rafmacalaba/datause-encoder-data huggingface dataset:
14 (row 4) or 0-15 bins (row 5), to minimize selection issues due to changes in economic systems and selective mortality or early retirement disproportionately affecting more experienced workers; (ii) Based on the 5-35 bins (row 6).<sup>21</sup>

**Excluding Selected Types of Workers.** Results hold for returns excluding female, parttime, self-employed and public sector workers, considering both monthly wages (row 7; N = 124; 101; 101) and hourly wages (row 8; N = 122; 98; 98).<sup>22</sup> Web Appx. Table A.14 (rows 2-5) shows results hold if we exclude each group of workers at a time. Excluding female workers slightly raises the returns in both developed and developing countries. If anything, the developed-developing gap slightly increases. Excluding part-time workers lowers the returns in developing countries when cohort fixed effects are included, thus also widening the developed-developing gap. Excluding self-employed workers does not substantially affect the returns. The developed-developing gap slightly increases. Finally, excluding public sector only slightly reduces the returns, and the gaps.

**Weights.** Results hold when country populations in 2017 are employed as weights, thus giving more weight to larger countries (row 9; N = 145; 122; 122).

**Complementarities.** Rows 10-13 examine if returns vary across education levels. According to UNESCO (2012), individuals finish high school after 12-13 years of education, thus 13 years is a reasonable cut-off. In developed countries, returns are slightly lower for more educated individuals (rows 10-11; 3.4*** vs. 3.2***; N = 143-122122 in row 10, 124-98-98 in row 11). In developing countries, returns are higher for more educated individuals (2.1*** vs. 1.7*** in col. (1)). However, we lose about 20-25 countries because we do not have at least ten individuals for each experience-education bin. To remedy that issue, we use country population circa 2017 as weights (rows 1213; N = 143-122-122 in row 12, 124-98-98 in row 13), making compositional changes due to smaller countries less consequential. Results are unchanged.<sup>23</sup>

**Main Returns.** In our simulations we use population-weighted education-specific

> 21Web Appx. Table A.9 shows that local returns are higher for lower-experience bins whereas Web Appx. Table A.12 shows that results hold for different combinations of local returns.

> 22Lagakos et al. (2018b) exclude: (i) female and part-time workers because potential experience is a better proxy of actual experience for male and full-time workers; (ii) self-employed individuals because their wages may also include the returns to the capital investments they make. In our case, I2D2 calculates their wage as the amount of salary taken from the business; and (iii) public sector workers because they receive nonwage compensation and their wages may not reflect the full payment for their labor.

> 23Results hold if we use 12 or 14 years or information about high school graduation (Web Appx. Table A.13).
McKelway, M. (2023). “Information, Norms, and Female Employment: An Experiment in India.” Mimeo.

Maity, B. (2020). “Consumption and Time-Use Effects of India’s Employment Guarantee and Women’s Participation.” _Economic Development and Cultural Change_ 68(4): 1185-1231.

Majlesi, K. (2016). “Labor market opportunities and women's decision making power within households.” _Journal of Development Economics_ 119: 34-47.

<mark>Murgai, R., M. Ravallion and D. van de Walle (2015). “Is Workfare Cost-effective against</mark> Poverty in a Poor Labor-Surplus Economy?” _World Bank Economic Review_ 30(3): 413-445.

<mark>Ralston, L., C. Andrews, A. Hsiao (2017). “The impacts of safety nets in Africa: what are we</mark> learning.” World Bank Policy Research Working Paper 8255.

<mark>Ravallion, M. (1990). On the coverage of public employment schemes for poverty</mark> alleviation." _Journal of Development Economics_ 34(1-2): 57-79.

<mark>Ravallion, M. (1991). “Reaching the Rural Poor Through Public Employment.”</mark> _<mark>The World</mark> Bank Research Observer_ 6(2): 153-175.

<mark>Ravallion, M. (1999). “Appraising Workfare.”</mark> _<mark>The World Bank Research Observer</mark>_ <mark>14(1): 3148.</mark> Ravallion, M., and S. Chen (2007). “China's (uneven) progress against poverty.” _Journal of Development Economics_ 82(1): 1-42.

Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

<mark>Tucker, J. (2008) Women, Family, and Gender in Islamic Law. Cambridge: Cambridge University Press.</mark> U.S. Agency for International Development and CARE (2008). The Coping Strategies Index.

Veit, C. T. and J.E. Ware (1983). “The structure of psychological distress and well-being in general populations.” _Journal of Consulting and Clinical Psychology_ 51(5): 730-742.

Verme, P. (2015). “Economic development and female labor participation in the Middle East and North Africa: a test of the U-shape hypothesis.” _IZA Journal of Labor & Development_ 4(1): 1-21.

World Bank (2018). _Economic Transformation in Djibouti: Systematic Country Diagnostic_ . World Bank.

World Bank (2019). _Challenges of Inclusive Growth: A Poverty and Equity Assessment of Djibouti_ . World Bank.

World Bank East Asia and Pacific Gender Innovation Lab (2020). “Impact Evaluation 31
# **1 Introduction**

Theories of international trade, such as the factor proportions model, often assume that countries use similar technologies in production or that technological differences are Hicks neutral.<sup>1</sup> In contrast, models of biased technical change assert that innovation and technology adoption are determined by local factor endowments. This paper marries these two literatures. It proposes a matching mechanism between factor endowments and technologies in open economies, and it studies how the cross-country pattern of trade changes once technology choices are considered.

The theory concerns economies that are open and differ in their factor endowments. Economies are composed of multiple goods, which can be produced with a range of factor-complementary machines. These machines are traded in a global market, which is characterized by a monopolistic competitive structure. The model is tractable even though it predicts that unit factor input requirements within industries can vary across countries.

The econometric analyses utilize data on factor endowments, intellectual property rights (IPRs), and net exports of computers and components, an industry that has received much attention in the technology adoption and growth literature. The data set covers 73 countries during 1980-2000. The empirical models test for the existence of multiple technological country groups in the data, and estimate the factor proportions model in a two-stage estimation procedure. The technology selection function is modeled as an Ordered Probit, where endowments and IPRs determine technology choices. The trade specialization equation follows closely the standard specification of Rybczynski functions found in the trade literature.

The econometric results from our preferred estimator suggest the existence of up to four distinct technological groups that differ in terms of their unit factor input requirements in the production of computers. The evi1The term factor-proportions refers both to relative abundance of factors of production and relative intensity with which different factors of production are used in the production of different goods. As Krugman and Obstfeld[23] explain ”... because the HeckscherOhlin theory emphasizes the interplay between the proportions in which different factors of production are available in different countries and the proportions in which they are used in producing different goods, it is also referred to as the factor-proportions theory.” 1
LIS data highlights two main caveats: first, the variable used for instrumenting the FLFP was not available, which meant that we had to restrict the estimation to only the OLS model; secondly, the target welfare variable was consumption and not income. However, the results for Morocco and Jordan appear to be in line with expectations while Egypt has the right sign, but the coefficients are not significant.

# a. The FLFP and inequality macro-model

In Section 4, we proceed with a multivariate analysis to show the inverted U-shape relationship between FLFP and inequality. We follow a general formulation taken from the literature ( <mark>Förster and Tóth, 2015)</mark> that has enough explanatory variables but, at the same time, does not cover all the inequality determinants as they are not easily available for many developing countries and for all the years being analyzed. The scope of this regression is to show that – once controlling for a set of socio-economic variables and using different models – the relation between FLFP and inequality is concave, our focus is, thus, mostly on the signs and significance of the FLFP expressed in polynomial form.

From the World Bank database, we collected data about inequality (Gini index), labor force participation and other variables and included them in the analysis as “controls”. All in all, our data set had a total of 1,025 observations.<sup>9</sup> We estimate the following fixed effects panel model: where:

- _i_ = 1,  ,127 stands for the country and _t_ = 2002,  ,2018 is the year;

- _GINIi_ , _t_ is the value of the Gini index calculated on households’ income in country  at year _i t_ and is the dependent variable;

> 9 Data are not available for all nations and years, so the panel is not balanced. The World Bank produces the data on “International Migrant Stock, percentage of population” by intervals of five years (the last update was in 2015): in this situation we interpolated the data to reconstruct the value of the variable in the years between updates.

8
Nations (2013). These data are used to calculate the youth and elderly dependency ratios in each year of a given simulation, and are in turn used to help determine domestic savings behavior.

The first factor is for the growth of GDP per capita. The second and third terms are for the youth and elderly dependency ratios, respectively. The function is parameterized following the empirical estimates of Loayza et al. (2000). These coefficients differ for countries based on their identification as either low or high income in 2007, and are constant over the time horizon of the simulations.<sup>9</sup> The coefficients for the growth term are positive for all countries which imply that as countries grow they save more. The coefficients on the dependency ratio terms are negative for all countries. So, as dependency ratios rise, the propensity for households to consume rises and savings as a share of GDP fall, with the magnitudes of the elderly dependency ratio coefficients being greater than that that of the young dependency ratio coefficients. Since investment is modeled as being savings driven, total global investment is driven by total global savings, with the amount of investment in a given country being a function of both domestic savings as well as the current account balance, which is determined exogenously. The additional implication of the savings driven investment assumption is that that as dependency ratios fall in a given country, domestic savings will rise, which in turn will boost investment. The opposite would hold true for a country where dependency ratios are rising.

While the numerical analysis will ultimately account for the full effect of all the different drivers of consumption, savings and investment, it may be useful to see how sensitive the μ<sup>s</sup> parameter is to dependency ratios. Figure 2 illustrates how the dependency ratios’ contributions to the savings share parameter change over time for four regions. This is done by applying youth and elderly dependency ratios calculated from United Nations (2013) to the β<sup>y</sup> and β<sup>e</sup> coefficients considered in LINKAGE. Since all regions considered are low income countries, the β<sup>y</sup> and β<sup>e</sup> are the same for all, and so differences in the savings share parameter values across regions are driven solely by the differences in dependency ratios.

> 9 Coefficient values can be found in Table A2.

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# **Box 3.  Identification Of The Normative Tax Structure**

Identification of the normative tax structure can best proceed by determining whether a given provision responds to one of the following questions:

- **1)** Is the provision necessary to determine the base of the tax, normatively defined, in accordance with the fundamental nature of the tax?

- **2)** Is the provision part of the generally applicable rate structure?

- **3)** Is the provision necessary to define the taxable units liable for the tax?

- **4)** Is the provision necessary to assure that the tax is determined within the time period selected for imposition of the tax?

- **5)** Is the provision necessary to implement the tax in international transactions?

- **6)** Is the provision necessary to administer the tax?

Source: P.R. McDaniel, and S. Surrey, “ _International Aspects of Tax Expenditures: A comparative Study_ ,” Kluwer, 1984.

For example, for a typical middle income country, the guideline must be modified with additional criteria (Box 4) because this economy has a high share of population in agriculture sector (45 percent of population), a large share of employment at minimum wage and a relatively large share employed in the informal sector and unrecorded sectors (about 25 percent of GDP). That difficult economic structure, coupled with the past economic instabilities reflected in high and highly variable inflation rates, lead to specific choices of tax structures in the country. Its tax collection mostly depends on tax withholding. Therefore, additional criteria (in Box 4) have been used in identifying tax benchmarks for PIT, CIT and VAT and special customs tax.

Nevertheless, the identification of the normative tax structure is a starting point and checkpoint during the process of classification. Some provisions are on the borderline creating difficulty in determining what provisions should be included or excluded from the normative tax structures.

As a result, not all conceptual deviations from the normal tax structure should be classified as tax expenditures. To properly classify a provision as a tax expenditure or as part of the normative structure requires consideration of not only to tax theory, but also the legislative history of existing provisions, the reasons offered by proponents of a new tax proposal, and the defenses relied on rules that are proposed to be terminated or modified (P. R. McDaniel and Stanley S. Surrey, 1984).

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_Rural-urban gradient in Latin America and the Caribbean_ _page_ 23 <!-- Start of picture text --> Proportion rural<br>0.7<br>0.6<br>0.5<br>0.4 150<br>0.3<br>WDI<br>0.2<br>0.1<br>0<br>El Salvador Domin. Rep. Guatemala Venezuela Ecuador Chile Mexico Colombia Suriname Cuba LCR Totals Argentina Costa Rica Paraguay Peru Honduras Brazil Nicaragua Bolivia Panama Uruguay Guyana<br>Trinidad & Tobag<br><!-- End of picture text -->

**Figure 6:  Census measures of rurality compared to definition based on <150 person/km2 and >1 hr travel time criteria (GPW 3)**

(WDI: World Development Indicators, data for 2002)
Wholesale and Retail Trade; (vii) Transport, Storage and Communication; (viii) Finance, Insurance, Real Estate, and Business Services, (ix) Community, Social, and Personal Services. The data was obtained from the United Nations’ National Accounts database. We also construct the ratio of debt liabilities in total foreign liabilities as our proxy of the debtequity ratio —our measure of vulnerabilities in financial openness. The data to construct this ratio was obtained from Lane and Milesi-Ferretti (2006).

Finally, Table A.2 presents a more detailed description of the sources of the data for all the variables involved in our econometric analysis.

# **3.2 Estimation Technique**<sup>**8**</sup>

The proposed panel data regression poses some challenges for estimation. The first is the presence of unobserved period- and country-specific effects. The inclusion of periodspecific dummy variables can account for the time effects while country dummies deal with country-specific effects. The second challenge is that our variables of interest —trade and financial openness —are likely to be jointly endogenous with shocks to the RER; hence we need to control for the biases resulting from simultaneous or reverse causation in our RER volatility equation.

Our baseline regression equation of RER volatility follows the following specification: where the dependent variable is the standard deviation of annual changes in the RER (in logs) using monthly data for the 1975-2005 period. The matrix **O** _it_ contains information on our variables of interest: trade and financial openness. We use either the policy or outcome measures of trade and financial openness. However, our benchmark result would be the one that uses exports and imports as percentage of GDP (in logs) as our indicator of trade openness, and foreign liabilities as percentage of GDP (in logs) as our measure of financial openness. Finally, the matrix **Z** _it_ comprises the control variables for the RER volatility regression equation: the standard deviation of Home-Foreign labor productivity growth, monetary and fiscal policy volatility, dummy variables for fixed and intermediate exchange rate regimes, dummy for currency crisis, and the level of income per capita (at the start of the 5-year period).

However, as we said above, it is highly likely that shocks to the RER (or RER volatility) may have an effect on trade and financial openness. Hence, we need to find appropriate

> 7 Note that all standard deviation measures were taken for annual changes during 5-year periods.

> 8 The present sub-section draws heavily from Loayza, Fajnzylber and Calderón (2005).

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Poverty incidence in Eastern Europe decreases with age, and reaches a minimum at

**ages** 35-44 in Bulgaria, ages 45-54 in Hungary, and ages 55-64 in Poland. After those ages, the increase in poverty incidence is quite rapid and severe, except in Poland (as we noted earlier, this is due to the generous pension system in Poland). In Bulgaria and Hungary, poverty rates among people over 75 are close to twice the national average. The vast majority of people in that age group are women, and their poverty rates are higher than for the men in that group. Actually, the gender-breakdown of Table 8 (not shown here) reveals that poverty rates among the elderly are higher for women in general than for men. At lower ages though, the gender gap is not very pronounced, and for some ages poverty is lower among women than men.

Gender is thus a relevant poverty dimension in Eastern Europe primarily for the elderly, especially at very high ages, and for female-headed households. For many women, the labor market changes of transition have had major implications. Prior to transition, women were expected to work full-time but the state provided day care for their children. Transition has led to a drop in female labor force participation (not all of it voluntarily) but it has also led to a reduced supply of affordable day care centers (World Bank, 1996e)." Both factors may well affect female-headed households disproportionately.

**The** effect oftransition on women is discussed further by Einhorn (1993), Funk and Mueller (1993), Chase (1995) and Fong **(1996).**

39
the presence of a large and relatively unproductive agricultural sector.

Relative to the existing literature, this paper provides new evidence about the effect of a quasi-exogenous income shock on productive diversification for rural households, and has the advantage of exploiting a plausibly permanent income shock. It is also one of the first papers to analyze the impact of variation in quota policy in China on investment in non-agricultural production. The paper proceeds as follows. Section 2 describes the data and institutional background, and Section 3 presents the identification strategy. Section 4 presents results and robustness checks, and Section 5 concludes.

# **2 Data and institutional background**

This section briefly summarizes the data employed, provides an institutional overview of the grain quota system, and describes the information available in the dataset about the quota system.

## **2.1 Overview of data sources**

The data employed here is the China Research Center for the Rural Economy panel dataset, collected in a sample of 206 villages in 13 provinces in China between 1986 and 2001, excluding 1992 and 1994.<sup>2</sup> Provinces observed include Shanxi, Jilin, Jiangsu, Zhejiang, Anhui, Henan, Hunan, Sichuan and Gansu. The surveys prior to 1993 were considerably briefer, and thus the primary analysis is restricted to the post-1993 panel; 1993 is identified as the baseline year.

In addition, the analysis employs two climatic or agronomic data sets. The first is monthly climatic data from a network of weather stations in China collected by the Carbon Dioxide Information Analysis Center (CDIAC). Using data on the latitude and longitude of the county centroid, climatic measurements for each county and year are

> 2A randomly selected sample of households in each surveyed village forms the panel; the mean number of households in a village-year cell is 69.

4
very rapid, averaging 50 percent per annum during 2006-08. The NBT’s unencumbered international reserves have been depleted (also because of the NBT’s quasi fiscal activities) and stood at only just over one month of import cover and the equivalent of only 44 percent of reserve money at end-2008. Finally, the banking system is very heavy dollarized, with foreign currency deposits comprising 64 percent of total deposits.

**Table 4**

# **Current Account Balances and International Reserves in Armenia, the Kyrgyz Republic and Moldova, 2008**

||Current account balance as<br>percent of GDP|Gross international reserves<br>in months of imports|
|---|---|---|
|Armenia|-11.4|3.9|
|Kyrgyz Republic|-10.7|3.0|
|Moldova|-16.7|3.2|

Source: Current account balances, ECA database; gross international reserves, IMF Macroeconomic space in Armenia, the Kyrgyz Republic and Moldova is limited by BOP constraints, because these economies also face an adverse external shock which exacerbates an external position which was displaying signs of vulnerability even before the shock. Table 4 provides data on their current account balances and gross international reserves. Each of the three countries had a large current account deficit in 2008, in excess of 10 percent of GDP in Armenia and the Kyrgyz Republic and nearly 17 percent of GDP in Moldova. They held a larger buffer of gross international reserves than Tajikistan, between 3 and 4 months of import cover in each country, but this is not sufficient to fund sustained BOP deficits.

15