rafmacalaba/data-use-mentions-extended huggingface dataset:
The dependent variable, r r r i i , is the dummy variable indicating whether an SOE _i_ in city _c_ and two-digit industry PPr _j_ is privatized in year rr _t_ . Since privatization is rarely reversed, each SOE’s observations after the year of privatization are dropped from the sample for the _current_ empirical exercise. A refers to a series of age dummies of the party secretary, being one if the party secretary in city ssi _c_ and year _t_ is _s_ years old, with _s_ taking the values of 51 rr and above. We treat party secretaries younger than age 51 as the reference group. We include city, industry and year fixed effects, and cluster the standard errors at the city level to accommodate for correlation in the error term within a city.

We control for firm characteristics, the party secretary’s background, and the city’s macro conditions (i.e., i ). The firm-level characteristics, all once-lagged, include labor productivity ( _Labor prod_ ii , the logarithm of the ratio of sales to the number of employees), the XX leverage ratio, the logarithm of total employees, and the return on asset ( _ROA_ ). The characteristics of party secretaries include the indicator of having college education ( _College_ ), the indicator of having work experience in the central government ( _Central Experience_ ), and in the provincial government ( _Provincial Experience_ ). The macroeconomic conditions in the city include the GDP growth rate and GDP per capita at the city level.

According to the OLS results in Table 2, the probability of the privatization of local SOEs drops abruptly when the age of the local party secretary passes the threshold of 58. The coefficients of the age dummies for age between 51 and 57 are all statistically insignificant, indicating no differences in the tendency to pursue privatization relative to that of a younger politician. However, when he becomes 58 years or older, the probability of an SOE proceeding with privatization drops by 4 percentage points. This is a large magnitude: relative
15 with transitory income shocks such as rainfall, nutritional and health status, or situationspecific variables such as arrears in pensions in Russia, or even measures of subjective well-being, could help capture better the transitory component in consumption.<sup>9</sup> In this paper, we focus deliberately on the relatively sparse set of assets that is readily available in most conventional surveys and that has been commonly found to explain well variation in household consumption levels. Including more time variant variables from outside the questionnaire would possibly contribute to greater precision and less bias but would likely increase the data compilation efforts in practice.

To better capture economies of scale associated with the consumption of goods that have a public good flavor and to increase the predictive power of the consumption model, household demographic information (asset class 2) can be incorporated. At the same time, however, one might expect that assets such as labor related variables and education variables (asset class 2 and 3) would be more prone to parameter instability following structural or policy-induced economic transformation.

Accordingly, poverty predictions derived from consumption models using different asset class combinations will be compared. From within each of the asset classes, care will be taken to specify models that achieve satisfactory explanatory power but that also minimize model error. A balance is sought whereby additional regressors that add to explanatory power can be added only if parameter estimates are precisely estimated. Concerns about overfitting also prompt a preference for relatively parsimonious specifications. As a basic starting point, it is generally appealing to check the performance of a particular specification on a random sub-sample of the survey data

> 9 As they often represent important reasons for changing returns in assets, they could further mitigate the likelihood of violating the parameter stability assumption.
policies on Foreign Direct investments (FDIs) affect the probability of awarding a procurement contract to a foreign firm.<sup>4</sup> Fronk (2014) estimates the effect of TAs in a gravity model using U.S. federal – procurement data as such, he has one buyer (the U.S.) from multiple suppliers. While measuring precisely procurement purchases (at least by certain public entities and for values above certain thresholds)<sup>5</sup> , the contract-level data cover only a single or a few countries

> 3Cole et al. (2017) extends the model of McAfee and McMillan (1989) to establish an equivalence between price preferences in procurement auctions and import tariffs.

> 4Gourdon and Messent (2019) estimate the effect of TAs on the value of procurement contracts awarded by the EU on non-EU firms using both contract-level and aggregate data. Their set of TAs is thus limited to those signed by the EU.

> 5 The TED (Tenders Electronic Daily) database includes contracts awarded by public authorities (at the national and sub-national level) in countries of the European Economic Area and by EU institutions. Reporting is a requirement if the value of the contract is above a certain threshold (around 5 million euros

6
., Costa Junior, C., Brandão Junior, A. et al., 2018).

We obtained emission data from the latest version of System for Estimating Greenhouse Gas Emissions (SEEG version 10, 2022). An initiative of Climate Observatory, SEEG estimates are based on Brazilian Inventory (BI) of Anthropogenic Emissions and Removals of Greenhouse Gases from the Ministry of Science, Technology, and Innovation (MCTI) that are consistent with guidelines of Intergovernmental Panel on Climate Change (IPCC). SEEG reproduces and validates BI data, and improves its coverage and granularity (de Azevedo, T., Costa Junior, C., Brandão Junior, A. et al., 2018). For our analysis, we 10
interviewed in all the 3 waves of the HFPS. Likewise, Table 6 reports the results for a model that includes fixed effects by region. In both cases the results in Table 4 hold.<sup>17</sup> Finally, we evaluate whether the mechanism underlying the gender differences in the impact of WFH on job losses is related to occupational segregation by gender. For this, we estimate a variant of model 2 that includes fixed effects by pre-pandemic occupation using the 2-digit ISCO classification. Table 7 reports these results. The coefficient associated to the interaction between gender and WFH is still negative and significant in the sample of households with children, which means that even among women in the same occupation, those with children were the ones for whom the ability for WFH was pivotal. This suggests

> 14Table A.3 reports the results of estimating a variant of model 2 where instead of the adjusted WFH index we separately incorporate the job characteristics and internet access. Our main results hold, as well as when we differentiate between the different types of internet access (Wi-Fi or mobile network).

> 15Unfortunately, the HFPS does not allow for a more complete characterization of the composition and structure of the household. For instance, we do not have information for children younger than 5 nor can we identify which household members are the parents; neither their marital status nor if there are elderly people in the home.

> 16The regressions for households with and without children are similar, in particular they include the same controls, so in principle the results are comparable. As is usual when analyzing heterogeneities, the omission of an unobservable control could generate a larger bias in one of the samples, distorting the results of the comparison.

> 17It would also have been relevant to check the robustness of the results after controlling for the prepandemic economic sector, but unfortunately this variable has 60 percent missing observations in the HFPS.

12
of informality, yet strong positive effect on welfare gains, GDP growth and wages. Similarly, Maican and Orth (2018) analyze Swedish retail food stores data and establish that, by enhancing competition, lower entry costs increase welfare in differentiated product markets.

According to Bruhn (2013), different types of business owners in the informal economy react to reforms in different ways. Analyzing the effects of a business registration reform in Mexico, the author explains why this reform did not impact all the informal business owners in the same way. The results demonstrate that the owners of informal businesses were more likely to register them if prior to the reform they possessed similar personal characteristics to those of formal business owners. Such characteristics included gender, age, marital status, education as well as being a head of a household and/or a migrant. In turn, owners of informal businesses with traits similar to those of wage workers, were more likely to become wage workers rather than formalize their businesses even when regulations became more conducive.

At the same time, Demenet (2016) finds that by moving from informal economy to formal registration, firms get greater access to new equipment and can increase their scale of operations, resulting in higher competitiveness and productivity. Curtis (2016) takes the example of China to examine the impact of business reforms on the country’s total factor productivity growth. Building a model that captures both the private and state sectors, the author finds that entrepreneurship-friendly business reforms lead to expansion of a private sector and exit of least productive state firms from the market, hence increasing total factor productivity gains. Furthermore, Djankov et al. (2018) analyze panel data for 189 economies from 2005 to 2013 to suggest that effective business regulations can contribute to poverty reduction; mostly due to stimulation of job creation and promotion of entrepreneurship. The 7
- Das, Jishnu, Stefan Dercon, James Habyarimana and Pramila Krishnan. 2007. “Teacher Shocks and Student Learning: Evidence from Zambia.” _The Journal of Human Resources_ 42(4):820-862.

- Dobbie,W., and Fryer Jr, R. G. (2013). Getting beneath the veil of effective schools: Evidence from New York City. _American Economic Journal: Applied Economics_ 5(4): 28–60.

- Halpin, Peter F., and Michael J. Kieffer. 2015. “Describing profiles of instructional practice: A new approach to analyzing classroom observation data.” _Educational Researcher_ 44(5): 263-277.

- Hamre, Bridget K., Robert C. Pianta, Andrew J. Mashburn, and Jason T. Downer. 2007. “Building a science of classrooms: Application of the CLASS framework in over 4,000 US early childhood and elementary classrooms.” _Foundation for Childhood Development_ .

- Hanushek, Eric A. and Ludger Woessmann. 2008. “The Role of Cognitive Skills in Economic Development.” _Journal of Economic Literature_ 46(3): 607-68.

- Hanushek, Eric A. and Ludger Woessmann. 2015. _The Knowledge Capital of Nations_ . Cambridge: The MIT Press.

- Kane, Thomas J., Eric S. Taylor, John H. Tyler, Amy L. Wooten. 2011. Identifying Effective Classroom Practices Using Student Achievement Data.” _Journal of Human Resources_ 46(3): 587-613.

- Kane, T. J., and D. O. Staiger. 2008. “Estimating Teacher Impacts on Student Achievement: An Experimental Validation.” NBER Working Paper No. 14607.

- Kane, Thomas J., and D.O. Staiger. 2012. Gathering Feedback for Teaching: Combining High-Quality Observations with Student Surveys and Achievement Gains. Bill & Melinda Gates Foundation.

- Martin, Gayle and Waly Wane. 2016. “Education Service Delivery in Tanzania.” World Bank Report No. AUS5510
**24** **<u>│</u>**

observed on aggregate with domestic firms do not appear to be substantial, job creation also appears to be associated with seasonal dynamics; that is, business cycles and varying global macro conditions specific to the time period of the surveys.

The debate, however, tends to focus not just on any job creation, but on “good” jobs associated with high wages, labor market security, and quality of the working environment. And while there is no strong evidence that FDI also improves non-wage working conditions (OECD, 2008), there is evidence that foreign-owned firms pay higher average wages --10 to 70 percent higher, according to estimates.<sup>4</sup> This finding is corroborated across countries, development levels, sectors, skill level and over time, with developing countries enjoying much larger premia than developed countries.

Several factors <mark>justify higher wages by foreign multinationals, all related one way or another</mark> t <mark>o productivity advantages. Firms acquired or established through FDI may be more productive than other domestic firms, thus earning higher profits and paying</mark> higher wages. Second, foreign affiliates may pay more because their parent firms are relatively more productive (having had to overcome the liability of foreignness to undertake FDI), and thus have higher profits and can pay higher wages. Third, foreign firms might try to prevent technological spillovers through labor turnover by paying higher wages. Fourth, the FDI wage premium may compensate for higher labor demand volatility in foreign plants or for a higher foreign plant closure rate. Fifth, non-market forces, such as equity considerations, could also play a role (Bernard and Sjöholm, 2003; Egger and Kreickmeier, 2013; Fabri et al., 2003; Fosfuri et al., 2001; Heyman et al., 2007; Javorcik, 2014; Tomohara and Takii, 2011).

The World Bank Enterprise Surveys confirm the
minikit saturation, although these estimates are less precise than those of the impacts of assignment to receive a minikit; we note that we also fail to reject that the sum of the coefficients on assigned minikit and minikit saturation (the effect on adoption in a fully treated compared to an untreated water user group) is zero. Fourth, we find strong positive selection into using a minikit: farmers who grew horticulture in 2016 Dry, who are 30.6pp more likely to grow horticulture in 2017 and 2018 Dry, are 13.1pp more likely to use a minikit in response to assignment to receieve a minikit receipt.

We interpret these results as corroborating evidence that information and financial constraints are not dominant constraints to adoption of irrigation. Most farmers assigned to receive a minikit do not pick it up and use it, and the farmers who do

> 47After matching names from the lists of water user group members to our baseline survey, we found that 32% of households either had multiple household members on the lists of water user group members or had a single household member listed multiple times; these households are more likely to be assigned to receive a minikit and may differ from other households.

43
# (a) RRAs (%) and log of real GDP per capita

<!-- Start of picture text -->
-1 0 1 2 3<br>Ln real GDP per capita<br>HIC RRA obs HIC fitted values<br>DC RRA obs DC fitted values<br>(b) RRAs (%) and agricultural comparative advantage<br>-1 -0.5 0 0.5 1<br>Ag comparative advantage<br>HIC RRA obs HIC fitted values<br>DC RRA obs DC fitted values<br>400<br>300<br>200<br>100<br>0<br>Relative Rate of Assistance (%)<br>-100<br>400<br>300<br>200<br>100<br>0<br>Relative Rate of Assistance (%)<br>-100<br><!-- End of picture text -->

Figure 6. RRAs (%) mapped on real GDP per capita and agricultural comparative advantage, 1955–2007. Agricultural comparative advantage is defined as agricultural net exports divided by the sum of agricultural exports and imports.

Source: Anderson 2010, Figs. 2.2 and 2.3.

56
.7|25.8|29.4|33.2|21.8|44.9|
|Ethiopia|66.6|82.0|66.0|60.9|77.97|56.41|101.81|
|Ghana|75.7|85.1|56.1|66.9|66.88|59.17|93.06|
|Kenya|162.6|194.6|120.6|141.5|170.55|153.91|440.24|

Overall, the sensitivity analysis clearly implies that our finding that resource misallocation is the highest in Kenya and the least in Cˆote d’Ivoire is robust to alternative assumptions regarding the measures of labor inputs, elasticity of capital and outliers. However, the figure that emerges from comparing Ethiopia and Ghana is rather mixed, the relative ranking changes depending on the alternative assumptions.

# **10 Concluding Remarks**

This paper has examined the effects of resource misallocation induced by firm-specific distortions on aggregate manufacturing productivity using comprehensive and comparable data on manufacturing firms in four Sub-Saharan Africa countriess - Cˆote d’Ivoire, Ethiopia, Ghana and Kenya.

Our main results are as follows. First, we found evidence that firm-level distortions have a quantitatively sizable detrimental impact on aggregate productivity, through the misallocation of resources away from more efficient to less productive ones, in all four countries we study. The counterfactual exercises imply a substantial productivity gain from the removal of distortions in Kenya, with a range of estimates from 162.6 to 194.6, depending on the model assumptions. The potential productivity gains are also large for Ghana (with range from 66.6 to 82.0 percent) and Ethiopia (with a range from 75.7 to 85.1 percent). For Cˆote d’Ivoire the gains are from 31.4 to 44.7 percent, much smaller than the other three countries. These results
At 54 percent, a relatively high share of respondents in Tajikistan said they were either already vaccinated or planned to be in July 2021.<sup>6</sup> Only about 17 percent of respondents said they either probably or definitely would not be vaccinated. By March 2022, vaccination intentions had risen to 92 percent, with only 4 percent responding that they would not be vaccinated.

Following the introduction of vaccination mandates in Uzbekistan and Tajikistan and a new wave of infection that rapidly spread in Central Asia in July and August 2021, all three countries later achieved significantly higher vaccination rates, with Tajikistan highest among them as of June 2022. By June 2022, about 9.29 million people were fully vaccinated in Kazakhstan (49.5 percent of the population including children), 15 million in Uzbekistan (43.9 percent), and nearly 5 million in Tajikistan (52.2 percent).<sup>7</sup>

# **Figure 1: Self-Reported Vaccination Intentions**

# _(A) Kazakhstan_

<!-- Start of picture text --> Self-Reported Vaccination Intentions in Kazakhstan<br>May-22<br>Apr-22<br>Mar-22<br>Feb-22<br>Jan-22<br>Dec-21<br>Nov-21<br>Oct-21<br>Sep-21<br>Aug-21<br>Jul-21<br>Jun-21<br>May-21<br>Apr-21<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Definite or Already Lean yes Lean no Definite no<br><!-- End of picture text -->

> 6 Due to differences in fieldwork and questionnaire updating protocols, questions on vaccine hesitancy were added in July 2021 in Tajikistan.

> 7 All estimates from the _Our World in Data_ dashboard: https://ourworldindata.org/covid- <u>vaccinations?country=OWID_WRL , retrieved June 8, 2022.<
is that the cross-country variation in institutions induced by these historical circumstances has a large effect on income per worker.

Using a similar methodology, Glaeser et al. (2004) questions Acemoglu et al. (2001)’s view on the importance of institutional quality for economic development, and argues for a central role of human capital (as measured by educational attainment). The starting point is the observation that European settlers did not just bring European institutions with themselves, but also their human capital. Indeed, Glaeser et al. (2004) show that the instrument used by Acemoglu et al. (2001) is positively correlated with current educational attainment, and argues that institutional quality is a product as opposed to a cause of the process of economic development. This view is supported by a regression specification where institutional quality and educational attainment are simultaneously instrumented for, from which it emerges that educational attainment has a stronger explanatory power for economic performance. This conclusion has been in turn questioned by Acemoglu et al. (2014), which argues that the instruments used by Glaeser et al. (2004) are not valid sources of exogenous variation.<sup>3</sup> Recent work has shifted the focus from a national to a subnational setting. Gennaioli et al. (2013) use data at the regional level across 110 countries to examine the relative importance of human capital and other possible determinants of local economic performance. Within-country comparisons allow the authors to control for unobservable factors varying at the country-level, such as national institutions, national culture and national language. In this subnational regression specification, educational attainment emerges as the most important predictor of regional economic performance. Moreover, Gennaioli et al. (2013) use firm-level data to document that entrepreneurs’ and managers’ human capital plays a particularly important role in explaining differences in firms’ productivity. However, Acemoglu et al. (2014) argue that country fixed effects are not sufficient to eliminate omitted variable biases, and that the measure of institutions used by
females’ and males’ wage offers, job tenure, and job offer arrival rates; gender roles regarding leisure or home production and fertility patterns; or more generally, policies affecting overall Roma labor market conditions.

**Counterfactual experiments show that males and females do not react symmetrically to policy interventions, and that most labor gender gaps can be closed by providing wives the same labor market conditions as husbands.** All the policy experiments increase or reduce Roma husbands’ and wives’ parameter values. <mark>Focusing on husbands, increasing the frequency of receiving a job offer, decreasing the frequency of layoffs, and reducing the search cost are the policies that most increase the husbands’ labor participation. Focusing on wives, the policy that induces more wives to participate in the labor market is increasing Roma husbands’ and wives’ wages.</mark>

**Future extensions of the model could endogenize the fertility decision to reflect the impact of**

**labor market changes on the number of children** . Another useful extension could introduce the role that taxes and subsidies have on the spouses’ decisions. Moreover, because the Roma population in many cases live in extended families, including other household members’ labor market states and time decisions should adjust better the Roma labor market decisions. Sharing a dwelling with extended family could be a strategy for Roma households facing reduced labor opportunities. Currently, the RRS only applies the time usage module to a random household individual; to apply the modeling to extended families, time usage modules must collect data from all household members.

**Another useful future extension could make a General Equilibrium Search Model like the one developed by** **<mark>Hanming and Shephard (</mark> 2019), but this development will rely on future data availability.** Hanming and Shepard have labor supply and demand information and data from employer-provided health insurance. In this paper we do not have firms’ information; the estimated labor market parameters do not allow