide two independent identification devices to estimate the hypothetical treaty-shopping cost parameter. By combining this estimate with the data, we delineate the implications of treaty-shopping for investment and government revenue in source countries.

# **A.   Estimation of Total Effects**

Direct evidence on the effect of DTT builds on the following system of equations � where Y�,� ��FDI�,�, Tax�,�� is a vector including the logarithm of the aggregate stock of foreign direct investment in country s and year t, FDI�,� and the logarithm of the aggregate corporate income tax revenue in country s and year t, Tax�,�. The indicator vector variable DTT�,� equals one for country s if a treaty with Mauritius is effective in year t and zero otherwise. We include country-specific fixed effects μ� ��μ���,�, μ���,�� to account for differences in FDI and tax revenue that exist independent from the tax treaty framework. Accordingly, the coefficient vector β ��β���, β���� measures the relative change in FDI and the relative change in tax revenue, following treaty conclusion with Mauritius. These DD estimates of the treatment effect build on the premise that treated and control group would have moved in the same direction in the absence of an intervention; the common-trend assumption (see Angrist and Pischke, 2009). While there is no test to verify this assumption formally, we review whether the experimental groups followed similar trajectories before treaty-conclusion.

We include a broad set of control variables (X) that may affect FDI and tax revenues, drawing on the World Bank’s World Development Indicators (WDI) and the IMF’s WEO database. Specifically, we control for the magnitude of international trade, by including the log of imports and exports, market size and potential demand in source countries, including GDP and GDP per capita, GDP growth, and measures to control for the overall
Overall, the data reveal important target areas from a gender perspective (for example, younger women and the prominence of market-related constraints) that policy could be targeted towards — but more refined data in this category of constraints would be useful. Specifically, looking more carefully within market access issues related to distance and cost — for example, commuting times, how often individuals can reach markets, infrastructure quality and modes of transport for products — would better inform how policy might be directed, particularly since distance/cost affect marketing of products as well.

In surveys with a community module like the LSMS, better data on access to child care and caregiving arrangements can also help in understanding women’s labor underutilization amid rural economic transitions. This could include, for example, community-level data on care centers along with existing data collection on local schools — including how long schools and centers are open — as well as other questions on informal caregiving arrangements among family members or other individuals within the community, which could be more relevant for poorer, rural contexts.

## Summary and ways forward

There are large survey data gaps in understanding how rural economic transition is associated with changes in individual employment outcomes. Data on constraints faced by rural women in seeking better economic opportunities or improving productivity are also very important for policy targeting (for example, in technology adoption programs). Box 7 summarizes these issues and priorities for survey data collection going forward.

Box 7. Features of work amid rural transition, and barriers to economic mobility

- Consistent individual-level data on work-related migration across surveys is needed (including data on individuals from the household that have migrated for work, place of migration, sector of work, and remittances) to understand how rural-urban migration has affected rural women left behind.

- Survey questions on skills acquired (or needed), as well as questions on economic mobility (including, relevant to women, information on respondents looking to move to paid work in the formal sector, and those transitioning from home and subsistence production into market work
Health experts were by far the most likely group of people to be able to reduce recipients’ vaccine hesitancy, according to phone survey respondents and participants in the control group in the online experiment (see Figure 8). Respondents that did not plan on being vaccinated or were unsure were asked “Would you be more likely to receive the COVID-19 vaccine if any of the following individuals/authorities receive or recommend the vaccine?” in both surveys based on the same list of groups of people. In the phone survey where respondents could select all that applied, around half (48%) of respondents selected health workers. In the online experiment where respondents selected the main group most likely to change respondents’ minds, around two-thirds (66%) of respondents selected health workers. In addition, phone survey respondents were asked their most preferred way of receiving information about the vaccine and around four out of five (80.1%) respondents stated face-to-face communication from health workers (see Figure A5 in Appendix A). Collectively, these results provide evidence that advice from health experts about the COVID-19 vaccine is likely to positively influence people’s willingness to get vaccinated.

**Figure 8 – Top three groups in people who could change respondents’ minds about getting the vaccine**

<!-- Start of picture text --> a) Phone survey (late May/early June 2021) b) Online experiment (late June/July 2021)<br>.5 .8<br>0.49<br>.4 0.66<br>.6<br>.3<br>0.29<br>.4<br>.2<br>0.19<br>.2<br>.1 0.20<br>0.12<br>0 0<br>Family and Friends Religious leaders Health experts Family and Friends Religious leaders Health experts<br>Share of respondents that selected this answer Share of
# The Early Labor Market Impacts of COVID-19 in Developing Countries: Evidence from High-Frequency Phone Surveys<sup>∗</sup>

Melanie Khamis Daniel Prinz David Newhouse Amparo Palacios-Lopez Utz Pape Michael Weber Keywords: COVID-19, Employment, High-frequency phone survey, Developing countries JEL Codes: J21, O12, E01

> ∗Khamis: World Bank, Wesleyan University and IZA; Prinz: World Bank and Harvard University; Newhouse: World Bank and IZA; Palacios-Lopez: World Bank; Pape: World Bank; Weber: World Bank. The corresponding author is David Newhouse: dnewhouse@worldbank.org. This work was prepared as part of the World Bank’s JobsWatch Covid-19 initiative. The authors are grateful to Sukti Dasgupta (ILO), Sangheon Lee (ILO), Truman Packard, and Nobuo Yoshida for helpful comments, and to Benu Bidani, Ambar Narayan, Michal Rutkowski, Carolina Sanchez-Paramo, and Ian Walker for their guidance. This work was made possible through a grant from the World Bank’s Jobs Umbrella Trust Fund, which is supported by the the UK’s Foreign, Commonwealth Development Office/UK AID, and the Governments of Norway, Germany, Italy, and Austria; the Austrian Development Agency; and the Swedish International Development Cooperation Agency. The authors further gratefully acknowledge financial support from the Korean Trust Fund (KTF). The team is also grateful to the Poverty and Equity Global Practice and the Data for Goals group for collecting, harmonizing, and sharing the phone survey data, and to Denis Medvedev and Leonardo Iacovone for providing aggregate indicators from firm surveys. Aggregate indicators from the high frequency phone surveys are available at the High Frequency Phone Survey dashboard at: https://www.worldbank.org/en/data/interactive/2020/11/11/covid-19-high-frequencymonitoring-dashboard
estimation methods, on the other hand, “borrow strength” across areas through a regression framework that is quite flexible and can incorporate different types of data, including census data, remote sensing data, and administrative data, to name but a few. In traditional unit-level models like Elbers, Lanjouw, and Lanjouw (2003), the key predictors were household-level values derived from a census. However, this requires that there exists a (recent) census with variables also available in a survey with monetary poverty measures. This is a restrictive requirement, as censuses like this are few and far between in many developing countries. In this paper, we instead use AGEB or municipality aggregate variables when we implement the household-level models. Implementing a household or unit-level model with only aggregate variables is sometimes referred to as a “unit-context” model.

In all three methods discussed here, poverty or welfare is regressed on a selection of predictive features and random effects, which we discuss in more detail in the methods section and the appendices. Thus, each in-sample target area has two separate estimates: the direct survey estimate and the synthetic prediction estimate created through the regression. The key to small area estimation is the combination of these two separate estimates, based on relative variances. Intuitively, when the sample estimates are more precise for an area relative to synthetic predictions, there is “less strength” that can be borrowed from the prediction and the direct sample estimates are given more weight in the estimate. When the sample estimates are less precise relative to the synthetic predictions, on the other hand, areas can “borrow more strength” through the regression (Jiang and Rao, 2020). However, note that not all municipalities in Mexico appear in our sample. As such, this means that some municipalities do not have a direct estimate. For these out-of-sample areas, the sample contains no information and therefore gets zero weight in the estimate, which is a purely synthetic prediction from the regression.

The three methods also differ in sometimes important
Table 5: Average Trade-Flow vs. Trade-Consumption Elasticities

|Dep.|Var. :|(1)<br>_m_<sup>_DD_</sup><br>_z,t_|(2)<br> ̃_q_<sup>_DD_</sup><br>_z,t_|Diference|
|---|---|---|---|---|
|_Panel A:_ All Goods|||||
|_τ _<sup>_DD_</sup><br>_z,t_||-8.9<sup>_∗∗∗_</sup><br>(1.1)|-7.7<sup>_∗∗∗_</sup><br>(1.1)|16%|
|_Panel B:_ Phaseout Cat|egories||||
|1 _{ Phased } × τ _<sup>_DD_</sup><br>_z,t_||-13.2<sup>_∗∗∗_</sup><br>(1.6)|-10.9<sup>_∗∗∗_</sup><br>(1.6)|21%|
|1 _{ Others } × τ _<sup>_DD_</sup><br>_z,t_||-6.6<sup>_∗∗∗_</sup><br>(1.4)|-6.0<sup>_∗∗∗_</sup><br>(1.3)|10%|
|Year FE||✓|✓||
|HS6 FE||✓|✓||
|Observations||15,153|15,153||

_Note:_ All estimates are obtained form equation 10 and by varying
municipalities to provide a far more disaggregated look at convergence over a longer period, while also addressing distributional concerns. The data set includes variables on income, poverty, and inequality, spans 22 years (five waves of data over 1992–2014), and covers 2,361 municipalities. To the best of our knowledge, this is the first study that implements Ravallion’s (2012) method using municipal-level data.

The rest of the paper is organized as follows. Section 2 reviews the relevant empirical and theoretical literature on convergence, highlighting the scarcity of work exploring within-country poverty convergence at a high level of geographical disaggregation. Section 3 presents the methodology used to construct the dataset, which is a novel exercise applying small area estimation techniques to household surveys and population censuses to compute comparable measures of mean household per capita income, poverty headcount ratios, and the extent of inequality at the municipality level. Sections 4 and 5, respectively, test for convergence in mean per capita incomes and poverty headcount ratios across municipalities. The analysis emphasizes comparisons among subgroups of municipalities that exhibit sizable disparities (for example, urban versus rural, and municipalities located in states along the U.S. border versus the rest) as well as across subperiods to contextualize the various changes Mexico has experienced over the years (that is, economic crises, ups and downs in overall poverty rates, and the expansion of public expenditure). Both analyses explore the role of redistributive programs, such as targeted cash transfers to the poor and fiscal redistribution to subnational governments, in driving these changes. Section 6 digs deeper to explore the role of the initial distribution of poverty and inequality in determining the speed of convergence and decomposes the estimated magnitude of poverty convergence. Section 7 brings together the main messages of each section to conclude. Taken together, the findings suggest that income and poverty convergence have taken place at the municipality level in Mexico during 1992–2014 and that redistribution has played an important role.

# **2 Literature review and past evidence in
analysis was done separately by gender. Table 2 presents the two-year transition matrix used in the model, building on the LFS trends and ELMPS transition probabilities by gender to update the HIECS. About 18 percent of the sample of working-age males and 8 percent of the sample of workingage females are reallocated across states. Overall, the main transitions that needed to be modeled relate to stopping work and becoming informal. The adjustment also considers demographic changes by raking HIECS weights based on population estimates disaggregated by age categories and gender, as the working age population is growing.<sup>9</sup> Once the labor market states are updated, labor income is adjusted to reflect earnings growth over the period. Incomes are updated based on the nominal growth rate of their destination (FY20) income source over two years. The income growth rates are computed by labor market state, gender, and age group (ages 15-24, 25-34, 35 and above).

> 8 Attrition in the ELMS is about 20 percent over the period 2012-2018. 9 Population projections were obtained from the UN Population Prospects.

7
.003)|(0.001)|(0.003)|
|Region dummies|✓|✓|✓|✓|✓|✓|
|Occupation dummies|✓|✓|✓|✓|✓|✓|
|Observations|1,713|1,713|1,713|4,305|4,305|4,305|

_Notes_ . *** p<0.01, **<0.05, *<0.1 _._ A linear probability model’s coefficient estimates and standard errors are reported. This table uses panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. The sample is restricted to those aged at least 20 years old in 2012 and at most 59 years old in 2018. All control variables refer to 2012. Panel weights are used.

# **6. Concluding Remarks**

The persistence in and the rigidity of labor market states were always key characteristics of the Egyptian labor market. This paper revisited these questions relying on transition matrices to examine the dynamics of labor market transitions post-Arab Spring. The analysis relies on the two most recent rounds of the Egypt Labor Market Panel Surveys (ELMPS) and exploits the panel structure of the data to track individual employment status and job trajectories between the two rounds.

21

**Official Use**
for assessing creditworthiness, such as employment data and demographic data are also absent from a significant proportion of PCRs.

# **_3. Economic research, macroeconomic policy and control_**

Credit bureau information can also be important for macroeconomic intelligence. Credit bureau information can be considered by region, by sector, by quality of borrower and used to analyze the credit market and interest rate developments in detail. Moreover, the dynamics of assetquality can be highly illuminating in assessing activity and growth, by region or sector and macroeconomic risks. For example, an increase in the movement of loans to lower grades in a particular sector or region may highlight particular economic problems and risks in that area or sector. This information may be invaluable for a Central Bank to understand the transmission of 19