rafmacalaba/data-use-mentions-extended huggingface dataset:
(Capasso, Ohnsorge, and Yu 2022). However, the direction of causality has yet to be identified, especially in a multi-country context. This section aims to demonstrate the presence of a causal link from financial development to informality by employing an instrumental variable approach. However, before we move on to the instrumental variable estimation, we document the association between output informality and financial development without any presumption of causality.

# **2.1 Data**

As summarized by Svirydzenka (2016), financial development materializes in different forms— as also reflected in the wide range of indicators of financial development used in the literature. Indicators range from domestic credit to the private sector to GDP ratios to equity market capitalization, bond market turnover, and the number of commercial bank branches per 100,000 adults, among others. Virtually all of these indicators, except those relating to equity market development, are significantly correlated with a range of informality measures (Capasso, Ohnsorge, and Yu 2022).

Here, for our purpose, we focus on banking sector development and, specifically, access to banking services. Informal workers tend to be lower-income and lower-skilled, and informal firms tend to be smaller, less productive, and active in less capital-intensive sectors (Ohnsorge and Yu 2021). Hence, the main point of interaction of informal workers and firms with the financial system is likely to be banks, not equity or bond markets. Their transactions with banks, if any, are likely to be small in magnitude and founded on relationship banking since relationship banking, which can also be measured by a denser branch network, can decrease the probability of credit rationing despite higher screening and monitoring costs (Presbiter and Rebelotti 2014). This is the reason why, in the presence of a larger number of opaquer borrowers, banks can grant more credit by employing relationship lending technologies. One study for India, for example, showed that greater banking system access, including branch density, was associated with significant increases in enterprises in the informal
or time. Crowdsourced data can help act as an additional input that can be used by policymakers in better understanding the situation. By using a clustering algorithm to identify and rank crash locations, we find that the top 15% of crash clusters (66 of 435) account for half of all crashes. Knowing that a small portion ( _<_ 1%) of the road network hosts 50% of RTCs in the crowdsourced data can help reduce an intractable problem to a more manageable one. This analysis shows the potential for using these data to complement road safety diagnostics and to guide investments and planning in road safety in Kenya and in other contexts, especially those with similar data deficiencies and with sufficient social media density like India and the Philippines [35].

The approach can be extended to other events reported on social media, whether related to disaster relief, crime, personal safety, urban mobility, or road maintenance. The work on disaster relief and response makes prominent use of geoparsing of tweets [36–43]. Geoparsing of tweets that lack geolocation information could enable more comprehensive crime analytics [44–46]. Improved algorithms can lead to faster and better geolocation of events, which would help urban planners and policy makers improve responses and better target interventions.

3
an increasing pattern of earnings up to the 1970-1974 cohort, and then earnings decline for the latter cohorts. The age coefficients imply a regular inverted U-shape for the age-earning profile. The negative coefficient of the interaction between the cohort and age variables is important. This implies that the earnings of younger cohorts start to decline at a younger age over time.

**Table 4: Cohort regressions. Log Hourly wage. Restricted to workers with college.**

||Males|Females|
|---|---|---|
|Cohort|0.1624|0.3269|
||[0.083]|[0.063]|
|Cohort<sup>2</sup>|-0.0224|-0.0331|
||[0.006]|[0.005]|
|Age|0.0625|0.0943|
||[0.016]|[0.012]|
|Age<sup>2</sup>|-0.0014|-0.0018|
||[0.000]|[0.000]|
|Cohort*Age|-0.0082|-0.0125|
||[0.002]|[0.002]|
|Constant|3.5632|2.8814|
||[0.264]|[0.199]|
|Observations|117|117|
|Adj. R-squared|0.931|0.955|

Notes: Authors’ calculations using quarterly Labor Force Surveys. Cohort starts in 0 and ends in 7, where 0 is the oldest cohort and 7 is the youngest cohort. Age starts at 0 (Age-23). The constant can be interpreted as the average log hourly wage for the oldest cohort with an average age of 23.

In order to disentangle the roles of labor demand across age groups, we analyze the pattern of employment by occupation level
annual ambient air quality standard for India set by the Air Prevention and Control of Pollution Act (1981). Concentrations are measured in _μg/m_<sup>3</sup> . All data comes from the MERRA-2 satellite system. In columns 1-3 the coefficient can be interpreted as the approximate percentage change in the outcome after MNREGA was statutorily implemented in a district. In column 4 the coefficient is the change in percentage points in the percent of months that exceed the Indian Ambient standard of 40 _μg/m_<sup>3</sup> . In columns 1-3 the base regression is a fixed effects Poisson of the form _log_ ( E [ _Eimy|_ **X** _imy_ ]<sup>)</sup> = _βPostimy_ + _γi_ + _δmy_ where _Eimy_ is the outcome in district _i_ in month _m_ in year _y_ . In column 4 I use a linear fixed effects specification of the form _Timy_ = _βPostimy_ + _γi_ + _δmy_ where _Timy_ is an indicator for whether district _i_ had PM2 _._ 5 levels that exceed ambient standards in month _m_ in year _y_ . Post _imy_ is a dummy variable equal to one after MNREGA treatment takes effect in district _i_ . _γi_ are district fixed effects while _δmy_ is a year by month fixed effect. In all columns I include controls for the monthly average cloud cover, precipitation and minimum and maximum temperature in district _i_ in month _t_ . N refers to the number of districts _×_ months included in each regression. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. The mean of the outcomes prior to MNREGA for each is presented. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

# **10 Figures**

42
1 Introduction In the past two or three decades most Latin American countries have redefined their development strategies, moving away from import-substitution regimes towards policies promoting integration with the global economy through exports and foreign direct investments (FDI). This important shift has been accompanied by an intense academic debate regarding the relationship between integration with the international markets and domestic growth. Despite the general presumption of a positive impact of trade liberalization on economic growth, there is still disagreement among economists about the nature of this relationship (Baldwin 2000). Most of the controversy is explained by the difficulty in identifying the underlying mechanisms driving this relationship (Winters 2004). Furthermore, since trade liberalization is often just one element of a more comprehensive set of market-oriented reforms it is hard to disentangle its effect from the impact of other policies.

This paper contributes to this debate by developing an innovative microeconometric approach that can disentangle the various channels through which integration with the global markets –via international trade– can affect firm-level productivity. Our empirical analysis is based on Mexican firm-level data covering 1993-2002,<sup>1</sup> a period of economic integration between Mexico, the US, and Canada within the North American Free Trade Agreement (NAFTA). The present study, defines NAFTA as a process of economic integration that goes beyond a simple tariff-reduction scheme and, instead, encompasses a set of institutional rules within which foreign trade and investment take place. The objective of this paper is to measure the impact of NAFTA on the productivity of Mexican plants.

The present study is related to various strands of literature. The pioneer set of studies collected in Roberts and Tybout (1996) analyzed the evolution of firm-level productivity dynamics in response to trade reforms and economic integralation for various developing countries. More recently, the interest has moved towards the identification of the different channels and mechanisms behind the impact of trade reforms on productivity (Aghion, Burgess, Redding, and Zilibotti 2004, Girma, Greenaway, and Kneller 2004, Pavcnik 2002, Tybout 2001, Amiti and Konings
projections model. Orange whiskers indicate 90 percent confidence interval. Methodological details can be found in annex E. Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2012, 2018, 2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016). Sample includes unbalanced panel of 32 advanced economies 97 EMDEs for 1981-2020.

29
) complemented the 2005/06 Uganda National Household Survey (UNHS) with a harvest diary. The UNHS is a standard multi-purpose household survey with a sample of some 7,500 households from 750 enumeration areas (EAs) which, in rural areas are mostly equivalent to villages. Information on agricultural production was collected in two visits, each covering one of the country‟s two main cropping seasons. The first and second visit collected data on agricultural production for July to December 2004 and January to July 2005, respectively.

We expect that having a diary would significantly reduce three sources of bias that might possibly have led to under-reporting of crop output. First, as it records output immediately upon harvest, _recall bias_ , whereby in interviews that occur long after the crop has been harvested respondents may under-report the actual amount of output obtained, should be minimized. Second, diaries are expected to reduce _respondent bias_ that might arise from the fact that recall surveys are implemented by a dedicated team that has limited time available for finalizing the interview and often interviews only one member of the household. Diaries, by contrast, can be regularly reviewed together with the local monitor and the danger that a household member other than the one responsible for production provides is asked to provide 5
a large share of unskilled youth. As a result, job creation and skills development are critical priorities. To address these challenges, the government launched the National Policy for Skill Development and Entrepreneurship in 2015, and its 12<sup>th</sup> Five-Year Plan set a goal of training 400 million workers by 2022.

India’s national labor market data are mainly collected through National Sample Surveys, some rounds of which have dedicated sets of employment questions. The Central Statistical Organization also conducts an Annual Survey of Industries and quarterly employment surveys designed to track industrial and workforce development trends in selected economic sectors. External surveys, such as the International Finance Corporation’s Enterprise Surveys, periodically analyze India’s business environment, and especially its formal sector (IFC 2014). Sample surveys of households and industries are typically conducted by the government and have traditionally served as the primary data source for understanding labor market trends and skills demand in India.

> 3 World Bank World Development Indicators website.

6
<!-- Start of picture text --> Pupil Classroom Ratio (PCR)<br>© Ratio MESIP over Non-MESIP districts<br>1.25 |<br>1.20 +] I<br>I °<br>1.15 >| ° ° I °<br>| °<br>° I<br>1.10 -| |<br>1.05 +<br>I<br>1.00 4<br>0.95 —<br>2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2010-20<br>Notes: Figures are WB calculations using EM|S databases for the school years 2010-11 to 2019-20<br><!-- End of picture text -->

<!-- Start of picture text --> Pupil Teacher Ratio (PTR)<br>© Ratio MESIP over Non-MESIP districts<br>1.25 5 |<br>1.20 + I<br>1.15 ~ I<br>e 8 |<br>ee |<br>° I<br>1.10 +} |<br>I<br>1.05 ~<br>I<br>1.00 +<br>0.95 —<br>2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2010-20<br>Notes: Figures are WB calculations using EMIS databases for the school years 2010-11 to 2019-20<br><!-- End of picture text -->

<!-- Start of picture text --> Pupil Classroom Ratio (PCR) in Lower Primary<br>© Ratio MESIP over Non-MESIP districts<br>1.25 |<br>I<br>1.20 +] °<br>I<br>°<br>I<br>I<br>115-0 I<br>I Ps<br>|<br>I
# **1 Introduction**

The increase in gasoline prices stemming from the oil crisis overshadowed the United States presidential debate of October 1980. Ronald Reagan and then-President Jimmy Carter were going head to head in the election. Carter’s loss that year coincided with a peak in oil prices. Other modern US presidential incumbents – for example, Presidents Ford and George H.W. Bush - also lost their reelection bids following oil price spikes. This anecdotal evidence points to a broader question about the role of exogenous shocks in determining electoral outcomes.

Our paper is the first effort to systematically examine the effect of oil price shocks on the reelection probability of political incumbents in oil-importing countries. We use a novel dataset of 207 national elections in 50 mostly oil-importing democracies, which crucially also includes polling data in the run-up to elections. The polling data allow us to explore the effect of exogenous shocks not only on the election outcome, but also on voting intentions in the months leading up to the vote. Depending on the political system, the dataset includes elections of the chief executive in parliamentary or presidential systems. As large oil imports leave a country vulnerable to changes in _international_ crude oil prices, we rely on these prices as an exogenous source of _country-specific_ variation in terms of trade. Specifically, we create a measure of ’oil price shock’ by interacting the fluctuations in international oil prices (over which individual countries do not have explicit control) with the country-specific intensity of oil imports relative to GDP.

An oil price shock one year prior to an election is found to significantly reduce the likelihood of reelection for the incumbent party. Specifically, a 1% increase in our oil shock measure decreases the likelihood of incumbent re-election by nearly six percentage points. Furthermore, novel polling data confirm that these shocks operate by reducing the popularity of the sitting government. We show that both right-wing and left-wing incumbent parties are susceptible to crude oil price increases. We verify that the winning parties are more likely to belong to the opposite end of
# Economic Consequences of Trade and Global Value Chain Integration: A Measurement Perspective

Alessandro Borin<sup>1</sup> , Michele Mancini<sup>2</sup> , and Daria Taglioni<sup>3</sup>

> 1Bank of Italy

> 2Bank of Italy

> 3World Bank

Originally published in the Policy Research Working Paper Series on _September 2021._ This version is updated on _April 2025._ To obtain the originally published version, please email prwp@worldbank.org.

**_Keywords:_** _Global Value Chains (GVCs); GVC Measures using Inter-Country Input-Output Tables; GVC Exposure; GVC Risk and Resilience Assessments._

**_JEL Codes:_** _E16, F1, F14, F15_

**_Online Appendix:_** _The Online Appendix is available here_
based on data from household or establishment surveys in a single country. “Macro” analysis is concerned with many of the same variables but at level of national economies, using cross-country comparisons based on aggregate data.

The research on the impacts of unions and collective bargaining has been carried out against the backdrop of significant changes in these institutions in recent decades. In many developed countries, union membership has been declining and bargaining has become more decentralized (OECD 2006). The situation in developing countries is not as clear but the limited data available suggest similar trends.<sup>42</sup> Finally, any discussion of unions and collective bargaining in the context of developing countries must recognize the reality that these institutions in their traditional form are not well suited to much of the employment structure. This is especially the case in low-income countries where most people—such as farmers, the self-employed, and family workers—are not engaged in an employer-employee relationship. This is creating impetus for innovative institutions such as associations of self-employed workers. While these organizations may have some important impacts on the variables of interest in this review, they are not included.

> 42 Evidence on some developing countries can be found at the Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts (ICTWSS) database, 1960–2010, Amsterdam Institute for Advanced Labour Studies.  The database is available at http://www.uva-aias.net/208.
Employment statistics show that in Rwanda, like in many countries in the region, youth face a challenging school-to-work transition: 28% of youth are unemployed, with women facing unemployment rates that are 7 percentage points higher than those of men (Rwandan National Institute of Statistics, 2022). Youth who are able to secure an income generating activity overwhelmingly do so in the informal sector, which accounts for 94% of the income generating activities. The data present a mixed picture of the labor market for tertiary-educated Rwandans, who face a higher unemployment rate of almost 34% but are also far more likely to eventually find formal-sector jobs: more than half of working, tertiary-educated Rwandan youth hold formal jobs.<sup>5</sup> In this context, it is reasonable to expect that a soft skills training, if effective, could improve not only informal income generating activities, but also access to formal jobs. Moreover, improved soft skills may also help participants adapt to changes in work requirements and retain their jobs (Deming, 2017; Heinrich and Housemann, 2021).

The Rwanda Development Board advertised the program on several online job boards and invited youth who graduated from university or technical/vocational education programs in the previous two years to apply. Around 2,400 participants applied to the program, and close to 1,800 completed an online baseline survey, many more than the 450 available vacancies. Of these, 500 women and 500 men were randomly selected and invited to attend information sessions where public lotteries to assign the vacancies of the training were conducted. The lotteries selected 450 participants who were assigned to the training (225 women and 225 men) and 450 participants who were assigned to the control group. Approximately 74% of participants from all around Rwanda attended the bootcamp, even though for logistical reasons it was held in the South of the country following strict COVID-19 guidelines.

Our results indicate that the program facilitated an accelerated entry into the labor market in a period characterized by COVID-19-related disruptions. Using data from
households that decide to stay in conflict zones is scarce in Colombia. This paper contributes to better understanding how households cope with conflict shocks, and how labor markets become important mechanisms for preventing further losses stemming from violent conflict.

The paper also contributes to the economic literature on the use of labor markets to hedge against the consequences of economic shocks. Empirical evidence on this issue is scarce and what data are available are restricted to small samples rich in information about the use of time, or large samples with limited labor information (Kochar 1999; Rose 2001; Cameron and Worswick 2003; Ito and Kurosaki 2009). The survey used in this paper collects detailed information about households’ use of time and participation 3
and the rich implies evidently that there is no sizeable middle class.<sup>5</sup> As we will see later when applying relative distribution methods, the distributional changes that occurred between 1991/92 and 2012/13 hollowed out the middle of the Ghanaian household consumption distribution and increased the concentration of households around the highest and lowest deciles, hence leading to an increase of polarization.

# **2.2 Relative distribution methods**

## _2.2.1 Basic concepts_

> 4 The gap between 90-th and 10-th deciles is probably a lower bound of the real one. In general, household surveys do not contain good estimates of upper percentiles of welfare (Alvaredo and Piketty, 2010). When using consumption to rank welfare, as it is normally done in low/er middle income countries, the situation is further aggravated. Consumption is very accurate in capturing the well-being of poorer people, yet it is rather imprecise in capturing that of people living in upper percentiles.

> 5 In this paper we will analyze the median-based approach to the measurement of polarization. Since it subdivides the population into two subgroups – those above the median and those below the median, respectively – we refer to this as the case of “bi-polarization”. For a detailed explanation of the main differences not only between the study of inequality and that of polarization, but also between the concept of bi-polarization and that of “multi-polar” polarization, see e.g. Chakravarty (2009, ch. 4), Deutsch et al. (2013) and Chakravarty (2015).

5
# _v. References_

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Del Granado, J., D. Coady and R. Gillingham (2010), The unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries. IMF Working Paper, WP/