rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: Assignment to treatment and control groups <!-- Start of picture text --> Treatment assignment<br>Treatment Control<br>Treatment 1 Treatment 2 Control 1 Control 2<br>Books & Books No intervention Training and<br>Training (in May 2007) before May Books (in Oct<br>2008 2007-Mar 2008)<br>(in May 2007)<br><!-- End of picture text --> To reduce spillover eects and ensure the political feasibility of providing schools with dierent inputs, a given province was allowed to have either treatment or control schools. Then, in these selected schools, a class from two grades (specically, from third and fourth grade) was randomly selected and surveyed.<sup>3</sup><sup>_,_4</sup> In the upcoming sections, we discuss the limitation of conning treatment and control schools in selected provinces, instead of allowing each province to have both types of schools, and we cluster standard error at province level to correct for this limitation. Finally, students within a class were randomly selected if the class size was more than 20; otherwise the whole class was surveyed.

The baseline survey was conducted during April-May 2007, just before the end of the academic year, and it encompassed 137 schools, 141 teachers and 2,612 students. A follow-up survey was conducted in April 2008. In the follow-up survey, additional schools, classes, teachers and students were surveyed to address initial imbalances in

> 3The classes were sorted alphabetically, and if there were at least 20 students in the rst class of each grade, it was selected as a sample. Otherwise, next class with at least 20 students was selected. If such class did not exist, the class with the highest number of students was selected.

> 4In 2004, Mongolia has began a transiting from a ten-grade education system, with four primary school grades, to a twelve-grade system (Yang and Sato, 2009).

6
tariff barriers implied that most tariffs were redundant, that is the tariffs exceeded the differential between internal and external prices (see Hay 2001 and Kume et. al. 2003). We assume that individual firms take these changes as exogenous and hence use the complete series. Since our interest in the end, is to identify the maximum contributions of trade variables to the evolution of the dependent variables, rather than identify individual effects, we include both variables simultaneously despite some clear conceptual overlap. Figure 8 shows the dramatic effects of reforms: a reduction to one-third of the level of effective protection (from 1988 to 2002) and doubing of import penetration rates (during the same period). This, along with the fact that higher reductions in the formal hiring rates observed in diverse industries (see Figure 9) appear accompanied by higher import penetration ratios<sup>14</sup> suggests that trade liberalization may have been important.

We develop three proxies to capture the impact of elements of the Constitutional reforms discussed in the introduction. Unlike the trade variables, we do not have a continuous series of arguably exogenous innovations across time, but rather a reform implemented at one moment.<sup>15</sup> Hence, we are especially dependent on the cross sectional variation in the impact of the reforms for identification. We calculate prereform values for formal workers by sector and then interact them with a dummy for the Constitution. This effectively exploits the cross sectional impact of the reforms across several dimensions.

_Union density:_ As discussed above, the reforms generally shifted power toward the unions and hence we would expect sectors with greater union representation to be more affected. The National Household Survey (Pesquisa Nacional Por Amostra de Domicílios<sup>16</sup> PNAD), a complementary employment survey with greater coverage but no

> 14 The relation between changes in hiring rates and effective protection seems to be orthogonal before controlling for any other effect.

> 15 Impacts of Constitutional changes appear to be of different magnitude for formal and informal workers. For instance, Figure 8 shows a differentiated evolution of the aggregate incidence of overtime and of the tenure of workers before dismissal between both sectors. Nevertheless the time variation of the proxies can certainly be treated as exogenous and attributed to the Constitutional change just for the periods in the neighborhood of 1988.

> 16 Data to compute the union density by industrial sector and to identify the sources of provision of health services before reforms was drawn from this source.

16
slightly more frequent power outages, more interruptions in phone and internet services, and higher costs for getting access to phone and internet services (see Panel D in Table 1).

With respect to _government regulation and expropriation_ , we focus on regulations on firm entry as well as expropriation activities of the government. Since comprehensive measures of entry reforms are infeasible, and the enforcement of entry regulations varies across regions, we focus on the measurable effects of entry regulation, the _degree of competition_ , which is captured by the Herfindahl–Hirschman Index in a specific sector (referred as HH-index hereafter). Relying on the annual surveys of industrial firms—the most comprehensive panel data of Chinese firms--we explore how competition has evolved and differed across regions. The degree of competition has risen substantially over time (Figure 2a), especially after the 2001 entry to the WTO. The increase in competition persisted for three years afterwards, stabilizing during 2004 to 2007. Firms in inland regions face much less competition than those in coastal regions (see Figure 2b), which feature more developed markets and denser concentration of firms.

Government expropriation of firms is captured by three variables. The first is the tax burden, perhaps the most common source of complaints from entrepreneurs worldwide. In developing economies, the informality of tax collection process, coupled with poor administration and corruption, places a disproportionate burden on those who comply, and thus distorts competition. The second variable measures extralegal fees and special assessment fees collected by local governments, which are more arbitrary and likely more harmful than taxes (Fisman and Svensson, 2007). The third variable is firms’ expenses on entertaining local cadres and cultivating public relationship (referred to as ETC),<sup>12</sup> which are shown to be mostly related to bribery and corruption and a good proxy of corruption (Cai et al., 2011).

The extent of government expropriation faced by private firms has experienced little improvement despite the rapid growth of the private sector in China (see Panel A in Table 2). The burden from conventional taxes as well as extralegal fees has not shown a clear trend over the last two decades. On average, private firms spend 2% of their revenue on entertainment (and travels), but larger firms and firms in coastal regions face less severe expropriation (see

> 12 ETC also contains legitimate expenses for firm business operations.

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<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Strongly Disagree Slightly Neither Slightly Agree Strongly<br>disagree disagree agree nor agree Agree<br>disgree<br>2015 2016 2017<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A15: Fear for the future of South Sudan

# Biggest fear, future of South Sudan

<!-- Start of picture text --> 100 Other reasons<br>80<br>Bad economy<br>60<br>Health and disease problems<br>40<br>20 Poverty<br>0<br>Lack of economic opportunities<br>2015 2016 2017<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017

## **Table A1: Regression results for poverty and consumption**

|VARIABLES|(1)<br>Poor<br>(below<br>USD 1.90<br>PPP)|(2)<br>Poor<br>(below<br>USD 1.90<br>PPP)|(3)<br>Log(real<br>consumption)|(4)<br>Log(real<br>consumption)|
|---|---|---|---|---|
|Survey year: 2016|-0.286|0.031|-0.524|-0.934|
||(0.268)|(0.259)|(0.449)|(0.611)|
|Survey year: 2017|-0.051|0.260|-2.541***|-2.916***|
||(0.228)|(0.221)|(0.344)|(0.455)|
|Inflation*Post|0.252|0.060|-0.829***|-0.575*|
||(0.165)|(0.161)|(0.257)|(0.338)|
|Conflict|0.001|0.002|-0.005**|-0.006**|
||(0.001)|(0.001)|(0.002)|(0.003)|
|Female household head|0.149***|0.203***|-0.030|-0.135*|
||(0.051)|(0.065)|(0.050)|(0.080)|
|Education level of household head_Primary|-0.077|-0.081|0.066|0.120|

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# **1.  Introduction**

Empirically identifying fiscal multipliers requires a strategy to isolate changes in public spending and/or taxes that are plausibly uncorrelated with contemporaneous economic shocks. In this paper I propose a novel method of identifying such fluctuations in public spending that relies on two features unique to many low‐income countries: (1) borrowing from the World Bank finances a substantial fraction of public spending, and (2) actual spending on World Bank‐financed projects is typically spread out over several years following the original approval of the loan. The first fact means that fluctuations in spending on World Bank‐financed projects are a significant source of fluctuations in overall public spending in these countries. The second fact means that fluctuations in World Bank‐financed spending in a given year are largely determined by fluctuations in project approval decisions made in previous years, and thus are unlikely to be correlated with shocks to output in the current year. I use World Bank project‐level disbursement data to isolate this component of public spending associated with past project approval decisions, and use it to estimate government spending multipliers in a sample of 29 mostly low‐income countries where this source of fluctuations in public spending is large relative to the size of the economy.

The recent financial crisis has renewed interest in the long‐standing question of the size of fiscal multipliers. Knowledge of the size of the multiplier is crucial to informing policy discussions about the appropriate scale and duration of fiscal stimulus packages in response to macroeconomic crises. Years of intensive and creative research have, however, yielded a bewildering array of estimates of the multiplier, ranging from zero and even negative to well above one. Nearly all of this evidence comes from a handful of developed economies, and is based on one of three primary identification strategies. The first are VAR‐based identification schemes, of which Blanchard and Perotti (2002) is a leading example. These studies rely on the availability of quarterly data, together with the assumption that discretionary changes in fiscal policy take sufficiently long to implement that they cannot react to contemporaneous economic activity within a quarter.<sup>1</sup>

> 1 This identification strategy is infeasible in the majority of developing countries, and especially in the poorest low‐ income countries that are the focus of this paper, as most do not report fiscal or macro data on a quarterly basis. For a sample of 27 middle‐income countries, Ilzetzki and Végh (2008) and Ilzetzki, Mendoza and Végh (2010) are able to assemble quarterly data in order to analyze the cyclical effects of fiscal policy in these countries using standard VAR‐based identification strategies that have been applied to industrial countries.  However, there is no overlap between their sample of emerging market economies with available quarterly data and my sample of low‐ income aid‐dependent countries.

2
_Figure 5 Age effects in the household saving rate, by categories of households_ <!-- Start of picture text --> Household saving rate, by Household saving rate, urban vs.<br>consumption quintile rural<br>0.35 0.3<br>0.3<br>0.25 0.2<br>0.2<br>0.1<br>0.15<br>0.1<br>0<br>0.05<br>2528313437404346495255586164<br>0 -0.1<br>-0.05 2528313437404346495255586164<br>-0.1 -0.2<br>Age of the household head Age of the household head<br>Q1 Q2 Q3 Q4 Q5 urban rural<br>Age increments (rel. to age 25) Age increments (rel. to age 25)<br><!-- End of picture text --> In keeping with saving rate patterns, patterns in household wealth accumulation are similar across consumption quintiles 2 through 5 (Figure 6, upper panels). Residential wealth increases at similar rates. Financial wealth stagnates early on, then picks up after age 45, and levels of debt decline steadily. Patterns differ somewhat for the first consumption quintile: residential wealth growth is markedly slower, even expressed in terms of their lower consumption levels. As we describe below, this appears to correlate strongly with the urban/rural divide.

Urban and rural households exhibit the same rate of growth in residential wealth, but their financial wealth evolves differently (Figure 6, lower panels). Urban households improve their situation steadily by increasing their financial wealth and reducing their debt throughout the life cycle. Rural households see a deterioration of their financial wealth and reduce their debt at a slower rate. Several explanations are consistent with these patterns. Rural households may be exposed to more frequent or damaging shocks early in the life cycle than the urban households, depleting their buffer stock. It is also possible that they benefit from better village networks to cope with economic uncertainty, reducing the need to accumulate liquid wealth in their 30s and 40s. Finally, since liquid wealth is also accumulated to pay for predictable lumpy expenditures such as schooling expenditures and ceremonial costs, the patterns exhibited by these expenditures could differ from those of wealthier urban households.

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# **APPENDIX – Definitions**

# **A1. Bank Variables**

The source of all these variables is the BANKSCOPE data base. The time series of the variables at the country level are obtained by taking bank-level averages in each country.

**Net interest Margin** (in percent of total assets): The net interest income as a percentage of total assets. The higher the ratio the cheaper the funding or the higher the margin the bank is obtaining. It is an indicator of the operational performance.

**Non-interest income** (in percent of total assets): It is the ratio of other operating income to total assets. It is an indicator of the operational performance. It indicates to what extent fees and other income represent a greater percentage of earnings of banks. The higher this ratio is the better.

**Pre-tax operation income** (in percent of total assets): The ratio of profit before tax to total assets.

**Overhead Costs** (in percent of total assets): It is a measure of efficiency. It is an indicator of the quality of operations.

**Loan Loss Provision** (in percent of total assets): Loan loss provision in percent of total assets. It is an indicator of asset quality. Ideally this ratio should be low.

**Equity** (in percent of total assets): As equity is a cushion against asset malfunction, this ratio measures the amount of protection afforded to the bank by the equity they invested in it. The higher this ratio the more protected is the bank.

**Non-interest assets** (in percent of total assets): The ratio of non-interest earning assets to total assets.

**Customer and short term funds** (in percent of total assets): This is the ratio of deposits to total assets. It indicates liquidity of banks.

**Tax rate:** It is the ratio of taxes paid to profit before tax.

**Foreign bank share:** The ratio of total assets of foreign banks to total assets in the banking sector.

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# Maternal Education and Child Mortality

A World Bank study investigating the relationship between maternal education and child mortality using data from 17 countries' found that "regardless of measurement technique, it appears that an additional year of schooling reduces mortality for infants and children by 9 per 1,000. There is no evidence that this effect differs by urban and rural areas, but there is evidence that it is cumulative and becomes stronger as a woman ages... The consistency of these results is rather remarkable considering the wide range of environments." The average of the significant coefficients for wife's education was -.18. For husband's education the average was much lower: -.08. In the individual studies, in all cases except urban Colombia, the husband's education has half (or less) of the effect of the wife's education.

# Source: Cochrane and others 1980

40. Exglaining the shortfall in female schooling. Many studies show that as parental incomes rise, girls tend to "catch up" to boys in schooling; the income elasticity of education for girls exceeds that for boys. Increases in either parent's income are associated with increases in children's education over the long run. But these patterns vary across countries and cultures. In Latin America, women have almost caught up with men in basic education in most areas; in much of Africa, South Asia and the Near East they still lag seriously behind men in years of schooling. Substantial increases in income and many years may be needed before girls progress rapidly in some areas. Hence the need to see what measures might help short circuit the process. Family and community attitudes and the type of schooling available all apparently matter, but more research is required to sort out what matters most in which circumstances.

41. Why the demand for female education is low. Poorer parents often feel that educating sons is a necessity but educating daughters a luxury. This is reflected in the "catch-up" pattern for girls as parental incomes rise. There are several possible reasons why, but they come down to this: Either parents do not realize how high the returns to girls' education are, or parents know more about the actual (social and economic) costs and benefits of education thar. many researchers do.

lBolivia, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Nicaragua, Paraguay, Peru, Thailand, Jordan, Tanzania, Uganda, and Malaysia.
<u>Figure 11. Growth Rate of the Addressed Demand to Egyptian Exports</u> <!-- Start of picture text --> 30%<br>20%<br>y = ‐0.0102x + 0.1394<br>10% R2 = 0.1259<br>0%<br>‐10%<br>‐20%<br>‐30%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> � Note: Addressed demand is calculated using this formula DEMi= ∑��� αij Mj with n the number of trade partners, _j_ partners, _αij_ the weight of Egyptian exports to country _j_ in its total exports αij =Xij/ΣXij and _Mj_ the total imports of country _j_ . _Source: Constructed by the author using International Trade Center data._

# _5.2.3 Trade Policy_

Amalyzing Egypt’s trade policy can also help explain why Egypt is under‐trading in some products and over‐trading in others. Despite significant reforms and liberalization efforts, significant trade barriers prevent the country from fully exploiting its trade potentials and maximizing its gains from trade. Egypt remains among the group of developing countries with the highest frequency index and coverage ratio of NTMs.<sup>9</sup> These non‐ tariff measures vary between technical requirements, conformity assessment, export related barriers and rules of origin. Egypt has signed several preferential trade agreements, but some of these agreements remain shallow and overlapping, leading to a more complex trade regime with a limited impact on trade. This section, looks at Egypt’s trade policy from the tariffs perspective to assess the country’s openness to trade. It also examines several non‐tariff measures that are hindering the fast development of exports. These include administrative and technical barriers to trade, as well as other sanitary and phyto‐sanitary (SPS) measures. It also discusses why the country’s preferential trade agreements have not yielded the expected results.

# _a. Tariffs_

Since Egypt’s accession to the WTO, nearly 99 percent of Egypt's tariff lines are bound. Most Favored Nation (MFN) tariffs applying to non‐agricultural products are generally lower than those applying to agricultural goods, with an average of 12.8 percent and 66.4 percent respectively. For some agribusiness products, average tariffs can exceed 1,000 percent, such as those on beverages and spirits. Egypt’s tariffs for 2017 are based on the HS 2012 version of the Harmonized Commodity Description and Coding System and contains 7,850 tariff lines at the HS 10‐digit level, of which 99.7 percent carry ad‐valorem duties.

Tracing Egypt’s tariffs policy over time shows that, on one hand, applied tariffs have dropped significantly between 1995 and 2012, with simple and weighted averages of applied tariffs falling from 24 percent and 16.7

> 9 The frequency index captures the percentage of products that are subject to one or more NTMs, while the coverage ratio captures the percentage of imports that is subject to one or more NTMs.

14
manually geo-referenced.

Table 4 shows that there is a robust and significant relationship between violent conflict and its legacy and unit costs. Countries which are in conflict have about 30% higher unit costs. Although the coefficient on the post-conflict dummy is positive, it is not significantly different from zero. We find evidence for the higher costs in politically unstable countries also when using the political instability measure from the Worldwide Governance Indicators (where we use the continuous measure as well as a dummy variable for whether the measure is above the median of the sample). Countries that are above the median of the sample in terms of political instability, face about 15% higher costs. The size and significance of the coefficients is robust to omitting GDP per capita, or controlling for contemporaneous GDP per capita.<sup>27</sup> The estimated effect appears in line with Benamghar and Iimi (2011), who find that halving security incidents would reduce procurement costs by 10% and cost overruns by 15%.

The ruggedness of the terrain in a country, surface area and population density of a country are significantly and positively associated with unit costs. Building a road in a more rugged terrain is likely to involve higher unit costs of construction and maintenance. Column (1) suggests that a 1% increase in the ruggedness of a country is associated with about 0.07 % higher unit costs. The surface area and distance to the nearest ice-free coast are highly correlated, so that when we include the surface area we cannot estimate the coefficient on the distance to the nearest ice-free coast precisely. The positive coefficient on the surface area therefore is likely to pick up both the effects of being landlocked, leading to higher transport costs, as well as the fact that perhaps constructing and maintaining roads in larger countries involves higher organizational costs. Population density is also positively and significantly associated with unit costs, indicating that unit costs rise by about 10% for an increase of one hundred people per square kilometer. One possible reason is that, if population density is high, then the value of having a road is higher and building a road is more likely to be justified in a cost-benefit analysis. An alternative explanation could be that price levels and wages are higher in cities and this is reflected in the final costs.<sup>28</sup>

> 27Results available upon request.

> 28A further explanation could be that road specifications are higher in urban areas, but this difference

17