rafmacalaba/data-use-mentions-extended huggingface dataset:
keeping the RER undervalued and/or determine the size of the undervaluation. This will allow us to test whether the ― _mercantilist_ ‖ view of the exchange rate policy is empirically valid. To accomplish this task we gather an unbalanced panel dataset of 79 countries, of which 21 are

> 1 Recent research on the ―mercantilist‖ view of exchange rate policy suggests that the accumulation of international reserves by some countries such as China and Argentina are aimed at keeping the real exchange rate undervalued; therefore, promote growth through rising exports (Rodrik, 2008). Others suggest that accumulating reserves may soften the blow of adverse financial and real shocks –that is, demand for reserve hoarding is precautionary (Aizenman and Lee, 2007; Cheung et al. 2007). 2In order to compute our theory-based measure of RER misalignment a long-run RER equation from a theoretical model that considers the equilibrium real exchange rate (ERER) as the relative price of tradable to non-tradable goods. The building blocks of the model will follow Balassa (1964) and Samuelson (1964) for equilibrium in the tradable and non-tradable goods market, and Mussa (1984) and Frenkel and Mussa (1985) for the inter-temporal BOP equilibrium.

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Similarly, for CIF/FOB ratio, using (1.2), we can express the Discrepancy Index in terms of r as: In comparison to the CIF/FOB ratio, the Discrepancy Index is a **nonlinear function** of measure r . Notice that slope of DI is steeper when r is in interval [-1,0] compared with when r is positive. This signifies that DI is more sensitive to changes in r when r is in [-1,0] than when r is positive. In the context of empirical trade data, this implies DI more heavily penalizes situations when imports are underreported as opposed to when imports are overreported. We find these functional properties important because structural reasons that result in import overreporting are guaranteed as opposed to structural reasons for import underreporting (which are not).

# 3.2 Aggregate indicators based on the discrepancy index

The Discrepancy Index is a bilateral asymmetry measure that quantifies the level of divergence between reported data and its mirror on a specified product and year. The product itself may be on any level of product aggregation if the level of product aggregation is consistent in both records. That is, we can quantify the discrepancy of reporting between a pair of countries on a disaggregated and sectoral level. However, countries trade in various products (or sectors) with a large set of trading partners. Therefore, the value of the Discrepancy Index on a single transaction may not be representative of the reporting patterns of the involved countries or products. This necessitates creation of aggregate level indicators that can summarize important properties of the distribution of Discrepancy Indexes. In this section, we discuss the aggregate level indicators used throughout this report.

The definition of the Discrepancy Index in (1) outlines a variable whose value is determined by selecting an ordered pair of countries ( A , B ) and product C ( DIABC = DI((A, B), C) ). Thus, calculating the DIABC for all country pairs and product combinations (for
(Chatterjee et al. 2015). Access to roads or transport is also associated with increased access to nonagricultural employment that affects women more than men, especially in communities with more egalitarian gender norms (Lei et al. 2019). Other measures to empower women can reinforce this (Fletcher _et al._ 2017).

Social norms also have an important role in mitigating female autonomy (Debnath 2015). Evidence on spouses' preferences and community attitudes towards work by married women in central India suggests that women's labor force participation may negatively affect their spouses’ social standing, leading to many husbands being opposed to their wives' taking up of employment (Bernhardt _et al._ 2018). Interventions to change social norms may thus hold promise to increase female labor force participation in the medium term.

Indeed, while short-term interventions involving testimonies by working women or discussions within the household had no effect (Dean and Jayachandran 2019), young rural women who, over a 3-year period, were offered training to acquire skills needed to join the business process outsourcing industry were significantly less likely to get married or have children during this period, choosing instead to enter the labor market or obtain more schooling or postschool training (Jensen 2012). Similarly, financial literacy

> 1 India ranks 149 of 153 in the Economic Participation and Opportunity sub-index of the 2020 World Economic Forum’s Global Gender Gap Index, before only Pakistan, the Republic of Yemen and Iraq. Although levels of gender inequality across Indian regions vary with agricultural endowments that affect demand for and value of female labor (Carranza 2014), such intra-country variation cannot explain low overall levels of female participation.

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Finally, controlling for land area and crop mix, male farmers have a productivity advantage over female farmers. This is largely explained by their greater access to resources, including agricultural inputs, transportation, credits, unpaid agricultural family labor, and mechanization. Our result therefore highlights the continued need to level the playing field between men and women. Further, our analysis shows that the male productivity advantage persists even after adjusting for agricultural resources and other endowments. The remaining unexplained part may possibly reflect an unequal pattern of division of labor driven by gendered social norms. Analysis based on the Sri Lanka Time Use Survey 2017 suggests that women working in agriculture spend significantly less time on paid employment and more time on unpaid domestic work, whether compared to male workers in agriculture or to their counterparts working outside agriculture (World Bank 2021b). Thus, interventions that could ease women’s household responsibilities, for instance, are likely to help increase women’s agricultural productivity and generate more crop income.

# REFERENCES

- <mark>Aguilar, A., E. Carranza, M. Goldstein, T. Kilic, and G. Oseni. 2015. “Decomposition of Gender Differentials in Agricultural Productivity in Ethiopia.”</mark> _<mark>Agricultural Economics</mark>_ <mark>46: 311–</mark> 34.

- <mark>Akresh, R. 2005. “Understanding Pareto Inefficient Intrahousehold Allocations.” IZA Discussion</mark> Paper 1858, IZA, Bonn, Germany. http://ftp.iza.org/dp1858.pdf.

- <mark>Ali, D., D. Bowen, K. Deininger, and M. Duponchel. 2016. “Investigating the Gender Gap in</mark> Agricultural Productivity: Evidence from Uganda.” _World Development_ 87: 152–70.

- <mark>Appleton, S., and A. Balihuta. 1996. “Education and Agricultural Productivity: Evidence from Uganda.”</mark
2- digit CNAE level), location (at the 2-digit zip code level), number of workers, and the number of years with a relationship with the bank. There are two advantages of studying the universe of working capital loans. First, working capital is the main non-earmarked credit product of firms in Brazil, representing 44% of non-earmarked total outstanding loans. Second, working capital financing is almost exclusively accessed through the free market, as it accounts for a negligible share of earmarked loans (only 0.3%). Thus, the pricing of a working capital loan is decided by the issuing bank and is not subject to interest rate caps.

Our second data set includes information on the origination of earmarked loans for every firm-bank pair in our sample. Each record includes the date and credit type of all earmarked loans originated in the country. Two patterns emerge from the earmarked credit data. First, the relationship of earmarked credit between a lender and its borrowers is highly persistent. More precisely, 92% of the firms that obtain their first earmarked credit and receive consecutive earmarked loans will use the same bank. Second, firms that receive earmarked loans obtain these loans on a frequent basis. The median time between consecutive earmarked loans in the data is nine months. Based on these patterns, we create an indicator variable, _Earmark_ , that records when an earmarked credit relationship starts between a firm-bank pair.

> 11 Each rating is associated with a minimum loan accounting provision, which is an estimate of the probability of default. The possible ratings are AA, A, B, C, D, E, F, G, H. However, we exclude from our sample all ratings below E, which account for less than 1% of the observations and are loans associated with evergreening processes.

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poverty. The former were important as public social service delivery was where the largest share of donor funding went in the post-HIPC period. Human welfare outcomes such as school enrollment and infant mortality were regularly scrutinized as part of MDG monitoring, while employment outcomes were not. Indicators of poverty reduction (as measured by household consumption) were given attention as well, and still remain the ultimate goal of both SSA country development strategies and donor finance (see a recent story on IDA results, 2000-2010 for an example<sup>2</sup> ).

A second reason for the poor quality of data is that it is not easy to collect data on employment and earnings outcomes in low-income Sub-Saharan African countries (hereafter, SSA). Survey research on the measurement of employment outcomes tends to take place in OECD countries, where most of the experts on the measurement of employment and earnings work. But the employment pattern in SSA is not at all similar to that found in OECD countries. In OECD countries, almost all employment takes the form of wage and salary employment, where employed labor force participants work for an unrelated individual according to an agreement covering the terms of the task and the remuneration. Job search involves entering the labor market and trying to find someone to hire you on acceptable terms. In SSA, employment takes the form of self and/or household employment, where a task is performed for family profit or gain (including agriculture for home food consumption). Most labor force participants never even enter the labor market. Yet SSA statistics offices routinely adopt survey instruments or modules for collecting data on employment supplied by statistical agencies in OECD countries. They may modify the surveys to fit local context, but without the benefit of local survey research or knowledge on the best techniques to use when measuring household or self-employment. This results in employment and earnings data that lack relevance to the country setting, and mismeasure the key features of employment (non-sampling error). Not surprisingly, in their detailed analysis of surveys from 23
, labor force participation, income share of labor, investment rate, openness to international trade, world governance indicator, life expectancy, innovation, employeeemployer cooperation, severance pay, labor tax and social contribution rates, inflation rate, gross government debt as a share of GDP, and budget balance as a share of GDP.<sup>18</sup> The X3 variable includes income groups; X1*X3 refers to the interaction term between income groups and UB and ALMP; and ei denotes robust white noise residuals. The analysis also controls for fixed effects across regions.

Outliers were removed using a standard method.<sup>19</sup> To increase the number of observations, control variables were averaged over 2017–2019 (real GDP per capita, income share of labor, labor force participation, world governance indicators, life expectancy, severance pay, and labor tax and contribution

> 15 Models using micro data or data from certain local geographic areas are not able to capture the secondary effects of UB and ALMP on other sectors and geographic locations, and yield results that are limited to that sector or location.

> 16 Since UB and ALMP data used in this analysis did not change much from mid-2019 to mid-2020, it was not possible to conduct a panel data analysis. Moreover, given that the labor market outcome indicators were only available until 2019, we used the 2019 values of UB and ALMP in the regression analysis instead of their 2020 values. Number of countries in the analysis changes based on the availability of indicators on labor market outcomes and control variables.

> 17 The maximum length of the UB indicator is the average of the maximum length of UB specified in labor laws for employees with one-, five- and ten-year tenures.

> 18 Please see Table A9 in the appendix for the definition, source, and summary statistics of all variables used in the regression model and Table A10 for the sample size and summary statistics of indicators used in the analysis after dropping outliers.

> 19 We
<!-- Start of picture text --> Fig 3.3a China’s rapid transformation in Fig 3.3b .... But slow transformation of the<br>export Structure: following the Comparative employment structure<br>Advantage<br>1.0 100<br>0.9 Misc. 90 Tertiary Sector<br>manufact<br>0.8 80<br>ured<br>0.7 articles 70 Secondary sector<br>0.6 Machinery 60<br>and<br>0.5 50<br>transport<br>0.4 Crude equipment 40 Primary Sector<br>0.3 Mfg'ed- 30<br>fuels and<br>material<br>0.2 Minerals 20<br>0.1 Food & 10<br>0.0 animals 0<br>Source : Justin Lin and Yan Wang 2008 and updated based on UN COMTRADE data<br>1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007<br><!-- End of picture text --> The evolution of China‘s export structure in the last three decades reflects the significant structural transformation in flying geese style that enabled the country to graduate from an exporter of labor-intensive products such as apparel, textiles and leather to more sophisticated ones such as home appliances, office machines and electric machinery (Figure 3.3). Such a transition from low- to middle and high value-added products could not have been possible had the original endowments of the Chinese economy remained unchanged during the last three decades. With annual savings of around 40 percent of GDP and dramatic improvement in education, its endowments of human, physical and financial capital have evolved significantly- a topic covered by many studies. Most importantly, its stock of technological capabilities has benefited from importing and adapting foreign technologies of production and mastering their use
experience and counseling in a protected environment. Qualified applicants were assigned to either training or control group _randomly_ . Members of the treatment group received job training for 9 to 18 months depending on their profile and the site. The nonexperimental data for the comparison group are contained in the file PSID_CONTROLS.TXT. This file holds relevant information on 2490 individuals drawn from the Population Survey of Income Dynamics (PSID). This sample consists of all male heads of household less than 55 years old who were continuously observed in the PSID, and not classified as retired in 1975.

Table 3.1. Descriptive Statistics for the Underlying Data

|**Variable**|**Treated**|**Comparison**|
|---|---|---|
|Age|25.82 (7.16)|34.85 (10.44)|
|Education|10.35 (2.01)|12.12 (3.08)|
|Black|0.84 (0.36)|0.25 (0.43)|
|Hispanic|0.06 (0.24)|0.03 (0.18)|
|Married|0.19 (0.39)|0.87 (0.34)|
|No High School Degree|0.71 (0.46)|0.31 (0.46)|
|Real Earnings in 1974|2095.57 (4886.62)|19428.75 (13406.88)|
|Real Earnings in 1975|1532.06 (3219.25)|19063.34 (13596.95)|
|Real Earnings in 1978|6349.14 (7867.40)|21553.92 (15555.35)|
|Zero Earnings in 1974|0.71 (0.46)|0.09 (0.28)|
|Zero Earnings in 1975|0.60
a detailed description of the BACI data set.

**Import tariffs** : We use annual data on bilateral applied import tariffs by country and product (SITC 2-dig., Rev. 4) from the TRAINS database of the United Nations Conference on Trade and Development. While these data span our sample period, the extent of time coverage differs considerably across countries.

**Initial GDP per capita** : We use data on GDP per capita of each developed country (in constant 2000 USD) from the World Development Indicators of the World Bank. We use values for the first year the country is observed in the estimation sample.

# **B.2 Variable definitions and summary statistics**

This section describes in detail the variables used in the econometric analysis. Table A2 provides summary statistics on each of these variables.

- _log_ (1+ _exp_ ): logarithm of one plus the FOB value of bilateral exports in thousands of US dollars of developed country _n_ to developing country _m_ in sector _i_ in year _t_ .

- _log_ (1 + _imp_ ): logarithm of one plus the FOB value of bilateral imports in thousands of US dollars from developed country _n_ to developing country _m_ in sector _i_ in year _t_ .

- _log_ (1 +<sup>_<u>robots</u>_</sup> _hours_<sup>):logarithmofoneplustheratiobetweenthestockofrobotsandthenumberof</sup> working hours (in millions) in developed country _n_ in sector _i_ in year _t_ . The stock of robots was estimated suing the perpetual inventory method based on the observed stock of robots in the IFR data and using a depreciation rate of 10%.

- _log_ (1 +<sup>_<u>obs.robots</u>_</sup> _hours_<sup>):logarithmofoneplustheratiobetweenthestockofrobotsandthenumber</sup> of work hours (in millions) in developed country _n_ in sector _i_ in year _t_ . The stock of robots was directly observed in the IFR data set.

- _Initial
curve lies below the solid reference curve. This indicates that Engel curves have drifted to the left, a signal that the national CPIs in these countries may overstate the increase in cost of living. In Ghana, Mozambique, and Uganda the direction of the CPI bias does not seem consistent across the income distribution. In Ghana, for example, the CPIs appear downward-biased among those people at the lower end of the income distribution, while they appear biased in the opposite direction among people at the upper end of the distribution. The pattern is reversed in Mozambique and Uganda.

While the illustrations in Figure 1 show a clear drift in the Engel curves for the countries in our study, movements in food budget shares could also be attributed to changes in relative prices, household characteristics, etc. Thus, we use regression analysis to condition on these potentially confounding influences and assess the actual drift in the food Engel curve.

Table 4 reports the results of ordinary least square (OLS) regressions of the Engel functions. All regressions are weighted by population-level sampling weights (as discussed later in this section, unweighted regressions are run as a robustness check). As explained in section III, we apply equation (4) to Ethiopia, Ghana, Mozambique, and Uganda (linked to the availability of crosssectional price data) and equation (10) to the rest of the countries. We also limit our sample to urban populations for all countries except Mauritius, Nigeria, and Rwanda.

Across all countries, the estimated regression coefficients for CPI-deflated total expenditure are negative and statistically significant. These results are consistent with Engel’s Law that food budget shares decline as households become richer. The coefficients of the various household characteristics show that, in general, food shares are higher among households of larger size, with younger children, and with heads who are married or older. Food shares decline among households in which the heads have higher educational attainment. There is no clear pattern across countries between the gender of the household head and the food budget
Nevertheless, this study, based on data from China, six major host African countries and 35 firm interviews, provides strong evidence that private Chinese investment in Africa is gathering momentum and starting to make an impact in some places. It reinforces what many have suggested, i.e., China’s overall role in global cross-border investment is likely to continue to grow in the near future, and the role played by its private sector especially deserves focused attention.

This phenomenon itself raises many more questions: Will private Chinese investment be a blessing or curse to Africa? Will it contribute to Africa’s industrialization or will it kill native “infant” industries? Will Chinese private businessmen provide valuable jobs for locals, or will they simply exploit the cheap labor? What influence will this investment have on the local business environment, fostering healthy law and order and economic competition, or aggravating local corruption? Finally, what impact will this new development have on China?

Answers to these questions have important policy implications for African host governments, as well as for China. Like any FDI, private Chinese OFDI carries not only new opportunities but also special challenges. How the host governments meet those challenges will determine the net impact of the investment for their economies. There are also implications for the home government as well as the involved Chinese firms. The following suggests several “do’s and don’ts” in terms of government policies, especially those for the host countries, that can influence the outcome of this investment.

# 1. Policy implications for host countries’ industrialization

First and foremost, the rising role of private Chinese investment appears to offer a great opportunity to African host countries, especially those which aspire to transform their traditionally resourced-based economies into more industrialized societies. Industrialization is a process involving many steps. The labor-intensity and relatively low technology associated with the current wave of Chinese firms relocating to Africa provides a desirable first step in this process. Such industries are easy for less developed host countries to absorb and adopt;
Global Findex survey provides information on financial resilience by asking respondents whether they would be able to come up with an amount equal to 1/20th of gross national income per capita in local currency within the next month. It also asks what their main source of funding would be.

The results show that adults in high‐income economies are far more likely (73 percent) to say that they could raise emergency funds than are adults in developing economies (50 percent) (figure 13). Most respondents in high‐income economies who report that they could come up with the emergency funds say that they would rely on savings, whereas most respondents in developing economies say that they would turn to family or friends or use money from working.

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