rafmacalaba/datause-encoder-data huggingface dataset:
(where numbers in parentheses refer to activity classification codes of the database): Food and Beverages (4); Textiles and Wearing Apparel (5); Wood and Paper (6); Petroleum, Chemical and Non-Metallic Mineral Products (7); Metal Products (8); Electrical and Machinery (9); and Transport Equipment (11).

The most recent analyses of GVC linkages in Africa include Allard et al. (2016) and AfDB, OECD, UNDP (2014). Allard et al. (2016) make use of the EORA database to estimate upstream linkages in GVC participation for countries in the region as the outcome of a range of control variables that are also of interest to this study. The report by AfDB, OECD, UNDP (2014), looks specifically at the role of GVC participation in the context of a strategy for the industrialization of an economy. It is also based on estimates of upstream linkages (the share of FVA in country exports) as well as downstream linkages (the share of DVX in county exports) for a wider range of two-digit or three- digit ISIC industries than those reported in Allard et al (2016).<sup>3</sup> Allard et al (2016) conclude that many countries in the region have comparative advantage in tasks that might have high shares in the value added of final goods in manufacturing industries. This is consistent with the even stronger conclusion of AfDB, OECD, and UNDP (2014), also based on the EORA database, that in Africa as a whole –including North Africa –local manufacturers are more integrated into GVCs than domestic firms in agriculture, mining or services.

The main working hypothesis of this paper is that natural resource endowment and economic geography are major influences on countries’ linkages and role in manufacturing GVCs. These influences can be estimated and identified in the framework of an econometric factorproportions- based gravity model of “supply-side differences” between countries as partners in trade in goods and services or trade in tasks. Antras and de Gortari (2016) provide a theoretical

> 3 The industries covered in AfDB, OECD, UNDP (2014) include textiles and apparel, food and beverages, electrical goods and machinery, transport equipment, wood and paper, and the catchall category of “other manufacturing”.

6
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31
# **1. Introduction**

The business environment (BE) and entrepreneurship of developing and transition economies have received considerable attention from both researchers and policy makers over the decades (Berkowitz and DeJong, 2005; Dethier et al., 2010; Djankov et al., 2005; 2006; McMillan and Woodruff, 2002; World Bank, 2005; Xu, 2011). Indeed, creating a sound business climate is regarded as a key strategy for development, as it offers opportunities and incentives to entrepreneurs and firms to invest, create jobs, and expand business, consequently contributing to growth and poverty reduction (Stern, 2002).<sup>2</sup> To help improve BEs in the developing world, the World Bank has conducted firm surveys (i.e., the World Bank Enterprise Surveys, or WBES) to consult managers/owners about the obstacles of doing business in more than 135 economies since 2002.<sup>3</sup> The availability of cross-country firm-level data allows researchers to study the impact of BEs at the firm level. Numerous studies have been devoted to discussing how entrepreneur behaviors and firm performance are affected by a broad range of BE factors such as legal environments, property rights protection, infrastructure, access to finance, regulation, corruption, and crime rates.<sup>4</sup> Perhaps because most existing data such as the WBES surveys evaluate the BE at a specific year, few studies explore how a broad range of BE aspects and entrepreneurship have changed over time. As a result, little is known about how the BE has evolved, its likely impact over the decades, and how entrepreneurship and firm characteristics respond to such changes in the BE. Such a gap renders our understanding about the BE incomplete. For instance, do various BE elements differ in their speed of adjustment? If so, what is the economic and political logic behind such disparities? Once some BE elements evolve, do entrepreneurs and firms take advantage of such changes?

We fill some of these gaps in the literature by examining how a broad range of BE measures and entrepreneur characteristics evolved in China over 1993-2012. Although chosen

> 2 According to Stern (2002), business climate refers to “policy, institutional, and behavioral environment, both present and expected, that influences the returns, and risks, associated with investment” in a region.

> 3 An alternative and complementary effort of the World Bank to facilitate reforms in the business environment is the Doing Business project, which captures the country-level business environment (see Djankov et al. 2002, 2003, 2007, 2008, 2010).

> 4 See Xu (2011) and Ayyagari, Demirguc-Kunt and Maksimovic (2012) for a comprehensive review on the effects of business environments on development.

2
( _α_ and _β_ in equation (3)). In this case the quantification of the results is very similar.

Table 1: The impact of Chinese unilateral trade liberalization on the extensive margin of exports

|Dependent variable|Log|Exportedproducts|of frm _f_ in country|_j_ inyear_t_|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Tariff_fj,t−_1|-0.492***|-0.497***|-0.453***|-0.167|
||(0.136)|(0.136)|(0.137)|(0.150)|
|Tariff_fj,t−_1_×_China_j_||||-1.586***<br>(0.312)|
|RER_j,t−_1||-0.311*|-0.045|-0.141|
|||(0.164)|(0.170)|(0.171)|
|GDP_j,t−_1|||0.316***|0.235**|
||||(0.094)|(0.096)|
|Firm fxed effects|Yes|Yes|Yes|Yes|
|Country fxed effects|Yes|Yes|Yes|Yes|
|Year fxed effects|Yes|Yes|Yes|Yes|
|Observations|34,327|34,327|34,327|34,327|
|_R_<sup>2</sup>|0.542|0.542|0.543|0.543|

_Notes_ : The regressions are OLS estimations of equation (2) for the period 1999-2005. The dependent variable is the logarithm of the number of products exported to country _j_ in year _t_ by firm _f_ . Fixed effects by firm, country and year and a constant are included in all specifications. The destinations are China and other Asian countries that already integrate WTO such as India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand. Tariff _fjt_ is the tariff measure faced by firm _f_ when exporting to country _j_ in year _t_ . Tariff _fjt ×_ China _j_ is an interaction term between the firm level tariff measure and a dummy variable equal to one when the country of destination of exports is China and zero otherwise, China _j_ . GDP _jt_ is the natural log of the GDP of country _j_ from the Penn World Tables. RER _jt_ is the bilateral real exchange rate between France and China and countries in the control group using producer prices of France and importer countries from the International Financial Statistics (IFS). Heteroskedasticity-robust standard errors clustered by firm-country pairs are reported in parentheses. _∗∗∗, ∗∗,_ and _∗_ indicate significance at the 1, 5 and 10 percent levels respectively.

Table 2 reports similar results for the intensive margin of exports. After controlling for observable country level characteristics in columns (2) and (3), the average reduction of Asian tariffs leads to almost 3 percent increase in French firms’ export sales. The coefficient of the interaction term between the tariff measure with the China dummy variable in column (4) shows a larger effect due to Chinese liberalization relative to the average Asian tariff cuts. This allow us to conclude that China’s tariff reductions increase by a larger amount export sales of French firms towards this destination relative to the others: Chinese tariff cuts boost by almost 19 percent (7 times _β_ ) French export sales. While the net effect of Chinese liberalization on French firms export sales is 21 percent (7 times ( _α_ + _β_ )).

18
Ethiopian data the Singh-Maddala ranks first (being observationally equivalent to the GB2). For income data, the results in table 4 are somewhat mixed. The GB2 is clearly the best model for Burkina Faso, Ethiopia, Ghana, Malawi (for 2017 income data), Nigeria, Rwanda, Tanzania, and Uganda, whereas for Kenya, the Malawi’s 2013 household incomes and the Niger’s 2011 data, the Singh-Maddala seems to be as good as the GB2. A similar conclusion applies to Niger’s 2014 data, but in this case the Dagum model fits the data better than the alternatives. In general, the GB2 model gives the best fit to both consumption and income data in 7 of the 14 cases analyzed: Ethiopia 2014, Ghana 2005, Nigeria 2004, Rwanda 2014, Tanzania 2009, Tanzania 2013, and Uganda 2005. For the remaining country-year pairs, the following parametric distrbutions are instead selected as imputation models:

- Burkina Faso 2014: Dagum for the consumption distribution and GB2 for the income distribution

- Ethiopia 2014: Singh-Maddala for the consumption distribution and GB2 for the income distribution

- Kenya 2005: GB2 for the consumption distribution and Singh-Maddala for the income distribution

- Malawi 2013: Dagum for the consumption distribution and Singh-Maddala for the income distribution

- Malawi 2017: Dagum for the consumption distribution and GB2 for the income distribution

- Niger 2011: Dagum for the consumption distribution and Singh-Maddala for the income distribution

- Niger 2014: GB2 for the consumption distribution and Dagum for the income distribution

The suitability of fit of the functional forms chosen according to the model selection methods is evaluated by comparing the sample values of distributional indicators reported in table 1 with their counterparts implied by the fitted models—see the last four columns of table 3 and table 4.<sup>21</sup> Specifically, in figures 3 and 4 the comparison relies on checking for overlap between 95 percent confidence intervals of theoretical and sample indicators to draw conclusions about the accuracy of selected distributional statistics deduced by parameter estimates. The results suggest that for most of the indices (the mean, the Gini coefficient, the MLD, and the Theil index), the best-fitting models produce theoretical values that are quite often in a close agreement with the corresponding sample values—the respective confidence intervals overlap in a way that let us exclude that the predicted values, and the sample estimates of chosen

> 21 The analytical expressions for all indices considered here, which are functions of the estimated parameters of the GB2 and its nested distributions, can be found, inter alia, in Kleiber and Kotz (2003, ch. 6) and Jenkins (2009).

18
in productivity may be due to better sorting across sectors, where home production is one of the sectors. Second, we explore the possibility that preschool availability may lower worker turnover among female employees, which also may result in productivity gains. Both channels are consistent with an increase in wages of the plants’ workers. Hence as a further implication of both channels, we check also whether the increase in preschool availability leads to an increase in workers’ wages, consistent with Pissarides (2000) and Beerli (2021).

# 5.1. Improvement in sorting across sectors

Hsieh et al. (2019) look at how improved allocation of talent increases aggregate productivity. Their paper introduces forces such as group-specific social norms to the standard Roy (1951) model which act as distortions to the allocation across occupations. If individuals sort not only based on their ability or preferences, as is the case in the standard Roy (1951) model, but also on the basis of other factors, such as discrimination or social norms about who should take care of the children, such sorting reduces the average quality of workers in an occupation, and hence productivity. Hsieh et al. (2019) demonstrate that between 20 percent and 40 percent of US growth from 1960 to 2010 was driven by improved allocation of talent across occupations, including women leaving the home sector for market work.

Lack of childcare may be one such barrier that lowers the likelihood that women sort into market work. Removing this barrier is likely to bring qualified women into the labor market, increasing productivity. We use two data sets to test how preschool expansion impacted female employment. First, we rely on the manufacturing survey to estimate the impact of preschool expansion on employment of women in the plants in our sample. Specifically, we estimate equation (1) with the number and share of female workers in a plant as a dependent variable. We use the sample of all manufacturing plants, and then limit it to the sample for which TFPR and TFPQ measures are available. Table 9 presents the results. One additional preschool per 1,000 children in a district increases the share of female workers by 0.4 percentage points, or 1 percent, at the plants with average share of female workers (41 percent). The coefficient is significant at the 1% level. The results are very similar for both the full sample and the sample restricted to plants for which TFP measures are available. We also find evidence of impact on the number of female workers. However, these results are weaker as the coefficients are estimated 16
Table 15: Impact of 3G/4G access on non-food consumption by group (fixed effects models)

||Female|Headed|Consu|mption|Re|gion|
|---|---|---|---|---|---|---|
||No|Yes|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|0.043|-0.088**|0.061**|-0.027|-0.009|0.047|
||(0.027)|(0.041)|(0.029)|(0.032)|(0.034)|(0.033)|
|Medium or strong 3G/4G|0.072***|0.013|0.080***|0.070**|0.050*|0.036|
|for one year|(0.028)|(0.039)|(0.031)|(0.032)|(0.030)|(0.039)|
|<br>Medium or strong 3G/4G|<br>0.066**|<br>0.023|<br>0.103***|<br>0.105***|<br>0.022|<br>0.021|
|for two years or more|(0.027)|(0.042)|(0.031)|(0.030)|(0.027)|(0.043)|
|Medium or strong 3G/4G|0.070**|0.040|0.130***|0.105***|0.009|0.025|
|for three years or more|(0.028)|(0.044)|(0.034)|(0.030)|(0.028)|(0.045)|
|# of observations|10,772|2,959|7,246|6,297|6,747|6,985|
||Age House|hold Head|Localit|yType|Edu|cation|
|||50 or|||Primary|Less than|
||Under 50|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|0.017|0.013|0.035|-0.014|0.011|0.077|
||(0.031)|(0.030)|(0.028)|(0.045)|(0.028)|(0.047)|
|Medium or strong 3G/4G|0.046|0.055*|0.070**|0.038|0.055**|0.030|
|<br>for one year|(0.031)|(0.030)|(0.029)|(0.041)|(0.027)|(0.047)|
|Medium or strong 3G/4G|0.064**|0.040|0.076**|0.052|0.050*|0.043|
|for two years or more|(0.030)|(0.028)|(0.030)|(0.037)|(0.025)|(0.047)|
|Medium or strong 3G/4G|0.065**|0.048|0.117***|0.011|0.047*|0.093*|
|for three years or more|(0.032)|(0.031)|(0.033)|(0.039)|(0.026)|(0.054)|
|# of observations|6,738|6,574|9,442|4,290|8,645|2,076|
||Liter|acy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|0.050|0.007|-0.011|0.044|||
||(0.036)|(0.028)|(0.029)|(0.030)|||
|Medium or strong 3G/4G|0.087**|0.056**|0.065**|0.058*|||
|<br>for one year|(0.038)|(0.028)|(0.030)|(0.031)|||
|Medium or strong 3G/4G|0.077*|0.050*|0.070**|0.046|||
|<br>for two years or more|(0.039)|(0.027)|(0.028)|(0.031)|||
|<br>Medium or strong 3G/4G|<br>0.110**|<br>0.045|<br>0.083***|<br>0.047|||
|<br>for three years or more|(0.044)|(0.028)|(0.030)|(0.033)|||
|# of observations|4,976|8,587|6,423|7,309|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. *** p<0.01, ** p<0.05, * p<0.1.

24
# Contents

|**1.**|Introduction ............................................................................................................................ 2|
|---|---|
|**2.**|Global and regional trends of COVID-19 and government responses .................................... 4|
|**3.**|Methodology .............................................................................................................................7|
|_a_|_._<br>_CCMs as Treatments_............................................................................................................7|
|_b._|_Endogeneity of Containment Measures_............................................................................. 9|
|**4.**|Econometric Results ............................................................................................................... 11|
|_a_|_._<br>_Adoption of CCMs_............................................................................................................... 11|
|_b._|_The timing of adoptions and lifting of CCMs_..................................................................... 15|
|_c._|_The effectiveness of CCMs_.................................................................................................. 17|
|**5.**|Summary and Conclusions .................................................................................................... 22|
|Ref|erences ..................................................................................................................................... 24|
|App|endix ....................................................................................................................................... 25|
# **1.   A Framework for Land Policy**

Around the world, in both developed and developing countries, policy makers use a variety of tools to manage and accommodate urban growth and redevelopment. Government officials have three main concerns in terms of land policy (i) accommodating urban expansion, (ii) providing infrastructure, and (iii) managing density. Together, the planning of infrastructure and urban expansion, land use and density policies combine to shape the spatial structure of cities. When effective, urban planning aligns land use, population and employment density and infrastructure to optimize urban form and economic productivity (Joshi and Kono, 2009). Density is in fact a key driver of the demand for urban infrastructure and largely determines the functional efficiency of cities. Land instruments for accommodating growth and financing infrastructure are therefore most useful when placed in the context of an overarching policy framework that helps in sequencing among policies.

At early stages of urbanization, policies that facilitate rural-urban land use conversion will be critical to support urban expansion. Creating strong institutions that facilitate this transformation and help minimize transaction costs and reduce information asymmetries will contribute to enhancing the fluidity of incipient land markets. Clear definition of property rights and valuation will set the basis for well functioning markets (see Table 1).

As urbanization advances and reaches intermediate levels, population pressures in cities will increase and policies that promote the coordination of land use management and infrastructure development, and manage densification gain importance. Well designed policies may allow cities to grow vertically with the same parcel of land accommodating higher value economic use, and supporting more people and infrastructure. The challenge here is to allow for rapid substitution between land and capital, where land shortages will be managed through higher levels of capital investment. While the core land institutions mentioned above remain important, city and neighborhood level zoning, building codes, and FSIs gain importance.

Finally, as demand for urban built space increases with incomes, cities spread out (sprawl), and individual cities become part of a larger metropolitan area. Policies that promote coordinated and consolidated management of metropolitan areas will gain importance in the land policy portfolio for countries at middle incomes and cities at mature stages of urbanization.<sup>6</sup> However, no matter the stage of urbanization or the levels of incomes, urban transformation is buttressed by strong institutional foundations that govern the functioning of land markets. These include (i) clear definition and protection of property rights, (ii) institutions that enable independent valuation and public dissemination of land values across uses, and (iii) a judicial system that can handle disputes that may arise in the process.

> 6 World Bank (2008), Dowall (1992) , and Bertaud (2004, 2010)

2