rafmacalaba/datause-encoder-data huggingface dataset:
in table A3 in the appendix. We also control the level of development of the economy using real GNI per capita. Inequality is controlled for using the country-wide Gini index. Both variables are prominent in the literature (Fajnzylber et al., 2002; Soares, 2004; Demombynes and Ozler, 2005). Increasing inequality typically increases the proportion of poor who may engage in criminal activity as well as the greater reward for engaging in criminal activity. For cases where data for the exact date are unavailable, we use data for the closest date available. The exact years of data used for GNI per capita and the Gini index are presented in table A3 in the appendix. We finally control for country size using the total population of the country. These data are available from the World Bank’s World Development Indicators. Data source and description of the variable can be found in table A1, with summary statistics in table A2.

# **3. Estimation**

We estimate the following equation (1) using OLS when the dependent variable is crime losses over sales, and Logit when the dependent variable is a dummy variable indicating whether a firm experienced crime in the last fiscal year.

- (1) _crimeij_ = β 1 _GDPgrj_ + β 2 _Policelag j_ + β 3 _Femij_ + β 4 _GNIcap j_ + β 5 _GINI j_ + β 6 _Population j_ + β 7 _Smallij_ + β 8 _Mediumij_ + β 9LargeCityij + β 10 _Manfij_ + ε _ij_

Where _crime_ is the losses due to crime as a % of sales or a dummy variable representing whether a firm experienced crime in the last fiscal year, _GDPgr_ is the real GDP per capita growth rate, _Policelag_ is the lagged number of police per 100,000 population, _Fem_ is a dummy representing female ownership, _GNIcap_ is the real Gross National Income per capita, _GINI_ is the Gini coefficient, _Population_ is the total population of the economy, _Small_ and _Medium_ are firm size 11
transformation, although the supremum Wald test suggests the presence of a structural break in 1978, the other two tests fail to reject the null hypothesis of no parameter shifts.

The results also confirm that the optimal transformation parameter is close to one for the linear model, with quadratic trend and break year of 1993. Indeed, statistics reported in Table 4 (last two columns) favor rejection of the null hypothesis that the optimal transformation parameter is equal to zero (logarithmic transformation) but fails to reject the null hypothesis that the parameter equals one (linear transformation). Overall, the results suggest that a linear specification best captures global aggregate yield growth, which is consistent with some previous single-commodity studies (Grassini et al., 2013; Hafner, 2003).

Based on the findings reported in Tables 3 and 4, we conduct regression analyses using the linear specification for the global aggregate as well as income and regional aggregates, with the results summarized in Tables 5 to 7. In each table, panel A reports parameter estimates of the base model, panel B shows the corresponding estimates which include a time-squared term, and panel C presents the results allowing for a structural break using piecewise linear regression. Before discussing the results, several key points should be noted. First, we test for residual autocorrelation in each regression using the Durbin-Watson test where the results suggest the presence of residual autocorrelation in almost all models (detailed results can be found in Baffes and Etienne 2024, Appendix A). To address this, we use the Newey-West standard errors instead of the conventional standard errors in Tables 5 to 7.

Second, we re-estimated the models using the Prais-Winsten FGLS estimator, which directly accounts for autocorrelation, rather than simply adjusting the standard errors as in the Newey-West approach. The results (reported in Baffes and Etienne 2024, Appendix A) indicate that most of the statistical significance remains consistent and the estimated coefficients are closely aligned with those in Tables 5 to 7. Third, as the PhillipsPerron stationarity statistics confirm, most of the residuals do not contain a unit root, suggesting that the statistical properties of the residuals generally do not change over time.

Lastly, the results from AIC vary depending on the yield indices analyzed. For example, the base model with a linear trend exhibits the lowest AIC for the aggregate, Big-4, and AE yield indices (Table 5). In contrast, for Other and EMDE yield indices, the models with a structural break and a quadratic trend, respectively, give lower AIC values. The model with a quadratic trend is preferred for cereals & grains and other crop group, the linear trend model for MENA and SEAO, and the structural break model for all other commodity and regional groupings (Tables 6 and 7).

_— 14 —_
<!-- Start of picture text -->
> 1000<br>=<br>2 800<br>2<br>s<br>% 600<br>3<br>2a<br>S 400<br>oO<br>o<br>55 200<br>Ss<br>0<br>2020 2030 2040 2050<br>HB East Asia & Pacific MM) Europe& Central Asia<br>HE Latin America& Caribbean IN Middle East& North Africa<br>HE North America South Asia<br>(9) sub-Saharan Africa<br><!-- End of picture text -->

<!-- Start of picture text --> = 50 2.15 3.65 6.85<br>=a<br>B 40<br>5<br>Qa 30<br>2<br>rs}oO | |<br>N 20<br>oO<br>oD<br>g°<br>8 ole<br>(v4 2030 2040 2050 2030 2040 2050 2030 2040 2050<br>GMM East Asia; & Pacific. MO) Europe & Central Asia:<br>ME Latin America & Caribbean Ml Middle East & North Africa<br>ME North America South Asia<br>M9) Sub-Saharan Africa<br><!-- End of picture text -->

<!-- Start of picture text --> 40<br>30<br>@<br>Nn<br>(e)<br>Q<br>£<br>o<br>2Oo<br>a 20 | |<br>oOaa |<br>O) | |<br>|as om NGA PAK PH IND Jri~ VNM LotMEX themt BRA IDN ‘ CHN DEU JPN<br>0<br>Countries ordered from lowest to highest GHG/capita in 2019<br>MN 2019 MN Addition to reach target at $2.15<br>Addition to reach target at $3.65 [NN Addition to reach target at $6.85<br><!-- End of picture text -->
- Christensen, J.H.E., and G.D. Rudebusch. 2012. “The Response of Interest Rates to US and UK Quantitative Easing.” _The Economic Journal_ 122:385–414.

- Colletaz, G., and C. Hurlin. 2006. “Threshold Effects of the Public Capital Productivity : An International Panel Smooth Transition Approach.” LEO Working Papers/DR LEO 1669, Orleans Economics Laboratory, University of Orleans, Orleans.

- Cunliffe, J. 2019. “Financial stability post Brexit: risks from global debt, a speech at CFO Agenda, London, May 7, 2019.” Speech, Bank of England.

- D’Amico, S., W. English, D. López-Salido, and E. Nelson. 2012. “The Federal Reserve’s Large-scale Asset Purchase Programmes: Rationale and Effects.” _The Economic Journal_ 122:415–446.

- De Castro, F., J.J. Pérez, and M. Rodríguez-Vives. 2013. “Fiscal Data Revisions in Europe.” _Journal of Money, Credit and Banking_ 45:1187–1209.

- Dell’Erba, S., R. Hausmann, and U. Panizza. 2013. “Debt levels, debt composition, and sovereign spreads in emerging and advanced economies.” _Oxford Review of Economic Policy_ 29:518–547.

- Driscoll, J.C., and A.C. Kraay. 1998. “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data.” _The Review of Economics and Statistics_ 80:549–560.

- Ebeke, C., and Y. Lu. 2015. “Emerging market local currency bond yields and foreign holdings -A fortune or misfortune?” _Journal of International Money and Finance_ 59:203–219.

- Engen, E.M., and R.G. Hubbard. 2005. “Federal Government Debt and Interest Rates.” _NBER Macroeconomics Annual 2004_ 19:83–138.

- Erce, A., and E. Mallucci. 2018. “Selective Sovereign Defaults.” International Finance Discussion Papers 1239, Board of Governors of the Federal Reserve System.

- Fernández, M., Andrésand Klein, A. Rebucci, M. Schindler, and M. Uribe. 2016. “Capital Control Measures: A New Dataset.” _IMF Economic Review_ 64:548–574.

- Fok, D., D. Van Dijk, and P.H. Franses. 2005. “A multi-level panel STAR model for US manufacturing sectors.” _Journal of Applied Econometrics_ 20:811–827.

22
the ECA region: Hungary, Slovak Republic, Slovenia, and Tajikistan. As with a pairwise comparison of countries at a point in time, a comparison for a single country at two points in time would be more convincing if it were based on a common set of sources.

Again, one could do this in principle, by going to the component data sources, but many of them would be costly or impossible to access for most data users. A second-best solution would be for TI and WBI to add to their web sites a tool that allows purer comparisons over two time periods (or across two countries) by computing customized indexes based only on sources common to both years (or countries). _Interdependence of Sources_ Intuitively, if several sources assess a country more favorably in year 2 than in year 1, we can infer more confidently that an actual improvement occurred than if evidence of progress were based on a single data source. This intuition is valid only to the extent that different sources represent independent judgments. In classifying which countries have improved or worsened to a “statistically significant” degree over time, both WBI and TI assume that assessments from each source are fully independent.<sup>19</sup> However, many of their sources clearly are not independent. The CPIA process takes into account numerous expert assessments and firm surveys, and ratings often are adjusted to be more consistent with rankings from those sources. The expert assessments (of the “centralized” type) in turn often consult each other, and sometimes adjust ratings for outliers. The EIU provides little information on definition or methodology for its corruption rating, as noted by Lambsdorff (2005b). He shows that the EIU ratings are

> 19 Setting aside the technical problems, Lambsdorff (2005a, 2005b) at least is careful to interpret changes in the TI index as shifts in _perceptions_ of governance.  Kaufmann (2005) on the other hand interprets statistically significant increases in the WBI indexes as improvements in _governance_ which demonstrate that “countries can substantially improve” their quality of governance “even in the short term.”

21
14

# **4  Evolution of Social Indicators during the Global Crisis**

## **4.1  Income growth**

Evidence points to a sharp deterioration in economic and social well-being indicators since the onset of the crisis in 2007. Economic performance has suffered, but not uniformly across the world. While some emerging market countries had marked improvements in their GDP per capita compared to the precrisis period, many others experienced deterioration (figure 6). Overall, 95 countries around the world are estimated to have their per capita incomes reduced in 2009 at the peak of the crisis. Per capita GDP fell on average by 3 percent in the Euro Area countries, with the largest declines found in countries hardest hit by the economic and financial crisis in Europe (e.g., Greece, Ireland, Iceland, Italy and Spain). Previous analysis focusing on a set of developed and developing countries over four decades suggests, indeed, that an increase in GDP volatility from normal to crisis related levels can reduce long-run per capita GDP growth by around 2 percentage points a year.<sup>40</sup>

**Figure 6. Impact of the crisis on GDP per capita**

<!-- Start of picture text --> Greece<br>Ireland GDP per capita, constant PPP<br>Luxembourg<br>Italy (% change between 2007 and 2012*)<br>Iceland<br>Spain<br>Slovenia<br>Denmark<br>Portugal<br>Finland<br>United Kingdom<br>Estonia<br>Hungary<br>New Zealand<br>Norway<br>Euro area (17)<br>Netherlands<br>United States<br>Japan<br>EU (27)<br>France<br>Belgium<br>OECD<br>Canada<br>Czech Republic<br>Mexico<br>Sweden<br>Austria<br>Switzerland<br>Australia<br>South Africa<br>Germany<br>Russian Federation<br>Turkey<br>Israel<br>Slovak Republic<br>Chile<br>Indonesia<br>Korea<br>Poland<br>China<br>-25 -15 -5 5 15 25 35<br><!-- End of picture text --> _Source_ : Authors’ computations based on OECD data.

* 2011 for Australia, Chile, Canada, Greece, Japan, Mexico, New Zealand, Turkey, United States, Russian Federation, and South Africa, and 2010 for China and Indonesia.

40 Hnatkovska and Loayza 2005.
youth today remain unexposed to other parts of their country before they enter the world of education or work.

The confessional system and the inequalities it breeds have also played a key role in perpetuating inequality of economic opportunity among youth, placing further strains on social cohesion. Youth in areas where services and infrastructure are inadequate are more likely to have limited access to economic opportunities and improvements in quality of life. Evidence indicates that area of residence and parents’ education at birth<sup>4</sup> largely contribute to youth exclusion and limit their socio-economic mobility (World Bank, 2016). The poor quality of public school education generates large inequality of opportunity among youth with regards to human capital accumulation and obtaining high-quality jobs. Additionally, for many young Lebanese, finding job opportunities is more a function of confessional policy and ‘wasta’ (connections) rather than meritocracy.<sup>5</sup> Youth economic exclusion is manifested in worsening labor market outcomes. In 2010, youth (1524) unemployment reached 34 percent, and the percentage of youth not in education, employment, or training (NEET) reached 21 percent in 2007. These poor labor market outcomes have likely worsened as a result of the sluggish economic growth of the past few years, which can be partly explained by the impact of the Syrian conflict on Lebanon’s economy. Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘soft skills’.

To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924/2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon.

> 4 Father’s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016).

> 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.

5
_Willingness to Pay for Avoided Forest Loss and Species Threats_ Table 1 lists our primary estimation results using data on _all respondents_ and all choice questions. We show results from two nested empirical model specifications (Models 1-2). Model 1 is a mixed logit model that predicts program choices as a product of random coefficients for the program attributes, including a log-normally distributed random coefficient for the program cost and normally distributed random coefficients for forest and species loss. Model 1 incorporates the environmental attributes of the program (avoided forest loss, reduced species threats) as correlated random coefficients to further increase econometric flexibility. As discussed above, this enables examining to what extent people view forest losses and species threats as complements versus substitutes. Besides modeling unobservable preference heterogeneity through random coefficients, Model 1 also incorporates observable heterogeneity in the program cost coefficient (see Equation 2). Model 2 extends from Model 1 by adding controls for biased program perceptions, as explained above. Coefficients for neither the socioeconomic characteristics to model observed heterogeneity nor the variables to control for biased program perceptions are included in Table 1 to save space.

The mean coefficients for avoided forest losses and reduced species threats in Table 1 are measured in dollars and express households’ annual WTP, on average, per 1-percentage point of improvement. The program cost coefficient is estimated as a log-normal coefficient in all models, so it needs to be exponentiated to obtain its absolute value.<sup>3</sup> The Status Quo coefficient is also estimated directly in U.S. dollars. It denotes the value respondents attach to maintaining

> 3A log-normal coefficient takes only positive values so we enter the program cost coefficient as its negative in the WTP estimation routine. In addition, we scale program cost by diving it by 100 to facilitate estimation. .

18
Table S.35: Checking on multiple-hypothesis testing

|Dependent variables:|One|if pass|||||
|---|---|---|---|---|---|---|
||Dec.<br>2013|Dec.<br>2014|||||
||(a)|(b)|||||
|Any product|0.011|-0.043|||||
|Original p-values|(0.660)|(0.226)|||||
|Adjusted p-values|||||||
|Bonferroni|(1.000)|(1.000)|||||
|Holm (1979)|(1.000)|(1.000)|||||
|R-squared|0.324|0.145|||||
|No. of obs.|850|845|||||
|Dependent variables:|Per-day h|ours using lig|ht sources|Lo|g of expendit|ures|
||Flashlight|Kerosene|Kerosene|Education|Kerosene|Total|
||(total)|lanterns|lamps||||
|||(total)|(total)||||
||(c)|(d)|(e)|(f)|(g)|(h)|
|Any product|-0.349*|-1.474***|-5.986***|0.025|-0.829***|-0.040***|
|Original p-values|(0.059)|(0.000)|(0.000)|(0.483)|(0.000)|(0.001)|
|Adjusted p-values|||||||
|Bonferroni|(0.820)|(0.000)|(0.000)|(1.000)|(0.000)|(0.015)|
|Holm (1979)|(0.351)|(0.000)|(0.000)|(1.000)|(0.000)|(0.010)|
|R-squared|0.058|0.175|0.383|0.220|0.390|0.472|
|No. of obs.|847|847|847|842|631|847|
|Dependent variables:|Per-day m|inutes (averag|e from Sep 2|013 to Apr 20|14)|One if|
||spent on:|||||attend|
||Study at|Study at|Study at|Study at|Other||
||school|home|home|home|||
|||(total)|(day)|(night)|||
||(i)|(j)|(k)|(l)|(m)|(n)|
|Any product|2.198|15.628***|-6.177**|21.805***|-6.031|0.063***|
|Original p-values|(0.420)|(0.004)|(0.011)|(0.000)|(0.506)|(0.006)|
|Adjusted p-values|||||||
|Bonferroni|(1.000)|(0.056)|(0.159)|(0.000)|(1.000)|(0.089)|
|Holm (1979)|(1.000)|(0.036)|(0.079)|(0.000)|(1.000)|(0.051)|
|R-squared|0.137|0.172|0.211|0.219|0.451|0.154|
|No. of obs.|620|620|620|620|620|2646|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) *** denotes significance at 1%, ** at 5%, and * at 10%, corresponding to the original p-values based on standard errors robust to heteroskedasticity and clustered residuals within each village in columns (c) to (h) and within school-grade in the remaining columns. (2) In columns (c) to (h), the baseline controls include household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number). In the remaining columns, the baseline controls additionally include age (years); education (years); and gender (dummy).

80
Table 5: Marginal effects of distance to roads in good condition on non-agricultural and skilled manual employment

||No|n-agricultural em|ployment|Sk|illed manual emp|loyment|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All|Women|Men|All|Women|Men|
|Logdist. good road|-0.057**|-0.058*|-0.062**|-0.051**|-0.027|-0.066**|
||(0.024)|(0.033)|(0.029)|(0.021)|(0.029)|(0.029)|
|Age|0.022**|-0.003|0.036***|0.006|-0.001|0.019|
||(0.010)|(0.013)|(0.013)|(0.009)|(0.008)|(0.015)|
|Gender (woman=1)|-0.177***|||-0.198***|||
||(0.019)|||(0.018)|||
|Education in years|0.045***|0.062***|0.019***|-0.019***|-0.017***|-0.018***|
||(0.005)|(0.007)|(0.007)|(0.004)|(0.005)|(0.007)|
|Number of children under 5|0.029*|-0.025|0.066***|0.004|0.020|-0.015|
||(0.016)|(0.023)|(0.022)|(0.014)|(0.014)|(0.023)|
|Owns agricultural land|-0.171***|-0.216***|-0.093*|-0.016|0.008|-0.041|
||(0.043)|(0.064)|(0.052)|(0.030)|(0.042)|(0.050)|
|Other individual controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Geographical controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Region dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,885|981|904|1,885|968|904|

Geographical controls include: Distance to Marz center, Distance to Yerevan, Altitude, Slope, Population density, Nightlights composite, Aridity. Additional individual and household controls include: Age squared, individual weights, HH wealth categories, family size. Robust standard errors in parentheses * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE There is a wide disparity between urban and rural areas in access to the safest forms of water supply and sanitation. Whereas around 20 percent of the urban population has access to piped water, only 1 percent of rural counterparts have piped access. Access to standposts in urban areas is 26 percent versus less than 10 percent in rural areas (figure 15a). Similarly, whereas flush toilets are used by around 10 percent of the urban population, they are almost nonexistent in rural areas. Improved latrines are used by 12 percent of the urban population versus only 6 percent of the rural population (figure 15b). However, these figures do not reflect improvements since 2008.

The provision of water services is seriously affected by the poor performance of the Sierra Leone water utilities. The rapidly increasing population of Freetown is catered to by the Freetown water utility— Guma Valley Water Company (GVWC). The rest of Sierra Leone was supposed to be covered by Sierra Leone Water Company (SALWACO) and the Water Services department in the Ministry of Energy and Water Resources. However, SALWACO is not yet a functioning utility. As a result performance data could only be obtained from GVWC. In the case of GVWC,<sup>7</sup> the utility is struggling to cover 85 percent of its operational costs and is able to recover only 61 percent of its total costs (table 8). There is a lack of mechanism for setting tariffs to improve affordability of the service. The average effective tariff, at $0.22 per cubic meter, is just one third of the tariff charge in countries with comparably sufficient water resources, and well below cost-recovery levels. Distributional losses, at 40 percent of production, are twice as high as those in a well-run utility, and higher than those in utilities operating in comparable fragile states. Ongoing repairs of key water-supply veins is expected to improve this situation. But the GVWC only recovers 77 percent of total billings (table 9); as a consequence, GVWC‘s hidden costs of inefficiencies stand at a staggering 300 percent of its revenues (figure 17).

> 7 World Bank 2010. Restructuring paper on a proposed project restructuring of Freetown Water Supply Rehabilitation Project. Washington, April 22, 2010 GVWC‘s performance remains weak, reflected in slow collection efficiencies, slow consumer base, very high water losses due to water leaks, illegal connections due mainly to inadequate water distribution system. These deficiencies are the main reasons for the limited operational performance of the company to its customers in Freetown. In order to meet the demand in Freetown, GVWC will need more vigorous actions in terms of water network rehabilitation including extension of its water facilities production/distribution and much more improvement in the utility management.

34
<!-- Start of picture text --> Men<br>6<br>4<br>2: Ease ese == | | | |<br>0 || ee<br>Agriculture, Community, Electricity, gas, Financing, Manufacturing Mining and<br>hunting, social, and and water insurance, real quarrying<br>forestry, and personal supply estate, and<br>fishing services business<br>services<br>TM2017 TM2018 TM2019<br><!-- End of picture text -->

<!-- Start of picture text --> Women<br>6<br>4<br>02 | nig |<br>1<br>Agriculture, Community, Electricity, gas, Financing, Manufacturing Mining and<br>hunting, social, and and water insurance, real quarrying<br>forestry, and personal supply estate, and<br>fishing services business<br>services<br>mTM2017 TM2018 TM2019<br><!-- End of picture text -->