rafmacalaba/datause-encoder-data huggingface dataset:
First, consider banking-system fragility, which we measure as a dummy variable that equals one if the country experienced a systemic crisis during the period 1988-1999, and zero if it did not. While inherently arbitrary, we join Demirguc-Kunt, Detragiache and Gupta (2008), among others, and classify a systemic crisis as one where (1) emergency measures were taken to assist the banking system (such as bank holidays, deposit freezes, blanket guarantees to depositors or other bank creditors), or (2) large-scale nationalizations took place, or (3) nonperforming assets reached at least 10 percent of total assets at the peak of the crisis, or (4) the cost of the rescue operations was at least 2 percent of GDP. We conduct a logit estimation based on key regulatory variables. Since many studies find that macroeconomic instability induces banking-sector distress, we also include the average inflation rate during the five years prior to the crisis in countries that experienced a banking crisis. In countries that did not, we include the average inflation rate during the five years prior to the survey of bank regulatory and supervisory indicators (1993-1997).

One key finding on fragility from equation 1 is that regulatory restrictions on banking activities (Activity Restrictions) increase the probability of a banking crisis. Many argue that restricting banks from engaging in nonlending services, such as securities market activities, underwriting insurance, owning nonfinancial firms, or participating in real estate transactions, will reduce bank risk taking and therefore increase banking-system stability. We find no support for this claim. Rather, we find that restricting bank activities increases bank fragility. Fewer regulatory restrictions may increase the franchise value of banks and thereby augment incentives for more prudent behavior. Or, banks that engage in a broad array of activities may find it easier to diversify income streams and thereby become more resilient to shocks, with positive implications for banking-system stability.

The second key finding on fragility involves the diversification index, which includes information on whether there are regulatory guidelines concerning loan diversification and the absence of restrictions on making loans abroad. Diversification is negatively associated with the likelihood of a crisis but diversification guidelines have less of a stabilizing effect in bigger economies, as measured by the logarithm of GNP. The inflection point is quite high; diversification guidelines have significant stabilizing effects in all but the nine largest countries.

> monitoring index for the Table 1 regressions below based on the Survey I indexes.  This has little effect on the estimated results.

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were growing less, allowing the richest countries to maintain or even increase the income gap. Accordingly, the ratio of income of the richest 10 percent of countries to the poorest 10 percent of countries was 10.5 in 1975, but it increased to 18.5 in 2005. In 1975–80, South Africa’s GDP per capita was 17 times higher than that of Malawi. In 2000–05, the gap between the then-highest GDP per capita--the Seychelles--and Malawi had grown to 24 times. Africa is becoming an increasingly more unequal region in terms of its distribution of income among countries, despite the convergence of growth rates.<sup>7</sup>

**Figure 7: Coefficient of variation of GDP per capita and standard deviation of growth**

<!-- Start of picture text --> 12 1.4<br>1.2<br>10<br>CV of GDP per capita<br>(right scale) 1<br>8<br>0.8<br>6<br>0.6<br>4<br>0.4<br>SD of growth<br>(left scale)<br>2<br>0.2<br>0 0<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text -->

# **_Growth persistence_**

Another important aspect of long-term growth is whether it is persistent. Figure 8 shows the regression of average GDP per capita growth as a function of growth in 1976. We ran the following cross-country model: 76 Δ _Yi_ = α + β ( Δ _Yi_ ) + ε where Δ<sup>_Y_</sup> _i_ is the average growth of country _i_ , and Δ<sup>_Y_</sup> _i_ 76 is the growth rate of country _i_ in 1976, the first year in our series. The coefficient is positive and significant, ( β = 0.013, _t_ = 0.70).<sup>8</sup> So, there is no evidence of growth persistence.

> 7 We show below more evidence against the neoclassical hypothesis of income convergence.

> 8 Gabon grew 31 percent in 1976, biasing the results. We removed it from the regression.

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Figure 5: Placebo: Discontinuity in agricultural outcomes at payment threshold <!-- Start of picture text --> 0.25<br>G<br>G G<br>0.00 G G G G G G G G G G G G G G G G G weight<br>G 100<br>G 200<br>−0.25<br>−0.50<br>−50 −25 0 25 50<br>Min deviation from trigger (midpoint)<br>∆log planted in t + 1 (insurable crops) - Pre-CADENA<br>G<br>1.1<br>G G<br>G<br>G<br>G<br>G G G weight<br>0.9 G G G G 100<br>G G G G G 200<br>G G<br>G<br>G<br>0.7<br>0.5<br>−50 −25 0 25 50<br>Min deviation from trigger (midpoint)<br>Log hectares sowed in t+1<br>Log yield in t+1<br><!-- End of picture text --> (a) ∆log planted in _t_ + 1 (insurable crops) - Pre-CADENA (b) Log yield in _t_ + 1 (insurable crops) - Pre-CADENA Note. The figures depict placebo tests using our main specification with pre-CADENA data. We plot the local average of the respective outcome variable in 5 mm bins. The size of the circle indicates the number of observations in each bin. The blue lines represent linear regressions on the binned averages estimated separately on each side of the normalized threshold. The running variable is calculated by taking the minimum difference between realized rainfall and the threshold at the municipality level. The sample in these figures is limited to observations within the optimal bandwidth of 50 mm.

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# **3.3 Patterns within countries**

How are gender gaps shaped by urbanization? Though we lack panel data or repeated crosssections to explore trends over time, we can compare gender gaps between urban and rural areas. Own land has a central role in rural areas, as the main place to live and produce, but is less relevant in towns and cities where most jobs are outside of agriculture and housing can be rented. In urban areas, housing and residential land play a larger role than agricultural land, but even though city dwellers tend to earn more than their rural compatriots, they are often less likely to own a home (Pendall et al 2016 for the United States; Eurostat 2015 for European Union countries; Sato et al 2011 for China). This reflects partly that land is relatively scarce in urban areas, leading to highpriced real estate, but also better access to financial services, as an alternative means to store wealth. Because of this, some caution must be exercised in comparing ownership of land and housing property across rural and urban areas.

Figure 5 plots gender gaps in property ownership by country, separately for rural and urban areas. Gender gaps in sole ownership are almost always larger in rural than in urban areas. This pattern holds for the most part, though less pronounced, also for gender gaps in sole and joint ownership, though there are some exceptions. This, however, does _not_ mean that women are more likely to own property if they live in urban areas. It rather reflects that urban men are much _less_ likely to own property than their rural counterparts.

To further investigate distributional patterns, we estimate gender gaps by quintile, using the DHS household-level wealth index. As shown in Figure 6, gender gaps in sole property ownership are in most countries larger for the poorest than for the richest quintile, though there are some countries where the opposite pattern holds (Burundi, Jordan, and Mozambique). This income gradient is weaker for joint ownership, where nine out of 40 countries (excluding the Comoros) display larger gaps for the richest than poorest quintile. These differences in gender gaps along the distribution reflect a considerable degree of rural-urban differences. In other words, the poorest – – quintile is more likely to live in rural areas, where as shown in Figure 5 gender gaps in property ownership are larger than in urban areas.

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Samaniego, R. (2006): “Employment Protection and High-Tech Aversion,” _Review of Economic Dynamics,_ vol. 9(2), pp. 224—241.

Syverson, C. (2011): “What Determines Productivity,” _Journal of Economic Literature,_ vol. 49(2), pp. 326–365.

van Ark, B., M. O’Mahoney and M. Timer (2008): “The Productivity Gap between Europe and the United States: Trends and Causes.” _Journal of Economic Perspectives_ , vol. 22(1), pp. 25—44.

Verhoogen, E. (2008): “Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector,” _The Quarterly Journal of Economics,_ vol. 123(2), pp. 489—530.

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-29 -

17. If we look at shorter growth periods of five and four years for SSA, the simple positive linear relationship is statistically significant.

18. In regressions 3 and 6 of Table 7, IS, the share of investment in GNP is ade-' to the regressions which already include IOCR. In these regressions, IS is statistically significant, whereas, in regression 2 of Table 6, without IOCR in the regression, is not statistically significant. This result underscores the importance of efficiency of investment.

19. Developing country armed forces do spend funds on education and health care for the troops as well as on some infrastructure. However, these exenditures are not the bulk of military spending.

20. While the data includes almost all the 71 countries already used to test the milex-growth reladonship, over half of the potendal observations during the 1970-90 period are missing. Still, there are up to 191 three-year observations and 119 five-year observations, and regressions were run using three- and five-year averages of the expenditure data.

# 21. Detailed regression results are available from the author.

22. The major regressors tested (besides population - POP) are per capita product (PCP), neighbor's average military expenditure share (NMES), the dummies for political instability (PI, CW and IW), and the share of official transfers received in national product (TRANS). The civil war regressor was dropped because it proved insignificant. PCP tests whether richer countries are willing to spend a higher share of GNP on the military.

23. Without India (and China), population is not a statistically significant derminant of MES for the multi-year periods. Regressions were also run with anmual observations, though these are not reported in the table. For the anmnal observations, population and population squared influence MES; these are scale effects, but the coefficients are reversed with and without India. The annual regressions were run including and excluding India. India changes the effects of population on MES, but does not change other coefficients materially. Since India is obviously a population outdier, the muld-year period regressions reported here exclude India.

24. However, with annual observations, TRANS has positive and statistically significant coefficients.

25. In these regressions, PCP is measured in thousands of 1987 U.S. dollars. In some of the other tables and regressions, PCP is in 1987 U.S. dollars.

26. Janowitz (1977) noted that the developing countries experienced much greater increases in paramilitary forces (militia, political police, etc.) from 1966 to 1974/75 than in active duty regular forces. He also discusses the preference among military governments to use para-military forces rather than the regular army for internal control.
_Table 1. A summary of how US health plans treat health care received abroad_

|_Plan_|_US population covered in_<br>_year 2004_|_Health care received overseas covered?_||
|---|---|---|---|
|||_Emergency care_|_Non-emergency care_|
|_Medicare and_<br>_Medicaid_|26% of US population<br>(Mainly retirees, low-<br>income families &<br>disabled people)|Not covered, except when beneficiary is a border<br>resident, and lives closer to the foreign provider than the<br>US provider|Not covered|
|_Tricare_|3.5% of US population<br>(Active duty and retired<br>US military personnel<br>and their families)|Covered, with overseas network provider handling the<br>claim filing.|Covered, if beneficiary is stationed<br>or retired overseas, until the age of<br>65.|
|_Health_<br>_Maintenance_<br>_Organization_<br>_plans (HMOs)_|25% of all employees in<br>the US with employer-<br>sponsored health<br>insurance|Covered, but as an out-of-network benefit requiring<br>higher consumer cost-sharing. Initially, beneficiary<br>pays entire cost out-of-pocket and qualifies for<br>reimbursement only when claim form and itemized bill<br>are submitted toinsurerupon returnto the US.|Not covered|
|_Point of Service_<br>_plan (POS)_|15% of all employees in<br>the US with employer-<br>sponsored health<br>insurance|Same as HMO|Not covered|
|_Preferred_<br>_Provider_<br>_Organizations_<br>_(PPOs)_|55% of all employees in<br>the US with employer<br>sponsored health<br>insurance|Same as HMO. However, some plans have a network of<br>overseas providers who accept US insurance (e.g., Blue<br>Cross Blue Shield) and would handle claim filing on<br>consumer’s behalf.|Not covered by most plans. When<br>covered, e.g., World Bank<br>employee health plan, it is treated<br>as an out-of-network benefit<br>requiring higher consumer cost<br>sharing.|

_Sources:_ Centers for Medicare and Medicaid Services (2005), Tricare, Kaiser Family Foundation Study (2004), US Census Bureau (2004), Blue Cross Blue Shield Association, World Access, and the World Bank Medical Insurance Plan.

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of the growth process (e.g., Chenery and Syrquin **[1985])** have found this variable to be an important factor in determining the relationship between, say, openness and growth. We include it here mainly as a check on our results, rather than as part of a more elaborate model of the relationship between public expenditure and growth. The variable we use as a proxy for the level of development is the country's per-capita **GDP** in **1969.** When this variable is included, the results reported in Table 4 remain unchanged. The variable itself has a negative sign and is statistically insignificant".

# 3.4.3 General vs. Central Government Spending

As stated earlier, our data set covers the operations of only the central government. Ideally, one would like to examine the impact of total government expenditures that includes the operations of state and local governments as well as expenditures of government owned or controlled public sector enterprises, on economic growth. This may be particularly important in the case of health and education expenditures, where in some federal systems, the bulk of these expenditures are carried out **by** sub-national governments. To our knowledge such _comprehensive and consistent_ expenditure series (across countries and time) are not available. However, there are a few countries for which consolidated general government expenditures (i.e., operations of central, state and local governments) are reported in the **GFS.** In order to determine whether or not including the state and local government expenditure data qualitatively and quantitatively affects our results, we do a few diagnostic<sup>tests.</sup><sup>**Of**the</sup><sup>**69**</sup> countries in our sample, there are 12 (see data appendix for the list) for which consolidated

> "The _results_ for this specification are not reported in the paper.

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mean harvest expectation and mean realized harvest, to be larger where there was at least some loss. These observations provide suggestive evidence of an association between over-expectation and loss occurrence.

Taking an over-expectation as an indication of a loss, between 49 percent (ESS15, maize) and 57 percent (ESS18, maize) of plots record a loss. Notably, this also results in less variation between crops and there is no clear pattern of alignment between the loss incidence based on planted area and/or plant productivity losses and based on harvest expectations exceeding realizations (Table 6).<sup>43</sup> _<mark>Table 6</mark>_ <mark>: Loss incidence across different proxies.</mark>

|||Percent|ofplots|with crop|<br><br>losses based on..|
|---|---|---|---|---|---|
|**Country **|**Crop**|**Round**|**Area**|**Damage**|**Area or damage**<br>**Overexpectation**|
|||ESS13|12.7%|40.9%|41.4%|
||Maize|ESS15|19.4%|55.7%|56.2%<br>48.7%|
|Ethioia||ESS18|17.1%|43.3%|45.3%<br>56.5%|
|p||ESS13|21.2%|45.9%|46.7%|
||Sorghum|ESS15|33.8%|74.5%|75.0%<br>49.8%|
|||ESS18|25.5%|53.3%|54.8%<br>53.3%|
|||IHPS10|56.5%||36.3%|
|Malawi|Maize|IHPS13|55.1%||44.6%|
|||IHPS16|65.3%||60.0%|
|||IHPS19|61.0%||51.8%|

Note: Crop loss incidences (percent) according to different proxies, by survey round and crop: area harvested < area planted (area loss), damage on the crop (damage), any area or damage-related loss, or expected production < realized production (overexpectation). ESS = Ethiopia Socioeconomic Survey, IHPS = Integrated Household Panel Survey.

For post-planting expectations to provide a realistic proxy of the attainable harvest, they need to be aligned with the actual management decisions on the plot.<sup>44</sup> Table A 4 and Table A 5 ascertain this through two equivalent yield regressions for each survey round and crop: one using realized harvest as the outcome variable and one using harvest expectations. They generally confirm that the alignment of expectations with production factors, based on the model’s R<sup>2</sup> , is generally equally good or better than for realized harvest. This increases confidence in farmers’ expectations’ alignment with their reported behavior. However, some notable exceptions in Ethiopia (ESS15 across both crops) and Malawi (IHPS10) exist.

> post-planting visit (not reported). This, again, suggests that some of the loss already occurred before expectations were elicited which will likely bias them downward.

> 43 For cross-country, longitudinal figures on crop and livestock losses in urban vs. rural areas see (Chaudhary, 2021). 44 This is because farmers’ input use and management practices place an effective upper boundary on the harvest that can be achieved. If farmers thus declare one thing as their harvest expectation but their input use is inconsistent with this, the relationship between post-planting expectation and realized harvest is distorted by factors other than shocks, rendering them an unsuitable proxy for the attainable harvest.

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45 _211_ V. Masemann. "The 'Hidden curriculum' of a West African Girls' Boarding School." Canadian Journal of African Studies, 8 (1974):479-494.

**2i/** Daphne Glazer. "Problems of the Education of Girls in Nigeria." Aspects of Education, 19, 33-42.

_2.21_ While there is little empirical evidence regarding unsafe schools, anecdotal evidence abounds. Deble (1980, p. 89) notes that "many people still hesitate to trust the school where girls are concerned" [I. Deble. The School Education of Girls (Paris: UNESCO, 1980), p. 89]. Lycette cites a recent survey of rural households in India that found about one-fifth of girls were withdrawn from school at the onset of puberty, often because parents felt it was dangerous for an unmarried girl to be in public [M.A. Lycette. Improving Basic Educational Opportunities for Women in DeveloRing Countries (Washington, DC: International Center for Research on Women, 1986)]. The danger is, not surprisingly, sexual in nature. For example, a study of factors affecting repetition and dropout in Papua New Guinea identified "rape and [sexual] coercion" as a maior fear of parents about sending daughters to school [L. Yeoman. Universal Primary Education: Factors Affecting the Enrollment and Retention of Girls in PaRua New Guinea Community Schools. (Paper presented at the UNESCO Regional Review Meeting, Bangkok, Thailand, November 1985)].

# / Muckenhirn.

.1. Glazer.

1<sup>2</sup> ./UNESCO Statistical Yearbook. (Paris: UNESCO, 1986j.

33/ R.A. Garden. Second IEA Mathemat rcs Study Samoling Report (Urbana: University of Illinois, 1984).

24-/ While we recognize that paternal occupation, coded dichotomously, is a rather weak measure of socioeconomic status (SES), this was the only SES measure agreed upon for inclusion by IEA participants. Given the lower proportions of girls in school in Nigeria and in this study, and the fact that researchers have found that girls in school in developing countries are likely to come from more advantaged families (although these data do not show higher proportions of females with professionally employed fathers), we admit that the social background of students may be underadjusted in these analyses. We have no reason, however, to suspect that the underadjustment is different for boys than girls.

2/ As the investigation is framed as an analysis of covariance, the homogeneity of regression slopes was tested. The interaction terms between school sex group and other covariates were not significant as a regression step. Since the estimation of unbiased program effects with ANCOVA rigorously assumes parallel relationships of covariates and outcomes across all levels of the confounding variables, interactions between confounding factors and the school-type variables were examined and found to be non-significant as a regression step. See S. Anderson, A. Auguier, W.W. Hauch, D. Oakes, W.
**Infrastructure and Economic Growth in the Middle East and North Africa**<sup>**‡**</sup>

Paul Noumba Um<sup>§</sup> , Stéphane Straub<sup>†</sup> and Charles Vellutini<sup>‡</sup>

> ‡ This paper has benefited from discussions comments and suggestions from Laslo Lovei, Jonathan D. Walters, Antonio Estache, Farid Gasmi, and Auguste Kouame. It represents the views of its authors, and does not reflect the views of the institutions they are affiliated with. Any mistake is therefore the responsibility of the authors. We are also grateful to the valuable support provided by Loic Whitmore in the preparation of the infrastructure data set. § Lead economist, Sustainable Development Department, MENA Region, World Bank. Email: pnoumbaum@worldbank.org

> † Toulouse School of Economics (ARQADE and IDEI). Email: stephane.Straub@univ-tlse1.fr

> ‡ Toulouse School of Economics and ECOPA. Email: charles.vellutini@univ-tlse1.fr
HRKs. The highest per capita GDP is in Zagreb which displays more than the double of many other counties’ per capita GDP. Motorway and road densities are calculated by dividing their length to the area of county. When it comes to infrastructure indicators, Zagreb has the highest motorway density while Krapina has the highest state road density. Low skill ratio is the ratio of working age population without higher education divided by the total working age population. The population variable is defined as the size of the working age population. The patent variable shows the number of yearly patents per 10,000 adults. The entrepreneurs indicator provides the number of entrepreneurs per 1000 people in 2012.

The decomposition of exports by product categories is presented in Figure 5 and Table 10. Chemicals and Pharmaceuticals account for the largest share of exports, followed by metals, food products, and textiles. As can be observed in the chart below, the export structure has remained relatively constant between 2007 and 2012. Table 11 shows the origin diversity index by category. Manufacturing electrical equipment, transportation, as well as other manufacturing, display the highest degree of concentration, whereas manufacturing of rubber and other nonmetal minerals show the highest degree of origin diversity.<sup>7</sup>

**Figure 5: Total exports by product category**

<!-- Start of picture text --> 100%<br>Chemicals<br>90% 17% 19%<br>Metal<br>80%<br>8%<br>11% Food<br>70% 9%<br>Textiles<br>10%<br>60%<br>Electrical<br>50% Machinery<br>40% Transport<br>30% Wood<br>Rubber<br>20%<br>Agriculture<br>10%<br>Services<br>0%<br>Other Man.<br>2007 2012<br><!-- End of picture text --> _Source: Staff calculations based on data from the CBS_

> 7 According to our calculations, around 15 counties export rubber

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