rafmacalaba/data-use-mentions-extended huggingface dataset:
Turning now to absolute poverty, following past practice, “extreme poverty” is defined here by the fact of being poor by the standards used to define “poverty” in the poorest countries of the world. On that basis the World Bank’s current international poverty line is $1.25 per person per day at 2005 Purchasing Power Parity (PPP).<sup>12</sup> The latest (survey-based) estimate of the proportion of the population of the developing world living below $1.25 a day is 21% for 2010, down from 33% in 2000 and 43% in 1990 (Figure 3).<sup>13</sup> The overall poverty rate (“headcount index”) has been falling at a robust 1% point per year over 1981-2010. The regression coefficient of the poverty rate on the year in Figure 3 is -1.04 with a standard error of 0.05 (n=11). Figure 3 also gives the series for the poverty gap (PG) index. This is the mean gap below the poverty line, as a proportion of the line, expressed as a percentage. Equivalently, the PG index is the product of the headcount index and the income gap-ratio, given by the difference between the poverty line and the mean consumption or income of those living below the line, expressed as a percentage of the line. The PG index fell from 21% to 6% over the period 1981-2010; the regression coefficient of the index on year is -0.46 with a standard error of 0.04 (n=11). The income gap ratio also fell, from 0.41 to 0.31.

Progress has been uneven. Table 1 gives the regional breakdown of the headcount index for selected years. Figure 4 gives the series for three regions accounting for 95% of those living below $1.25 a day (in 2008). For East Asia, the rate of poverty reduction
Project production comprises a cycle of strategy development, identification, preparation, appraisal and executive board approval, implementation, supervision and completion, followed by evaluation.<sup>2</sup> There are numerous contributors at each stage. On the World Bank side, strategy development is the responsibility of the country management unit, in particular the country director who determines the size of the financing envelope; project preparation, appraisal, supervision and completion are largely the responsibility of the task team, comprising technical and fiduciary specialists, but with guidance from sector managers and country office representatives. To the extent that previous studies have estimated the impact of individual professionals, this has largely been limited to the project manager (commonly referred to as Task Team Leaders or TTLs in the World Bank) during project implementation. Almost none have identified or attempted to measure the contribution of other participants in the process.

Along these lines, one contribution of this study is to more comprehensively assess the contribution of project design and supervision to development effectiveness. We consider the usual explanatory factors of project level success in the literature, clustered around (i) country characteristics, such as institutional quality, political and economic stability, and economic growth trajectories; and (ii) project characteristics, such as duration, size, sector and lending instrument. Additionally, we include (iii) aspects of project design, such as the clarity of results frameworks, the number of components, and the characteristics of staff involved in design and (iv) aspects of project supervision, such as the intensity, quality, and continuity of oversight. For this, we combine data on World Bank projects’ outcomes with a novel data set derived from project preparation documents and internal staff records. Specifically, we examine the contributions of the TTL who prepared the project, the Practice Manager who oversaw them and the Country Director responsible for the country program within which the project was set, as well as the chain of TTLs who supervised the project until close.

A second contribution of
# **3 Related Literature**

Our analysis relates to several literatures. First, we contribute to the literature on social cohesion and interactions with host communities.<sup>7</sup> Alrababa’h et al. (2021) conduct a large-scale representative survey of public attitudes toward migration in Jordan. The authors find that while economic concerns do not drive Jordanians’ attitudes toward Syrian refugees, humanitarian and cultural factors matter. In particular, Jordanians who are more exposed to refugees’ challenging living conditions and who are less sensitive to cultural threat demonstrate more positive attitudes toward refugees. Ghosn et al. (2019) explores how an individual’s contact with refugees influence their attitudes about hosting refugees. They find that attitudes towards refugees are associated with whether individual respondents have had contact with Syrians in Lebanon —those with such interactions are significantly more likely to support hosting refugees, to consider hiring a refugee, or to allow one of their children to marry a refugee.<sup>8</sup> Alan et al. (2021) presents an experimental evidence from an educational program in southeastern Turkey that aims to build social cohesion in schools by developing perspective-taking ability in children. The authors find that the intervention increased the likelihood of forming inter-ethnic friendship ties as well as reducing ethnic segregation in the classroom and lowering victimization in school grounds.<sup>9</sup> Second, there is a recent literature on the impact of refugee movements and

> 7De Berry and Roberts (2018) argue that several factors mediate social relations in the context of forced displacement. These factors are: (i) perceptions of identity; (ii) pre-existing relationships between displaced and host communities; (iii) capacity/readiness of communities to host displaced people; (iv) duration of displacement; (v) perceived/real disparities between different groups affected by forced displacement; and (vi) patterns of settlement.

> 8In a conjoint survey experiment, Allen et al. (2021) study preferred policy
# **2. A model of long‐term growth with public capital**

In this section, we provide an overview of the model structure (Sections 2.1‐2.3) and some intuition on growth drivers (Section 2.4). Section 3 describes how these model equations enter the LTGM‐ PC Excel‐based tool which enables users to run policy simulations.

# _2.1 The production function_

To analyze the effects of public capital on growth, we adapt the Standard LTGM by splitting aggregate capital stock into public and private portions. We assume a Cobb‐Douglas specification, where the two capital stocks have unitary elasticity of substitution, in contrast to being perfect substitutes in the Standard LTGM.<sup>8</sup> Based on the models in Eicher and Turnovsky (2000) and Agenor (2013), we first consider the following production function at time, t : Each firm takes technology (TFP), A� and public services S� as given, that is, these are externalities to the firm. K�� is the private capital stock, h�L� is effective labor, which can be further decomposed into h� , human capital per worker and L� , the number of workers. 1 �β and β are private capital and labor income shares. Next, we consider the following specification for public services S� : G� is the efficient physical public capital stock – the public capital that is actually used in production. ζ captures whether public capital is subject to congestion (or not) – discussed further below. φ is the _usefulness_ of public capital (more technically the elasticity of output to efficient public capital).

Due to corruption, mismanagement or pork‐barreling, only a fraction θ� �1 of measured public capital is useful for production. The measured capital stock K��� is what is recorded in international statistical databases, constructed using the perpetual inventory method. θ� is the average _efficiency/
Table 3: Descriptive Statistics: Urban model

||**2004/05**|**2009/10**|**2011/12**|**2014/15**|
|---|---|---|---|---|
|Log HH per capita expenditure<br>Mean|4.50|4.61|4.74|.|
|HH size|||||
|1 or 2|0.07|0.08|0.08|0.09|
|3|0.10|0.11|0.12|0.13|
|4|0.22|0.24|0.24|0.25|
|5|0.21|0.20|0.20|0.20|
|District HH size|||||
|Share 1 or 2|0.06|0.07|0.08|0.08|
|Share 3|0.09|0.10|0.11|0.12|
|Share 4|0.20|0.23|0.23|0.24|
|Share 5|0.20|0.21|0.21|0.20|
|HH age structure|||||
|Share 0-14|0.31|0.29|0.27|0.26|
|Share 15-24|0.19|0.18|0.18|0.18|
|Share 25-34|0.17|0.17|0.17|0.17|
|Share 35-49|0.20|0.20|0.21|0.22|
|Share 50-64|0.10|0.11|0.11|0.12|
|District avg HH age structure||||
# Table 6: **The Effect of Blackouts on Expenses by Category**

All specifications include time fixed effects, firm fixed effects, and a vector of dummies indicating the number of days (of 5) for which a response about blackout status was reported by the firm owner. Standard errors are clustered at the neighborhood level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)<br>Wages<br>(GhC)|(2)<br>Inputs<br>(GhC)|(3)<br>Outsourcing<br>(GhC)|
|---|---|---|---|
|**Panel A: One-Person Firms**||||
|# Blackout days reported (out of 7 days)|x<br>x|-0.43<br>(0.50)|0.18<br>(0.24)|
|Outcome Variable Average|0.00|10.75|2.57|
|Observations|1,265|1,265|1,265|
|**Panel B: Firms With Workers**||||
|# Blackout days reported (out of 7 days)|-1.29**<br>(0.56)|0.61<br>(0.77)|-0.62<br>(0.40)|
|Outcome Variable Average|5.80|17.68|5.60|
|Observations|1,097|1,097|1,097|

Source: Author calculations from The Hohoe Garment Makers Project Data 21
# **1 Introduction**

Informality is pervasive in the developing world, accounting for about one-third of GDP and around 70 percent of employment (Loayza, 2018; Medina and Schneider, 2019). A large informal sector tends to be associated with low productivity, threatening developing economies’ long-term growth potential (Ohnsorge and Yu, 2021). Firm-level survey evidence shows that the average informal firm<sup>2</sup> in developing economies is only one-quarter as productive as the average firm operating in the formal sector (Amin and Okou, 2020).

The drivers behind the differences in productivity between the formal and informal sectors have been well examined. Some studies point to the formal and informal sectors catering to their own unique markets (Perry et al, 2007; La Porta and Shleifer, 2014). Other more recent studies point out that productivity differences between formal and informal firms largely reflect differences in observed characteristics: informal businesses tend to be smaller, are run by less educated individuals, employ less educated workers, and have limited access to finance, which, in turn, prevent them from adopting new technologies (World Bank 2019a).<sup>3</sup> Empirical evidence suggests that digital technologies, which are often not scale-intensive, benefit small firms, including those in the informal sector, by improving their access to markets (Jensen 2007, Muto and Yamano 2009). Paunov and Rollo (2016) show that cell phone use at the industrylevel positively improves sales for informal businesses. However, technology adoption remains low in informal businesses, and other supporting registration efforts, such as management training through business advisory services, vouchers, and other financing approaches, have yielded limited success (McKenzie and Woodruff 2008; Fafchamps et al. 2014; De Mel et al. 2011). The limited success of those programs is largely due to the limited skillsets of informal business owners, management practices, and constrained access to finance (Aga et al. 2021; Aberra et al. 2022)
aim to further account for the fact that not all individual analyses bear the same informational value. Which stems from not all sovereigns that default necessarily having a suitable synthetic control constructable from the donor pool (Abadie et al. (2010, 2015)). To illustrate, imagine an islandstate without any close by neighbors that is structurally different to any other economy in the world. Especially since we restrict the pool of possible samples to reduce the risk of overfitting, one would not be able to construct a suitable synthetic control for such a country.</mark><sup>17</sup> <mark>Hence, as is implied by the very nature of event studies, the results from that individual synthetic control</mark>

> 16 <mark>Given that we also use GDP per capita to match the pre-treatment trend, this pool-restriction is seldomly binding. We thus add the measure of institutional strength, to capture the underlying structural differences of countries from the same income group.</mark>

> 17 <mark>We restrict the donor pool to countries that are structurally similar to the treated unit, by only considering countries within one quartile of the distribution of GDP and institutional strength, proxied by the Polity IV.</mark>

13
recent advances include Blanchard and Leigh (2013), Guajardo, Leigh and Pescatori (2014) and House, Proebsting and Tesar (2015). In general, empirical studies using a sample prior to 2009 show mixed or supportive evidence of “expansionary austerity”, while data since 2009 points to the contrary.

This paper is organized as follows. Section 2 reviews a theoretical model of expansionary austerity and calibrate the model to deliver a definition of high-debt regime for each country in my sample. Section 3 introduces a new source of narrative data, checks the quality of the data in a VAR exercise and presents empirical results using the narrative series as a direct measure of austerity. Section 4 uses structural shocks recovered from a proxy structural VAR as an indirect measure of austerity and presents another set of empirical results under this alternative definition. Section 5 concludes.

# **2 Model-based definition of high-debt regime**

An empirical study of expansionary austerity has to start by defining when debts are ”too high”. The literature has so far used empirical debt thresholds advocated by policy institutions. In this section, I provide a way of defining high-debt regimes based on Sutherland(1997)’s theoretical model of expansionary austerity. I calibrate the model to provide a cutoff debt-to-GDP value for each country. The model is presented below and a cutoff value for each country derived.

The model is in continuous time and the environment features a finitely-lived representative consumer and a government. Let _Bt_ be the stock of per capita public debt, F deficit spending and r the interest rate paid on public debt. The evolution of government debt is governed by a Brownian motion, where _Wt_ is a standard Wiener process and _σ_ scales up the variance: 3
WPS3815

# **Getting Real about Inequality**

## **Evidence from Brazil, Colombia, Mexico, and Peru**

**Edwin Goñi, Humberto López and Luis Servén** * **The World Bank**

### **Abstract**

Consumption baskets vary across households and inflation rates vary across goods. As a result, standard CPI inflation may provide a very misleading measure of the inflation actually faced by poor households, more so the more unequal the distribution of aggregate consumption across households. Likewise, changes in observed nominal consumption inequality may be very different from those in true inequality, i.e., that measured using household-specific CPIs. We explore empirically these issues using household data covering nine episodes from four Latin American countries (Brazil, Colombia, Mexico, and Peru). We find that in these countries standard CPI inflation typically reflects the inflation rate faced by a rich consumer located in the 80 to 90 percentile of the distribution of consumption expenditure. In most episodes we also find that inflation was anti-rich -- i.e. the inflation faced by the richest consumers was higher than the inflation faced by the poorest consumers. As a result of this bias, the observed increases in nominal inequality generally exceed the actual changes in real inequality. These results are robust to correcting for quality change bias in the CPI, to the use of alternative price indices, and to the use of alternative inequality measures.

JEL classification codes: C43, D12, D31, D63, J10 World Bank Policy Research Working Paper 3815, January 2006 _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. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the
) and bank lending rates also come from the same source. Industrial production comes from the Central Agency for Public Mobilization and Statistics (CAPMAS) deflated by the CPI; and the stock market index ( _EGX30_ ) as reported by the Egyptian Stock Market. EGX 30 Index is a market capitalization-weighted index. Finally, lending rates were taken from CBE reports while T-Bill rates were gathered from Ministry of Finance reports<sup>9</sup> .

All nominal variables were deflated by the consumer price index. To partially control for endogeneity bias, the models use the lagged values for industrial production, bank deposits, bank reserves and the stock market index.

> 8 For further details, see appendix 1-4

> 9 To check whether our series are stationary or not, we ran the Augmented Dickey-Fuller test and found they all suffer from a unit root problem. After taking the first difference and running the ADF, we found our variables stationary. Finally, we conducted the Johansen’s test to determine whether our series are co-integrated or not. We found that they form a cointegrated vector, allowing us to estimate the model in levels.
- 15 _-_

Figure 4: Contractual Savings and Market Capitalization, 1996 <!-- Start of picture text --> N MCGDP<br>2-<br>ZAF<br>1.5-<br>05 LL~~~~D .5IN1.<br>CSAGDP<br><!-- End of picture text -->

> Notes: The fitted line is given by 5 = 0.177 + 0.958x, with a t statistic of 7.314 for the slope. See Table 12 in Appendix 2 for the list of countries.

Source: 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Figure 5: Contractual Savings and Value Traded, 1996 <!-- Start of picture text --> \/T/GDP<br>1.5<br>1- USA<br>NLD<br>5- SW A U S L-W<br>-5-<br>DEU<br>o _ aL<br>0 .5 1 1.5<br>CS/GDP<br><!-- End of picture text -->

> Notes: The fitted line is given by ', = 0.085 +0.480x, with a t statistic of 4.650 for the slope. See Table 12 in Appendix 2 for the list of countries. Source: 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.
average gross value of output per hectare, our proxy for agricultural productivity, is 25% lower for the female-managed plot sample.<sup>26</sup> The gender differences in agricultural productivity are also evident in the comparison of the Kernel density estimates of the log of gross value of output per hectare for male- and female-managed plots, as displayed in Figure 2. The overwhelming majority of the differences in the average values of the observable covariates across male- vs. female-managed plots in Table 1 are statistically significant at the 1 percent level. For the purposes of the ensuing discussion, we focus on the differences that are statistically significant at least at the 5 percent level.

The incidence of manager-head of household correspondence is 99 percent for the malemanaged plot sample, while the analogous statistic is 80 percent for the female-managed plot sample. Female-managed plots are, on average, overseen by individuals that are 5 years older and have 2 less years of schooling with respect to their male-managed comparators. A significantly higher percentage of female-managed plots exhibit manager-owner correspondence (77 vs. 58 percent) and 75 percent of the female-managed plot sample are exclusively femaleowned, featuring either a sole female owner or dual female owners within the household.<sup>27</sup> The incidences of joint ownership and exclusive-male ownership stand at 4 and 3 percent, respectively, among female-managed plots. In comparison, male-managed plots are distributed more evenly across the ownership categories of exclusive-male (43 percent), exclusive-female (23 percent), and joint male-female (15 percent).

Although the average GPS-based plot area is 0.39 hectare, female-managed plots are, on average, 12 percent smaller than their male-managed counterparts. The use of inorganic fertilizer is lower on female-managed plots, whether measured by incidence, average unconditional amount per hectare, or average conditional amount per hectare. These trends may signal gender differences in FISP fertilizer voucher distribution and redemption outcomes. In fact, the IHS3 data indicates that even though female-headed households are just as likely to receive a
highlight the conditions under which investment in both physical and human capital is likely to pay off.

## The Education Puzzle

In the earlier neoclassical models, education was not considered a major input for production and hence was not included in growth models.<sup>1</sup> In the 1960s mounting empirical evidence stimulated the "human investment revolution in economic thought" (Bowman 1966). The seminal works of Schultz (1961) and Denison (1962, 1967) led to a series of growth accounting studies pointing to education's contribution to the unexplained residuals in the economic growth of western economies. Other studies looked at the impact of education on earnings or estimated private rates of returns (Becker 1964; Mincer 1974). A 1984 survey of growth accounting studies covering 29 developing countries found estimates of education's

- Arnold C. Harberger described this period in a recent paper, "Many, maybe even most, economists expected that increments of output would be explained by increments of inputs, but when we took our best shot we found that traditional inputs typically fell far short of explaining the observed output growth....". ".. as the newly discovered residual loomed large in our professional thinking, our discussion centered on two potential explanations: 'human capital' and 'technical advance."' (Harberger, AER, 1998 p.1-2)

3
) and RCT (cols. 3–4) specifications. The point estimates are also of similar magnitude when we use either the near or far control villages, suggesting no substantial local spillovers. To formally assess the presence of local spillovers, we test whether the likelihood of conflict events differs between near and far control villages. We do not find evidence of local spillovers based on this test (cols. 5–6). The results thus indicate that the cash transfer program does not shift the location of conflict events between villages at the local level or increase the likelihood of conflicts in neighboring villages.

# **3.4 Additional Results from Household-Level Data**

The results disaggregated by type of event in Table 3 highlight that the increase in conflict was mostly driven by foreign terrorist groups. To better understand potential within-village effects, we complement the analysis of the village-level data with secondary household data from a smaller-scale RCT conducted during the first phase of the program (2012–2013).<sup>22</sup> We analyze households’ self-reported information on the incidence of various types of conflict involving community members, based on a follow-up survey collected two years after the start of the program among 3,500 households, of which about two-thirds lived in communities randomly assigned to the cash transfer and one-third in control villages.<sup>23</sup> Table A10 shows that we do not find evidence of a significant decrease or increase in any self-reported conflict, including within households, within the community, or between the community and other nearby communities.<sup>24</sup>

> 21For a discussion of the spatial decay of violence, see Mueller et al. (2022).

> 22This analysis should be considered exploratory as these were not the main outcomes of this smaller-scale RCT, which is discussed in Premand and Barry (2022).

> 23See Premand and Barry (2022) for details on the RCT design, sample, and baseline balance. While the experiment is generally well