rafmacalaba/data-use-mentions-extended huggingface dataset:
absence of longitudinal data. The analysis focuses on the dynamics of poverty in Bhutan between 2017 and 2022. This case study is interesting because the observed and documented reduction in poverty appeared particularly significant (the poverty headcount ratio was 28 percent in 2017, and only 11.5 percent in 2022) and, as recorded by the results obtained with the decomposition _à la_ Datt and Ravallion (1992), unusually little of this decrease is explained by the role of economic growth, but rather by redistributive effects. In light of this, an analysis based on synthetic panels can help identify the flows responsible for observed changes in poverty.

21
028<br>(67.791)|8.483<br>(21.619)|
|Observations<br>|259|259|258|258|
|R<sup>2</sup>|0.229|0.178|0.273|0.216|

_Notes_ . Robust standard errors are reported in parentheses for columns (1) and (2) and bootstrap standard errors are reported in parentheses for columns (4) and (5). *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind).

# **4.4 Robustness Checks**

We conduct a number of different robustness checks (Table 6). First, there is the possibility that the likelihood of a crash being recorded by the police may have been affected by the curfew policy if it changed the likelihood of reporting a crash to the police or if the curfew led to increased police presence that increased reporting. In order to check if this may be driving the results, we use a different source of data on crashes coming from Flare to analyze the impacts of the curfew on crashes. We see very similar results for the curfew hours and pre-curfew hours as with the police data (Panel A of Table 6). The coefficient for the decrease in crashes during the curfew hours is smaller; therefore, in the triple difference, we do not see a significant drop in crashes during the curfew period hours in comparison to the daytime hours. We see a very large and significant increase during the pre-curfew hours.

Since we have data on road traffic crashes from the police, we go further back in time to 2017 and use data from 2017, 2018 and 2019 as a comparison to 2020 and comparing the pre-COVID-19 policies period of Jan 1-March 15 with the
VAT rates in nine European (IFS, 2011a) and 20 OECD countries (OECD/KIPF, 2014). Both studies find that reduced VAT rates as a whole have a progressive impact, but that richer households benefit more in aggregate terms than poorer households. IFS (2011a) emphasize a key reason for the overall progressive results is the application of reduced rates to the majority of food in all nine countries. OECD/KIPF (2014) finds significant variation in the distributional impact of reduced rates across expenditure types. Reduced rates typically introduced to achieve distributional goals – such as on food, water supply and energy products – are found to have a small progressive effect, but to be poorly targeted. Reduced rates typically introduced to address social, cultural and other non-distributional goals – such as reduced rates on books, restaurant food and hotel accommodation – are often found to be so poorly targeted that they have a regressive effect.<sup>26</sup> Meanwhile, Warwick et al. (2022) examine the overall distributional impact of reduced VAT rates in six developing countries (Ethiopia, Ghana, Senegal, Sri Lanka, Uzbekistan and Zambia). Additionally, they utilize input-output tables to estimate tax expenditures resulting from VAT exemptions. They find both progressive and regressive results, depending on the country, but that reduced rates and exemptions give significant benefit to richer households and are therefore an expensive way of attempting to support poorer households.<sup>27</sup> The impact of informality in developing countries illustrated by Bachas et al. (2023)

> 25 Similarly, current expenditure, and the VAT incurred on it, may have been funded from income earned in a previous year. See Thomas (2022a) for a more detailed discussion.

> 26 A range of other single-country microsimulation studies find similar results either for reduced VAT rates as a whole or for selected reduced rates such as on food (e.g., Davis and Kay, 1985, for the United Kingdom; Leahy et al
|**Mortgages**||||||
|No. of registered mortgages|6,542|2,227|872|1,445|1,998|
|<br>per year|168|117|87|289|400|
|Mortgage value (US $)|51,573|37,372|59,329|71,471|49,620|
|Plots reg. to male owners|0.529|0.732|0.672|0.461|0.296|
|<br>mortgage value (US $)|42,626|31,279|43,427|63,503|49,338|
|<br>Plots reg. to female owners|<br>0.209|<br>0.175|<br>0.222|<br>0.220|<br>0.234|
|<br>mortgage value (US $)|37,447|21,333|40,483|50,632|40,520|
|<br>Plots reg. by males & female jointly|0.183|0.009|0.032|0.245|0.395|
|<br>mortgage value (US $)|41,917|19,975|39,425|45,082|41,130|
|Plots reg. by companies|0.078|0.085|0.073|0.075|0.075|
|<br>mortgage value (US $)|175,531|131,375|255,818|266,687|131,479|
|Days for registration process|69|136|86|24|20|
|Days to get ministerial consent|94|99|79|||

_Source:_ LAA, land administration database and digitized records, 2020
handling practices. Under-pricing by small companies is often combined with under-reserving, leading to prolonged disputes and settlement delays. This affects the reputation of insurers and contributes to pervasive public mistrust. Competition for market shares in a small market can ultimately result in pyramidal structures, leading to larger losses and even insolvency. Table 4 shows MTPL loss ratios in countries for which the data is available on a net incurred basis.

**Table 4: MTPL Loss Ratios** **<u>(%) in Selected Countries</u>**<sup>**8**</sup>

|**Country**|**2004**|**2008**|
|---|---|---|
|Egypt|292|316|
|Jordan|87|101|
|UAE|76|89|
|Tunisia|114|87|
|Morocco|84|75|

Sources: AXCO, Supervisors Reports

**Property and construction is the second most important line of business, is clearly larger in oil producing countries and clearly associated with coverage of risks in oil and gas extractive industries.** Interestingly, revenues are larger in the oil producing countries outside the GCC.  It is also noticeable that agriculture does not yet generate a substantial income stream in the relevant countries (Egypt, Morocco, Syria).

**Transportation (MAT) premiums account for a relatively low share of the non-life premium in all sub-groups.** Not surprisingly, it is larger in the countries that have a major international airline, such as Egypt, the KSA and the UAE.  The strategic location of these countries between the Western and Eastern hemispheres is being exploited with a number of successful international airlines being established. The resulting hull and liability insurance business contributes to premium income, although most of this risk is reinsured internationally.

**Seven countries have shares in excess of 10% under the ‘Other’ heading.** In the case
_Table 6: Percentage of displaced individuals deprived in selected indicators by gender_

||Ethi|opia|South|Sudan|Su|dan|
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|
|Years of schooling|55|78<sup>***</sup>|36|63<sup>***</sup>|32|46<sup>***</sup>|
|School attendance|16|19<sup>**</sup>|21|29|23|23|
|Early marriage|3|13<sup>***</sup>|8|75<sup>***</sup>|6|50<sup>***</sup>|
|Unemployment|7|5<sup>***</sup>|2|0<sup>*</sup>|3|3|
|Legal id|45|46|48|74<sup>***</sup>|10|10|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1% ***, 5% ** and 10% * levels.

Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head. The largest differences between households headed by men and women is for early marriage (42.3 p.p.), waste disposal (37.2 p.p.), and
Rates of sales taxation are taken directly from the statutory rate schedule.<sup>32</sup> Rates of subsidization for energy products are taken from the Ministry of Petroleum and the World Bank (2017). When applied to purchases recorded in the HIECS, cumulative totals of energy subsidies and sales taxes (on goods) allocated in HIECS reach 34 percent, 42 percent, or 56 percent of the _scaled-down_ budget totals for those items, where the scaled-down budget totals are created by multiplying actual budget totals by the ratio of cumulative household consumption expenditure in HIECS to cumulative final household consumption expenditure in the national accounts.

Energy subsidies are also available to firms and industries using energy goods and services as inputs into production; other production inputs may attract sales taxes. Lower or higher input prices that result from subsidies or taxes, respectively, may be passed on to final prices for goods and services created using the subsidized or taxed inputs. This study estimates these indirect effects of energy subsidies and the sales tax regime and includes the indirect benefit (or burden) via the same imputation procedure based on consumption expenditure records described above.<sup>33</sup>

# **Excise taxes**

The GST was in effect in FY15 and includes provisions for the taxing of goods and services with a standard tax rate of 10 percent.<sup>34</sup> Certain commodities were taxed at 5 percent, and selected services were taxed at 5 percent or 15 percent. Some particular commodities are exempted from the GST, and others are assigned a special tax rate. For the purposes of this exercise, we refer to excise taxes as those goods assigned a special tax rate according to the table 1 of GST law. Goods included in this category are tea, sugar, beer, tobacco, and petroleum products (gasoline, kerosene, and so forth).<sup>35</sup> The CEQ exercise covers only a subset of these as the HIECS provided limited information for mapping them to
we make that effort and codify the 9,223 different answers to the 7 occupation variables in the HFPS. In particular, we map these answers to the 41 groups in the International Standard Classification of Occupations 2008 (ISCO-08) at 2 digits.

Having a codified occupation variable is important for two reasons. The first one is apparent: it allows us to explore heterogeneities by occupation. Which were the occupations hardest hit by the pandemic? Did people move across occupations as a reaction to the shock? The increasingly influential task-based approach stresses the role of occupations in accounting for different labor behaviors and dynamics (Acemoglu and Autor 2011). Having harmonized occupation variables is also relevant for a second reason: it allows us to construct measures of potential for work from home, a crucial factor to cope with the shock generated by the pandemic and the containment measures. We compute a measure of work from home following the proposal of Dingel and Neiman (2020) based on occupational characteristics from O*NET. Given that in some occupations the possibility of teleworking depends on having internet access, which is far from universal in Latin America, we adjust our individual measures of WFH by taking into account home internet access reported in the HFPS (Garrote-Sanchez et al. 2021).

Our main analysis is based on fixed-effects models of job losses and other labor outcomes. We focus the analysis on the effect of two factors that may account for the severity of the shock: the individual’s potential for work from home (WFH) and some sociodemographic characteristics such as gender and number of children at home. We control for several factors and include fixed effects by occupation and by country. We also explore the role of the differences in the stringency of the social-distancing measures across countries and over time.

We first confirm two important results: (1) the impact of the COVID-19 shock was strongly decreasing in the possibility of working from home, and (2) women were more likely to lose their jobs than men. Importantly, we also find
wage legislation, more (or less) liberal severance pay, reform in the pension regimes etc. These accompanying domestic reforms often concern labor—whether they are “anti” or “pro” labor. Sometimes “anti” labor legislation accompanies openness reforms because it is felt that liberalization in the foreign arena can be emptied of content (or cannot produced the desired results) if there is no improvement in the domestic legislation, i.e. if the latter is deemed too restrictive. Mexico provides one such example (Robertson, 2000 and Hanson and Harrison, 1999). Alternatively, labor policies can, for a segment of labor force at least, become more generous if that is the short-term cost the government needs to pay in order to convince trade unions not to wreck the reforms. In that case, more generous severance pay, low interest loans to start own businesses, early retirement schemes can all be used to reduce the resistance to reforms and to “buy off” potential losers. In addition to labor reforms, there may be also “accompanying” financial reforms: liberalization of interest rates, increased competition in the banking sector etc. All of this complicates any attempt to isolate the impact of trade reform on wage inequality. We shall therefore try to control for some of these other policies (labor markets, social transfers).

To measure labor market conditions, we use the Labor Market Data Base constructed by Martin Rama and Rachel Artecona (see Rama and Artecona, 2002).<sup>27</sup> Their database has, at five-year intervals (year 1975, 1980 etc.) a number of labor-related measures such as social security contributions (in percentage of gross salary), unemployment rate, replacement rate in case of unemployment etc. For our purposes, that is bearing in mind the model described above, two variables are of particular interest: share of labor force covered by collective agreements, and share of the unionized labor force. They do not allow us to distinguish between the
expected to be less productive (e.g., La Porta & Shleifer 2008), the actual dispersion is likely several times higher.<sup>1</sup> In this context, understanding mechanisms of expanding within productivity growth can be critical for Mozambique’s economic development, and a strategy that can complement well the continuation of policies to promote investment and economic transformation. Specifically, for policy, learning about the role firm capabilities can have in raising within sector productivity is important to potentially identify new drivers of growth for Mozambique.

Early studies showed that Mozambique scores low in measures of firm capabilities. It is ranked 137 out of 138 in the World Economic Forum’s Global Competitiveness Index (GCI), mostly due to low scores on weak higher education and training and limited business sophistication, and is ranked 148 out of 157 in the World Bank’s Human Capital Index, which measures the relative productivity of today’s generation based on their educational and health attainments. In an international comparison of management practices across 34 countries, firms in Mozambique reported the lowest adoption of business practices (Lemos & Scur 2014).

This paper uses new firm-level data on management and organizational practices, and firm-level skills, that was collected in 2018 as part of the World Bank’s Enterprise Survey to better understand the relationship between firm capabilities and firm performance in Mozambique. It expands from an early study about management practices (Lemos & Scur 2014) by covering also skills and training, and a wide range of sectors and locations in the country, in the context where Mozambique is facing a more difficult economic environment than in the high-growth period of 2000-2015. It also explores the importance of competition, contract enforcement and transaction costs in explaining differences in firm capabilities. Finally, it studies for the first time the relationship between firm capabilities and performance in Mozambique.

We find that larger firms, male-owned, with relationship with international markets (exporters, buying inputs from outside, with licensed technology), and those using internet
and cash transfers (9 percent). The average amount received by the first decile is also higher than for the rest of the deciles, in absolute terms. Despite the relatively modest cash amounts of both residents’ pension and cash transfers, they do help lower overall inequality as will be shown later. Still, these amounts are low, particularly in poorer provinces and rural areas, where the benefit is a small fraction of average wages. Compared to the average pension received by those that contributed to the Employee System, the average pension for those in the resident’s pension system is  10 times lower on average (World Bank, _China Economic Update, December 2023_ ). Considering cash transfers, overall coverage is low (14 percent of households) and significantly lower if we consider only the flagship cash transfer program Dibao. This program covers less than 4 percent of the population, compared with an average of 17.6 percent of the population among upper-middle-income countries (World Bank, ASPIRE database). Most households in the program are those whose members are unable to work due to age or disabilities. As such, the program has limited reach for households that may experience unexpected fall in income due to loss of employment or business.

**Fourth, and as expected, richer households contribute more to the fiscal system than poorer ones in relative terms, contributing to the progressiveness of the system.** Households pay into the fiscal system in the form of PIT, VAT, and consumption taxes on their purchases and through their contributions into the employee or resident

17
annual interest of LIBOR + 2.8 percent, there is also a requirement to pay a flat management fee of 0.7 percent, a commitment fee of 0.5 percent per annum and a one-time China Export and Credit Insurance Corporation (Sinosure) premium of 7 percent. The loan in Guinea [#16] attracts a management fee of 0.5% while that in Niger [#17] of 0.25%. The Glencore-Chad loan involved fees for a total of USD37 million plus 0.75 % annual fee on any outstanding amount, later lowered to USD 0.6 million [#7] & [#8].

We analyze how the rates of these loans compare to traditional loans in section 5/c. Given that data on fees is not systematic, these are not analyzed.

# g. Repayment schedules

In seven cases we observe RBLs with repayments set to yearly, quarterly or a daily volume of commodities used to service the loan. These repayment volumes are converted into their monetary value based on market prices and deducted from the total outstanding loan amounts. In some cases, the annual repayments change, with less to be repaid in earlier years. Some set a range of volumes (e.g., 150,000 to 200,000 barrels per day or up to up to 40,000 metric tons of cocoa). Some of these RBLs may require higher volumes within the set range to be shipped when prices are low, but still effectively cap the repayment value.

In some cases (especially RDA deals) the loan is to be repaid from a designated commodity income stream (or a percentage of it) and repayment speed depends on how much revenue is generated by that income stream. Examples of such earmarks include the Sicomines copper and cobalt mine in the DRC (85% of mine profits), the Agadem revenues in Niger (the government’s share of oil revenues) and the Badoit project in Chad (a fraction of profit oil and
potential mechanisms for how student learning might be affected. Most of the literature on changes in teacher pay measures the short-term effects of such policies, where the primary mechanism to improve student learning in the time frame studied is through the effort of teachers in the current system. However, over time, the mechanism associated with wage differentials and student learning is more likely to be through the selection of higher quality candidates into the profession.<sup>6</sup> Bau and Das (2020), which unlike the other studies follows teacher recruitment for a reasonably long period of time—four years—in Pakistan, nevertheless find no adverse selection effects in teacher quality from a reduction in salaries. Even

> 5 Other studies, such as Iwu et al. (2017) in Nigeria, also show positive associations between teacher pay and job satisfaction.

> 6 Teacher pay could also affect teacher turnover, as suggested by a survey of teachers in Nigeria (Okafor et al. 2016).

4
unknown, it is expected to potentially also play a major complicating role. Note that Ossa (2014) adopts a distinct approach that relies on quantitative modeling techniques to construct counterfactual estimates for the size of Nash (noncooperative) tariffs in a model featuring seven regions (including the US, EU, Japan, China, India, Brazil, and rest of the world). He computes the median tariff to be 58.1 percent, which is somewhat higher than estimates of the pre-GATT levels of average applied tariffs described above.

> 70The WTO (2007) provides an important survey of the first sixty years of the GATT/WTO system and includes additional, country-specific descriptions of tariff data over the decades for a number of countries not covered in depth here, such as Brazil, India, Senegal, Nigeria, Argentina and Korea (pp. 211-219).

39
1 if conflict in the past affects current poverty less and less over time, i.e. if countries are able to catch up and “repay their debt” after conflict is over. In the context of the literature discussion the parameter δ is best thought of as the (mental) health and skills of the affected population recovering in the years following conflict or as investors regarding a region or country as increasingly stable with lasting peace. We follow a two-stage procedure. We first calculate the value of C i for every country year under different assumptions on . We then estimate by running the regression in (1) repeatedly with different values of and taking the regression that maximizes the fit of the model. As a result, CCPPC CCPPCCPPbbPP an estimated equal to 0.88 is used throughout the study. This implies that, with stable peace, a δδ δδ δδ conflict debt shrinks to 52% or its original size after 5 years and to 28% of its original size in 10 years. δδ This is in itself interesting as it suggests a relatively quick recovery of poverty rates after conflict ends.

In Table A1 we show the evaluation of the conflict data using the conflict debt model. In the crosscountry data we show that the mean debt value is 0.177 but debt concentrates in few countries. Conflict debt takes a maximum value of almost 5 as the debt accumulates without peace.

# Cross-Country Results

The results provided by our conflict debt model are shown in Table 1. There is a robust relationship between conflict debt and the poverty rate where an increase by 1 in the debt is associated with an increase in the poverty rate of 1.767 percentage points in our preferred specification in column (3) which features country and year fixed effects. What is important to note here is how the country fixed effects reduce the size of the coefficient. This indicates that level differences in conflict and poverty between countries are a key driver of the relationship we see in Figure 1A. Once this is partialed out,