rafmacalaba/data-use-mentions-extended huggingface dataset:
Trade Organizations, Ngozi Okonjo-Iweala, is urging members to facilitate the transfer of technology for vaccine production to more countries. A spokesperson for the Switzerland-based public-private vaccine alliance GAVI has stated that the US supports manufacturers to transfer not only intellectual property but also know-how in a bid to boost global production (Zarocostas 2021).

# **Challenges**

Two of the common concerns raised about the prospects for manufacturing in African nations of are the business climate and the risk of political instability. These issues are real, but we believe they are much less important than a more fundamental issue: manufacturing around the world shows a pattern of rising capital intensity, which raises questions about the prospects for manufacturing in countries with a comparative advantage in low-skilled labor.

# _Rising Capital Intensity of Manufacturing_

Technological innovation in manufacturing in recent decades has favored capital over labor. At the same time, the spread of global value chains and increased openness to trade have had the effect of homogenizing technology around the world (Rodrik 2018). As a result, trade integration tends to reduce the employment intensity of manufacturing production in developing countries (Sen 2019). Thus, participation in global value chains tends to increase labor productivity, but not employment (Pahl and Timmer 2020). Indeed, Pahl et al (2019) show that technological change in formal manufacturing has led to employment declines in Kenya, Senegal and South Africa. This confluence of events makes it more difficult for low-income African countries to gain a foothold in formal manufacturing – even in their own domestic markets.

Using firm-level data from Ethiopia and Tanzania, Diao et al. (2021) show that the capital intensity of formal manufacturing in both countries far exceeds economywide capital intensity. This is especially true of the larger, most productive firms, where capital intensity approaches (or exceeds) levels observed in the Czech Republic, a country with per capita income around 20 times higher. High levels of capital intensity (and possibly of skill intensity as well, although the study does not measure that) appear
# 3.1 Financial Banking Data

The essential ingredient for this project is a micro data set that we built based upon the Basic Statistical Return (BSR)1A, maintained by the Central Bank (RBI). BSR-1A has details of each loan outstanding (above a threshold), reported annually by every branch of every scheduled commercial bank in India. The threshold over which individual account data are reported was Rs. 25,000 until 1998 and Rs. 2 lakh from 1999 onwards (the latter is about $4,000 using historical exchange rates). The universal and comprehensive nature of these financial data is substantially stronger than what can be assembled for most countries, including the United States for example. The BSR data have been used in recent research by Cole (2009), Das et al. (2016), Kumar (2016), and Das et al. (2018).

While the micro-data can only be accessed at the RBI by their staff, we were allowed to aggregate these data for external use. Our aggregations are at the level of the district x industry x year. Districts are administrative subdivisions of Indian states or union territories that provide more-granular distances from the various highway networks. We prepare our platform to resemble those used in prior studies on India's manufacturing sector to facilitate comparability.<sup>10</sup> Accordingly, the core sample contains 311 districts that account for over 90% of manufacturing activity in India. The excluded districts from the full set of 630 districts make very limited contributions to organized manufacturing.

Industry categories are 2-digit NIC for manufacturing and 1-digit for all other industry groups. We have invested substantial time in cleaning and validating the data and ensuring consistency across years. We explicitly designed our aggregations to avoid any kinks due to definitional changes across years. Our data span 1992 to 2013, with our analyses concentrated on the decade from 1999 to 2009 around the GQ reform.

# 3.2 GQ and NS-EW Mapping

We measure the spatial distance of Indian
government balance sheet (central government, central bank, SOEs) and calculates net financial positions in domestic and foreign currency (see box 2).

# **Box 2.**

# **Reporting on net financial position of the public sector in Chile. Implications for sovereign ALM**

The Ministry of Finance of Chile publishes on a quarterly basis a public debt statistical bulletin (Report on Public Debt Statistics<sup>_21_</sup> ) that presents simplified balance sheets of the central government and of the central bank, with details on the public‐sector debt (including guarantees, for example on students’ loans, as well as transfers to SOEs). These balance sheets distinguish assets and liabilities in local currency and in USD, presenting a net financial position in both denominations. For example, as of September 2017, both the central government and the central bank had a net credit (long) position in USD and a net debit (short) position in local currency.<sup>22</sup> Even though there is no formal coordination between the MOF and the Central Bank to implement a sovereign ALM strategy (e.g. composition of assets and liabilities to seek natural hedges), both institutions hold frequent discussions on recent developments in financial markets. Within the MOF, a committee composed of representatives from the Budget department, Treasury and the DMO, ensures coordination in the implementation of the debt strategy and the management of the assets of the government funds.

The ALM approach is currently used in Chile to support the decision on the currency composition of the assets of the Fiscal Stability Fund ( _Fondo de Estabilización Económica y Social_ – FEES), the revenues of which are generated by headline fiscal balances (in excess of 0.2 percent of GDP), and of the liabilities of the central government: ‐ the assets of the FEES are invested in securities denominated in currencies with low or negative correlation with the price of the copper (such as JPY); ‐ the
receiving any pay (in cash or kind) or other returns from a job or business while not at work?’</mark>_ <mark>), while Guatemala includes several questions on the type of contract (‘</mark> _<mark>Do you have a contract? Is this contract for temporary employment or permanent?’</mark>_ <mark>) and the insurance scheme (</mark> _<mark>‘Do you have a medical insurance? If yes, who pays for it?’</mark>_ <mark>). Finally, the 2013 Malawi LFS includes a detailed list of more than 10 questions to capture informal jobs.</mark>

# 4.7. Retrospective questions

Labor market dynamics are a central topic in labor economics and of interest to policy makers. Labor market phenomena such as school to work transitions, transitions from unemployment or inactivity to employment, the impact of policy changes on the distribution of workers across sectors or rapid transformation in labor markets are dynamic in nature. Analyzing these dynamic processes requires repeated cross‐sectional data or panel data. DHS and most LSMS surveys do not include a panel component and are implemented every three to ten years or on an ad‐hoc basis, whereas most LSMS‐ISA surveys include a (rotating) panel and are implemented every two to three years. The frequency of LFS is country specific. A growing number of countries are introducing sample rotation in LFS (ILO, 2013b).

An alternative to repeated cross‐sectional or panel data are retrospective questions. Respondents are asked to report their previous employment status by, for instance, listing all their jobs in the last five to ten years. Retrospective questions have two advantages. First, information can be collected from periods during which no surveys were implemented. Studying certain changes in the labor market (i.e. the impact of international trade, job polarization, ...) requires longer time horizons
.5|13.0|Yes|455.1|11.3|
|Pension and disability insurance|354.7|8.8|Yes|313.2|7.7|
|Health insurance|132.7|3.3|Yes|124.1|3.0|
|Unemployment insurance|19.3|0.5|Yes|17.8|0.5|
|**_Indirect taxes_**|755.5|17.7|Yes|363.0|8.9|
|VAT|453.5|11.2|Yes|267.7|6.6|
|Excise taxes|265.6|6.5|Yes|95.3|2.4|
|Other indirect taxes (customs)|36.4|0.9|No|||
|Other revenues|247.4|6.1|No|||

Sources: Authors' calculations based on Ministry of Finance _Note: “Included in analysis” refers to the items that can be identified in the household data for this analysis and the amount that can be estimated and reflected in the analysis._ Social security contributions (SSC) represent a large source of public revenues in Serbia, at 13 percent of GDP (pension contributions – 9.15 percent of GDP, health contributions - 3.4 percent of GDP and unemployment contributions – 0.6 percent of GDP). The social security system in Serbia consists of three components: pension and disability insurance, health insurance, and unemployment insurance. This is a Bismarckian social security system financed 6
13 employment. Thus, a MFG or FIRE revolution causes urbanization and production cities. **Proposition 4 (de-industrialization without de-urbanization and the transformation of existing production cities into consumption cities)** _When Lf is fixed, by definition U_ = 1 _− Lf is also fixed, implying that a productivity shock that decreases (increases) employment in manufacturing would lead to a corresponding increase (decrease) in employment in non-tradables._ Proposition 4 says that shocks to the manufacturing or FIRE sector can cause existing production cities to become consumption cities when de-urbanization is unlikely. In the next section, we show that urbanization rates almost never decrease and discuss why.

**Empirics.** The urban share and the employment composition of urban areas should depend on the resource windfall _R_ , (tradable) agricultural productivity ( _p_<sup>_∗_</sup> _f_<sup>_Af_), and urban</sup> tradable productivity ( _p_<sup>_∗_</sup> _m_<sup>_Am_).In our econometric analysis, we focus on the period 1960-</sup> 2020 and 116 countries that were still “developing” economies in 1960.<sup>15</sup>

We do not have reliable historical measures of _Am_ . It is also not obvious which price levels should be used for _p_<sup>_∗_</sup> _m_<sup>.FIREGDPisonlyreportedforsomecountriesandrecent</sup> years (previous ISIC classifications did not separate FIRE). MFG and FIRE employment is likewise only measured for some countries and years when there is a census or a labor force survey (surveys were rare before 1990). Productivity could then be high because employment is low and/or “selected”, for example if a country has a few MFG/FIRE firms and these belong to high-productivity subsectors or are politically connected. Given such issues, we use the GDP share of manufacturing and services (MFGSERV). Web Appx Fig. D.
1990 = 100) /1**

<!-- Start of picture text --> 400<br>350 Aluminum ($/t) Coal, Australia ($/mt)<br>Copper ($/mt) Crude oil avg spot ($/b)<br>Gold ($/toz) Iron ore (¢/dmtu)<br>300 Lead (¢/kg) Nickel ($/mt)<br>Steel cr coilsheet ($/mt) Zinc (¢/kg)<br>BW rough diamond ($/ct)<br>250<br>200<br>150<br>100<br>50<br>0<br><!-- End of picture text --> Sources: World Bank GEP database and Botswana Department of Mines Annual Reports (various issues). 1/ The price of rough diamonds is derived from the value of Botswana’s diamond exports and volume of production (carats recovered).

The relative absence of the extreme boom and bust cycles in diamond mining has helped Botswana achieve rapid economic growth. Although Botswana has experienced some output fluctuations over time (1981/82 and early 1990s), the magnitudes of these fluctuations were far smaller compared with those experienced by other resource-dependent countries. Since production in Jwaneng mine reached its full capacity in 1985, Botswana’s real output has more than tripled, with per capita income growing more than five times in Pula terms.<sup>4</sup> The mining sector—where the bulk of value added is generated by diamonds— has been the largest contributor to the country’s GDP, although recently its share has fallen to about 35-40

> 2 Since 2009 a small proportion of diamonds produced in Botswana is supplied domestically to facilitate the growth of local diamond cutting and polishing industries.

> 3 Other differences include the absence of universal world price per carat/gram of diamonds since prices vary widely depending on
# **References**

- Abramsky, Tanya, Karen Devries, Ligia Kiss, Janet Nakuti, Nambusi Kyegombe, Elizabeth Starmann, Bonnie Cundill, Leilani Francisco, Dan Kaye, Tina Musuya, Lori Michau and Charlotte Watts. 2014. “Findings from the SASA! Study: a cluster randomized controlled trial to assess the impact of a community mobilization intervention to prevent violence against women and reduce HIV risk in Kampala, Uganda.” _BMC Medicine_ 12(1):122.

**URL:** _https://doi.org/10.1186/s12916-014-0122-5_

- Ag ̈uero, Jorge M and Prashant Bharadwaj. 2014. “Do the More Educated Know More About Health? Evidence from Schooling and HIV Knowledge in Zimbabwe.” _Economic Development and Cultural Change_ 62(3):489–517.

- Aizer, Anna. 2010. “The Gender Wage Gap and Domestic Violence.” _American Economic Review_ 100(4):1847–59.

- Aizer, Anna and Pedro Dal Bo. 2009. “Love, Hate and Murder: Commitment Devices in Violent Relationships.” _Journal of Public Economics_ 93(3-4):412–428.

- Alleyne, Binta, Victoria H Coleman-Cowger, Laurel Crown, Maya A Gibbons and Linda Nicole Vines. 2011. “The Effects of Dating Violence, Substance Use and Risky Sexual Behavior Among a Diverse Sample of Illinois Youth.” _Journal of Adolescence_ 34(1):11–18.

- Angelucci, Manuela. 2008. “Love on the Rocks: Domestic Violence and Alcohol Abuse in Rural Mexico.” _The BE Journal of Economic Analysis & Policy_ 8(1).

- Baird, Sarah, Craig McIntosh and Berk Ozler. 2011.<sup> ̈</sup> “Cash or Condition? Evidence from a Cash Transfer Experiment.” _The Quarterly Journal of Economics_ 126(4):1709–1753. **URL:** _https://doi.org/10.1093/qje
<mark>default, which includes: (1) missed payments beyond the grace period; (2) material changes to the contract affecting creditors, including distressed debt exchanges that reduce the debtor’s obligations; or (3) unilateral changes imposed by the debtor resulting in diminished financial obligation.</mark><sup>13</sup> <mark>Farah Yacoub et al. (2023) strive to categorize defaults based broadly on this definition even for countries and time periods when rating agencies did not cover the debtor in question.</mark><sup>14</sup> <mark>Compared to available historical sovereign ratings, the default data set is broader both in the time and cross-country dimensions. All core results presented here remain significant and of similar magnitude when instead using a smaller data set that only considers defaults signaled by the three leading credit rating agencies merged with a study by Standard and Poor’s applying its methodology in retrospect (Standard and Poor’s, 2006).</mark> <mark>Because we strive to assess the impacts of sovereign default on social outcomes, we aim at limiting possible confounding drivers of poverty, such as wars (Bianchi and Sosa-Padilla 2022). We thus drop all those defaults, which coincide with inter-country wars, using data from Sarkees and Wayman (2010).</mark>

# **<mark>Data used to restrict the pool for synthetic controls</mark>**

<mark>In order to limit the risk of interpolation and overfitting, we restrict the pool from which the synthetic control is constructed to countries that are structurally similar to the treated unit (Abadie, 2021). To capture structural similarities, we rely on two variables: GDP per capita and Polity V. GPD per capita is measured in real terms and published by the Maddison Project, and serves as an alternative to splitting the sample by income group, as is often done in the literature. The Polity Project
and Lebanon.

Collecting new HBS data and enhancing data sharing are very different in terms of costs. Given the analysis above, collecting new HBS data is more of a priority in MENA than is irregular poverty measurement. Nevertheless, data sharing should be an indispensable part of any international organization’s data collection effort.

If collection of a new household budget survey is limited by lack of funds, statistical agencies may consider using the methodology developed by the World Bank and called “Survey of well-being via instant and frequent tracking or SWIFT 2” (Yoshida et al. 2015). The main idea behind this method is the following. Instead of implementing a survey that collects a large sample of consumption data, the statistical agency can collect a nationally representative subsample of both consumption and non-consumption data and a much larger sample of non-consumption data only. Using the first sub-sample, which includes both consumption and non-consumption data, imputation models can be developed, and consumption can be imputed into each household in sub-sample 2.

> 11 Atamanov and Krishnan (2016).

12
automobile industry could reduce the need for freight, causing workers in the freight industry to lose their jobs. Laid-off workers in the freight industry, however, may be less inclined to purchase new cars, therefore causing demand-driven unemployment in the automobile industry. As demonstrated by the aforementioned scenarios, job loss spillovers oftentimes occur simultaneously and in multiple directions, making the determination of causality within a particular spillover channel quite a challenge.

We overcome this identification challenge by utilizing Bartik job losses to capture exogenous job losses. We argue that tradable job losses driven by aggregate shocks are exogenous to a county and are not driven by a county’s fundamentals. Mathematically, Bartik job losses are defined as ∑ T T <u>,2007</u> U , where is the employment of ~~�~~ ii � llcc ii ii TT ii∈T llTT llcc,2007 × ∆logll UU llcc,2007 tradable industry _i_ in county _c_ and is the employment of all tradable industries in county _c_ TT cc,2007 before the Great Recession. The national job losses of industry ll _i_ during the Recession are U . Since there are more than 3000 counties in the U.S, changes in national tradable ii UU ∆logllemployment are largely exogenous to a county; that is, such changes are little affected by any county’s changes in fundamentals, such as productivity shocks or an increase in minimum wages during the Recession.

Although supporting sectors only accounts for just a fraction of employment, this does not imply that job losses spillovers via input-output linkages are insignificant. We focus on the relationship between tradable and supporting sectors because the econometric identification is clearer. Spillovers via input-output linkages could operate via other channels, such as intracounty tradable-tradable linkages (e.g. car assembly plants and car parts producers within a county) or inter-county linkages. However, these channels are more difficult to identify and establish a relationship from.

4
**FIGURE 6: GROWTH AND COMPOSITION OF PER CAPITA GDP, SENEGAL AND COMPARISON COUNTRIES**

<!-- Start of picture text --> Per Capita GDP Growth Rates, 1995‐2013 Composition of per Capita GDP by Sector<br>2.0<br>1.4<br>1.5<br>1.2<br>1.3 0.8 1.11<br>1.0 0.5<br>0.5<br>1.2<br>0.5 0.5 0.5 0.2 0.3 0.45<br>1.1 0.3 0.3 0.4 0.5 0.26 0.3<br>0.0 0.10.0<br>1.0<br>0.9<br>SSA WAEMU Senegal<br>SSA WAEMU Senegal Agriculture Industry Services<br>1995‐2005 2006‐2013 1995‐2005 2006‐2013 1995‐2005 2006‐2013<br>Cummulative growth index (1995=1)<br><!-- End of picture text --> Senegal’s poverty headcount fell by 20 percentage points during 1995-2005, nearly by 2 percent per annum, but by only 1.6 percentage points during 2006-2011. Although the slowing pace of poverty reduction is a natural consequence of the decline in growth from 4.5 percent to 3.4 percent, this is not a sufficient explanation. The additional explanation is that growth of services during the latter period was exclusive to a large segment of the workforce. The prominence contributions of communications and financial services—which are more capital and skill intensive—to growth during 2006-2013 is consistent with this trend.

The distribution of labor across sectors suggests that services-driven growth is relatively employment inelastic. Between 1995 and 2004, employment growth was overwhelmingly concentrated in the informal sector, which accounted for 97 percent of new jobs
times at hand, I define the commuter market access (CMA) for location <u>1</u> _n_ and sector _s_ as CMA _ns_ = _Wnsθs_<sup>.Thisisanindexoftheaccessibilityofjobsinlocation</sup><sup>_n_to</sup> employment in sector _s_ . Following Tsivanidis (2019) and Donaldson and Hornbeck (2016), I can solve the following system of equations to compute MA measures for both firms and workers specific to each sector and location: where _L_<sup> ̃</sup> _is_ represents the total amount of labor hired by location _i_ and sector _s_ ; _Lns_ corresponds to the total number of workers that reside in location _n_ and work in sector _s_ ; and FMA _is_ is a firm market access measure that captures whether firms in _i_ have good access to workers from sector _s_ .<sup>47</sup>

> 45These externalities are relatively large compared to the previous findings in the literature, where they are around 0.1. However, both numbers are still reasonable, especially in developing contexts. For example, Tsivanidis (2019) find that agglomeration externalities in Bogota are around 0.21, which is a larger value than those of previous findings.

> 46Section C1 in the appendix explains the procedure.

> 47Tsivanidis (2019) estimates these measures for Bogotá and shows that with data of commuting costs, and the number of residents and workers in each sector and location, the system of equation 5.4 has a unique solution. Another way to prove the existence and uniqueness of this system of equations is to apply the theorem from Allen et al. (2015).

28
Policy Research Working Paper 9370

# **Abstract**

The 19<sup>th</sup> International Conference of Labour Statisticians (in 2013) redefined labor statistics standards. A major change was to narrow the definition of employment to work for pay or profit. By the revised standards, farming that is only or mainly intended for own use is no longer considered employment, and such a farmer is no longer considered to be employed or in the labor force. This paper analyzes the implications of the revised standards on measures of employment in Sub-Saharan Africa obtained from multitopic household surveys. It shows that, in some contexts, 70 to 80 percent of farmers produce only or mainly for family consumption and are therefore, based on this activity, not considered employed by the revised standards. However, there is wide variation across countries and regions. Moreover, farmers are more likely to report intending to produce for sale at the end of the growing season of the main local crop than earlier in the season. Men are more likely than women to produce for sale. The revised standards lead to significantly lower employment-to-population ratios in rural Africa and change the sectoral composition of the employed population toward non-agricultural sectors. The paper concludes with recommendations for data producers and users.

This paper is a product of the Development Data Group, Development Economics. 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 igaddis@worldbank.org and apalacioslopez@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 quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly.