rafmacalaba/data-use-mentions-extended huggingface dataset:
, A. Geske (2006) “Towards a Fresh Start in Measuring Gender Equality: A Contribution to the Debate.” _Journal of Human Development_ 7(2): 275–83.

Dulfo, Esther (2012), “Women Empowerment and Economic Development,” _Journal of Economic Literature_ 50(4): 1051-1079 Dollar, David and Roberta Gatti (1999), “Gender Inequality, Income, and Growth: Are Good Times Good for Women?” Working Paper 20771, World Bank.

Economic Commission for Africa. 2004. The African Gender and Development Index. Addis Ababa: Economic Commission for Africa 23
dearer costs from politicians who allow or contribute to larger deadweight losses. We consider that the principal way firms realize the spoils of their influence is through demanding protection from competition, either through the restriction of new entrants or, commonly, through trade protectionism. This means that competition is likely oligopolistic, where firms are few and affect others, settling into a Cournot-Nash equilibrium (see Becker (1983)). That is, our expectations follow recent work on market power—in this case, emerging from influence. In this framework, we expect market competition to be limited, larger revenues to accrue to fewer firms, but for those revenues to be realized with less intensive input use, e.g. through a lower labor cost share of production (De Loecker et al., 2020).

Table 5 presents several regressions using the generic OLS form: where _Yi_ is one of four key outcomes. First, we include a dependent dummy variable that equals 100 (scaled for the easier interpretation of results) if a firm reports that in its main market it faces fewer than 20 competitors.<sup>39</sup> The second key outcome variable is the log of total sales, standardized to 2009 USD.

> 37Some readers will note that such barriers to entry (here, the higher relative cost of political influence in higher governance environments) and transactional costs mimic the industrial organization literature on productivity dispersion. 38Hillman (1982) and Grossman and Helpman (1994) establish these gains particularly in the form of trade protectionism.

> 39The ES data include a question that asks respondents to list the number of competitors they face in their main market, which included a pre-coded option of “too many to count”. The cutoff of 20 corresponds to the average median value of

26
tighter credit constraints experience both higher volatility of growth and lower growth rates. Acemoglu et al (2003) find that macroeconomic and financial variables do not have a significant role in explaining volatility once they account for institutions. Institutions are the key determinant of volatility; the only macro variable that may have some additional impact on volatility is the real exchange rate. These papers do not examine whether the effect of finance varies by size.

The paper is organized as follows: the first section examines the relationship between finance and economic volatility. In particular, it analyzes the relationship between total private credit in an economy and GDP or consumption per capita growth volatility and then considers the relationship of these two with household credit.<sup>3</sup> The second part of the paper is devoted to borrower characteristics, whether households or firms and growth in GDP per capita. I also examine what effect borrower characteristic may have on the composition of output by differentially affecting growth of certain sectors over others.

# **Data and Methodology**

The data for total private credit are taken from the World Bank’s Global Financial Database and covers 150 countries during 1964-2013. All regressions use 5-year averages. The data for household and firm credit are taken from the Bank for International Settlements. Most regressions cover around 40 countries during 1960-2013. The first section of the paper revisits the relationship between financial development (total private credit over GDP) and volatility in output and consumption per capita. The following section looks separately at household credit. All other data sources and time periods are described in Annex 1. The relationships are estimated using OLS, IV and GMM methods, as appropriate.

# **_Section 1. Volatility_**

This section examines the relationship between financial system development and volatility that was explored earlier in Easterly et al (2000) and has been explored since in various papers (Beck et al, 2001, Dabla-Norris and Srivisal, 2013). The base regression of interest

> 3 The analysis is also conducted for enterprise
<br>from census|Bank est. bared on official<br>contraceptive<br>prevalence<br>rate<br>of 26X|U.N. 1988 revision (pretim.)|

Notes: When a source for an estinmte is directly cited (e.g., UNPVSR 4/87), the estimate is taken as is from that publication. When an estimate Is "based on" a source, the estimate is not reported in but derived using data in that publication. When a source is given for a fertility or mortality estimate prior to 1985-90, the source for the corresponding 1985-90 estimate is a projection using the earlier estimate. Citations for the main documents referred to here (usually by abbreviations) are in the list of references.

- Nature of population estimate: F a de facto; J = de jure. No census, or no fertility ormortatity estinmtes and no projections made (the latter for smatt countries).

- a Census was for entire peninsula. b 55 people.

CBR Crude birth rate. CMR Crude death rate.

Celade Centro Latino Americano de Demograffa (1987). DHS Demographic **and** Health Survey. INED Institut National d'Etudes DOmographiques (Monnier 1987). TFR Total fertility rate. UNPVSR United Nations, Populatioa **n** d Vitat Statistics Report. USBOC United States, Bureau of the Census (1985).
>and<br>Futuna|Mar 69|<br>9 F|USBC 1985|||
|Western<br>Sahara|Dec 70|<br>76 F|UNPVSR<br>4/87|--|-.|
|Western Samoa|<br>Nov 81|156 F|U.N. 1984 assessment|BasedanUSBOC<br>1985|USBOC 1985|
|YeTenArab Rep.|Feb 86|9274 F|Bank projection from|Bank assessment ofWorld|Bank assessment ofWorld|
||||6/80 census|Fertility Survey|Fertility<br>Survey|
for investments in cognitive skill development may extend into early adulthood. The results emphasize the notion that skills interventions are not substitutes for adequate safety nets and points to important, dynamic policy trade‐offs related to productivity gains and poverty reduction. The findings also confirm the literature on the age‐malleability of noncognitive abilities and the synergies across various skill components.

# 2 The Evaluation Context and the Intervention

Youth employability and income‐generating potential are key to household well‐being in Sierra Leone, but a substantial share of the country’s youth, especially in urban areas, are not employed. More than two Sierra Leoneans in five (41.3 percent) are youth, defined in Sierra Leone as people ages 15–35 years. According to the country’s most recent labor force survey, more than half of youth are in the labor force (55.7 percent), but 26.9 percent are not in education, employment, or training.<sup>4</sup> This problem is more acute in urban areas, where the latter share is 32.3 percent, while the youth employment rate is 37.7 percent (compared with 58.8 percent in rural areas). Urban youth with some secondary education exhibit one of the lowest employment rates in the country (27.2 percent). Among urban youth with some secondary education who are employed, more than half are self‐employed (54.8 percent).

Skills are often mentioned as one of the key constraints on youth in accessing labor market opportunities and reaching their productive potential in Sierra Leone. Almost half of youth cannot read or write (46.5 percent), and the illiteracy rate is higher among young women than among young men (57.4 percent versus 32.9 percent). There is a large jump in earnings among people with some primary education relative to those with no schooling, and the jump at the highest levels of education is similar (Margolis et al.
local wind speeds at any particular locality where a tropical typhoon directly passes over or nearby. The estimated wind speeds from past typhoons were merged to contemporaneous household surveys (FIES) at the barangay level and consumption expenditures were then regressed against windspeed (or a related damage index) and socioeconomic household characteristics.

The estimates revealed that exposure of a barangay to a typhoon in the 6 months before the month of interview is associated with a statistically significant decline in total per capita expenditures and in specifically food and protein per capita expenditures. Expenditures on fruits and vegetables, cereals, education, medical services and nonfood items appear to be unaffected. These results are found to be robust to changes in the measure of the shock or to controlling for potential lagged effects, and unobserved household heterogeneity. The estimated coefficients from the regression model were also used to estimate ex-ante household vulnerability to poverty (i.e. the likelihood of household consumption falling below the poverty line) in the event of future natural disasters of different intensities.

It is important to bear in mind that the methodological framework employed in this paper for typhoons can also be employed with fairly minor modifications to identify the households vulnerable to poverty from a variety of other natural hazards. Models analogous to the wind field model for typhoons based on freely accessible remotely sensed data can be used for the estimation of the intensity of other natural hazards such as floods, earthquakes and even tsunamis (e.g., Skoufias, Strobl and Tveit, 2017). Thus, ex-ante estimates of the households vulnerable to poverty from floods and earthquakes can also be derived. Machine learning methods can also be used to improve the out-of-sample predictive ability of the regression models applied to retrospective data to estimate the relation between disaster intensity, welfare impacts, and household socio-economic characteristics. Finally, if the interest is on identifying the vulnerable to all kinds of covariate and idiosyncratic shocks, and not just to a specific shock as in this paper, it would be useful to explore and contrast the estimates of vulnerability to poverty
labor demand from relative wage trends controlling for relative labor supply.

Following the methodology laid out in Sanchez-Paramo and Schady (2003), Figure 25.C to Figure 25.E show relative labor demand for different elasticities of substitution across skill levels.<sup>23</sup> Labor demand for university relative to vocational education declined since the early 1990s, and labor demand for upper secondary to lower secondary education declined since early 1997. Labor demand for vocational education has risen throughout.<sup>24</sup> By contrast, Sanchez-Paramo and Schady (2003) show that relative labor demand for tertiary education increased over the 1990s for Argentina, Chile, Colombia and Mexico (and for Brazil since 1993).

23 Following Katz and Murphy (1992) and Sanchez-Paramo and Schady (2003), relative labor demand is backed out from relative wages and relative labor supply from a simple supply-demand model under the assumption of constant elasticity of substitution. The economy is assumed to be operating on the demand curve, and labor supply is taken to be inelastic in the short run. Furthermore, in order to net out compositional changes in the labor force, each cross-section is reweighed to replicate the average genderage (in five year brackets) structure over the entire period (Sanchez-Paramo and Schady 2003). In practice, the reweighing makes little difference to the findings.

24 Using LFS data from 1989 to 1995, Moenjak and Worswick (2003) find that at the upper secondary level, vocational education has a higher return than general education.

59
- **Peters, Margaret E.** 2019 _a_ . “Bilateral Labor Agreements Dataset and Additional Replication Data for: Immigration and International Law.”

- **Peters, Margaret E.** 2019 _b_ . “Immigration and international law.” _International Studies Quarterly_ , 63(2): 281–295.

- **Peters, Margaret E, and Michael K Miller.** 2022. “Emigration and political contestation.” _International Studies Quarterly_ , 66(1): sqab088.

- **Rios-Avila, Fernando, Arne J. Nagengast, and Yoto V. Yotov.** 2022. “JWDID: Stata module to estimate Difference-in-Difference models using Mundlak approach.” _Statistical Software Components S459114_ , Boston College Department of Economics, revised 08 Apr 2024.

- **Roth, Jonathan.** 2024. “Interpreting event-studies from recent difference-in-differences methods.” _arXiv preprint arXiv:2401.12309_ .

- **Roth, Jonathan, Pedro HC Sant’Anna, Alyssa Bilinski, and John Poe.** 2023. “What’s trending in difference-in-differences? A synthesis of the recent econometrics literature.” _Journal of Econometrics_ , 235(2): 2218–2244.

- **Saez, Juan Sebastian.** 2013. “Using bilateral labor agreements to increase trade in services.” World Bank Group.

- **Shrestha, Maheshwor.** 2019. “Death scares: How potential work-migrants infer mortality rates from migrant deaths.” _Journal of Development Economics_ , 141: 102368.

- **Shrestha, Maheshwor.** 2020. “Get rich or die tryin’: Perceived earnings, perceived mortality rates, and migration decisions of potential work migrants from Nepal.” _The World Bank Economic Review_ , 34(1): 1–27.

- **Shrestha, Maheshwor.** 2023. “A Deeper Dive into the Relationship between Economic Development and Migration.” World Bank Policy Research Working Paper 10295, World Bank.

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across locations. Nevertheless, once the Enron meltdown occurred, even these arbitrage strategies were used as evidence to convict Enron in the court of public opinion. A more independent regulatory process may have been able to distinguish between legal and illegal activities by the Enron traders. The actions by the state and federal criminal authorities in response to these memos have created significant regulatory risk for merchant energy suppliers. This has partially caused many of them to shut down or significantly downsize their energy trading affiliates. If one takes the position that many of the actions energy traders were arrested for in 2001 and 2002 routinely occurred in 1998 and 1999, then this sequence of events provides a valuable lesson about the importance of a regulatory process that is independent of the political process. If the regulatory process had been perceived as more independent, the Enron bankruptcy and prosecution of traders for illegal behavior could have occurred without the almost complete decimation of the energy trading sector.

# **3.5.2. UK Experience**

The transition from the Pool to NETA in the UK was largely a political decision. Virtually all of the academics studying the UK Pool argued against this change. Their argument was that the divestitures of generation capacity and new entry that had taken place throughout the 1990s had largely solved the market power problem that existed during the early and mid-1990s. There was very little rigorous empirical analysis of the likely benefits of the reform. The high cost of obtaining the bids and market outcomes data made it difficult, if not impossible, for researchers to undertake such an analysis. Moreover, because Ofgem was a major advocate of NETA, it had little incentive to undertake such an analysis. The significant wholesale electricity price decline that occurred following the implementation of NETA has fueled a debate over whether it or the significant generation divestitures and new generation entry during the 1990’s caused the price decline. Greater independence between the regulatory process and the political process could have allowed a more impartial and detailed analysis of the need to incur the significant cost associated
(UNEP-WCMC), is described in UNEP-WCMC (2002). Date of access: April 28, 2021 [link]. [g] The Armed Conflict Location and Event Data Project (ACLED) is described in Raleigh et al. (2010) [h] The precipitation data are produced by the Global Precipitation Climatology Centre (GPCC) and described in Schneider et al. (2015). Date of access: April 28, 2021 [link].

26
municipal budgets, as opposed to things like infrastructure construction that are not. This is evidenced in the survey data by several instances where municipal health projects are reported to involve the procurement and distribution of medicines and general health inputs.

The aspect of health service that the national health policy makes municipalities responsible for lends itself to providing greater benefits to the poor and reducing health inequality. Using community-based midwives and trained health workers to provide basic services, free of user charges, such as immunization, antenatal care, assisted deliveries, health outreach—have long been at the core of the primary health care approach advocated by WHO and UNICEF to reach disadvantaged populations and promote “health for all”. The bulk of municipal health spending goes towards the salaries of health personnel, predominantly nurses, midwives and community health workers that are stationed at village clinics. Using the Demographic and Health Surveys for the Philippines, Gwatkin et al (2007) show that the lowest quintile of households rely overwhelmingly on nurses or trained midwives; the richer quintiles rely instead on the services of doctors, who are not a significant part of municipal health policies or workforce.

However, despite the availability of these policy instruments, whether municipal governments indeed have sufficient incentives to avail of them to deliver services to the poor is a question of political economy. Actual benefit incidence of public health spending has been shown to be much less pro-poor than was expected given the concerted advocacy of the global health policy community (O’Donnell et al, 2007; Gwatkin et al, 2007). This paper suggests that part of the problem is that governments in developing countries, who rely on clientelist forms of political competition, have weak incentives to invest sufficiently in and effectively deliver health services to the poor.

Municipal health spending in the Philippines averages around 8 percent of total municipal spending, after more than 50 percent being allocated to general administration, and around 20 percent to the so-called “local development fund” that largely consists of infrastructure construction and repairs.<sup>18<
at lower levels of PCE, recall impacts are larger than at higher. The pattern bears more similarity to that of recall of acute events than of expenditures.

Table 6 repeats the basic regressions in Table 3 to show that the response heterogeneity by income visible in the figures is, indeed, born out statistically. In these analyses, we restrict ourselves to the observational data and augment the estimating equation with the inclusion of household PCE and household PCE interacted with recall period. The estimates largely confirm the results from the figures in the observational data. For all our outcome variables, the recall effect is greater for the poor than for the rich. These effects are uniformly significant and suggest, for instance, that at the household level, an increase in (log) PCE by 1 increases the size of the recall effect by close to 0.4 doctor visits per month. The duration estimates at the household level are particularly large. Since log PCE varies between 5 and 10 in our sample, our estimate suggests that monthly surveys may underestimate the total time spent sick by members of a household by as much as 18 days a month. The labor impact of sickness shows a similar pattern with declines in the importance of the recall effect at higher levels of PCE, although in this case the estimate is less precise.

# <u>Further Robustness Checks</u>

The recall period affects reports of morbidity and health seeking as well as the labor impacts of sickness. We investigate several ancillary specifications to check whether (a) these results could be driven by specific age populations, (b) specific respondents or, (c) whether the differential impact between the rich and poor could be picking up the effect of attributes correlated with PCE, notably education. For brevity, only the education results are presented in Appendix Table 2.

_Age Impacts._ One-third of our sample is below the age of 15. To check whether this recall effect is driven by high morbidity and differential recall for sickness among children, we
# **4.2 Distributional and Poverty Results for the BaU**

A moderate decrease in poverty between 2001 and 2015 results from micro-simulating the identified key structural trends on the Brazilian household data. Considering the full sample of all households, the headcount poverty ratio (P0) declines by about 6 percentage points (see Table 5). The reduction of the average normalized poverty gap (P1) and the poverty severity index (P2) indicates that those who remain poor move closer to the poverty line.<sup>30</sup> Inequality changes very little, as indicated by the 0.1 decrease in Gini coefficient (or as in the Theil or other inequality indices, not reported).

These average indices indicate that some progress in reducing aggregate poverty and inequality is achieved in a Business as Usual scenario, but these aggregate measures may conceal relevant distributional changes at a more disaggregated level. In fact, reaching stronger poverty reduction may require specific pro-poor policies which often rely, for their successful implementation, on more detailed information about disaggregated distributional effects.

A first obvious way to gather more detailed information is to analyze the poverty and inequality impacts separately for the agricultural and non-agricultural households.

_<u>Table 5: Poverty and inequality in the BaU scenario, by sectors</u>_

||2001<br>level<br>All hous|2001-15<br>change<br>eholds|2001<br>level<br>Non-agric<br>househ|2001-15<br>change<br>ultural<br>olds|2001<br>level<br>Agricul<br>househ|2001-15<br>change<br>tural<br>olds|
|---|---|---|---|---|---|---|
|PC income|314.9|1.5|351.9|1.2|148.3|2.
# _Independent variables_

Donor‟s ability to benefit from investments in country systems (<sup>_A_</sup> _i_<sup>in the model) is</sup> proxied by donor aid share, defined as the percentage of total aid (inclusive of aid not to the government sector) to recipient _j_ accounted for by donor _i_ . Higher donor aid shares are predicted to increase use of country systems. The sample mean for donor aid share is 7%, with a minimum value of 0.0014%<sup>6</sup> (Switzerland in Egypt in 2010) and a maximum of 83.6% (World Bank in St. Vincent in 2010).<sup>7</sup> Trustworthiness of recipient country systems (  in the model) is measured primarily by the widely used Control of Corruption indicator, from the Worldwide Governance Indicators project.<sup>8</sup> Weaker country systems, as measured by lower scores on Control of Corruption reflect increased risks to donors of corruption-related scandals, diversion of funds to lower priority uses, or inability to account for how funds were spent. The Paris Declaration explicitly asserts that corruption “inhibits donors from relying on partner country systems.” Use of country PFM systems is thus expected to be positively related to corruption scores.

The Freedom House civil liberties indicator is used as a supplementary indicator of trustworthiness of country systems. Civil liberties scores are higher in countries with a free and independent media, freedom of assembly, freedom for NGOs, and greater personal autonomy and individual freedoms.<sup>9</sup> The use of donor and other public funds is likely to be more

> 6 Values of 0 are not present in the sample because the dependent variables are all undefined for donor-recipient pairs with no aid transfers.

> 7 Larger donors at the global level do not always have higher average aid shares at the recipient level, as some donors concentrate their aid in fewer countries.  Portugal‟s