rafmacalaba/data-use-mentions-extended huggingface dataset:
the data for transition economies that tried to do just this, Commander and Svejnar (2009), found largely null results.

The three problems with attempting to uncover the relevance of elements of the business environment by estimating directly a production function augmented by business environment indicators can be avoided by taking a different approach. Following Carlin et al. (2006, 2010), we take as our starting point that the business environment is external to the firm and that to an important extent, firms in a country share the same environment. This suggests that firm level information be used in a different way from the augmented production function method. Specifically, we formulate predictions as to how the firm’s response to its business environment in terms of its expenditure on abatement and its evaluation of the costs imposed on it by deficiencies in the business environment varies with its characteristics, including its performance. In short, a firm-level assessment of the business environment is an endogenous variable in the modeling framework and as the dependent variable in estimations. The framework can be also used to address the relative importance of different aspects of the business environment in different countries.

# _Modeling framework: Summary_

The model is set out in Appendix 1 and we summarize it here. We use a simple single-period firm production function with 4 inputs, _L_ , _E_ , _B_ and _G_ , which are combined to product output _Y_ . _L_ is employment; _E_ is an intermediate input that is a flow of services which results from combining a public input _B_ with _G_ , an input the firm purchases in order to mitigate the effects of the unreliability of the public input. Firms also differ in productivity, which we capture with a productivity parameter _A_ . We index countries by _j_ and firms by _i_ . In our basic model, the public input _B_ that is supplied on identical terms to all firms in a country, so we write it as _B j_ . This captures the notion of a shared “business environment” – _B j_ varies across countries but not across firms
(World Bank, 2019). They are also often highlighted to counter the adverse labor market effects of increased technology adoption in developing countries. The mediating role of worker skills becomes even more urgent amid rapid advances in artificial intelligence, such as recent developments of Chat-GPT and GPT-4. While they are most likely required to achieve these goals, they are unlikely to be sufficient, given that differences in job task content are largely related to differences in technology use and participation in GVCs. In any case, policies to increase technology use and approaches to facilitate upgrading in GVCs should complement investments in skills, especially since technological change within GVCs tends to increase the relative demand for non-routine work (Reijnders & de Vries, 2018).

Our study has limitations. First, it does not claim to have determined a causal effect. Since the survey data were collected once per country, only cross-sectional analysis is possible. The analysis therefore cannot capture wage changes over time or cases where GVC participation created new labor market segments that did not exist before. In the future, the second round of PIAAC data collection will allow running a quasi-panel study to study the relationship between changes in GVC participation, technology use, and the supply of skills, with the RTI of particular occupations in various countries. Second, the survey data do not distinguish between domestic and foreign-owned firms, so it is unclear if FDI correlates with RTI differences within sectors. Lewandowski et al. (2022) showed that FDI is not a significant factor behind RTI differences between sectors, but there may be a relationship within sectors. Third, adult skill surveys have greatly improved our understanding of skills supply and the quality of education worldwide. It is possible, though, that literacy or numeracy measures are insufficient to fully understand factors behind differences in the nature of work, task content of jobs, and productivity. Differences in managerial and interpersonal skills may also contribute to differences in organizing and performing work. These skills are unfortunately not measured in the same survey data that capture worker tasks
may affect electoral outcomes seems reasonable. Since there may well be serial correlation and an effect of expenditures on elections, studies which disregard the possibility of simultaneity must be treated with caution (see, for example, Bruhn, Graham and Kane).

The standard solution to this simultaneity problem is instrumental variables. But the search for appropriate instruments is not simple: variables which affect elections tend to affect expenditures as well. The Peruvian case offers a partial solution to this problem, however, because we may legitimately be able to treat the 1990 election as exogenous to the model. FONCODES did not fund any projects until more than a year after the 1990 election. The election cannot therefore have been affected by FONCODES expenditures, equation (7) does not apply, and simultaneity should not be a source of bias in the parameters estimated in (4) for the December 1991-October 1993 period. The break in FONCODES expenditures before December 1991 may therefore work as a "natural experiment" which allows us to accurately identify the effect of an election on expenditures (Deaton, pp. 112-115). More generally, this result highlights the value of research which focuses on _new_ social programs to estimate the impact of elections on public expenditure patterns.<sup>17</sup> Estimates of the effect of elections on expenditures after October 1993 are more open to question because the 1993 referendum may itself have been determined in part by FONCODES expenditures in the preceding period.

Lack of comprehensive data and censoring are both potential sources of bias. Information on the results of the run-off 1990 presidential election is not available for 19 provinces. Also, seven new provinces were created between 1990 and 1995. As a result, there is data on FONCODES expenditures for 231 provinces, but data on elections for only 205 provinces.

There is nothing distinctive about the provinces for which electoral data is missing--they are not, for example, particularly poor or rich, or concentrated in one part of the country. As a safeguard, however, I
4 ture is supported by several studies in the United States and Europe which have identified an upward trend in risky sexual behaviors since the introduction of ART in 1996 (e.g. Gremy and Beltzer, 2004; Lakdawalla et al., 2006). More specifically, an association has been identified between decreased concern about HIV due to ART availability and unprotected sex, and in particular among men who have sex with men (e.g. Dukers et al., 2001; Kalichman, 1998; Lakdawalla et al., 2006; Mechoulan, 2007).

Investigations of disinhibition behaviors associated with increased access to ART in SubSaharan Africa are, however, limited. One of the earliest studies looked at change in the use of condoms by sex workers in Nairobi, Kenya (Jha et al., 2001). The findings are summarized in figure 1. There is at least some suggestive evidence that condom use by sex workers decreased when “fake” cures for AIDS were announced. Such a pattern is consistent with disinhibition behaviors, although the result may not be generalizable to the general population since it uses a very selected segment of the population. Cohen et al. (2009) is one of the few studies that use population based surveys to test risk compensation behavior in a Sub-Saharan African context. The authors found that in Kisimu (Kenya), ART-related risk compensation<sup>5</sup> , and the belief that ART cures HIV, were associated with an increased HIV seroprevalence in men but not women.

We differ from Cohen et al. (2009) in two ways. First, we focus on sexual behavior instead of HIV seroprevalence. Changes in sexual behaviors resulting from the introduction of ART can be viewed as a first order effect, while changes in sero-prevalence (as long as these changes are taking place through changes in sexual behaviors as theorized by Cohen et al. (2009)) are second order effects. Second, the panel structure of our data allows us to control for unobserved
It is useful to understand what labor markets and in particular employment trends looked like in the set of countries that we study pre-pandemic. Columns 12 –14 of Table 1 show the International Labour Organization’s pre-pandemic 1st quarter of 2020 and 2nd quarter 2020 data for the countries that have both quarters available and the change between the two quarters. For the subset of countries of the HFPS that have ILO employment data available for the two quarters, the range shows very small increases in employment (0.73) to large declines (-23.98).

# **3 Data and Methods**

## **3.1 High-Frequency Phone Survey Data**

We use harmonized data from the World Bank’s High Frequency Phone Surveys (HFPS). The surveys have been harmonized both ex ante and ex post in the data production stage, but differences across countries remain in terms of questionnaire and sampling design. The data used from the surveys cover 12 countries in the Sub-Saharan Africa (SSA) region, 12 countries in the Latin America & the Caribbean (LAC) region, 7 countries in the East Asia & Pacific (EAP) region, 6 countries in the Europe & Central Asia (ECA) region, and 2 countries in the Middle East & North Africa (MENA) region. Currently no countries in the South Asia (SA) region are included. Eight countries are low income, 17 are lower middle income, 10 are upper middle income and 4 are high income. Going forward, more countries will be added to the HFPS data, including several in South Asia. We analyze the first wave of data for each country, for waves collected between April and July 2020. Table 3 shows information on the set of countries included, the month data were collected, the number of survey respondents, the survey sampling, and the availability of some key variables. The final sample size for countries ranges from 692 (Paraguay) to 5,346 (Vietnam). The mean number
|0.25|0.16|***|0.30|0.18<br>***|0.34|0.27|**|

Source: LITS survey and authors‟ calculation. Note: The table reports means for each household characteristic by credit status. The credit status variables are as defined in Table 3.3.1. The household characteristics are defined as follows: Urban is 1 if the household is located in an urban area and 0 if it is located in a rural area. Income is the OECD equivalized expenses in USD per year. Self employed is 1 if the household derives its income predominantly from self-employment or farming and 0 otherwise. University degree is one of the respondent has a university degree and 0 otherwise. All means are adjusted for sample weighting in the LITS 2010 survey. The sample tests reports the results of linear independent sample tests which examine whether household characteristics differ for households with and without a specific credit status. ***, **, * denote significance at the 0.01, 0.05 and 0.10-level.

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.012)|(0.012)|(0.010)|(0.009)|
|Observations|61,811|134,751|168,739|61,811|134,751|168,739|
|Number of|149|233|263|149|233|263|
|districts|||||||
|Mean|0.552|0.548|0.539|0.552|0.548|0.539|

Note: Sample includes all mothers with and without any preschool-aged eligible child (age 3-6). Comparison group includes non-mothers and mothers of children with the wrong ages. Sample is restricted to individuals living in districts with population as indicated in the column headings. Population count was based in 1996 because it was the earliest Susenas data covering all 290 districts. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. DDD excludes while DDD-FE includes individual fixed effects. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Number of observations, number of districts, and work participation means for each column are indicated at the last three rows.

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(Bangladesh, Egypt, Mali, and Nigeria). Notably, in the models looking at tertiles of current conflict, the relationship between conflict and child marriage is consistent within each country. That is, no country has one tertile of conflict that significantly increased child marriage while another tertile significantly decreased it.

In order to explore whether _past_ conflict might affect child marriage practices going forward, this analysis also looked at a four-year lag of conflict in each country prior to the DHS (importantly, all models had year fixed effects to account for any time trends). Lags are important because conflict may have both immediate and longer-lasting effects on economic, social, and demographic drivers of child marriage and these drivers may also have longer-term, as well as immediate impacts. Among the four countries that saw decreases in child marriage associated with current conflict, one had a persistent decrease in child marriage both contemporaneously and also in later lags (Bangladesh). In two countries – Egypt and Mali – there are complex effects, which include both increases and decreases. In Nigeria, the contemporaneous decrease in child marriage disappears, but a decrease is seen in lag 4.

Among the eight countries that showed no association between contemporaneous conflict and child marriage, half (Burundi, Côte d’Ivoire, Peru, and the Philippines) showed a mix of significant contemporaneous and lagged effects (both increases and decreases). In Pakistan the only significant results were decreases. In Indonesia, Kenya, and Myanmar, there remained no significant effect. Among those countries that had a significant increase in child marriage with contemporaneous conflict, one showed a significant association between both lagged conflict and increased child marriage (Chad). In other cases, the association between current conflict and child marriage shifted to the lag, as with India and Ethiopia. And in DRC, Guinea, Colombia, and Cameroon, a more complex pattern emerged with significant increases contemporaneously and both increases and decreases emerging during the four lagged years.

In additional models, we used years of recent conflict
of political budget cycles, which relied on highly aggregated data on total expenditures and revenues, current and capital expenditures, or public deficits (Castro and Martins 2018; Akhmedov <u>and Zhuravskaya 2004; de Haan and Klomp 2013). These new data distinguish expenditures on</u> social assistance that involve direct transfers to citizens (such as cash and in-kind transfers) from expenditures on programs focused on job retention that are usually channeled through firms (such as wage subsidies or waivers or reductions in social insurance contributions).

The results of our empirical analysis demonstrate that the electoral cycle affects the composition of the social protection response. The longer the time since the last election in a country—and thus the sooner the next election date—the larger the share of the social protection pandemic budget allocated to social assistance and income protection and the lower the share allocated to job retention schemes. In contrast, the electoral cycle has no impact on the overall size of the budget allocated to social assistance, except in countries with high political competition. In these countries, the electoral cycle appears to affect the size of the social assistance budget by crowding out the resources allocated to job protection programs. The maturity of democracy, the intensity of political competition, and the rule of law in a country all seem to affect how the electoral cycle influences the composition of the social protection stimulus budget.

We identify a potential source of bias in our estimates – the postponement of elections due to COVID-19 restrictions. We address this by re-estimating our model, excluding countries that postponed elections. We find that this potential bias does not affect our main empirical results.

This paper contributes to the literature on the effects of electoral cycles on public expenditure in several ways. To our knowledge, it is the first study to analyze the effect of political budget cycles on the size and composition of social protection expenditures in the COVID-19 stimulus. Our analysis shows that the predictions of the political budget cycles theory apply even during systemic shocks like a pandemic. Also
029**|
|Constant|*|*|*|*|*|*|*|*|*|
||(0.040)|(0.047)|(0.038)|(0.080)|(0.042)|(0.039)|(0.048)|(0.058)|(0.070)|
|Observations|2,118|2,108|2,118|2,118|2,118|2,118|2,118|2,108|2,108|
|R-squared|0.003|0.281|0.102|0.009|0.036|0.215|0.084|0.096|0.291|

Note: Dependent variable is the log of per capita total expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes head of household and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

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# **6. Conclusion**

This paper uses data on monthly greenfield investment announcements from 2015 to 2023 to study how US-China trade tensions, the Covid-19 pandemic, and the Russian invasion of Ukraine are reshaping US investment. The paper shows that US multinationals reacted to recent shocks by mainly reshoring their activities. When comparing US multinationals to their EU counterparts, we find that investment patterns differed following the recent shocks. The evidence indicates that the reshoring trend by US multinationals started with the Covid-19 shock and accelerated with the Russia’s invasion of Ukraine. An increase in greenfield investment in neighboring countries has accompanied this reshoring trend. Furthermore, the results suggest that reshoring and nearshoring of investment projects is also associated with the reshoring and nearshoring of jobs.

These findings are particularly important for developing countries as a retreat from globalization by US multinational corporations could negatively affect their growth prospects. FDI has played an important in providing capital to, and fostering productivity growth, in developing countries (e.g., Alfaro and Chauvin, 2020; Smarzynska Javorcik, 2004). The linkages between multinational enterprises and domestic firms, especially in global value chains, have been associated with knowledge and technology spillovers (e.g., Dasgupta, 2012; Sinani and Meyer, 2004), which play a fundamental role in income convergence across countries.

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one standard error of the true poverty rate. The improvement after standardizing variables is consistent with earlier standardization evidence using household surveys and labor force surveys for Jordan (Dang _et al._ , 2017). Figure 2 provides a visualization of the key results from Tables 2– 4.

# _Within-year imputation with lighter questionnaires_

Tables 5 and 6 utilize the experimental TA 1 as the base round for imputing poverty rate in TA 2 and TA 3, respectively. Both tables yield the analogous results that Models 1–2 and 8–9 produce poverty estimates that are within one standard error of the expected value. By contrast, Models 3.1 19
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# **Appendix I: Overview of the Data Sources Underlying the Global Financial Development Database**

This is just a summary; for more on the Global Financial Development Database, including the individual country data and meta-data, see http://www.worldbank.org/financialdevelopment and http://data.worldbank.org/datacatalog/global-financial-development.

**<u>A Database on Financial Development and Structure</u>** (updated November 2010). This database was used a starting point for many of the basic indicators of size, activity, and efficiency of financial intermediaries and markets. Beck, Demirgüç-Kunt, and Levine (2010) describe the sources and construction of, and the intuition behind, different indicators and present descriptive statistics.

**<u>Bankscope</u>** by Bureau van Dijk was used to obtain and update data on banks. Bankscope combines widely-sourced data with flexible software for searching and analyzing banks. Bankscope contains comprehensive information on banks across the globe. It can be used to research individual banks and find banks with specific profiles and analyze them. Bankscope has up to 16 years of detailed accounts for each bank.

**<u>Bloomberg</u>** <u>,</u> **<u>Dealogic</u>** <u>, and</u> **<u>Thomson Reuters Datastream</u>** were used to obtain higher frequency data on stock exchange and bond markets that were aggregate on country level. **_<u>Doing Business</u>_** **<u>database</u>** . This database, a part of the _Doing Business_ project, offers an expansive array of economic data in 183 countries, covering the period from 2003 to the present. The data cover various aspects of business regulations, including those relevant to financial sector development issues, such as contract enforcement
firms with higher shares of workers at home ( ) were better able to mitigate firms’ sales shocks during COVID-19 or the resulting magnitude of job losses.<sup>30</sup> EEIIssh On the supply side of the labor market, the ability to work from home ( ), has been posited as an important determinant of jobs at risk, at least during mandatory firm closures (e.g., Hatayama et al., 2020). WWFFWW To test whether firms downsized less when their sector occupational structures were more conducive to working from home, we estimated the determinants of firms’ share of workers working from home, . In particular, we examined the role of physical demands, face-to-face interaction, and level of ICT use in a job, as EEIIssh well as the share of workers with internet at home according to the latest labor force data, averaging across occupations by sector.<sup>31</sup> Our results show that the ICT task index was statistically significantly related to firms’ for both countries. However, the physical demands and in-person face to face indices of sector occupations were not significantly related to in either one. Measured internet use at home was EEIIssh insignificant for both as well, possibly in part because data on internet use was not sufficiently current. The EEIIssh index (proposed by Hatayama et al., 2020) was statistically significantly related to as well, due to the component ICT use. The physical demands and face to face components of the index have (smaller, WWFFWW EEIIssh 30 In a small fraction of the specifications tried, the share of workers using ICT was negatively related to job loss, but this result was very sensitive to the inclusion of insignificant regressors.

31 Household internet usage was likely very high for PFPS workers by the time COVID hit. For the populations as a whole, 67 percent had internet access in Jordan in 2017 and 73 percent for Georgia in 2020.

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