rafmacalaba/data-use-mentions-extended huggingface dataset:
# **II. Research Strategy, Data, and Preliminary Comparisons**

We aim to understand how differences in business environments contribute to the gap in economic performance between China and India using firm-level data. Given the importance of productivity in income determination, we use total factor productivity (TFP) to measure firm performance. Indeed, TFP has been shown to be a major source of output growth (Solow 1957; Jorgenson and Griliches 1967). Moreover, TFP levels are highly correlated with income per capita (Islam 1995; Hall and Jones 1999). In a survey of economic growth, Helpman (2004, p. 33) states, ―There is convincing evidence that total factor productivity plays a major role in accounting for the observed cross-country variations in income per worker and patterns of economic growth. We therefore need to understand what drives the differences in total factor productivity.‖ For this paper, we focus on understanding the TFP differences at the firm level between the two countries. For robustness checks, we also used labor productivity, measured by value added per employee in logarithmic scale, as an alternative dependent variable and we found similar results. In our data, TFP measures are closely correlated with labor productivity, with a statistically significant correlation coefficient of 0.61.

We proceed in three steps. First, we estimate total factor productivity for each firm in our sample. We then use available data to construct proxies for known business environmental factors that affect firms. At the last step, we estimate the effects of the proxies of the business environment on firm productivity using regression analysis. Our presumption is that even firms in the same country face different business environments at the local level. A caveat is that our results are only as representative as our sample. For example, our data do not reveal policy impacts on firm entry and exits. Also, because our data cover only manufacturing firms, our results do not apply to nonmanufacturing sectors. Finally, our sample does not contain what some view as the most
Table 10: Impacts of Heat Exposure on Stream Choice

||Fraction of Students in Science|
|---|---|
|Lag 2: T_>_33C|_−_0_._0001<br>(0_._0001)|
|Lag 2: T 30-33C|_−_0_._0004<sup>_∗∗∗_</sup><br>(0_._0001)|
|Lag 2: T 27-30C|_−_0_._0001<br>(0_._0001)|
|Lag 2: T 24-27C|0_._00002<br>(0_._0001)|
|Lag 2: T 21-24C|_−_0_._0003<br>(0_._0002)|
|Outcome Mean:|0.63|
|Female Share Control<br>**Fixed Effects**|X|
|Year|X|
|Region|X|
|Observations|9,616|
|R<sup>2</sup>|0.322|

Notes. Estimates are from linear regressions using panel fixed effects run at the school level. The dependent variable is the fraction of students in each school who enroll in the science stream. All regressors include the number of days in the temperature bin during the time when these students were in their 10th grade. All regressions control for the number of days in the precipitation bins during the time that the current cohort of students were in their 10th grade. Errors are clustered at the region level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

29
the perpetration of physical IPV in the last year (the most common form of violence), a 3 percentage point (16.7 percent) increase for emotional violence and a 3.4 percentage point (42.5 percent) increase in sexual violence. Sole decision making by the wife, compared to joint decision making, is associated with a 10 percentage (62.5 percent) point increase in physical IPV, a 12 percentage point (66.7 percent) increase in emotional IPV and a 6.7 percentage point (83.7 percent) increase in sexual IPV. Using the same DHS data, we construct indicators representing the four blocks of alternate theories that may constitute important mediators for the relationship: attitudes towards violence, alignment of preferences, marital capital and bargaining. None of these covariates dampens the magnitude of the observed relationship between joint decision making and IPV, and in some cases they even strengthen the association.

Next, we re-run the analysis considering both wives’ and husbands’ views of who makes decisions. We find that the husband making decisions is associated with increased IPV compared to joint decision — making even when both spouses agree that the husband makes the decisions (i.e., in the absence of any contestation of power by the woman). This empirical finding contrasts with IPV theories emphasizing a divergence of preferences or contestation of power by the woman as a source of violence perpetration by the husband. Overall, we detect three broad categories of decision making patterns and their relationship with IPV. The first category, among which the highest levels of physical, emotional and sexual IPV are observed, are couples where the wife reports that she makes decisions (regardless of the husband’s view). The second category, with lower IPV compared to the first, are couples that disagree over decision making roles (but where the wife does not report making sole decisions) and couples that agree that the husband makes decisions. The third category, when spouses both report making joint decisions
: Evidence from the Exporter Dynamics Database” _Journal of Development Economics_ 119 (C): 121–37.

- Hauser, John, Gerard J. Tellis, and Abbie Griffin. 2006. “Research on Innovation: A Review and Agenda for Marketing Science.” _Marketing Science_ 25 (6): 551–765.

- Hoff, Karla. 2000. “Beyond Rosenstein-Rodan: the modern theory of underdevelopment traps.” Washington, DC: The World Bank.

- Kabuth, Oliver. 2003. “Pioneering Versus Following in Emerging Markets: The Case of the Automotive Industry in China and Brazil.” Doctoral Dissertation, University of St. Gallen, Switzerland.

Kay, John and Mervyn King. Forthcoming. _Radical Uncertainty._ Kenny, Charles. 2018. “Aid Transparency and Private Sector Subsidies at the IFC.” Center for Global Development (blog). January 17.

<u>https://www.cgdev.org/blog/aid-transparency-and-private-sector-subsidies-ifc.</u> 26
variables as Dingel & Neiman (2020a, 2020b), but instead of defining binary indicators, they allow both the WFH and physical proximity measures to vary between 0 and 1. Leibovici, Santacreu, & Famiglietti (2020) construct a contact-intensity measure for the United States using occupation-level information from O*NET and aggregating the possible scores for the question about performing tasks in close physical proximity to other people. The final measure can take a value from 0 to 100.

To measure jobs’ amenability to WFH, we exploit all the variables available in the data that describe job tasks related to home-based work. Instead of using a criterion based on satisfying at least one sufficient condition to classify occupations, we argue that the more (less) the conditions that are satisfied, the lower (higher) the amenability of a given job to be carried out at home. For example, according to our criteria, a job that satisfies three conditions would be less amenable to home-based-work than one that satisfies only one or two of those conditions. Accordingly, we also exploit categorical variables describing the intensity of different tasks, instead of transforming them into binary outcomes. While this approach still relies on the assumption that all

> 2 As a robustness check, we construct an index following a methodology more similar to that of Dingel and Neiman (2020a, 2020b) and Saltiel (2020) where only one condition needs to be satisfied in order for a job not to be amenable to WFH and find that it is highly correlated with our WFH measures (see Figure A 3).

3
_- 57_ Population figures are drawn from the World Bank's Social Indicators of Development 1996 and refer to 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1990.

General Government employment is from IMF Report No. SMI951277 and relates to 1995.

# **Korea**

Unemployment figures come from the CIA Factbook 1995 and relate to November 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Health sector employment comes from WHO, 1993 and refers to 1989. It is broken down as follows: 35,462 doctors, 29,368 dentists, and 28,103 nurses and forms part of Central Government. Central Government, Local Government and Education figures have been provided by the Korean Information Center of the Embassy of Korea. Data reflect situation as of 12-31-95. Central Government employment includes 278,837 central government employees, 3,040 legislative branch, 10,475 judicial branch and 2,113 others. Education employment in our data corresponds to the category: Public School Teacher (279,652). They are paid by the central government. Military employment data include conscripts, but not paramilitary forces, e.g., Civilian Defense Corps (3,500,000), or the Coast Guard (4,500). Consolidated Central Government wages and salaries and GDP estimate are for 1994, and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) are taken from the Economic Statistical Yearbook 1989 and refer to 1989.

# **Lao PDR**

GDP estimate is for 1993 and is from World Tables 1995. Wage bill and average wage estimate is taken from IMF Report No. SMI/9516 of January 12,1995 and relates to 1993. Manufacturing wage estimate is from IMF Report No. SMI96194 and relates to 1993.

# **Malaysia**

Population and
made of artificial or synthetic materials. In non-Russian markets, it is considered that such lining can provide extra quality, for example insuring the breathability of the shoe. It is unclear what scientific information would be used to justify the requirement of non-artificial linings.

# **3.3 Sanitary and Phytosanitary Standards**

Harmonization of Russia’s SPS standards with the WTO’s is in process. As with TBT, SPS measures are now meant to be harmonized at the ECU level, a process that began in 2010, when the ECU was formed: The ECU agreements provide for SPS harmonization with Russia’s WTO obligations. Accordingly, large trading partners such as those in the EU now inform potential exporters of ECU measures while negotiating memoranda of understanding with Russia.<sup>34</sup> The United States reports engagement with both Russian and ECU authorities on SPS issues (U.S. Trade Rep. 2014, p. 75).

Differences in national standards account for some of Russia’s trade frictions in agriculture. While the SPS agreement imposes basic requirements on admissible measures, differences in how WTO members

> 31 Amendment No. 286-FZ (December 2012); Resolution #474 “On Submission of Notifications about the Beginning of Turnover of Alcoholic Products on the Territory of the Russian Federation” (entry into force October 1, 2013); revisions to the “Technical Regulation on Alcohol Product Safety,” and Regulation Order #59 on alcoholic beverages.

> 32 USTR 2014 TBT Report, op.cit., p. 95 **_._**

> 33 European Commission Trade Market Access Database, http://madb.europa.eu/madb/barriers_details.htm?barrier_id=095224&version=5, downloaded June 27, 2014.

> 34 <u>http://ec.europa.eu/food/international/trade/sps_requirements_en.htm.</u>

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 17
Statistical Database and the EU-KLEMS dataset<sup>17</sup> . Our index of value added is constructed from the OECD dataset as the weighted growth of household ...nal consumption, gross capital formation and government consumption (where appropriate) at constant national prices, using as weights their respective shares of value added. According to our theory, these shares should be kept constant at their steady state level, but in practice we use shares averaged across the twenty years in our sample.

In constructing the growth rate of the "modi...ed" productivity residual, we subtract from the log-changes in value added, the log changes in the capital and labor stocks used to produce it, weighted by their average respective shares out of total compensation. Data on aggregate production inputs are provided by EU-KLEMS. Log-changes in capital are constructed using the estimated capital stock constructed by applying the perpetual inventory method on investment data. In our benchmark speci...cation, log-changes in the labor stock are approximated by log-changes in the amount of hours worked by persons engaged. Alternatively, we use a labor service index which is computed as a translog function of types of workers engaged (classi...ed by skill, gender, age and sex), where weights are given by the average share of each type of worker in the value of total labor compensation. We assume that economic pro...ts are zero in the steady state so that we can recover the gross (tax unadjusted) share of capital as one minus the labor share.

In order to compare welfare across countries, we combine data coming from the Penn World Tables and the EU-KLEMS dataset. More speci...cally, our basic measure of value added is constructed from the Penn World Tables as the weighted average of PPP converted log-private consumption, log-gross investment and log-government consumption, using as weights their respective shares of value added in the reference country; as in the within case, we use shares that are averaged across the
of our analysis, we report a summary of treatment effects in Appendix Table D.12.

# **3.3 Main Outcomes of Interest**

The focus of our study is the impact of payroll accounts on account use, consumer learning, and consumer protection. To measure how actively consumers engage with the account, we use both the self-reported survey data and the administrative transaction-level data. Intuitively, we would expect to see more active engagement with accounts under the payroll accounts treatment, as workers in this treatment condition receive direct deposit wage payments into their account by default and thus face much stronger incentives to actively engage with the account than workers in the bank or mobile account condition, for whom use of the account is optional.

We use two key outcomes to track how consumers learn to navigate the financial technology. Our first outcome, _outside transactions_ , is an indicator for whether a transaction was performed outside the workplace. As Figure 2 illustrates, most employees in our sample report being either “uncomfortable” or “very uncomfortable” using a bank or mobile money agent without assistance.

13
comparability purposes with other countries the analysis uses the total income definition, instead of the monetary income definition, to account for the imputed rent. In pursuance of a better understanding of the incidence of fiscal policy in Chile, the following income groups are defined: _poor_ , as those individuals with per capita income below the $4 a day poverty line —distinguishing within this group the “ultra-poor” (living with less than $1.25/day), the “extreme poor” (living on $1.252.5/day), and the “moderate poor” (living on $2.5-4/day)—; _vulnerable_ , as those with per capita income between $4 and $10 a day; _middle class_ , as those living on $10-50/day; and _wealthy_ , as those with per capita income above $50/day.<sup>18</sup> The analysis also considers the incidence on the extreme and moderate poor as defined using the official poverty lines in Chile, as well as on income deciles.

# **4. MAIN RESULTS**

# **4.1 Redistributive effects of Chile’s fiscal system**

Are fiscal interventions in Chile equalizing? Figure 2 shows that income inequality in Chile, as measured by the Gini coefficient, declines from 0.494 to 0.467 when moving from market income plus pensions to disposable income<sup>19</sup> —i.e., after the intervention of direct taxes, social contributions to health and unemployment insurance, and direct transfers—.

**Figure 2: Effects of fiscal interventions on income inequality** _(Gini coefficients)_

<!-- Start of picture text --> 0.55<br>0.50<br>0.494<br>0.487<br>0.45 0.467 0.464<br>0.420<br>0.40<br
around women in public transit. Social norm survey questions followed the same wording as that of the rider exit survey.

# **Sampling**

To select a representative sample of rush hour commuters, we use a simple sampling protocol based on ordering and counting individuals on the platform of each line at the main station, Central do Brasil, during the evening commute (5-8pm). Table A1, Panel B, summarizes patterns of response. A total of 1,078 commuters were approached, 555 women and 523 men. Ninety percent (90.1 percent) of women and 85.7 percent of men responded to the platform survey (Table A4 column 1), with an overall response rate of 87.9 percent.<sup>16</sup> We use administrative data on the number of SuperVia riders by line to apply sample weights to obtain estimates that are representative of the average rider.

After agreeing to participate in the platform survey, respondents are invited to participate in a series of IATs.<sup>17</sup> The IAT method and instruments are discussed in Section 4.3. Participants in the IATs are offered a compensation of R$30.00, or the equivalent of 7 hours of work at the minimum wage (about $7.50).<sup>18</sup> A booth was set up close to the platform and equipped with laptops on which participants took the test. The platform survey was conducted until 300 finished IATs were completed.<sup>19</sup> The response rate for the IATs is reported in Table A1. Conditional on being invited to take the IATs, the response rate was 40.6 percent. Women are slightly less likely to accept than men (38 percent versus 43.5 percent), but this difference is imprecisely estimated (Table A4 column 2: _p_ = 0 _._ 11). Women’s stated use of the reserved space is not significantly correlated with response
## **5.2. Robustness Tests**

Several sensitivities are performed to test the robustness of our results. Because transport corridors are often conceived to connect large urban centers (booming centers of economic activity), excluding these end points limits the analysis to the areas along the corridor that were likely connected more arbitrarily (randomly). Columns 3 and 4 in tables 2 and 3 report the estimation results when 20 percent of the corridor area at the end nodes of the transport corridor is excluded. Moreover, it could be that some corridors are not envisioned to connect cities that happen to be at the ends of the corridor, but rather several cities, some of which are located at the ends of the corridor and some along the corridor alignment. To account for this possibility, the human settlement data prior to the start of project were used to exclude built-up urban centers. These results are reported in columns 5 and 6 in tables 2 and 3. The results across the various sensitivities are largely consistent, with a few exceptions. First, the robustness results hint that trade openness at appraisal could have a positive impact on local economic activity in non-nodal areas (column 3), and the impact of general policy reforms in non-nodal areas could also be positive (column 4). Second, the robustness results hint that a larger connectivity increase could negatively influence local economic activity (table 3, column 4), once again hinting at the need to balance ambition in project scope with the accompanying operational complexity. These nuances in the results could warrant future research.

# **6. Conclusion**

This paper conducted a survey of large transport infrastructure (corridor) projects supported by the ADB, JICA, and World Bank. It used the survey data to catalog the project characteristics and relate them to success measures, focusing on a WEB—local economic activity. To this end, the paper used the differencein-differences methodology, conditioning on country context, project design and implementation, and complementary policies and institutions.
Policy Research Working Paper 10433

# **Abstract**

This paper studies the relationship between participation in global value chains, worker routine task intensity, and within-country wage inequality. It uses unique survey data from 47 countries across the development spectrum to calculate worker-level, country-specific routine task intensity and combines them with sectoral measures of backward and forward global value chains participation. Higher global value chains participation is associated with more routine-intensive work, specifically among offshorable occupations, especially in countries at lower development levels. The results by broad sectors contrast sharply: higher global value chains participation is linked to a higher routine task intensity in offshorable occupations in the industry but a lower routine task intensity in non-offshorable occupations in business services. Higher worker-level routine task intensity is strongly associated with lower wages, so global value chains participation indirectly widens the within-country wage inequality through this routine task intensity channel. At the same time, global value chains participation directly contributes to reduced wage inequality, except for the richest countries. Overall, this analysis finds that global value chains participation reduces wage inequality in most low- and middle-income countries that receive offshored jobs but widens wage inequality in high-income countries that offshore jobs.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 piotr.lewandowski@ibs.org.pl (corresponding author), karol.madon@ibs.org.pl and dwinkler2@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.
2019 2/1/2020 9/1/2020 4/1/2021 6/1/2022 1/1/2023 8/1/2023 3/1/2024<br>10/1/2017 12/1/2018 11/1/2021<br><!-- End of picture text -->

# **Sea Intelligence Schedule Reliability Index**

Sea-Intelligence consultancy<sup>5</sup> publishes a Schedule Reliability Index benchmarking carrier on-time performance within an 8-hour window. Despite being available globally and by shipping line, this metric unsurprisingly correlates with the Stress Index, as both leverage the same underlying vessel movement data. However, the Stress Index is port-centric and scalable, while the reliability measure focuses on services, trade lanes, and carriers.

# **Global Supply Chain Pressure Index**

The Federal Reserve Bank of New York’s Global Supply Chain Pressure Index<sup>6</sup> (GSCPI) is a meta-indicator that integrates several existing series to compound into supply chain disruption indicators. Global transportation costs are measured by employing data from the Baltic Dry Index (BDI) and the Harpex index,<sup>7</sup>

> 4 Each month, Sea-Intelligence measures schedule reliability across more than 11,000 vessel arrivals on average, in more than 270 ports, which is the underlying data for the monthly global on-time performance, as well as the individual carrier, trade lane and service on-time performance. The trade lane and service schedule reliability are based on a two-month rolling averages. In other words, February trade lane on-time performance is based on the average on-time performance of vessel arrivals in both January and February. The definition of “on-time” has in accordance with the widely used calendar-day definition been settled as arrival within plus or minus 1 calendar day from the proforma schedule.

> 5 https://www.sea-intelligence.com

> 6 <u>https://www.newyorkfed.org/research