rafmacalaba/data-use-mentions-extended huggingface dataset:
share of countries’ GDP. Panel (a) shows our estimates for the 13 sample countries, ranked by per capita log GDP (2015 constant USD from the World Bank). Panel (b) adds estimates of CIT expenditures from 66 additional countries, available in the Global Tax Expenditure Dataset (GTED), for a total sample size of 79 countries. The new estimates come from countries’ official tax expenditure reports, which based on international standards should be completed yearly. In practice, developing countries do not systematically produce tax expenditure reports, and when they do, the methods vary significantly depending on each country’s definition and statistical capacity. Panel (c) shows the correlation between our estimates of tax expenditures, and those from official tax expenditure reports, for the 11 sample countries where both are available. The dotted line is the 45-degree line. For sample countries, we adjust the GTED tax expenditure numbers when further details are available in the country’s tax expenditure report, to include all relevant expenditures. To compute aggregate tax expenditures from the microdata, we first use the difference between the top statutory tax rate and the firm-specific ETRs multiplied by their net profits to compute firm-level tax expenditures. We then aggregate these tax expenditures across firms, within country, and divide it by the sum of corporate tax liabilities, yielding a ratio of foregone CIT revenue to actual tax revenue. We multiply this ratio by the CIT collection as a share of GDP (using tax revenue data from Bachas et al. 2022) to obtain total tax expenditure as a share of GDP. This figure is discussed in Section 3.1.

29
requirements for traders and district-level entry taxes (Das-Gupta 2006). Thus, a consistent share of agricultural trade tends to occur at a short distance, making districts a suitable spatial scale to capture a substantial part of the first two channels. Consistent with these ideas, some studies have tried to capture the demand-side effects on agriculture through district-level analyses (Parthasarathy Rao et al. 2004).

There is also emerging evidence of increases in land prices in peri-urban and rural areas surrounding the urban agglomerates. The land values in these areas may be well above the discounted future stream of income from agricultural activity, inducing some landowners to sell the land (Jha 2008).<sup>15</sup> We begin from the following basic specification: where _H dtR_ is a measure of rural poverty in district _d_ at time _t_ , α is the district fixed effect, λ is the state-year fixed effect, _PdtU_ − _j_ is the urban population of district _d_ at time _t-j_ (where _j_ ∈ 2,1[] ), and _X_ is a vector of controls that includes other variables that are likely to have an independent impact on rural poverty, including, inter alia, the district’s rural population to control for the growth of the district’s overall population. The coefficient of interest here is _β_ , which measures the collective impact of the _location_ and _economic linkage_ effects of urbanization on rural poverty.

We use a standard Foster Greer Thorbecke (FGT) measure of poverty for _H dtR_ , the poverty headcount ratio. _PdtU_ − _j_ , the main independent variable, is computed as the number of cities in district _d_ . Because population figures from the census are 12
loan because they had sufficient capital (SACE 2014). State owned enterprises had financing from headquarters in China, and private companies accumulated savings and loans from family and friends. Manufacturing and construction companies take as much credit as other companies in Kenya, but only 16 percent of companies in the services sector have taken a loan or credit line. Larger companies tend to have better access to loans and credit. 95 percent of Chinese companies, however, have a local bank account and can access basic banking services.

# **4 Official Development Assistance from China**

## **4.1 Chinese aid is small compared to commercial activities**

Chinese aid to Sub-Saharan Africa was US $3.2 billion in 2013, and is quite small relative to its investment stock of US $32.35 billion in Africa; the small amount of aid supports China’s no handout policy (China Africa Research Initiative 2013). China is more of a business partner than a donor, but it does provide grants and other forms of aid (Wang and Bio-Tchane 2007). Chinese aid only started to become significant after 2002, and it became 1.23 percent of total loans and grants to Kenya in 2003 (Mwega 2009; Onjala 2008). China also accounted for 10 percent of loans and 20 percent of physical infrastructure assistance to Kenya in 2005 (UNDP 2005; Onjala 2008). Unfortunately, as we discuss in section 4.3, we do not have access to quality aid data.

30
dummy variables for each industry; represents dummies for each country. _EXTDEP_ is the industry-level dependence on external finance based on U.S. data, which is equal to external finance (capital expenditures minus cash flow from operations) divided by capital expenditures, as defined in Rajan and Zingales (1998). We then interact that variable with _FIN_ , which represents our indicators of financial development (private credit/GDP and stock market capitalization/GDP) in each country. A positive, significant coefficient for that interaction term is supportive of the notion that the labor growth of firms in externally dependent industries is faster relative to other industries in countries with relatively well developed financial sectors. Since our aggregate variable now varies at the country-industry level, we cluster our standard errors at the country-industry level in our analysis.

Because it focuses on within-industry, between-country differences in labor growth, this approach is complementary to our instrumental variables regressions (which focus on crosscountry differences). Moreover, the inclusion of country and industry dummy variables makes this approach less susceptible to criticism about omitted variable bias and model misspecification than more traditional cross-country regressions. RZ relied on industry-level data because it provided the most disaggregated comprehensive data on growth at that time, whereas our surveys enable us to examine firm-level growth in a consistent manner across countries. Finer disaggregation should improve the precision of our estimates relative to industry-level approaches.

Our main finding is that the interaction between industry-level dependence on external finance and private credit/GDP is significantly positively linked to labor growth in the sample of low-income countries (Table 7). By contrast, there is no significant relationship between labor growth and the interaction between stock market development and dependence on external finance in the sample of low-income countries. This is the same pattern as in our instrumental variables regressions. Our finding indicates that, when evaluated at the median industry level of external finance (0.24), the country at the 90<sup>th</sup> percentile of banking development would have a labor growth rate higher by
between tariffs and average wages, and a strong negative association between tariffs and the skill premium. Following a tariff reduction of 10 percent, for instance, the wage of an average unskilled worker would decline by 3.9 percent while the wage of an average skilled worker would decline by only 0.5 percent. This suggests that while average wages would decline, the skill premium would instead increase by 3.4 percent.

Our results suggest that tariff liberalization can help explain the decline in wages and the increase in wage inequality observed in Argentina both during the 1970s and the 1990s. There are important differences, however. The large tariff cuts of the 1970s can account for over one fourth of the 100 percent increase in wage inequality (i.e., concretely, for 27 percentage points). During the 1990s, instead, tariff reforms can only account for 10 percent of the observed increase in wage inequality. This implies that other concurrent factors, such as other policy reforms inherent to the 1990s (technological change, deregulation, privatization), also played a key role in the determination of the wages of Argentine workers during the last decade.

The remainder of the paper is organized as follows. In Section 2, we describe the data used in this paper and we motivate our work by describing the trends in trade liberalization and the trends in wage inequality. Section 3 presents the empirical analysis and Section 4 concludes.

# 2 The Trends in Tariff Reforms and Wage Inequality

A major input into our analysis is the historical data on Argentine trade policy and wages, spanning the 1974-2001 period. These data come from two different sources: customs data on imports and tariffs, and household survey data on wages and workers.

We begin by describing the customs data. We measure trade policies with sectoral tariffs. Data on ad-valorem import tariffs come from official Tariff Schedules, which specify the tariff rate levied on each item of the Harmonized System (HS). In order to make our trade data comparable with the wage data,
in the household; the more adults, the greater the negative error in consumption valuation for goods likely consumed outside the home such as Fruits and Meals Out. This appears true for recall and for diary formats and indicates additional attention is warranted for households with many adults but only one survey respondent. Recall modules administered to households with many children (but not diaries) tend to differ with respect to the benchmark for various basic foodstuffs such as Vegetables, Fruits, and Meat/Fish. In general, the recall modules undervalue consumption for these items, and this undervaluation increases for households with many children, indicating once more the importance of greater attention if recall surveys are administered to larger households.

Urban households administered recall modules also tend to underreport select food groups; this is especially so for the subset and collapsed list recall surveys (modules 3 and 4). However, such households also exhibit overreporting for other food groups. There are some other specific results for various food groups and either the educational attainment or wealth level of the household, but no systematic pattern emerges, making survey design lessons difficult to generalize with respect to value reported and household measures of economic status. This is in contrast to the results shown in table 3, which contains more clear patterns in consumption incidence error and selected household characteristics, whereby more disadvantaged households are more likely to omit the consumption of key food groups entirely.

# _Item characteristics and reporting error_

This section explores the relation between commodity characteristics and reporting error. Investigated commodity characteristics include (1) either commonly or uncommonly consumed items in terms of the consumption incidence as reported in the benchmark individual diary; (2) the monthly value of the consumed item; (3) the frequency of purchase/consumption; (4) the share of the item consumed from home production; and (5) the storability or perishability of the item.

Because most modules record consumption information on up to 58 individual food items, we construct the item-specific incidence and value error for 54 items relative to the
Our paper proceeds as follows. Section 2 provides a comprehensive description of pre-crisis situation in Tunisia including trends in poverty, composition of labor markets and individual and household characteristics that make the Tunisian population more susceptible to COVID-19. Section 3 describes the data and the empirical methodology to simulate the impacts of COVID-19 on labor income and consumption. It also discusses the magnitude of impacts in the presence of mitigation measures. Section 4 presents our results under two scenarios. Section 5 concludes and provides policy implications of our results.

We rely on the latest round of data available of the national household survey or EBCNV implemented in 2015. EBCNV 2015 is a quinquennial survey which is the eight survey of its kind carried out by the National Institute of Statistics. The seven preceding surveys were carried out in 1968, 1975, 1980, 1985, 1990, 1995, and 2005. The realization of these surveys coincides with the preparatory work for the Development Plans. The survey on the budget, consumption and household standard of living in 2015 covers data on Household expenditure and acquisitions during the survey period, Consumption food and nutritional situation of households, and Household access to community health and education services. While the National Institute of Statistics (INS) has conducted a household budget survey more recently in 2019, the official estimates are not yet published, and the data are not yet widely available and do not collect the consumption data.

# **2. Pre-Crisis Situation: Poverty and Labor Markets**

Poverty had declined in Tunisia in the pre-COVID-19 period, but significant disparities remained. The poverty headcount rate declined between 2010 and 2015 from 20.5 to 15.2 percent in the country as a whole (Figure 2, panel A). Nevertheless, significant disparities existed between urban and rural areas, and between coastal regions (where most economic activities are concentrated) and interior regions. Extreme poverty is predominantly a rural phenomenon. Moreover, a considerable share of the population in rural and lagging areas remains vulnerable<sup>4</sup>
proxy for profit, are more likely in countries with greater perceived corruption.

These results are consistent with the findings in Table 6. One way to rationalize the results so far is that misallocation of capital in an economy, for instance driven by socialist legal origins or corruption, creates profit opportunities for private equity investors in emerging markets. In contrast, investees in economically “freer” countries are less capital constrained, and so have lower returns. Yet, other factors, such as political risk, can harm returns.

**Differences from financial openness and development.** We investigate this misallocation hypothesis further by including in Equation (1) measures of financial openness and development that more directly capture access to capital, and the potential for misallocation due to capital constraints. The financial openness index from Chinn and Ito (2006) captures de-jure capital controls and private sector credit as a percent of GDP captures the depth of the banking sector. In this regression, we also include country fixed effects, to isolate variation within countries as these variables change over time, for instance those associated with liberalizing reforms. Table 8 reports these results.

For direct investments, in Table 8, Panel A, the OLS specification shows a negative association between both variables and performance, indicating that as financial markets open and credit mar-

> 20Table A8 reports results using the IRR as measure of performance. Table A9 reports results when restricting the sample to only realized investments. Results are qualitatively similar.

20
instrument. These attributes include the coupon rate, payment frequency, currency, issue date, and maturity date among others.

At the inception of each simulation and at the beginning of the simulation horizon, the debtstock matrix provides a description of the initial debt stock. The first task performed by the model involves, in fact, the initialization of this debt-stock matrix using the actual debt stock from the debt stock database. The majority of subsequent operations in the model are performed by either updating the debt stock matrix or by using the information embedded in the matrix to perform an ancillary calculation. In summary, the debt stock matrix, by virtue of the fact that it represents a description of the entire debt stock at any given point of time, is the central element of the TDSM implementation.

The principal application of any debt strategy model is the computation of the cost-risk distributions for a number of alternative financing strategies over a pre-defined time horizon. In the model, this is accomplished by a nested loop. The loop has three layers. The first layer loops over the number of financing strategies. The second layer loops over the number of stochastically generated interest and exchange rate outcomes. The third and final layer loops over each of the time steps across the simulation horizon.

The bulk of the computation occurs at each quarterly time step. At each of these quarterly time steps, a sequence of stages is performed.

- The first stage in this sequence is the computation of cash flows, performed by screening the debt stock matrix using the attributes corresponding to the instruments, kept in each row.

- Consequently, the matrix is updated by deletion of maturing bonds/bills.

- The next stage is the computation of the gross funding requirement, which equals the net cash flow at that time step. The inflows are determined by the assumptions regarding the primary surplus, privatization revenues, etc. for each quarter. These quantities are fed into the model at inception and assumed to remain fixed across all scenarios and financing strategies. The outflows
# **Literature**

This paper draws from two tightly linked branches of academic literature. First, it draws from the literature that addresses the effectiveness of measures to ease the business registration processes, particularly in the case of one‐stop shops, and second, it draws from the literature that concerns the rationale for informal firms to formalize. This paper aims to contribute to the first of these by documenting the introduction of a set of reforms aimed at abating the cost of registration in Rwanda and the consequences these reforms had across economic sectors on formal job‐creation and tax revenue. Lack of sufficiently detailed data prevents this paper from shedding light on the drivers of firm registration, and thus from contributing to the second branch of the literature.

## **One‐stop shops and business registrations**

In recent years, many countries have improved the process of business registration by reducing the time, cost, number of procedures and minimum capital requirements to start a business.

The World Bank Doing Business database indicates that more than three‐quarters of the registered countries have enacted at least one reform to improve the ease of registration. Reforms typically include reducing licensing requirements, simplifying registration processes, reducing direct costs, and improving coordination among regulatory agencies. One of the most popular types of reforms is to set up "one‐stop shop" registration centers.

A 2010 literature review on business entry reforms (Motta, Oviedo and Santini 2010) finds the introduction of business entry reforms, such as a one‐stop shop for business registration, to be associated with an increase in the number of registered firms. They also find that a reduction in the cost of registering a business induces more registration in industries with low barriers to entry than in industries with high barriers to entry.

Examples of positive associations between the introduction of one‐stop shops and increases in business registrations are found throughout the literature and across income levels. Reforms that cut registration time or cost (or both) by more than 40% during
_Nacional_ - STN).<sup>14</sup> .

# **4 Identification Strategy**

The results _−_ based financing (RBF) reform in _Ceará_ configures an opportunity to estimate if education incentives for mayors triggers student learning. Given that such a reform occurred at the state level and at clear points in time allows us to explore empirically the geographic and time characteristics in our identification strategy. That being said, we start by restricting our sample to schools located in the three immediate municipalities at the border between _Ceará_ and adjacent states ( _Piauí_ , _Paraíba_ , _Rio Grande do Norte_ and _Pernanbuco_ ). Figure 1 illustrates the municipalities included in the analysis. By limiting our sample to municipalities that are geographically close, but on different sides of the border, we intend to minimize the influence of time-invariant unobserved characteristics of schools and municipalities that affect student learning. Put simply, schools right across the border serve as a good counterfactual for the case that the RBF reform had not been implemented in _Ceará_ .

Another useful characteristic of the _Ceará_ reform is its clear timeline. We leveraged that the RBF and the technical assistance (TA) component were introduced at defined dates to compare the evolution of learning outcomes at the border at different points in time. To make this point clearer, consider the RBF and TA reform for 5<sup>_th_</sup> grades. In the first moment, the state congress enacted the RBF reform in mid-December 2007; so, from January 2008 on-wards, a fraction of Quota-Parte revenues started to be redistributed to municipalities based on learning outcomes. Then, in 2011, schools with students from 3<sup>_rd_</sup> _−_ 5<sup>_th_</sup> grade received a _non−mandatory_ package of training and pedagogical materials (the TA component). In a third and final stage, in 2012 the RBF formula was re-formulated to penalize municipalities with higher percentages of students

> 14 Public spending data can be accessed at `https
observables to predict the probability of formality. As the set of predictors expanded, the overlap between the two distributions declined. This reduced overlap means that, while we are better able to identify informal firms that look like some of their formal counterparts, there are fewer of them. With more detailed characteristics, we can see that fewer informal firms are similar to formal firms and fewer will be potentially formalizable. Informal firms operating outside establishments do not seem to have many counterparts that are formal and therefore are particularly unlikely to be formalized.

We used two different methods to group or cluster informal firms based on their observable characteristics and then classify these groupings according to their predicted probability of being formal. The first method was to group informal firms based on a combination of the most important predictors of formality, broken down into discrete categories. We made sure to aggregate these groups in such a way that no groups contained fewer than 10 informal firms. The second method was to use cluster analysis to create 10 clusters with maximum similarity between the observables in each cluster and dissimilarity across clusters. Using these methods separately on the EcC data and pooled ELMPS data, we identified the profiles of firms that were least likely to be amenable to formalization and those of firms that were most likely to have formal counterparts and be potentially formalizable.

The grouping analysis suggests that firms not amenable to formalization include young one- or two-person firms with no hired labor, irrespective of productivity. These firms make up 22% of informal firms in the ELMPS and 34% of informal firms in the EcC. Groups with low probability of formality also include older one-person firms with relatively low revenue per worker, which constitute another 20% of informal firms in the ELMPS. Cluster analysis on ELMPS data identified three clusters as least amenable to formalization, which we named “women-owned retail firms,” “youth-owned young firms,” and “smaller professional firms.” These clusters together make up 22% of informal firms and tend to skew young. Cluster analysis on the EcC