rafmacalaba/data-use-mentions-extended huggingface dataset:
employment coefficients, i.e., the number of workers needed to produce one billion dong worth of goods,<sup>12</sup> were calculated using the employment and production data from the annual _Enterprise Survey_ data and exports data from the UN Comtrade System at the two-digit VSIC level for the period 2000-2007. As the _Enterprise Survey_ data do not include household enterprises, Vietnam’s goods are assumed to be exported through formal enterprises.<sup>13</sup> The visual comparison between Figures 3.1-3.3 and Figures 4.1-4.3 reveals that the employment effect of exports is disproportionately larger for labor-intensive industries due to the relatively larger employment creation per dollar of goods exported in such industries. In contrast, the employment effect of increases in the value of mining sector exports is relatively modest due to that sector’s low labor-intensity despite the importance of the mining sector in Vietnam’s exports in value terms. Since Vietnam’s exports to the U.S. consist mainly of labor-intensive goods, if exports are evaluated in terms of their labor content, the share of the U.S. in Vietnam’s “industrial” exports for the year 2004 rises to about 28 percent. Specifically for the period 20012004, exports to the U.S. accounted for about 35 percent and 70 percent of _changes_ in Vietnam’s total industrial exports in terms of export values and labor content respectively. In the empirical section, I will use these large changes to identify the impact of exports on wages.

Appendix Table A.3 reports the evolution of Vietnam’s imports by the VSIC sub-categories for the period 2000-2008. Vietnam’s imports rose from $15,637 million in 2000 to $31,969 million in 2004, and, after Vietnam’s accession to the WTO in January 2007, increased at an accelerated pace to $80,714 million in 2008. Since United States goods enjoyed MFN tariff status in

> 12 The implicit assumption of this
/landscan.ornl.gov/documentation on August 3rd 2018.

- Socioeconomic Data and Applications Center (2018). Gridded Population of the World (GPW). Retrieved from http://sedac.ciesin.columbia.edu/data/collection/gpwv4/methods/method1 on November 19th 2018.

- Stevens, F. R., Gaughan, A. E., Linard, C., and Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. _PloS one_ , 10(2):e0107042.

- Sutton, P., Roberts, D., Elvidge, C., and Baugh, K. (2001). Census from heaven: An estimate of the global human population using night-time satellite imagery. _International Journal of Remote Sensing_ , 22(16):3061–3076.

- Varian, H. R. (2014). Big data: New tricks for econometrics. _Journal of Economic Perspectives_ , 28(2):3–28.

- Vogel, K., Goldblatt, R., Hanson, G., and Khandelwal, A. (2018). Detecting urban markets with satellite imagery. International Growth Centre Working Paper C-89448-INC-1.

- Wardrop, N., Jochem, W., Bird, T., Chamberlain, H., Clarke, D., Kerr, D., Bengtsson, L., Juran, S., Seaman, V., and Tatem, A. (2018). Spatially disaggregated population estimates in the absence of national population and housing census data. _Proceedings of the National Academy of Sciences_ , 115(14):3529–3537.

- Wooldridge, J. M. (2002). Inverse probability weighted m-estimators for sample selection, attrition, and stratification. _Portuguese Economic Journal_ , 1(2):117–139.

- WorldPop (2018). World pop website and methods page. Retrieved from http
4 finite samples and inefficiencies even asymptotically [Alonso-Borrego and Arellano **1996J.** To mitigate this problem, we use a _system_ estimator. Besides the _difference_ dynamic-panel equations, we simultaneously estimate the _level_ equations where the instruments are lagged values of the differenced regressors [Arellano and Bover **1995]. By** ameliorating the weak instrument deficiency, this _system_ estimator dramatically improves the consistency and efficiency of the estimator [Blundell and Bond **1997].** Thus, this paper uses two econometric procedures **-** a pure cross-sectional instrumental variable estimator and a GMM dynamic panel technique **-** to evaluate the impact of differences in banking sector development on economic growth, capital accumulation, productivity growth, and private saving.

The second major way in which this paper improves upon existing work is **by** using better measures of saving rates, physical capital, productivity, and banking sector development. Private saving rates are notoriously difficult to measure [Masson et al. **1995].** As detailed below, however, we use the results of a recent World Bank initiative that compiled high-quality statistics on gross private savings as a share of gross private disposable income for a broad cross-section of countries over the period 1971-1995[Loayza, Lopez, Schmidt-Hebbel and Serven **1998].** We also use more accurate estimates of physical capital stocks. Researchers typically make an initial estimate of the capital stock in **1950** and then use aggregate investment data to compute capital stocks in later years [King and Levine 1994; Nehru and Dhareshwar 1994]. These estimates use aggregate investment data that, for example, combine investment in residential structures with investment in equipment and machines, while employing a single depreciation rate. Recently, however, the Penn-World Tables (PWT) **5.6** constructed capital stock data based on disaggregated investment and depreciation data. While the PWT **5.6** discusses remaining measurement problems, these
- **Spillover from FDI in service sectors.** While most studies in the literature focus on spillovers from FDI inflows in manufacturing on manufacturing firms, research has also looked at the effects of FDI inflow in service sectors such as banking and telecommunication on manufacturing firms. Fernandes and Paunov (2012) <mark>examine the impact of FDI inflows in producer service sectors on the TFP of Chilean manufacturing firms and show that forward linkages from FDI in services explain 7% of the observed increase in Chile's manufacturing users' TFP. J</mark> avorcik and Li (2013) use data from Romania in 1997 – 2005 and find that an expansion of global retail chains leads to significant TFP increase in the supplying manufacturing sector. Arnold, Javorcik and Mattoo (2011) find a similar result in the Czech Republic and show that the presence of foreign firms in the service sector is associated with greater productivity in the manufacturing sector. A related study by Arnold et al (2016) finds that service reforms in India benefit both foreign and locally owned manufacturing firms, with the effect on the former being larger.

- **Mediating factors.** The existence and the magnitude of productivity spillover could be conditional on a variety of factors such as the absorptive capacity of countries and firms, forms of FDI, and domestic policies. For example, countries and firms with a stronger absorptive capacity such as R&D capacity and relatively skilled labor would be more likely to experience productivity spillovers. For example, Blalock and Gertler (2009) find that Indonesian firms with more R&D, more educated workers, and smaller technology gaps from foreign firms tend to benefit more from FDI. Another study by Javorcik and Spatareanu (2008) finds that ownership structure can also be a mediating factor. They find that vertical spillovers tend to arise from projects with shared domestic and foreign ownership but not from fully foreign owned plants. Du et al. (2012) examine how industrial policy – specifically tariff liberalization and tax
opportunities between the developing and developed worlds.<sup>16</sup> Second, there is signicant spatial heterogeneity at the regional level and across countries within a region. The Latin America and Caribbean region experienced the worst educational

> 15The 56 estimates are for the same birth cohorts as our sample: 1960s to 1990s birth cohorts. Bjorklund and Salvanes (2011) report a range of 0.40-0.60 for developed countries with the estimates for the United States among the highest. Prag et al. (2019) report an average of 0.49 from a meta analysis of the studies on sibling correlation in income and education published between 1972-2018 (includes both developing and developed countries).

> 16The existing cross-country evidence for developing countries based on father-child correlation in schooling also nds lower mobility in developing countries (see, for example, Neidhofer et al. (2018)). However, the existing evidence cannot answer whether this pattern holds for broader measures of socioeconomic status. We provide the rst evidence that this conclusion holds even when we capture a broad set of observed ad unobserved family and neighborhood factors.

5
# **IV. Descriptive Analysis**

_Table 1_ presents some descriptive statistics using data on 23 SSA countries (Table A1 presents a list of these countries) that have data on both manufacturing GVC and skilled/unskilled labor contents of exports. First, let’s look at the GVC participation index. The larger the ratio, the greater the intensity of a country’s involvement in GVCs. _Table 1_ shows that on average the intensity of involvement for SSA countries in manufacturing GVCs is approximately 44 percent. This means that less than half of total manufacturing exports in SSA are GVCs related exports. Since GVC participation has only two components reflecting upstream and downstream linkages in the chain, _Table 1_ provides suggestive evidence that the intensity of involvement for SSA countries is greater in backward (24%) than in forward (20%) manufacturing GVCs. This signifies that SSA countries incorporate more foreign inputs in their manufacturing exports (backward linkages) than they supply intermediate manufacturing inputs to the exports of other countries (forward linkages).

**Table 1: Summary Statistics – African Countries**

|**_Variable_**|**_Obs_**|**_Mean_**|**_Std. Dev._**|**_Min_**|**_Max_**|
|---|---|---|---|---|---|
|DVX|508|7,593,820|5,179,819|1,887,196|17,500,000|
|DVA|508|34,800,000|20,500,000|11,700,000|73, 500,000|
|FVA|508|8,972,219|5,984,821|1,936,014|19, 900,000|
|DVX_share|508|0.1995|0.0150|0.1683|0.2203|
|FVA_share
percent of children in non-RSE households attending school on average over this two year period.

In contrast, panel B shows no significant effect of the RSE on school attendance in Vanuatu. One reason may be that starting in 2010, when the final round survey was in the field, primary schooling became fully subsidized whereas previous fees were 7000 vatu per year (10 percent of per capita average income). Moreover, many schools had allowed students to remain enrolled even with unpaid fees from previous years; the main incentive to clearing these debts was that it allowed students to sit the leaving examinations at the end of Grade 6 and 8. Hence, RSE workers reporting the payment of school fees as a motivation may have repaying school fee debts, which would not show up in current enrolment except for a possibly higher transition rate to high school (which also depends on examination performance). Our data do not capture this higher transition rate, partly because rural students often leave their households to live with urban relatives so as to attend secondary school (national secondary enrolment rates are below 40 percent and are much lower in rural areas, in part because of geographic inaccessibly of schools).

The apparently divergent impacts of RSE participation on school enrolment also may reflect the nature of the selection into the RSE in the two countries. In Vanuatu, the households participating in the RSE are relatively better off, and their children have higher baseline school attendance rates than non-RSE households. It is therefore possible that credit constraints were not limiting schooling for this group. In contrast, the Tongan RSE households were relatively poorer at baseline than the non-RSE households, and had lower school attendance rates. The extra income earned through the RSE allows them to then catch-up to (and surpass) the school attendance rates of the non-RSE group.

Our data do not show any evidence that the RSE has fostered the development of nonagricultural businesses among the households in our sample in Tonga. None of the households surveyed mentioned investment in a business as a main use of the money
# **Introduction**

The sudden onset of the COVID-19 shock in early 2020 resulted in substantial disruptions to economic activities as a large share of the global population was confined at home and a myriad of restrictions aimed at reducing the spread of the virus were put in place. Between January and June 2020, the volume of global trade retraced by 13 percent with a sharp rebound from then onwards (World Bank, 2020). The sudden drop in the supply of products from China led to debates on whether the increased exploitation of global comparative advantage, which in the decade prior to COVID-19 turned China into the world’s manufacturing powerhouse, had produced a dangerous dependence. The unprecedented social distancing restrictions brought by the COVID-19 pandemic presented additional challenges to global production with reductions in in-person production.

An unexplored question we address in this paper is which are the supply-side characteristics of products that determined the vulnerability, or on the contrary, the resilience of their trade to the pandemic in 2020. We focus on how the following characteristics of exported products mediate the impacts of the COVID-19 crisis: unskilled labor-intensive production, production relying more on imported inputs, on inputs from dominant suppliers or for which China is a dominant supplier, and complex knowledge-intensive production.

Our identification strategy is based on a stringent difference-in-differences specification with bilateral monthly product exports (at Harmonized System (HS) 4-digit level) by all countries to 29 major markets - the 27 European Union (EU) countries, Japan, and the United States (US) – as the dependent variable. The main regressors are interaction terms between measures of COVID-19 incidence (death over population) and proxies for production vulnerability. We capture the effect of the COVID-19 shock by exploring how the response of exports for a given exporting countryimporting country pair at the product level over time is mediated by production vulnerability 2
productivity (see, for example, Aghion et al. 2004, Djankov et al. 2002, Djankov, McLiesh and Ramalho 2006, Gaviria 2002). Last, access to finance is captured via a dummy variable equal to 1 if the firm currently has a bank loan or a line of credit and 0 otherwise ( _Credit_ ).

Macro-level factors such as the quality of governance, institutions, human capital, and market size have been found to be important for the overall growth and development of the private sector. Regarding market size, the general belief is that a larger market size improves firmproductivity through several channels such as increasing returns to scale in the production process, increasing returns to scale in knowledge creation, higher returns from innovation activity that often involves fixed costs, greater human capital accumulation due to externalities, better returns to public goods like physical infrastructure, greater competition in the product-markets (see for example, Aghion et al. 2005; Aghion and Howitt 1998; Lucas Jr, 1988, Romer 1986). Therefore, we proxy market size by the (log of) population of the country taken from World Development Indicators, World Bank (WDI).

For human capital, there is a rich literature discussed above documenting the importance of human capital for overall growth and development. We use three measures of human capital, namely gross enrollment rates in primary education, gross enrollment rates in secondary education, and gross enrollment rates in tertiary education, taken from WDI.

Several studies show that well-functioning institutions and good governance provide an enabling environment that makes it easier for firms to do business, improve their efficiency and growth over time. This happens through several channels such as correcting for market failures and collective action problems, protection of private property against expropriation by state and private agents, provision of physical and financial infrastructure, ensuring competitive markets that reward efficient firms and weed out inefficient ones, reducing regulatory burden on the firms, 14
any impact of water infrastructure also on boys’ school attendance. We consider boys aged 5 to 15, and use hauling time as the main variable of interest, using as instruments the one-period lagged hauling time, the dummy variable indicating whether the household head is a widow(er) or is divorced, and households’ place of residence (rural or urban community). Model (8) is estimated on a sample of 1,174 observations. Estimation results are shown in Table 7. Interestingly the effects of hauling time on school attendance are almost exactly the same for boys as for girls, a result which is in line with findings described in Koolwal and van de Walle (2013). However the impact of household size and composition on school attendance is very different for boys and girls. Girls living in larger households are less likely to attend school in general, whereas household size does not affect boys’ overall school attendance. We however find that the higher the 16
Another method is **propensity score adjustments** , whereby the probability of response is modeled (typically using a probit or logit model) using the response status from the phone survey as well as observable household/individual characteristics for the entire sample (both responders and nonresponders) derived from the representative survey (Little, 1986). The probability of response for each sampled unit is then predicted using the model parameters and the inverse of the predicted response probability (or propensity) applied to the base weights. This approach could introduce extreme weight adjustments and thereby increase the variance of the weights (and estimates obtained). However, the risk of this can be reduced by forming response classes based on the predicted probability (e.g. probability deciles) and the average predicted probability within the class used as the adjustment factor. The propensity modeling approach is particularly attractive for telephone surveys drawn from representative surveys that contain a diverse set of characteristics that can be incorporated into the propensity model.

A further method that can be employed is through **calibration of nonresponse** <mark>(Lundström & Särndal, 1999; Andersson & Särndal, 2016)</mark> **<mark>.</mark>** Calibration is a weight adjustment technique utilized in many surveys whereby weights are adjusted so that the weighted sample composition matches known characteristics of the underlying population. Typically, auxiliary data from population censuses are utilized for these calibrations. However, this approach can be extended to also include sample-based estimates from a representative survey used as a frame <mark>(Andersson & Särndal, 2016)</mark> . In this case, the weights for the interviewed sample will be calibrated across all considered characteristics such that the weighed profile of characteristics obtained from the interviewed sample closely match those from the full representative survey sample. Provided the characteristics included in the calibration are closely associated with nonresponse, the calibration will serve to counteract the nonresponse bias. There are many complex calibration models that could be used
A number of findings stand out based on the host government data.

#  **_In all six host countries, the numbers of Chinese investment projects proved to be several times higher than those recorded by the MOC system, confirming the suspicions of many in China._**

Figure 4 below shows the number of Chinese investment projects that are “in operation” in each of the six host countries, contrasted to the number of projects reported by the MOC database. To be conservative, in all countries except Rwanda, the research uses numbers of projects “in operation,” rather than “committed.”<sup>14</sup> It is quite astonishing to see how closely the Chinese estimated, i.e., what is happening on the ground is significantly higher, by 2-7 times, than what is captured by MOC data in all six countries.

**Figure 4: Chinese Investment in Six African Countries, as of end-2011** (by project number)

<!-- Start of picture text --> 700<br>605<br>600<br>478<br>500<br>400<br>313<br>290<br>300<br>212<br>200<br>123 111 113<br>87<br>100 16 7 25<br>0<br>Nigeria Zambia Ethiopia Ghana Liberia Rwanda<br>Data from China Ministry of Commerce Data from Host Country IPA<br><!-- End of picture text -->

# _Source: Shen and Zhai._

More specifically, there are 2.9 times as much Chinese investment projects in Nigeria, 2.5 times in Zambia, 2.6 times in Ethiopia, 3.6 times in Rwanda, 5.5 times in Ghana, and 7 times in Liberia.

Host country data do not distinguish private and government ownership. However, the differences are ostensibly caused by private projects assuming SOEs generally comply
0.010)|(0.010)|(0.012)|6|
|Any assistance from any|0.17|0.20|0.21|0.18|8|
|source|(0.052)|(0.119)|(0.119)|(0.096)||

Source: Own calculations based on HFPS-Wave 1.

Notes: Sample limited to urban household heads. Nonfarm (farm) income is reported for agricultural (nonagricultural) enterprises since income is reported at the household level while the source of income is only defined for the household head. Similarly for reported changes in income for wage employees.

> 31 Countries included: **Total income decline** (Ethiopia, Malawi, Nigeria, Senegal), **Non-farm income** (Ethiopia, Malawi, Nigeria, Sudan, Senegal, Sierra Leone, South Sudan, Zimbabwe), **Farm income** (Ethiopia, Malawi, Nigeria, Sudan, Sierra Leone, South Sudan, São Tomé and Príncipe), **Business revenue** (Burkina Faso, Ethiopia, Malawi, Nigeria, Sudan, Sierra Leone, South Sudan, Zimbabwe), **Wage income** (Ethiopia, Malawi, Nigeria, Sudan, Senegal, Sierra Leone, South Sudan, Zimbabwe), **Sold assets** (Ethiopia, Nigeria, Sudan, South Sudan), **Private assistance** (Burkina Faso, Ethiopia, Malawi, Nigeria, Sudan, Zimbabwe), **Public assistance** (Ethiopia, Malawi, Nigeria, Sudan, South Sudan, Zimbabwe), **Any assistance from any source** (Burkina Faso, Ethiopia, Malawi, Nigeria, Sudan, Sierra Leone, South Sudan, Zimbabwe).

15
Goodhart, C. and Perotti, E., 2012. _Preventive Macroprudential Policy._ http://www.voxeu.org/article/preventive-macroprudential-policy Hanson, S., Kashyap, A., and Stein, J., 2011. _A Macroprudential Approach to Financial Regulation._ Journal of Economic Perspectives, 25(1): 3–28.

IIF - Institute of International Finance, 2012. _Capital Flows to Emerging Economies_ . Research Note, June.

IIF - Institute of International Finance, 2012. _“Shadow Banking”: A Forward-looking Framework for Effective Policy_ , June.

International Monetary Fund, 2009. _Lessons for Monetary Policy from Asset Price Fluctuations_ . World Economic Outlook, October.

International Monetary Fund, 2010. _The IMF FSB Early Warning Exercise: Design and Methodological Toolkit_ . www.imf.org/external/np/exr/facts/ewe.htm Klein, M.W., 2012. _Capital Controls: Gates and Walls_ . Fall 2012 Brookings Panel on Economic Activity, Washington, September 13-14.

Koenker, R. and Hallock, K., 2001. _Quantile Regression._ Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.

Lim, C., Columba, F., Costa, A., Kongsamut, P., Otani, A., Saiyid, M., Wezel, T., Wu, X. _Macroprudential Policy: What Instruments and How to Use Then?_ International Monetary Fund Working Paper N. 11/238.

Magud, N. and Reinhart, C., 2006. _Capital Controls: An Evaluation_ . NBER Working Paper N. 11973.

Mishkin, F., 2009. “Will Monetary Plicy Become More of a Science?”, _in_ Deutsche Bundesbank, ed., _Monetary Policy Over Fifty Years: Experiences and Lessons_ , London: Routledge, pp.81-107.

Mishkin, F., 2011. _Monetary Policy Strategy: Lessons From The Crisis_ . NBER