rafmacalaba/data-use-mentions-extended huggingface dataset:
the first evidence with a theoretical underpinning on how IO linkages facilitated by HSR connecting exporting firms with their domestic suppliers, enable the substitution of domestic for imported materials, thus pushing up the firm and aggregate DVAR in exports.

The rest of the paper is structured as follows: Section 2 covers the background of HSR development in China. Section 3 introduces the new approach to estimate firm-level DVAR using only customs transaction data, and section 4 details the empirical specifications and variable measurements. Section 5 presents the effects of HSR expansion on firms’ DVAR, along with robustness checks. The model is outlined in section 6, and conclusions are drawn in section 7. The Appendix provides a detailed discussion on the datasets used and the construction of key variables. It also provides additional robustness checks on the city-level impact of HSR connections on firms’ DVAR.

# **2 Background of HSR Development in China**

In the late 20th century, China invested heavily in transportation infrastructure, such as railways and expressways, to promote economic development. However, the previous transportation infrastructure construction mainly focused on freight transport, while passenger travel remained comparatively underdeveloped, suffering from low-quality service and constrained travel speed.

To mitigate the overloaded transportation network at the time and provide significantly improved passenger travel services, the Medium and Long Term Railway Plan (MLTRP) was approved in 2004, initially proposing the construction plan of the HSR network. High-speed rail typically refers to rail systems designed for trains traveling at speeds exceeding 250 km/h.6 The MLTRP proposed four horizontal and four vertical form corridors, linking nearly all major cities in China. Accelerated by the fiscal stimulus aimed at helping the economy recover from the global financial crisis, the construction of the HSR speeded up since 2008. In 2016, the MLTRP was further revised to expand China’s highspeed rail network to eight horizontal and eight vertical corridors. After years of development, China now boasts the largest high-speed rail system worldwide (Lawrence et al., 2019). By the end of 2015
and pessimistic regarding socioeconomic trends, and optimistic and pessimistic regarding climate change impacts).

The results are unequivocal: the impact of climate change on poverty is conditioned by overall development progress. In particular, the impacts of climate change are lower in scenarios with high structural change away from agriculture, high productivity growth in the agriculture sector and high levels of social protection. In scenarios where these conditions are not met, climate change could bring up to 1 million additional people in extreme poverty by 2030.

# Even without climate change, very different futures can be imagined for poverty and development in Vietnam

We explore a wide range of uncertainties on future structural change, productivity growth or demographic changes and create a large number (several hundreds or thousands) of scenarios for future income growth and income distribution in Vietnam (Table 1). We then analyze the resulting space of possible future poverty and income distribution using several indicators like the income of the bottom 20%, the number of people living in extreme poverty, the income share of the bottom 40%, or GDP growth.

Consistently with our goal of exploring the largest possible uncertainty, the range of income growth in our scenarios is much larger than the one explored in other scenario exercises, such as the SSPs (Figure 1). All the hypotheses that were made on the input parameters appear however possible (or at least, non‐ impossible) and based on historical data (see Rozenberg and Hallegatte, 2015).

3
Barro, R., Redlick, C., 2011. Macroeconomic e¤ects from government purchases and taxes. The Quarterly Journal of Economics 126, 51-102.

Blanchard, O., Perotti, R., 2002. An empirical characterization of the dynamic e¤ects of changes in government spending and taxes on output. The Quarterly Journal of Economics 117, 1329-1368.

Browning, E. 1975. Labor supply distortions of social security. Southern Economic Journal 42, 243252.

Carroll, R., Cline, R., Neubig, T. 2010. The macroeconomic e¤ects of an add-on value added tax. Ernst & Young.

Chahrour, R., Schmitt-Grohe, S., Uribe, M. 2012. A model-based evaluation of the debate on the size of the tax multiplier. American Economic Journal: Economic Policy 4, 28-45.

Cloyne, J., 2013. Discretionary tax changes and the macroeconomy: New narrative evidence from the United Kingdom. American Economic Review 103, 1507-1528. Driscoll, J., Kraay, A., 1998. Consistent covariance matrix estimation with spatially dependent panel data. The Review of Economics and Statistics 80, 549-560.

Frankel, J., Vegh, C., Vuletin, G. 2013. On graduation from ...scal procyclicality. Journal of Development Economics 100, 32-47.

Feldstein, M. 1995. The e¤ect of marginal tax rates on taxable income: A panel study of the 1986 tax reform act. Journal of Political Economy 103, 551-572.

Gavin, M., Perotti, R. 1997. Fiscal policy in Latin America. NBER Macroeconomics Annual 12, 11-61.

Gil, P., Marti, F., Morris, R., Perez, J., Ramos, R. 2017. The output e¤ects of tax changes: Narrative evidence from Spain. Central Bank of Spain Working Paper 1721.

Harberger, A. 1964a. The measurement of waste. American Economic Review 54, 58-76.

Harberger
POLICY **RESEARCH WORKING PAPER** 1752

# Summary findings

Using the household survey and other data sources, Canagarajah and Thomas analyze returns to education and other aspects of Ghana's labor market profile from 1987 to 1991.

The labor force grew slower than the population did between 1980 and 1990, but the supply of labor is expected to increase as the population of youth is expected to grow faster from 1990 to 2000. And labor force participation rates for 26- to 45-year-olds have been increasing rapidly.

Over time, the average labor force participation rates of women have become equal to men's; that of children younger than 15 has remained unchanged at 38 percent. More than half of Ghana's child laborers are employed in agriculture.

The formal sector's share of employment is on the decline, while the private informal sector's share has increased, especially in urban areas. Over time, the informal sector (in which most workers have a primary education or less) has absorbed more labor than the formal sector (in which most workers have middle or secondary schooling).

Unemployment is pervasive in urban areas, and is less visible in rural areas. Labor productivity may not have increased and is possibly declining.

Between 1987 and 1992, there was reverse migration, with many people moving from urban to rural areas, mostly for family reasons. Employment-related migration has also been on the increase.

As is true elsewhere, the level of education affects participation in the labor force. Literacy rates for women are lower than those for men, which is one reason men dominate the private formal sector. The rate of return to education increases with higher education and work experience. The return for each additional year of schooling ranges from 4 percent to 6 percent in Ghana, quite high for a Sub-Saharan African country. Private and social returns to education are greater for primary than for secondary or postsecondary education.

- - This paper a product of Human Development 3, Africa Technical Families is part of a larger effort
Figure A.2: Corporate Tax Expenditures as a Share of GDP <!-- Start of picture text --> (a) Administrative micro-data (b) Global CIT Expenditures<br><!-- End of picture text --> <!-- Start of picture text --> Our Estimates<br>CRI Government Estimates<br>HND<br>SWZ<br>RWA<br>DOM<br>GTM MEX<br>SEN<br>UGA<br>ECU MNE<br>ETH ALB<br>6 7 8 9 10 11 6 7 8 9 10 11<br>GDP per capita (Log) GDP per capita (Log)<br>(c) In-sample Comparison: Our Estimates vs. Gov-<br>ernment Estimates<br>CRI<br>HND<br>SWZ<br>RWA<br>DOM<br>MEX GTM<br>SEN<br>UGA<br>ECU<br>ALB<br>0 .5 1 1.5 2<br>CIT Expenditures: Gov. Estimates(% of GDP)<br>2 4<br>1.5 3<br>1 2<br>.5 1<br>CIT expenditures (% of GDP) CIT expenditures (% of GDP)<br>0 0<br>2<br>1.5<br>1<br>.5<br>0<br>CIT Expenditures: Our Estimates(% of GDP)<br><!-- End of picture text --> Note: This figure shows estimates for the size of corporate income tax expenditures as a share of countries’ GDP. Panel (a) shows our estimates for the 13 sample countries, ranked by per capita log GDP (2015 constant USD from the World Bank). Panel (b) adds estimates of CIT expenditures from 66 additional countries, available
to solve students’ dynamic optimization problem for a given value of Θ. Appendix C provides a full description of the algorithm. The estimation of **_δ_** and **_ξ_** is nested within the model solution for a given of value of Θ, in the spirit of Berry, Levinsohn and Pakes (1995).

In anticipation of next section, a couple of remarks are in order. First, our model solution perfectly replicates observed enrollment rates by type by construction. Hence, enrollment rates are not a moment to match in the estimation. Second, our model solution attempts to replicate observed dropout rates at the (year, ability quintile, income) level, but only does it with mixed success (see Appendix C.3 for further details). Thus, dropout rates are a moment to match.

# **7 Estimation**

In this section we describe the estimation strategy and identification. We also present parameter estimates, describe the model’s fit, and address the role of effort given our estimates.

> 15 We use tuition at public institutions because there is always a public institution that the student can attend. Modelling the choice of college type (public or private) is beyond the scope of this paper.

> 16This creates, in effect, four college attainments: high school, college, some college (one year), some college (two or more years). The two “some college” categories correspond to college dropouts. We work with two rather than one dropout category because their wages are quite different and can hence affect dropout timing.

20
# Figure 1. Global output growth and relative per capita incomes

A. Actual output growth <!-- Start of picture text --> Percent 2000-2021 average<br>8<br>4<br>0<br>-4<br>World AEs EMDEs<br>2011 2013 2015 2017 2019 2021 2011 2013 2015 2017 2019 2021 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> C. Per capita income in EMDEs relative to advanced economies <!-- Start of picture text --> Percent<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> B. Potential output growth <!-- Start of picture text --> Percent<br>Potential growth<br>10<br>Actual growth<br>8<br>2000-2021 potential growth<br>6<br>4<br>2<br>0<br>World AEs EMDEs<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> D. Per capita income relative to advanced economies, 2022 <!-- Start of picture text --> Percent<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, EMDE = emerging market and developing economy, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = SubSaharan Africa.

A. Sample of 181 countries.

B.-D Based on production function
``` Decompositionofgrossexports: Perspective:sectbil Exporter:CHN Importer:USA Sectorofexport:17 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`107292.76`|_`100.00`_|
|`Domestic content (DC)`|`79824.00`|_`74.40`_|
|`Domestic Value-Added (DVA)`|`79815.64`|_`74.39`_|
|`VAX -> DVA absorbed abroad`|`79373.64`|_`73.98`_|
|`Reflection`|`442.00`|_`0.41`_|
|`Domestic double counting`|`8.36`|_`0.01`_|
|`Foreign content (FC)`|`27468.76`|_`25.60`_|
|`Foreign Value-Added (FVA)`|`27465.88`|_`25.60`_|
|`Foreign double counting`|`2.88`|_`0.00`_|

According to WIOD 2014 data, Chinese value-added potentially exposed to this tariff turns out to be around $79.8 billion, as shown by the value reported for the `Domestic Value-Added (DVA)` . This is higher than the $77.4 billion of Chinese value-added traced in the same export flow using an “exporting country” perspective (see the previous `icio` output above). Thus, if we had used the latter perspective, we would have understated the Chinese exposure. Again, each empirical question calls for the best suited perspective: the _default_ “exporting country” is more useful if the aim is to assess GVC participation or to retrieve measures of value-added trade consistent with the figures at a
among return migrants than nonmigrants in all three countries. The difference in employment rates between the two groups is statistically significant and larger in Nepal and Pakistan compared to Bangladesh (Table 2). This finding is consistent with the results of Arif (1998) and Ilahi (1999) using earlier data from Pakistan.

Figure 4, however, shows that the gap in employment rates between return migrants and nonmigrants declines with the number of years since the return. While the employment gap between returnees and non migrants is large one year after return, it diminishes with duration of stay, driven by both a decline in the unemployment rate and an increase in labor force participation.

Table 3 shows the effect of the duration of stay on return migrants back home, controlling for other individual worker characteristics: the negative association between past migration overseas and the likelihood of being employed decreases with duration of stay at home for all three countries. In Nepal, there is no statistically significant difference in the likelihood of being employed between nonmigrants and return migrants who have returned 3 years ago or

> 13The mean age of return migrants who are out of the labor force in their sample is 45 years old.

12
and external imbalances such as large budget and balance of payment deficits and accumulated foreign debt. As a result the government embarked on the Economic Reform and Structural Adjustment Program (ERSAP) in 1991 which entailed the privatization of SOEs and the removal of various subsidies among other things. Changes in labour market characteristics had already came about starting from the 80s when the government had directly interfered with the market by suspending the public sector employment guarantee scheme for graduates and by introducing direct rationing of enrollment by the Ministry of Education. These two major turns in the economy have resulted in profound changes in female employment in the following years.

Following the ERSAP, state-owned textiles and clothing (TC) enterprises were downsized, thus paving the way for some private sector growth. This growth has caused this sector to increasingly attract female workers. In fact, the entire increase in private sector employment between 1998 and 2006 was on account of the feminization of just two sectors, these are food processing and TC (Assaad and El-Hamidi 2009). Many more women are employed in TC compared to the national average. At the national level in 2007, about 20% of the private sector’s labor force were women, this share doubles to about 40% in TC (El-Haddad 2015). This _relative_ contribution remains true to date as our data confirm. Female participation in the clothing sector remains at 40%, while it had dropped to just 17% in textiles.<sup>1</sup> Female participation in manufacturing remains also more or less put at about 18% of total employment in the sector (EIFBS data). But these persistent patterns mean that the female Egyptian labor market is also clearly highly segmented.

> 1 And to 15% in the food, beverage and tobacco sector.

3
each year subject to an imposed import restriction.

Figure 10 illustrates interesting features on the use of these TTBs over time. First, there are spikes for the United States in 1992 and 2001 and for the EU in 2001; empirical evidence described in more detail below links significant increases in TTB use to recessionary periods (especially unemployment rate increases) as well as real exchange rate appreciations. Second, India began using TTBs in 1992 and China in 1997; import coverage levels for India exhibit a consistently upward trend. Third, for China, the EU, and US, the significant deviation in 2001-2003 between the ‘all TTB’ series and ‘antidumping only’ series reflects the previously discussed global safeguards that each applied over steel products. Fourth, there is a slight increase for these economies in the ‘flow’ of products subject to new TTB investigations during the Great Recession period of 20082009, but it is not nearly as sizeable as in other periods of macroeconomic downturn.

To what extent has research linked changes in TTB levels to macroeconomic shocks such as real exchange rate appreciations and increases in the unemployment rate? Furthermore, can this partially explain why applied MFN tariffs were not responsive to the massive aggregate-level fluctuations taking place during the Great Recession, as discussed in Section 2.1? Knetter and Prusa (2003) and Irwin (2005) provide evidence from data through the 1990s that exchange rate appreciation significantly impacts antidumping use for a number of high-income economies. More recently, Bown and Crowley (2013a, 2014) confirm these results in the more comprehensive TTB data in cross-country samples of five high-income economies and 13 emerging economies, respectively, covering the period of 1988-2010. They find for the EU and the US, the flexibility of the real exchange rate, and in particular the sharp depreciations that subsequently took place (after initial sharp appreciations in 2009) likely contributed to the dampening pressure on demands for import protection during the Great Recession. Second, over time, the emerging economies’ collective TTB responsiveness to macroeconomic shocks (including
WFP and GoE. With time, LEAP focused increasingly on the development of the LEAP tool and provision of the tools intermediary and final outputs.

> 41 A dekad is a time interval of approximately ten days. Every month has three dekads, such that the first two dekads have 10 days, and the third is comprised of the remaining days of the month.

42 The Water Requirements Satisfaction Index (WRSI) is an indicator of crop performance based on the availability of water during a growing season. A WRSI value of 100% would indicate that water requirements for plant growth are fully satisfied.

10
Figure 3: Correlation between likelihood of laying off workers and change in sales revenue.

<!-- Start of picture text --> Likelihood of laying off workers Fraction of workers laid off<br>.25<br>10<br>.2<br>.15<br>5<br>.1<br>.05<br>0<br>0<br>-100 -50 0 50 -100 -50 0 50<br>Change in sales Change in sales<br>Men-led firms Women-led firms Men-led firms Women-led firms<br>Average fraction of workers laid off<br>Average fraction of firms thay lay off workers<br><!-- End of picture text --> Note: Binned scatterplots. Computation use weights equal to the inverse of the number of observations in each country. Variables in both axes are residuals from linear projections on fixed effects for size, sector, income group, geographical region, timing of the survey, and severity of the shock.

# **6.2 Digital technology adoption**

In this section we examine gender gaps in technology adoption during the COVID-19 crisis. Studies prior to the crisis have tested whether women are more or less likely than men to use digital solutions. Fatehkia et al. (2018) estimate gender gaps in both internet penetration and mobile phone usage across countries using digital trace data from Facebook, and find that gaps in internet usage and mobile phone usage between men and women decrease with the stage of development of the country.<sup>21</sup> EIGE (2016) documents that women’s usage of the internet to sell goods is 5 pp lower than men’s usage, while women’s usage of the internet to buy goods is 7 pp less than men’s usage. Finally, (Ono and Zavodny, 2003) examine differences in men’s and women’s usage of the internet using data from several surveys
In all of the above cases, I map the respective GTAP sectors to Agriculture assuming that the larger share of value added accrues to it as compared to Industry. In the next section, I discuss how I use this data to quantify the gains from changes in distortions.

# **4 Quantitative Analysis**

I begin the quantitative analysis by calibrating the technology parameters θ _j_ and α _j_ . Equation 7 pins down the value of θ _j_ which is the share of sector _j_ ’s value added in the total value added. Using the sector value added data from the WDI database, I am able to estimate θ _j_ for multiple country-periods.7 As discussed before α _j_ maps to the capital share of income within a sector. I estimate α _j_ indirectly by estimating the labor share of income within a sector. To do this, I employ the GTAP data that reports both the value added as well as the labor income at the sector level. However, the GTAP data are available for only three years – 2004, 2007 and 2011. I estimate the labor shares for all the three years and then consider the average of the three estimates for our analysis. The estimated labor shares for the three years vary over a narrow range for a large number of countries. Hence, I take the average of the three years to serve as a reasonable benchmark and use a time-invariant set of sectoral labor shares for each country in the quantitative exercises that follow.

## **4.1 Estimating Distortions**

Once I have the estimates of φ _j_ and α _j_ , I employ equation 6 to arrive at the estimates of sectorspecific distortion τ _j_ . However, note that for any fixed country-period I have two independent equations and need to solve for three sector-specific distortions. But, since these distortions only determine the allocation of resources across sectors, I can normalize τ _j_ for any one sector and solve for distortions in the other two sectors relative to the distortion in the chosen sector. In this case,
# **2 Facts on Reserves and Central Bank Independence**

This section presents empirical evidence on the interaction between reserves and central bank independence. Subsection 2.1 describes the data sources used. Subsection 2.2 documents that central banks with more independence tend to accumulate more reserves. Finally, subsection 2.3 provides anecdotal evidence to support a key assumption of the model.

## **2.1 Data**

I use annual data from 1970 to 2017 for 11 Latin American countries.<sup>11</sup> As is common in the literature, I exclude data for default episodes, following Catao and Mano (2017).

For central bank independence, I use a _de jure_ CBI index from Garriga (2016). Following Cukierman, Webb, and Neyapti (1992), the index is based on 16 criteria coded on a scale from 0 to 1 (lowest and highest independence, respectively) and reflects the central banks’ political, financial, and policy independence from the government. For international reserves, I use data from the International Financial Statistics. As defined by the IMF (2001), reserves are "official public sector foreign assets that are readily available". This definition includes foreign currencies and foreign-currency deposits and securities, special drawing rights (SDRs), and the reserves position at the IMF. Following the standard convention, I exclude gold. For public debt, I use data from the IMF Historical Public Debt Database for the 1970-2012 period, and from the IMF World Economic Outlook Database for the 2012-2017 period. In line with my motivation, I consider all forms of defaultable public debt. This includes all maturities, and debt denominated in domestic and foreign currency as well as issued domestically or externally. For GDP, I use data from the World Development Indicators Database. For spreads, I use the Emerging Market Bond Index Plus (EMBI+) for the 1994-2017 period. For inflation rates, I use data from the International Financial Statistics.

> 11I focus