rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 11 Neutral Distribution vs. Random Allocation to Simulate Remittances in El Salvador<br>A) Differences Between Projected and Actual B) Poverty MSE<br>Data<br>2.50 2.00<br>2.00<br>1.50<br>1.50<br>1.00<br>1.00<br>0.50<br>0.50<br>0.00 0.00<br>2017 2018 2019 2017 2018 2019 2017 2018 2019<br>Poverty at $6.85 Gini Neutral distribution<br>Neutral distribution Random allocation Random allocation<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

As expected in a country like El Salvador, results suggest that using random allocation to model international remittances slightly improves the model's performance along the whole time series compared to neutral distribution. The difference between the simulated and actual poverty rate at 6.85 USD reduces for all years by around 0.14 percentage points (p.p.) (Figure 11.A). Still, the reduction in inequality is smaller (approximately 0.08 p.p.). In addition, results suggest that the average MSE for poverty and vulnerability is also lower with the random allocation approach (Figure 11.B), especially in years where the model's bias is larger such as 2017 and 2019.

> 23 Own calculations using SEDLAC 2019.

27
<mark>Table 2a: Transition Matrices - Synthetic vs. Actual Panel Data Chile 1996 and 2006</mark>

|<br>Status in 1996, 2006|<br>Lo<br>|<br>wer Bou<br>Estimate|<br>nd<br>s|<br>Truth|<br>Up<br>E|<br>per Bou<br>stimate|<br>nd<br>s|
|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|Poor,Poor<br>|7.46<br>|6.68<br>|5.35<br>|4.64<br>(3.19,6.07)|2.96<br>|2.65<br>|2.46<br>|
|Poor,Non-poor<br>|7.70<br>|10.35<br>|11.09<br>|19.59<br>(16.87,22.29)|25.39<br>|20.55<br>|21.78<br>|
|Non-poor,Poor<br>|0.13<br>|0.92<br>|2.25<br>|2.96<br>(1.79,4.11)|4.64<br>|4.94<br>|5.13<br>|
|Non-poor,Non-poor<br>|84.71<br>|82.06<br>|81.31<br>|72
Figure 8 Trends in non-monetary indicators correlated with household welfare, DHS data 2003-2018 <!-- Start of picture text --> a) Access to electricity b) Access to improved water source<br>90 100<br>80 90<br>70 80<br>60 70<br>50 60<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0<br>0<br>2003 2008 2013 2018<br>2003 2008 2013 2018<br>National Urban Rural<br>National Urban Rural<br>c) Access to improved sanitation d) Secondary school attendance<br>90 80<br>80 70<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10 10<br>0 0<br>2003 2008 2013 2018 2003 2008 2013 2018<br>National Urban Rural National Urban Rural<br>Share of households with electricity access (percent) Share of the population with an improved water source (percent)<br>(percent)<br>Net secondary school<br>Share of households with an attendance rate (percent)<br>improved santiation<br><!-- End of picture text --> Note: each panel shows trends in non-monetary indicators highly correlated with household welfare and monetary indicators of poverty using data from the DHS 2003, 2008, 2013, 2018. Trends are presented separately for households living in rural and urban areas as well as at the national level. Panel a shows the share of households with access to electricity, panel b shows the share of households with access to improved water source, panel c shows the share of households with
# **Data Appendix**

**1. Quarterly Public Finance Database:** This dataset includes quarterly time series for total government revenue, tax revenue, revenues from social security, total expenditures (current and capital), interest payments and transfers.  The data refer to the consolidated general government or the consolidated central government (including social security), depending on the existence of quarterly accounts for the general government in each country.  Data are from national sources, with the exception of the UK and Canada from the OECD Economic Outlook and Belgium, Bulgaria, Czech Republic, Cyprus, France, Germany, Hungary, Poland, Slovakia and Slovenia from Eurostat.  Links to these sources are provided below.  For the Latin American and Asian countries in the sample the data correspond to central government accounts. For the remaining countries the data refers to the Consolidated General Government.

**2. Annual Tax Revenue Breakdown:** This dataset contains the breakdown of tax revenues by their components: Personal Income Tax Revenue, Corporate Income Tax Revenue, Value Added Tax Revenues (or Sales Tax Revenues, for countries with no VAT) and Social Security Contribution Revenues.  This database was constructed separately from the Quarterly Public Finance Dataset since these series are not always reported together and, many times, the tax revenue breakdown is in annual frequency.  For Latin American and Asian countries, where Central Government is being considered, only federal tax revenues are included.  Data was taken from national sources, with the exception of Argentina, Chile, Colombia and Mexico from ECLAC; Canada, UK, France, Germany, Belgium, Czech Republic, Poland, Slovakia and Hungary from OECDstats and Cyprus from the IMF’s IFS.

**3. Statutory Tax Rates:** The data related to the statutory tax rates, comprised by tax brackets and rates of Personal Income Tax, Corporate Income Tax, Social Security and VAT, comes from the OECD Tax Database for OECD countries ( _www.oecd.org/tax_ ); from
10 rebound from the expenditure cuts that were made in response to the debt crisis in 1982. By 1994, however, total spending on education had risen to 5.4 percent of GDP, or almost a full percentage point above its previous peak of 4.5 percent reached in 1982.

<!-- Start of picture text --> Figure 3<br>MEXICO<br>Education Spending per Student Capita<br>3500.00 -<br>8 3000.00 Prvate<br>o 2500.00<br>Z2000.00-<br>1500.00- Muipa<br>aC<br>1000.00<br>O 500.00 Federal<br><!-- End of picture text --> Source: Informe de Gobiemo, 1989 and 1997.

Figure 3 describes the evolution of funding for education in Mexico since the early 1980s. Private spending represenited less than 10 percent of total expenditures in education in 1987, and has fallen to below 5 percent in recent years. (This figure somewhat underestimates total private costs, which, with some adjustments noted in the Annex could be around 27% of total education spending.) Similarly, the share of state and municipal spending peaked at 17% of total spending in 1990, but since then also declined to less than 10 percent of total spending. The federal government, on the other hand, currently accounts for 87 percent of total sector spending. These figures are not far out of line with those from an OECD review of education expenditures, which indicated that in 1994, about 19 percent of spending on education in Mexico came from private sources, compared to a country average of 13 percent for the whole of the OECD.<sup>9</sup> Mexico does not stand out particularly in the preceding comparison. As in most countries, the public sector directly or indirectly finances the lion's share of education expenditures. Mexico does stand out in contrast to its
22 internal remittances, and 22.7 of such income from international remittances. In addition, households in the second lowest decile group receive almost 20 percent of their total household expenditure (income) from international remittances.

The fact that households in the bottom income decile groups are receiving a larger share of their total household expenditure (income) from international, as opposed to internal, remittances, serves to explain why international remittances have more of an impact on reducing the depth and severity of poverty in Ghana than internal remittances. When households in the poorest (and next to poorest) decile group receive international remittances their expenditure (income) increases by over 20 percent. This in turn has a huge effect on any poverty measure – like the poverty gap or squared poverty gap -- which considers both the number and the distance of poor households beneath the poverty line. By contrast, internal remittances account for a much smaller share of total expenditure (income) for households in the two poorest decile groups. As a consequence, when poor households in Ghana receive internal remittances, the poverty indices which measure both the number and distance of households beneath the poverty line do not show the same type of changes as with international remittances. In Ghana international remittances reduce the depth and severity of poverty more than internal remittances because poor households are receiving a greater share of their expenditure (income) from international remittances.

# 7.  Conclusion

This paper has used a large, nationally representative household survey (N=5998 households) to analyze the impact of internal and international remittances on poverty in Ghana. Four key findings emerge.
23 Mercosur countries had been liberalizing imports on a most favored nation basis for several years when, in 1991, they introduced their first widespread set of preferential tariff cuts. This is the year for which the UN COMTRADE records indicate that intra-block trade accelerated sharply. If the most dynamnic products in Mercosur's intra-trade, or those that were shifting most rapidly toward the region, had disproportionately high preferences this would suggest that Mercosur trade barriers were a factor in the re-orientation of exports. Evidence relating to this point could come from an analysis of the margins of preference that Mercosur's trade barriers provide member countries. Are these high enough to account for the increases in intra-trade that occurred during the 1991-94 period when tariff preferences on all but a few products were being implemented.

Several, sources of statistics on Mercosur's tariffs and NTBs are available for analyses of these points. First, a cooperative project between UNCTAD and the World Bank, named SMART -- Software for Market Analysis and Restrictions on Trade, compiled statistics on many OECD and developing countries' pre-Uruguay Round trade barriers (see UNCTAD and the World Bank, 1989 for a description of the SMART database and operating system). Since both Brazil and Uruguay's 1988/89 tariffs were included in these records (along with data on Brazil's nontariff measures) they provide partial details on Mercosur's trade barriers at very fine levels of detail. These two countries account for over 60 percent of Mercosur's total imports with the result that the SMART records provide a useful profile of the _structure_ of external protection. However, it should be noted that Mercosur countries (particularly Brazil) have subsequently implemented major unilateral MFN tariff reductions so the earlier statistics are not a reliable guide to current levels of protection. For this reason, post-Uruguay tariff data were drawn directly from the World Trade Organization's Integrated Data Base (IDB). Where there were known exceptions and departures from the reported WTO statistics (as was the case with tariffs and nontariff restrictions on
census of all manufacturing establishments with ten workers or more that use power-driven machinery.<sup>24</sup> Besides recording information on plant location, industry, legal form and employment, the LMMIS includes relatively detailed balance sheet accounts regarding assets and inputs use.

The data used in this paper are an unbalanced 13-year panel from 1996 to 2009, comprising 13,975 plantyear observations for 3,772 unique plants. This paper uses a definition of high-growth that considers growth over a three-year window, and hence requires that a plant be observed for at least three consecutive years. This limits the number of plant-year observations for this study to 5,962, equivalent to 1,218 unique plants. Of these, 57 establishments are dropped due to lack of information on capital assets or raw materials as TFP cannot be estimated for these plants.

Although the count of registered formal manufacturing plants grew rapidly from 623 in 1996 to 1,948 in 2009, plant activity has been less dynamic.<sup>25</sup> Average plant size fell from close to an average of 136 employees in 1996 to less than 70 employees in 2009, while the median plant size remains close to 20 employees throughout the sample period. These results imply that new plants entering the market are mostly small and hence may lack the necessary economies of scale needed to serve the global market. There is a lot of churning in Ethiopian manufacturing with gross entry rates recording an average 7.6 percent per year, while the exit rates among new plants are also high. Research suggests that 60 percent of manufacturing plants in Ethiopia exit the market within 3 years of entry (Gebreeyesus, 2008).<sup>26</sup> Manufacturing in Ethiopia is concentrated in five main towns, with Addis Ababa hosting over 61 percent of plants in 1996, while the other four towns being Dire Dawa, Bahir Dar, Hawassa and Nazreth add another 10 percent to total plant count. By 2009, the importance of the capital city declined to
We test our theory empirically focusing on the case of Indonesian manufacturing, where our estimates suggest that the vast majority of plants exert some degree of labor market power. Specifically, we take advantage of quasi-exogenous variation in investment restrictions across 346 narrowly-defined manufacturing product markets in Indonesia. Such restrictions are implemented by the government through the publication of the Negative Investment List (NIL), a Presidential regulation which details the conditions that new investors have to fulfill to register a company in any Indonesian sector. After addressing concerns of potential endogeneity of changes to the NIL and product market trends, we exploit changes in restrictions across product markets in 2011 to estimate the causal impact of product market regulation on wage markdowns.

To estimate markdowns, we use granular information on prices and quantities of 9- digit products and intermediate inputs from a highly representative sample of Indonesian manufacturing plants.<sup>3</sup> This constitutes an advantage over previous studies, which often rely on industry-level price indices to deflate nominal quantities, incurring in several sources of bias (Bond et al., 2021; De Loecker et al., 2016; Foster et al., 2008). Importantly, we also disentangle wage markdowns from price markups, which are embedded in the “naive” comparison of marginal product of labor and the wage paid to employees.

Using this approach, we provide reduced-form estimates of the elasticity of wage markdowns to investment restrictions, which is around 0.25. This implies that NILrelated entry barriers in the product market have increased markdowns by around 4.3% in our sample. The results are robust to a battery of tests and are also consistent with an event study regression based on a change of the NIL as the “event”. The absence of pre-trends in the event study further relieves endogeneity concerns for the instrument. In addition, we probe the robustness of our results using an alternative identification strategy based on a Bartik instrument exploiting the differential exposure to regulated product markets across 274 commuting zones. Such design minimizes the potential
farmers in Kagera have expanded into new crops, such as cabbages, tomatoes, green peppers and vanilla, while others have diversified into non-farm activities, such as Nile perch fishing, mining, trading and so on (De Weerdt, 2010). These sectoral shifts have coincided with substantial migration within and outside the region (Beegle, De Weerdt and Dercon, 2011).

Overall, the Kagera population is of diverse ethnic make-up with Haya and Nyambo tribes dominating in the north, and Subi, Sukuma, Zinza and Hangaza in the south. Projections based on the 2002 census put the population of Kagera at a little over 2.5 million people (URT, 2006b). Under half of the population was aged 0–14 years and around 5 percent were over 65 years old. Kagera is known for being one of the early epi-centers of HIV/AIDS with the first cases having been detected at Ndolage Hospital in 1983. Kwesigabo et al. (2005) studied trends in prevalence rates and note their steady decline over the years. While urban Bukoba recorded a peak of 24 percent prevalence in 1987, other districts had figures well below that. The Tanzanian Commission for AIDS (TACAIDS), the National Bureau of Statistics (NBS) and ORC Macro (2005) put the region wide prevalence in 2004 at 3.7 percent of individuals aged 15–49, well below the national average of 7 percent. Finally, in the early nineties the region hosted several hundreds of thousands of refugees from Rwanda and Burundi. The economic impact of this has been described by Baez (2007) and Maystadt (2010).

The baseline Kagera Health and Development Survey (KHDS) consists of 915 households, sampled to be representative of Kagera and interviewed up to four times from autumn 1991 to January 1994 and then twice more in 2004 and 2010. A more formal comparison of the baseline KHDS data with the Tanzania’s national 1991/2 Household Budget Survey, shows that across a range of demographic and socio-economic indicators,
containment measures over time and across countries. While most countries experienced an initial shock that restricted mobility between March and April of 2020, some countries, like the Philippines and Indonesia, experienced more prolonged reductions in mobility with little recovery over time (Figure 1a). Other countries such as Myanmar and Mongolia showed signs of recovery in mobility in May 2020, soon after the initial lockdown, but later in the year faced sharp declines in mobility due to a renewed surge in COVID-19 cases. Finally, countries like Lao PDR, PNG, and Vietnam experienced relative normalcy in mobility during 2020 after the initial shock, reflecting comparatively low local transmission of COVID-19 until 2021.

Multiple rounds of the HFPS have helped capture the socioeconomic and distributional impacts of the pandemic at varying periods of each country’s experience during the pandemic. For example, in Indonesia and the Philippines, the first survey round occurred at a time when mobility was on average more than 40 percent lower than baseline levels, while the subsequent round with employment and income data occurred at a time of relative improvement (Figure 1b). On the other hand, the first round of the HFPS in Mongolia and Myanmar was implemented at a period of comparative recovery, while later rounds (rounds 3 and 4 for Mongolia, round 4 for Myanmar) took place during a time of lockdown in each country.<sup>13</sup> While mobility varied relatively less across rounds in Lao PDR, PNG, and Vietnam, it was still below pre-pandemic levels in Lao PDR and Vietnam during the included survey rounds.

Figure 1a. Some countries experienced more severe containment measures that restricted mobility Percent change in mobility compared to pre-pandemic baseline Figure 1b. Survey timing and frequency differed across countries HFPS field dates and mobility index

> project - but found that in some countries, there was a significant disconnect between restrictions imposed and actual movement of people as indicated by the mobility data (in some countries, measures were stringent but mobility during the same period varied significantly), particularly as
# **Appendix B: Documentation of the Calculation of Ownership Shares for Kenya**

# **I. Telecommunications Shares in Kenya**

The primary source of data was various publications of Paul Buddle Communications, including ―Kenya—Telecoms Market Statistics and Forecasts,‖ March 20, 2008. Table 10 contains mobile phone subscription statistics by company and Table 2 lists the number of fixed-line phone subscribers. We defined market share as the share of total subscribers, summing fixed-line and mobile subscribers.

The telecommunications companies are: Telkom Kenya, Safaricom and Celtel. Ownership shares are as follows. France Telecom purchased 51% of Telkom Kenya in 2007 with the Government of Tanzania holding the remaining 49 percent.<sup>32</sup> . Vodafone held 35% of Safaricom network, with the remainder held by Telkom Kenya (60%) and a local company Mobitelea (5%).‖<sup>33</sup> . ―Celtel was acquired by MTC of Kuwait for US$3.4 billion in March 2005‖. MTC was later renamed ―Zain Group‖.<sup>34</sup> The results for market share by country (in percent) are as follows: Kenya, 26; EU, 49; EAC, 0; COMESA, 0; Rest of World, 25.

# **II. Bank Shares in Kenya.**

# **Bank Market Shares**

The data source for bank market shares was Bankscope, an on-line data source for about 29,000 banks world-wide.<sup>35</sup> Through Bankscope, we obtained data on total assets by bank in Kenya, owners -shareholders of the bank and the percent of the bank owned by each owner-shareholder. Market share of each bank was defined based on the bank’s assets as a share of total bank assets in the country. We divided the regions into the European Union, East African Customs Union, COMESA and Rest of the World.<sup
In addition, the paper finds that remittances tend to decline more in municipalities experiencing higher export growth.

This paper contributes to the literature in several ways. It is the first study to focus on the role of exports at the local level in a developing country using unique panel data from poverty maps, combined with data on exports at the municipal level. Second, it uses an instrumental variable that allows the causal impact of exports to be disentangled. The instrument combines the initial export structure by sector at the municipal level with global trends in exports from developing to developed countries by sector. Municipal-level trends in this instrument are thus not affected by local-level shocks. Third, it provides new evidence on the channels that could help explain why increasing exports may not necessarily translate into higher income growth 4
# **A.2 Cross-National Health Indicators**

**Figure A.2:** Health Expenditure and Under-5 Mortality in 2010

<!-- Start of picture text --> 200<br>SLE<br>150 GG<br>MLI<br>NER<br>BEN GG<br>SSD GIN GNB<br>100 MOZ LBR<br>TGOMWIBDI<br>GMBCOM G UGA<br>TZA<br>MDG SEN RWA<br>ERI<br>50<br>5 10 15 20<br>Health expenditure per capita<br>(Constant 2005 USD)<br>Under−5 Mortality Rate (per 1,000 Live Births)<br><!-- End of picture text --> We use measures from the World Development Indicators from 2010 for under-5 mortality (per 1,000 live births) and health expenditure per capita (in constant 2005 USD). The sample includes countries that the World Bank classifies as low income. Sierra Leone (SLE) appears to the upper right.

A4