rafmacalaba/data-use-mentions-extended huggingface dataset:
findings are similar to the findings of Kingdon and Soderbom (2008), who use a different survey and sample.

# **7.     Labor Supply - Total Hours Worked**

# **7.1.   Method**

The labor supply models usually consist of two stages, the first being the decision to work and the second being hours worked. Since the earnings data are not reported for informal sector workers, I use the following models to estimate hours worked.

First, I restrict the sample to paid employees and estimate the hours worked: where  1 is the coefficient for earnings, X is the vector of other variables that may influence the hours worked, and u is the unobserved variables with zero mean.

Second, I include all workers in the labor market in both formal and informal sectors. Since earnings are not reported for unpaid family workers and own-account workers, employment status (ES) rather than earnings data are included.

In the last model, I use the earnings equation (equation 4) to predict the earned wages for unpaid family workers and own-account workers, assuming that the return on education is the same for paid employees and informal sector workers. This is a strong assumption because of the selection bias between workers in the formal sector and workers in the informal sector. Thus, the results have to be interpreted with caution. In the case, the model is as follows: 16
<mark>differently. We conclude the empirical section by showing how GSPS indicators correlate at the country-level with one of the most widely used sets of governance indicators, the Worldwide Governance Indicators (Kaufmann, Kraay, and Mastruzzi 2010).</mark>

# **<mark>Literature Review and a Conceptual Framework of the Global Survey of Public Servants</mark>**

Early surveys of public servants by the World Bank were undertaken around 2001 under the umbrella of the ‘Governance and Anti-Corruption Surveys’ initiative (Recanatini et al., 2010). These surveys, as the name suggests, focused on the determinants of corruption and potential policy impacts. In academia, many US-based public administration academics have used the Federal Government’s staff survey, the Office of Personnel Management Federal Employee Viewpoint Survey (OPM FEVS), or complements to it (see for example the body of work of Sanjay K. Pandey). There are a small number of exceptions of research based on surveys from elsewhere in the world, with Grødeland et al. (2001) an early example, but the overall scale of surveying has been far more limited than that of citizens, households or firms (Rogger 2017). Though some governments have undertaken surveys of their officials, regular and consistent surveying within government is a recent phenomenon in most governments (Khurshid and Schuster, forthcoming).

Each of these initiatives has, understandably, taken a rather partial view of the determinants of government functioning. In contrast to surveys of private enterprise, for which the economic theory of the firm presents a clear benchmark model, there exists no consensus on the underlying production function of public administration that would guide measurement selection. This is true both theoretically as well as empirically, where the very fact that there has been so little analysis of granular data on public officials has limited the capacity to create stylized facts of public service. As such, this section lays out a conceptual 4
of larger ones, including both domestic firms and foreign exporters. This increases the market power of non-exiters. Our finding that NTMs are mostly associated with changes at the extensive margin of trade is also consistent with our working assumption that they mostly increase fixed rather than variable costs.<sup>32</sup> Our results in Table 7 and Table A.35 are consistent with those in Fernandes et al. (2015) and Fontagn ́e et al. (2015). Fontagn ́e et al. (2015) use French firm-level data to show that SPS concerns raised at the WTO entail compliance costs, raising unit values and inhibiting entry. In Fernandes et al. (2015) the focus is on the impact of a specific type of NTMs, namely pesticide standards, on firm exports of agricultural products. Still, the authors show that more restrictive standards in the importing country relative to the exporting country, lower firms’ probability to export as well as their export values and quantities. While these results help understand the mechanisms through which NTMs affect trade, they, however, do not speak directly about their impact on market power, which cannot be directly measured in the absence of information on the entire set of firms, both foreign and domestic. This makes apparent how complementary our approach is to these papers, as it allows us to circumvent this problem and uncover that NTMs have unambiguous and opposite effects to that of tariffs when it comes to market power.

Our interpretation of the effect of NTMs is in terms of compliance costs. An alternative, and empirically undistinguishable interpretation is that higher NTMs, by increasing the quality requirements for products or reducing information frictions, may increase the demand for high quality goods. Therefore, high quality firms raise their market shares and hence their market power in those destinations, while low quality firms disappear. Both interpretations are consistent with our main finding that NTMs raise market power at the firm-level. The fact that unit values are positively correlated with NTMs in Table 7 fall
on the roughlysales tax return, as shown in table VI. The table reports the pre-reform mean in the treatment group, and the coecient _β_ for dierent specications, either using the raw data, or winsorizing by the 99.9th, 99th and 95th percentile, in addition to the rst percentile. As several outcome variables take value zero for a large share of observations, we use levels rather than logs, and report the treatment eect as marginal eect on the pre-reform average. However, the results are qualitively similar when using log(outcome+1) or a dummy variable indicating if the outcome is non-zero.

To visualize the identifying assumption and treatment eect on total tax payment, Figure VIII plots the month-on-month change in total tax payment for the treatment and control group, together with the DiD coecient estimates from Equation 2. For the purpose of this gure, the data is winsorized by the 99th percentile. Total tax payment is the sum of the tax withheld and the taxpayer's payment. Despite seasonal uctuations with peaks during the December shopping season, the two groups exhibit parallel pre-reform trends, which conrms the suitability of the control group. At the time of the reform, tax payments in the treatment group increase sharply by about 33% and remain at this higher level for the next 15 months.<sup>50</sup> As there is no corresponding decrease in tax payment in the control group, and the treatment group represents a substantial share of total sales, the withholding rate reform increased aggregate sales tax revenue by 8.4%. Figure IX illustrates this results, using a simple linear regression discontinuity design on demeaned semester-wise revenue data (from ocial government statistics). We display the evolution of revenue from the quarterly simplied regime tax as a counterfactual, to show that the discontiuity at reform time is not driven by business cycle uctuations.<sup>51</sup> A concern is that taxpayers may have
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performance would have deteriorated markedly, with both the Sargan and the Godfrey tests pointing to the existence of some form of misspecification.

Data on Morocco was then used to estimate the export equation (equation 6). As a measure of W1, that is, the sensitivity of labor costs to capacity utilization, first the minimum wage rate and then the consumer price index (CPI) were used. Both indicators can provide only a very imperfect approximation to W1. The estimates were not particularly sensitive to either of these two indices, but both the Chow (X<sup>2</sup> 1-2.96) and Godfrey (X<sup>2</sup> 1 - 3.73) tests pointed to a serious misspecification problem in the equation using the wage rate. Thus we report only the results using the CPI. For Turkey, both capacity and relative prices turn out to be significant determinants of the export supply decision. Again, although the coefficient on capacity utilization has the right sign, it is not well determined. Moreover, it was not possible to impose the restriction that p X 0 (X<sup>2</sup> 1 - 13.0). The statistical properties of the equation, as indicated by the usual battery of statistical tests, were satisfactory, with no obvious indication of misspecification.
equilibrium models of the economy, with linkages across sectors and firms, are needed to gauge more complex medium-term effects.

Despite these shortcomings, our simple framework proved useful to inform policymakers in our partner countries in real time, at the onset of the lockdowns. Researchers in high-income countries have recently used a wealth of digitally-generated high-frequency information on economic activity to study the impact of COVID-19 (e.g. Chetty et al. 2020). Many developing countries nowadays generate similarly detailed digital information (e.g. transaction-level records from e-billing systems and credit/debit card reporting), but still need to build the infrastructure to use these data for timely economic analysis. This project illustrates the benefits of using harmonized administrative tax data for just-in-time policy analysis, and aims to create a bridge for researchers and policy makers to work with such data. Administrative tax data remain selected, since they only cover the formal sector, and should thus be complemented with survey-based studies of the informal sector (Alfaro et al. 2020). Yet, formal firms are the relevant group to study from a public finance perspective, as they remit the lion’s share of taxes and because they can be reached directly with tax policy measures and liquidity provision.

This paper is organized as follows. In section 2 we present the data and the modeling assumptions. In section 3 we estimate the direct impact of the pandemic on firms’ profitability, payroll and exit rates. In section 4, we examine the implications for public finances and potential policy responses through wage subsidies. Section 5 concludes.

# **2 Data and Modeling Assumptions**

## **2.1 Data**

Our study uses firm-level corporate tax records from 10 countries – a diverse group of countries spanning three continents.<sup>4</sup> Table 1 shows the descriptive statistics by country, ranked in increasing order of GDP per capita. Countries’ GDP ranges from 643 USD per capita in Uganda to 12,027 USD in Costa Rica. Population
households decreased and the decreasing trend lasted for seven years. During 2007-2013, households’ real income on average decreased by 35%, resulting in cutting off their spending on education by 43%. The reduction in education spending reflects both young children not attending school and parents cutting back on school expenditures.

The identification strategy of this paper uses variation in the impact of sanctions across industries. This study thus compares the educational outcomes of children in the most affected industries, before and after the sanctions, with a control group of industries not significantly exposed to the sanctions (a difference-in-difference approach).<sup>5</sup> I define households in which the head works in either the oil and gas industry or energy supply as the treated group. The oil and gas industry is directly affected by the sanctions and the energy supply industry is highly dependent on oil exports. Following Abadie and Gardeazabal (2003) and Abadie et al. (2010), this paper uses a synthetic control method (SCM) and weight industries in the control group to construct a synthetic control that matches treated households for a 12-year pre-sanctions period. The synthetic control group includes information, education, and health industries with weights 0.148, 0.169, and 0.683, respectively. These industries are heavily regulated by the government. Therefore, the sanctions have little effect on wages and employment levels of these industries.<sup>6</sup> Moreover, these industries are not dependent on trade, thus making them unaffected by the changes in the exchange rate. The raw data confirm that households in these industries experienced the lowest incidence of family income changes after the sanctions.

> 5My methodology, which relies on difference-in-differences, can only capture differential impacts on the most affected households relative to the less affected households and does not capture the general effects of the sanctions.

> 6Those working in the information, education, and health industries are mostly public sector employees for whom the minimum wage and the minimum percentage change in wage
more pronounced compared to patterns across a broader group of LMIC countries (Kugler et al. 2021). By February/March 2021, however, those disparities largely disappeared. Instead, lower education turned out to be the main predictor of joblessness in February/March 2021 among those who were working preCOVID-19. This suggests that re-integration of the least educated inactive or unemployed persons into the labor force should be a priority for governments, as apparently the extended public works programs implemented in Ethiopia, Nigeria, and Uganda (Gentilini et al. 2020) have not been able to absorb this group.

The relative vulnerability of NFE income offers further guidance for policy design. A large share of households relies on this income generating activity making it very important for the economy and overall well-being. However, these enterprises are relatively difficult to target due to the small scale and informal nature of the activities. It would be beneficial to provide easier procedures for registration to improve targeting possibilities in the future and to design safety net policies particularly targeted at NFEs during crises such as the pandemic.

Finally, this paper has illustrated how high-frequency phone surveys can be a valuable tool in capturing data on outcomes, such as employment status, that are prone to changes and fluctuations. These surveys can also complement face-to-face surveys on more structural characteristics of households and individuals.

21
1.4 data

17 each year, and does not predict how firm productivity changes year to year. Because I do not have a variable time dimension in my econometric model, the effects of explanatory regressors are on an equilibrium level of FDI. This is better reflected by FDI stock because it is far less volatile on an annual basis than FDI flows. Importantly, the extensive margin of FDI measured by whether multinational firms from country i operate in country j can only be estimated by FDI stock, not flows.

For the main explanatory variables, I use the new IAB indicators of FDI regulation across 87 economies, prepared by the World Bank Group in 2010. With 21 countries from Sub-Saharan Africa, 20 from Eastern Europe and Central Asia, 14 from Latin America and Caribbean, 10 from East Asia and Pacific, five from South Asia, and five from the Middle East and North Africa, this sample of 87 countries can be considered random, and a fair representation of the actual world. In 200708, they constituted 87.1 percent of global population and 77.9 percent of global output (Table 15). The sample includes 15 high-income OECD countries.

As shown in Figure 4, in terms of market size, the mean and standard deviation of the sample of all countries<sup>19</sup> in the world and the subsample of 87 IAB countries are almost identical. However, within the IAB sample the distribution of the subgroup with zero FDI observations is remarkably different from the subgroup with non-zero observations. The sample selection bias is, therefore, likely to be more prominent within the IAB sample.

The 23 IAB indicators measure, among others, openness to FDI through equity ownership permitted in 11 sectors (11 indicators); time, procedures and regulations for starting a foreign business (three indicators); arbitrating commercial disputes (three indicators); and accessing industrial land (6 indicators). The data are compiled from detailed surveys filled out by over 2350 local experts from leading law and
# I. **INTRODUCTION**

Economists agree that the ownership of a firm affects its productivity. The theory of the firm suggests that the productivity of a firm is affected by various ownership or control rights: cash flow rights, internal incentives, managerial incentives, and production decision rights (Holmtrom and Tirole, 1989). Subsumed within this literature may be the contract that firms sign with governments. Moreover, the recent surging literature on the relationship between firm-specific human capital and growth suggests that it may also be important to examine the dynamic effects of change in the organization of a firm (Lucas, 1988; Romer, 1986). The empirical literature on the relationship between ownership and productivity, however, has not caught up: it is almost exclusively of static nature, and the ownership variables are usually represented by dummy variables such as private (or state) ownership, collectives, etc (Boardman and Vining, 1989). An exception is Ehrlich et al. (1994): they analyze the dynamic effects of private versus state ownership, find that state ownership is associated with lower growth rates, and suggest that it is because private ownership facilitates the accumulation of firm-specific human capital.

This paper continues this direction by examining the effects of ownership on both productivity levels and growth rates. Controlling for specific features of ownership, I focus on the effects of decentralized control rights and cash flow rights on growth. In particular, I consider how changes in ownership and control rights affect both SOEs productivity levels and growth rates. Among these changes are appointing new managers, delegating production autonomy to managers, reducing the share of output under the government's plan, using performance contract and firm-level pay sensitivity, and allowing the managers to determine wages of employees. This rich description of ownership and control, seldom tried in the literature, is available for a panel data set of Chinese SOEs.

I
Figure 16: Household Food and Non-food Consumption, 2014/15 <!-- Start of picture text --> Non-Poor Poor<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>Rural - Food Rural - Non-Food Urban - Food Urban - Non-Food<br>SDG<br><!-- End of picture text --> _Source:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

Both food and non-food expenditures remain lower in rural Sudan compared to urban Sudan (Figure 17). These differences are significantly higher for non-food expenditure, whereas only small differences exist for food expenditure. However, as there are differences in prices between the rural and urban areas, food consumption is likely higher in rural areas, something which is to be expected, due to the increased physical demands of agrarian work. Differences in non-food consumption, where the urban population on average consumes almost twice the monetary amount as the rural sector, are unlikely to be due to different prices. As we note in the next subsection, such non-food expenditure may account for differences in asset ownership between urban and rural Sudan. Some assets, such as those that aid in communication and transport, can improve the productive capacity of farmers. Therefore, improving market infrastructure, to increase the opportunity for both food and non-food consumption in rural Sudan, is vital for improving rural livelihoods and alleviating poverty.

Figure 17: Per Capita Food and Non-food Consumption, 2014/15 (a) Per Capita Non-food Consumption <!-- Start of picture text --> 6000<br>Urban Rural<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>SDGs<br><!-- End of picture text --> 18
# **Appendix B: Further description of the food and non-food consumption aggregates in the TZNPSs and the experiment**

The main consumption aggregate in NPS consists of the food and non-food consumption expenditure aggregates.

# _Food component_

Food consumption is made up of expenditures on thirteen food categories: cereals and cereal products; starches; sugars and sweets; dry pulses; nuts and seeds; vegetables; fruits; meat products and fish; milk and milk products; oils and fats; spices and other foods; non-alcoholic beverages; and alcoholic beverages. It includes all sources of consumption, such as purchased food, meals eaten outside, food produced at home, and gifted food. The values of food that has been eaten and nonpurchased food that was consumed are incorporated into the welfare measurement.

# _Non-food component_

NPS collects data covering a wide array of non-food items, including health, education, utilities, and energy sources (including water, kerosene, electricity, matches, bulbs, and charcoal), furnishings and household items, clothing, transport, communication, recreation, and other non-food consumption. Each non-food category has a specific reference period based on its frequency of purchase or consumption: expenses for mobile phones and personal care are gathered over the last month, and expenses on furnishings over the past twelve months.

The information about some non-food goods and services was excluded from the consumption aggregate in earlier rounds of NPS because those items were not collected or due to incomparability with other rounds. Based on this information, we can identify two methods for defining the consumption aggregate. The new classification method includes three additional items in non-food consumption - the use value of durables, imputed rent on user-occupied property, and clothing expenses - while the old method excludes them. Hence, the new method yields higher consumption figures for non-food and total consumption, by the inclusion of durables and imputed rent. We employ the new method to construct the consumption aggregates for analysis. The descriptive statistics for the consumption aggregate calculated using the new method and the old method
depressed agricultural productivity and reduced incentives for improving productive techniques. The political crisis in Afghanistan starting August 15, 2021, caused a large economic contraction, with GDP per capita dropping 23 percent between 2021 and 2022. Since then, the economy has seen slow recovery in agriculture and stabilization in the service and industry sectors. Using the macroeconomic data on a nonneutral distribution, projections of poverty in 2023 are between 63 and 59 percent (See Annex D for details).

**Figure 6: Evolution of sectoral growth**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40%<br>Agriculture Industry Services GDP per capita<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022p 2023<br><!-- End of picture text --> _Source: NSIA and World Bank projections_ _Note: Macroeconomic data up to 2021 come from the NSIA, while 2022 and 2023 data are World Bank projections. After 2021, the NSIA stopped producing official national accounts data. GDP data for 2022 and 2023 are projected information produced by World Bank staff using information from different monitoring sources._ Poverty projections using economic growth do not account for household adaptation strategies, economic stabilization, or conflict reduction. These conditions make it feasible for rural areas to experience lower poverty rates in Q3 2023 than in the corresponding periods in 2019/20 and 2021. In the case of Afghanistan, welfare monitoring shows that to cope with the economic instability after August 2021, households mobilized extra women and youth labor. This has resulted in structurally higher labor force participation and unemployment compared to 2020, especially among women and youth. Increasing wages and recent deflationary dynamics contribute to the observed improvement in Afghan households' capacity to satisfy basic needs.