rafmacalaba/data-use-mentions-extended huggingface dataset:
grouped into turnover quartiles.

# **4. Empirical methodology**

The effective tax rate of the presumptive tax is estimated by dividing the tax liability by the turnover, as shown in equation 1. In the literature on corporate tax burdens, this indicator takes a backward-looking approach, which measures the tax burden the enterprises face after all investment and planning decisions have taken place (Nicodème 2002).<sup>14</sup> This is a first-order approximation of the tax burden, which does not account for behavioral responses to taxes. The disadvantage of using turnover (rather than profits) is that it assumes that the profit margins are constant across firms (Nicodème 2002). Nevertheless, turnover is used in this paper because it is the basis for assessing the presumptive tax. Since businesses cannot deduct costs to reduce taxable income in the presumptive tax regime, turnover is equivalent to taxable income. It is also difficult to get reliable estimates of profits in the data because household business expenses cannot be easily separated from the overall household expenses. For example, respondents can misclassify goods and materials for household use as business expenses; owners’ payment to themselves as employee wage costs; and household rent as business rent (see de Mel, McKenzie, and Woodruff, 2009 for a discussion).

Two measures of tax liabilities are used to estimate the tax burden of the presumptive tax. The first measure is the self-reported tax payments made in the 12 months prior in the ESS data, which is what the businesses claimed to have paid in taxes on income and profits. The second measure is imputed tax

> 14 In contrast, forward-looking tax rates are calculated to estimate firms’ decisions about investments, financing, or locations (Lazar 2014).

13
zero elsewhere. In the empirical estimations, we make use of district data: the district maize balance establishes whether a district is a buyer of seller district (see below).

To capture developments in road infrastructure and road quality, developments in transportation technology, changes in the structure of the trading sector, and developments in telecommunication infrastructure, a general trend variable is included. The trend variable takes a value of one at the start of the sample period and increases by one each period. Partial derivatives with respect to trends are expected to be negative, reflecting cost reductions caused by structural developments, such as the roll-out of the mobile phone infrastructure, which are shown to reduce the information and search costs of trade (e.g., Aker 2008; Jensen 2007; Muto and Yamano 2009).

There is a distinct seasonality in maize prices. Because the domestic maize trade is determined by maize prices in different locations, it is likely that seasonality in maize prices also translates into seasonality in transaction costs. Therefore, a set of monthly dummy variables— binary variables with a value of one for a specific month and zero elsewhere—is included to account for seasonality in transaction costs.

To further generalize the specification, we allow for seller-specific and buyer-specific seasonal patterns by interacting monthly dummies with seller and buyer dummies. The estimated equation is summarized as follows:ln (tctkj) = β0 + β1ln (diskj) + β2ln (pf,t) + β3trendt + where _tc_<sup>_kj_</sup> is the transaction cost of trading goods from _k_ to _j_ ; _dis_<sup>_kj_</sup> is the distance between location _j_ and _k_ ; _pf_ is the transport fuel price; trend is a trend variable; _μk, νj_ , and _δm_ are seller, buyer, and monthly fixed effects; _λkm,_ and τ _jm_ are seller-monthly and buyer-monthly fixed effects; and _ε_<sup>_kj_</sup> is an error term with a zero mean and a constant variance (
country/sector (for investments) or country/product (for trade) pair is scored against the panel of country/sector or product pairs where investment or trade success is observed. The intuition behind this being that investment and trade success reveal the conditions under which they become possible, and therefore a good policy strategy would be to try to meet _at minima_ similar conditions to hope for a similar outcome. The results are presented on a simple Excel spreadsheet that allows to read an assessment of the country performance on each of the variables selected by the model.

There are different ways to build a benchmark. A simple way, as was done for the CBT, would be for each variable and each sector (product) to observe where a country ranks relative to successful peers. One way to do this would be to use quantiles defined according to performance (from worst to best) and score each country’s performance in a variable/sector (product) according to which quantile it would fall into. Another method would be to use an indexing measure such as the Distance to Frontier score (a normalized measure of the percentage of the best score) such as used in the Doing Business rankings to see how far a country is from the best practice. Thus, it would become possible to assess for each sector how a country fares along each variable relative to countries with successful outcomes.

These methods, however, would only use the model for the selection of the significant variables but leave aside the information that the model produces on the effect of each variable score on the likelihood of investment and RCA success. We therefore take an approach which utilizes the model insights to calculate benchmarks. Our goal is to produce categorical scores (from 1: worse to 5: best) for each indicator previously selected by the model and relative to which each country’s performance on this indicator can be benchmarked. To construct scores, we must define four thresholds which map the entire universe of values for each indicator into the
0.021)|(0.021)|(0.020)|(0.022)|(0.021)|
|Ethiopia-Nigeria|0.007|0.000|0.003|0.838|0.218|0.000|
|Ethiopia-Uganda|0.340|0.585|0.000|0.000|0.000|0.006|
|Nigeria-Uganda|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|9,127|9,127|9,128|9,127|9,127|9,128|
|R-squared<br>_Note_: Each column r|0.006<br>eports results from|0.122<br>a single regres|0.078<br>sion of a binary var|0.160<br>iable equal to 1 if|0.067<br>the respondent|0.038<br>was familiar with|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent was familiar with the action and 0 otherwise. The binary variable is then regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

29
Figure 2. As with the mobile access figures, the usage rates for particular mobile functions are strikingly high. At least once a day, 81 percent of Kenyan adults make or receive a call, 61 percent send or receive a text message, and 22 percent send or receive money or pay a bill using a mobile phone.

Differences by gender in mobile phone usage for particular functions are small. Nearly the same number of women as men make or receive a call on a daily basis (80 percent versus 82 percent). The gender gap is slightly higher for daily use of text messages (58 percent versus 65 percent) and for daily use of mobile money (21 percent versus 24 percent). A comparison of urban and 3
Policy Research Working Paper 10970

# **Abstract**

In Paraguay, poverty reduction has stalled since 2014 due to a deceleration in economic growth, which has been argued to be partly due to a series of climate-related events. Nevertheless, little is known about the impacts of climate-related shocks on the poor. This study analyzes the extent to which short-term weather shocks have affected incomes and poverty in Paraguay. It combines data from the yearly household survey series, the Permanent Continuous Household Survey; the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate temperature data; and Climate Hazards Group InfraRed Precipitation with Station data from 2004 to 2019. The research design exploits variation in weather shocks across districts and time, using ordinary least squares pooled regression analysis. The results show that heat shocks led to significant household income reductions: 5 percent in urban areas and up to 8.8 percent in rural areas, on average. Heat shocks also increased poverty by 1.7 and 4.2 percentage points in urban and rural areas, respectively. Floods primarily affected urban areas, increasing poverty by 1.9 percentage points. The impacts vary substantially across regions and household characteristics: female-headed households in rural areas are particularly vulnerable to heat shocks, while households that are active in the primary sector suffer most from both heat and drought shocks. These findings evidence the disproportional impacts of short-term weather shocks on income and poverty across regions and household characteristics. The results highlight the need to consider actions to promote adaptation and climate risk strategies tailored to subpopulations that are vulnerable to climate, to enhance overall resilience in the country.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lgayoso@worldbank.org
# **Box 1: Global gains from migration**

Even though quantitative estimates of the direct gains from migration are difficult to obtain, economic simulations suggest that an increase in South-North migration would produce substantial income gains in the long-run; these income gains could exceed those from comprehensive trade liberalization; and the destination countries in the North would capture one fifth the overall benefits of increased immigration (World Bank 2006, Winters _et al._ 2003, Anderson & Winters 2008, van der Mensbrugghe & Roland-Holst 2009). Documented welfare gains from South-North migration work primarily though the increase in the available labor force. Ortega and Peri (2009) found that immigration increases employment in the destination countries in the North one for one, implying no crowdingout of natives. This result implies that immigration increases the total GDP of the receiving country without affecting average wages or labor productivity.

Immigration has also been observed to boost productivity through innovation and specialization. Data from the United States show that one percent increase in the share of migrant university graduates increase the number of patent applications and grants issued per capita (Chellaraj _et al._ 2008, Hunt & Gauthier-Loiselle 2008). However, burdensome regulatory requirements and procedures that foreign doctors, engineers, architects and accountants have to meet in order to practice in the destination country can impose significant financial and other costs on these highly skilled immigrants (Mattoo and Mishra 2009). Also the less-educated immigrants increase labor productivity as they complement the uneducated local labor force that, based on their knowledge of the local language and institutions, will be better able to specialize in more productive complementary tasks (Peri & Spaber 2009). Furthermore, immigrants are often willing to do jobs that locals no longer are interested in, such as care for the elderly (UNDP 2009, p. 85). Also, the availability of low-cost childcare by the immigrants can enable young local women to go back to work (Kremer & Watt 2006) thus boosting economic development further.

Incoming migrants need to be integrated into the labor
available in the region where they were born. By the same token, Dahl and Sorenson (2011) found that companies perform better, survive longer and generate higher profits when located in regions in which their founders have lived longer, this effect being similar in size to that associated to previous experience in the same sector.

Following this line of reasoning, both spinoffs (entrepreneurs leaving a mother firm to found a new business<sup>27</sup> ) and ‘serial entrepreneurs’ (founders who have previously run other businesses) may have an advantage compared with “ _de novo_ ” entrepreneurs. For example, Hirakawa, Muendler and Rauch (2010), using microdata from Brazil over the 1995-2001 period, found that spinoffs are characterized by larger entry sizes (see Section 4.2.1) and lower exit rates than new firms not generated by a parent company. Similarly, the role of past experience and path-dependence is confirmed by the fact that serial entrepreneurs are more likely to replicate the success of their past companies than single venture entrepreneurs or serial entrepreneurs who failed in their prior business (see Gompers _et al_ ., 2006).

Turning our attention to a managerial perspective, new founders who had previously been employed as top managers in the same sector and who had better access to relevant information are expected to exhibit better post-entry business performance (for an empirical validation of these relationships, see Cooper, Gimeno-Gascon and Woo, 1994; Cressy, 1996; Arrighetti and Vivarelli, 1999; Lee and Tsang, 2001; Shane, 2001; Vivarelli, 2004).

Some studies confirm the positive link between previous job experience and a firm’s postentry performance in the case of DCs too. For example, McPherson (1996) found a positive relationship between annual employment growth and previous experience of the founder in similar economic activities for entrepreneurial firms in Swaziland and Botswana, while Vijverberg (1991) and Goedhuys and Sleuwaegen (2000), both studying Côte d’Ivoire, found that job experience previously acquired in
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Policy Research Working Paper 10923

# **Abstract**

Using global data from the World Bank’s Enterprise Surveys that includes the precise geo-location of surveyed firms, this paper examines how dry spells and precipitation shocks influence firm performance. The study finds that firms in areas that experience dry spells have lower performance in terms of sales. This is particularly true for smaller firms and those in developing economies. A higher number of extreme dry days also increases the chances that a firm will exit the market. The main channels are largely through labor productivity and infrastructure service disruptions such as water and power outages. There is also some evidence of limited access to finance due to negative precipitation shocks.

Governance may be an exacerbating factor, with negative precipitation shocks increasing exposure to corruption. Yet, there is also some indication that digitally connected and innovative firms are more resilient to negative precipitation shocks. Process innovation, website ownership, and use of technology licensed from foreign firms mediate the effects of negative precipitation shocks on firm performance. However, there is little evidence of adaptation. Negative precipitation shocks have no effect on the presence of green management practices or green investments for a subset of firms for which such data is available.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Office of the Chief Economist, Planet Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aislam@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly
population, according to the results reported in section V of this paper.

The remainder of the paper is structured as follows: section II describes the data sources used in the analysis. Section III describes the setting, reviews relevant literature, and provides detailed statistics on domestic mobility rates, statistics on propiska registration, cost-of-living indexes, spatial earnings differentials, and measures of housing affordability in Uzbekistan. Section IV provides both descriptive and experimental results on citizens’ preferences for reform. Section V describes the association between highly publicized involuntary demolitions and citizen support for reform. Section VI concludes.

7
marriage between men and women in Mali, only 48 percent of women 60 and above are married compared to 90 percent of men (Commission on Population and Development, 2000). There are considerably more widows than widowers. Indeed, 3 percent of men aged 56 and older are identified as widowers versus 52 percent of women in the same age group in the 2006 ELIM data.

Malian women tend to have different, and typically significantly inferior, human capital <u>endowments</u> to men. According to the 2006 DHS, only 17 percent of Malian women are literate compared to 37 percent of men; the breakdown for rural and urban areas is 8 versus 26 percent, and 35 versus 61 percent, respectively.<sup>6</sup> An alarming 88 percent of rural women have had no schooling at all. This is true also of 59 percent of urban women. Although the percentages of men without instruction are also high at 69 and 39 percent in rural and urban areas, they still fare considerably better in this respect than women. Far fewer girls attend school: the ratio of girl to boy attendance is 75 percent at the primary school level and only 35 percent at the secondary school level. Greater illiteracy and lower educational attainments generally, place stronger limitations on women‘s access to employment and government services. Malian women also face pronounced health risks and complications associated with the near universal practice of girls‘ circumcision in often unsanitary conditions, and repeated child bearing as adults. A woman has 6 to7 births on average, and these are associated with high rates of maternal mortality ─ 670 deaths per 100,000 live births in 2008 (Hogan et al., 2010) ─ as well as pronounced morbidity. At 250 per 1000 births, rates of infant mortality are also extremely high. Demographic and Health Surveys indicate high rates of anemia. On the whole, women are likely to suffer from lower physical resilience to shocks for a substantial part of their adult lives.

A number
# Learning Poverty: Measures and Simulations<sup>*</sup>

## João Pedro Azevedo<sup>♦</sup>

JEL Classification Codes: O12, O15, I21, I24, I25 Keywords: Learning Poverty, Primary Schooling, SDG, Learning Losses, COVID-19

> * I would like to thank Paola Ballon, Paul Corral, Diana Goldemberg, Amer Hassan, Ambar Narayan, Yevgeniya Savchenko, Umar Serajuddin, Shabana Singh for providing comments to earlier drafts of this paper. I would also like to thank Jaime Saavedra, Omar Arias, Christian Aedo, Harry Anthony Patrinos, Silvia Montoya, Kirsty Newman, Koen Geven, Michael Crawford, Reema Nayar, Halsey Rogers, and Syedah Aroob Iqbal for questions and discussions that inspired different parts of this paper. I would like to thank the producers of all the learning data used in this paper—the national governments and the teams at the International Association for the Evaluation of Educational Achievement (Progress in International Reading Literacy Study and Trends in International Mathematics and Science Study), United Nations Educational, Scientific and Cultural Organization (Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación), Conférence des Ministres de l'Education des Etats et Gouvernements de la Francophonie (Programme d'analyse des systèmes éducatifs), and Southern and Eastern Africa Consortium for Monitoring Educational Quality, as well as the Organisation for Economic Co-operation and Development; and the staff from the education management information systems departments of all ministries of education, without whom none of this work would have been possible. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> ♦ Lead Economist, HEDGE, Education Global Practice, World Bank.
balance. In summary, our DCGE model is well suited to capture path dependent effects within a consistent macroeconomic framework.

# _Baseline scenario_

In order to estimate the economic impact of climate change for Tanzania, we first specify a baseline scenario that reflects development trends, policies and priorities in the absence of climate change. The baseline provides a reasonable trajectory for growth and structural change of the economy from 2007 to 2050 that can be used as a basis for comparison.

Economic growth in the DCGE model is determined by rates of factor accumulation and technical change. For population and labor supply, we assume that Tanzania’s population will continue to grow but at a decelerating rate (i.e., 2.0 percent today falling to 0.3 percent by 2050). We assume that the expansion of cultivated crop land will slow such that growth in agricultural production becomes increasingly dependent on the adoption of improved technologies rather than land expansion. As described earlier, the crop models use historical climate data to define year-on-year yield fluctuations in the baseline for each crop and region. Exogenous long-term agricultural productivity growth is set at 0.8 percent per year in agriculture and 1.2 percent in non-agriculture. Improvements in the education levels of Tanzania’s workforce are assumed to continue, with supply and productivity rising faster for skilled and semi-skilled workers than for unskilled workers (i.e., at 2.0 and 1.5 percent per year, respectively, compared to 0.5 percent). Under the above assumptions, Tanzania’s economy gradually develops, with agriculture’s contribution to gross domestic product (GDP) falling from 27.8 to 14.1 percent during 20072050. Overall, per capita GDP grows at an average 2.2 percent per year in the baseline, leading to significant improvements in average household welfare.

# _Economy-wide modeling results_

The DCGE model uses the crop yield results from Section 2 to estimate the economy-wide impacts of climate change. We first discuss the macroeconomic results from the model,
0.511|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if she goes out without telling<br>him?<br>0.501|

Notes: The _Global Index_ (Cronbach’s alpha = 0.71) is created using all variables reported above with the exception of those contained in the _Beliefs on others_ index. All variables were re-oriented so that the impact of treatments on each component of the index should be positive. The column on the right reports the loading factors used for the construction of the indexes with principal component.

44