rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 6: Non-linear pricing structure**

Consumer equilibrium Intra-marginal prices and income effect <!-- Start of picture text --> Good 2 Good 2<br>Electricity (Good 1) Electricity (Good 1)<br><!-- End of picture text --> Taylor (1975) has reviewed a number of studies that estimated the elasticity of demand to changes in prices for electricity. The models presented are estimated longitudinally with time data or cross-section with data on states or provinces. These models share some essential elements that can be summarized in the following equation Where the subscript _i_ =unit of observation; = quantity consumed of electricity; = household income; = electricity expenditure; = Price of electricity where this price can be alternatively the marginal price or the average price; _X_ = vector of control variables; = price elasticity and = income elasticity. These models can control for various factors such as substitutes products, household white goods, climate conditions, household size and other factors specific to the case-study.

In our case, we assumed that the researcher has only one cross-section survey which means that the unit of observation is the household and the number of observations relative to the marginal price will be the number of price blocks present in the electricity system. This may be a problem for the estimation of elasticity if the number of blocks is small. In principle, one could use average price instead of marginal price to overcome such problem. However, the use of average price may cause endogeneity. This is because we use the quantity to compute the average price and this will make the average price correlated with the error term. Alternatively, as reported by Taylor (1975), we can use the per capita expenditure on electricity (up to the block before the last so as to exclude the block of the marginal price) together with the marginal price of the last block of consumption as follows: Where = marginal price and = expenditure on electricity up to block b-1 of
0.96|3|0.79|0.87|0.75|
|1 male + 1 female + 2 children|0.47|1|1|4|1|1|1|
|1 female + 3 children|0.48|0.86|0.97|4|0.85|1|1|
|1 male + 1 female + 3 children|0.45|1.20|1.03|5|1.21|1.12|1.25|
|1 male + 2 female + 2 children|0.44|1.27|1.05|5|1.29|1.12|1.25|
|1 male + 1 female + 4 children|0.43|1.39|1.06|6|1.42|1.22|1.5|
|1 male + 1 female + 3 children +<br>elderly|1<br>0.43|1.46|1.07|6|1.49|1.22|1.5|
|1 male + 1 female + 4 children +<br>elderly|1<br>0.42|1.66|1.10|7|1.71|1.32|1.75|
|1 male + 1 female + 5 children|0.42|1.59|1.08|7|1.64|1.32|1.75|

**Source** : Authors’ calculations based on HIECS 2015. **Note** : The calorie requirements considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

Figure 1 plots the three equivalence scales as a function of
wages. However, with mobile labor, local wages will remain nearly unchanged in either of these cases. Before presenting the test based on local wages, we note that there is no direct restriction on mobility of labor within Sri Lanka in contrast to some other countries such as China (Hukou) and Vietnam (Ho Khau). Indeed, labor market in Sri Lanka has been characterized by considerable geographic mobility with a large proportion of workers employed in manufacturing in cities (e.g. garments) coming from rural areas. Data from census 2012 show that about 27 percent of population in two prominent destination districts (Colombo and Gampaha) are migrants. About a fifth of Sri Lanka’s total population are migrants. This evidence suggests substantial mobility of population within Sri Lanka.

With geographically mobile workers, the impact of land restrictions on local wage provides a credible test of whether migration channel is indeed driving the results reported earlier: there should be a negative effect on wages only if migration cost is the relevant channel. As discussed above, the estimates in Table 6 provide clear evidence of a negative effect of land restrictions on local wage, thus strongly reject both the rights insecurity and collateral mechanisms. Note that a lower wage is not consistent with a labor supply response to property rights insecurity, even when geographic mobility of labor is limited.

There are two additional pieces of evidence that contradicts the existence of a significant collateral and credit constraint effect. First, the evidence that land restrictions do not affect self-employment in non-agriculture contradicts a collateral and credit effect, because a reduction in credit availability should affect non-farm self-employment (entrepreneurship) the most. The second piece of evidence comes from the fact that manufacturing is the most capital intensive among all of the sectors in our context. Thus a decline in capital due to tighter credit constraint would mean a larger negative impact of land restrictions on employment probability in manufacturing than services. The evidence discussed above suggests the opposite, probability of employment in services declines by a larger magnitude than that in
15.44|65.5|

Source: IEG database and staff calculation.

*The difference between the two ERRs is calculated for every project reporting an ERR at appraisal and completion as: 100*(ERR at completion – ERR at appraisal)/ERR at appraisal. The mean difference was then taken on the percent of differences.

**Model 3 Results:  Does EA matter to the success of a project, after controlling for country, project and team effects?**

The results for model 3, which examines how the frequency and discrepancy in ERRs are correlated with project performance, are summarized in Tables A.6 to A.9. OLS regressions are estimated for the case where the dependent variable is the project rating on the 6 point scale. Logit regressions are used for the binary dependent variable where project performance is represented as either successful or unsuccessful.

Tables A.6 and A.9 show that reporting the ERR in both the PAD and ICR is strongly and significantly associated with project performance. Tables A.8 and A.9 show that the discrepancy between ERR reported in the PAD and ICR is negatively and significantly associated with project performance. Specifically, the wider the discrepancy (after controlling for the optimism bias at project preparation), between the ERR reported in the PAD and ICR, the lower the project performance. These regressions provide strong evidence for the impact of the quality of project economic analysis on project performance, with results that are robust across the various regression specifications and estimation methods.

21
# **References**

- Acemoglu, D., and F. Zilibotti. 2001. “Productivity Differences.” _The Quarterly Journal of Economics_ 116:563–606. 3.2

- Alvarez-Cuadrado, F., and M. Poschke. 2011. “Structural Change Out of Agriculture: Labor Push versus Labor Pull.” _American Economic Journal: Macroeconomics_ 3:127–58. 1

- Artuc, E., D. Lederman, and G. Porto. 2015. “A mapping of labor mobility costs in the developing world.” _Journal of International Economics_ 95:28–41. 11

- Basu, S., and D.N. Weil. 1998. “Appropriate Technology and Growth.” _The Quarterly Journal of Economics_ 113:1025–1054. 3.2

- Bernard, A., and C. Jones. 1996. “Productivity across Industries and Countries: Time Series Theory and Evidence.” _The Review of Economics and Statistics_ 78:135–146. 6, 3.3.2, A.2

- Bryan, G., and M. Morten. 2019. “The aggregate productivity effects of internal migration: Evidence from Indonesia.” _Journal of Political Economy_ 127:2229–2268. 11

- Caselli, F., and S. Tenreyro. 2005. “Is Poland the Next Spain?” NBER Working Paper 11045, National Bureau of Economic Research, Cambridge, MA. 6, A.2

- Chen, C. 2020. “Technology Adoption, Capital Deepening, and International Productivity Differences.” _Journal of Development Economics_ 143:1–26. 2

- de Vries, G., L. Arfelt, D. Drees, M. Godemann, C. Hamilton, B. Jessen-Thiesen, A. Kaya, H. Kruse, E. Mensah, and P. Woltjer. 2021. “The Economic Transformation Database (ETD): content, sources, and methods.” WIDER Technical Note 2/2021,
*|**% discount rate**|||**5% discount rat**|**e**|
|**Elasticity **|3% discount rate|5% discount rate|2020|2021|2022|2020|2021|2022|
|1|184 90.44|23 209.13|2 477 719.53|10 595 024.57|2 274 324.65|3 110 023.79|13 298 833.06|2 854 723.33|
|1.4|6 531.54|8 198.37|875 226.69|3 742 573.85|803 379.73|1 098 581.09|4 697 663.93|1 008 399.06|

Notes: A constant value per statistical life year (VSLY) is estimated by using two discounting rates: 3% and 5%. Based on information from the WHO's Global Health Observatory Data Repository, we estimate that the remaining life expectancy is half of the Ivoirian life expectancy at birth. The term elasticity refers to the value of a statistical life (VSL) 's income elasticity, or the proportional change in the VSL brought on by a change in income. For 2013, the USA's GNI per capita (PPP, current international US$) was US$ 54 380. This amount is used as the basis for all calculations. The low levels of VSL are equal to US$ 4,200,000 for the USA. All VSL estimates are adjusted for inflation using the GDP deflator, as well as for potential changes in real income. All estimates are in US$.

7
participant welfare; and Section 5 concludes.

# **1.1 WWS Program Description**

During 2008-10, as the global financial crisis unfolded in Latvia, labor market conditions worsened and unemployment reached historic highs. A large and increasing proportion of registered unemployed people was not receiving unemployment benefits, or was receiving very low benefit amounts (Hazans, 2012). In September 2009, as a safety net response, the Government of Latvia implemented an emergency public works program (WWS) with financial support from the European Social Fund (ESF) and technical assistance from the World Bank. The goal of the WWS program was to reduce the severity of the social consequences of the global financial crisis on Latvians through temporary labor-intensive job creation. Central government expenditures on the WWS program amounted to about LVL 8.0 ($16) million in 2009; LVL 27 ($54) million in 2010; and LVL 20 ($40) million in 2011 (Government of Latvia, 2011).<sup>7</sup> During 2010 and 2011, Government expenditures amounted to about 0.25 percent of GDP, or 2.0 to 2.5 times expenditures on the main poverty-targeted social assistance program. i.e. the Guaranteed Minimum Income (GMI) program (World Bank, 2010). Government administrative data show that the WWS program created more than 110,000 temporary jobs over 2009-11.<sup>8</sup> All registered unemployed people who were not receiving unemployment benefits were eligible to participate in the WWS program and opportunities were provided on a firstcome, first-served basis.<sup>9</sup> The WWS program participants were eligible to participate up to six months with a two week minimum requirement. There was no limit to the number of

> 7On March 31, 2011 the exchange rate was: e1.00: US$1.42: LVL 0.704

> 8There is significant variation in the duration of participation in the WWS program, with some
due to low-quality housing and distant cyclone shelters, they experienced higher income

> 10They tend to use cross-sectional or longitudinal data from primary surveys or nationally representative household surveys such as the Vietnam Household Living Standards Survey, the Household Income and Expenditure Survey of Bangladesh, or the Indonesian Family Life Survey.

> 11See, for example, Arouri, Nguyen, and Youssef (2015), Karim (2018), and Kurosaki (2015).

8
.73|
|Uruguay|2.70|8.50|2.99|10.15|4.27|8.87|6.64|1.18|2.78|1.76|3.37|1.73|3.10|2.35|
|USA|1.11|4.03|1.99|5.04|2.24|4.65|3.23|1.08|2.61|1.22|2.81|1.77|2.85|2.36|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

36
9 often on Ge from the IMF's Government Finance Statistics Yearbook (GFSY)), care must be taken in making any comparisons across studies.

# B. OECD EXPERIENCE

The sizeable increase in the share of government expenditure out national income is a relatively recent phenomenon in OECD countries. Data for the United States illustrate this point (Table 1). While no one series runs from 1800 through the present, the general trend is clear. Federal government expenditures, measured in current dollars, account for a minimal share of nationai income until World War I. A significant dip characterizes the inter-war period, but thereafter the ratio of U.S. Government expenditures to GNP steadily marches on rising more than sevenfold between 1929 and 1984.

Peacock and Wiseman(1961) report a similar overall trend for the United Kingdom.10 In fact, the same historical trend applies to OECD nations as a group. Employing the narrower Gc definition of government spending, Kuznets (1966) (Table 2) shows that the share of Gc out of GNP in both Europe and the United States did not reach double digits until the 1940's, and often not until the 1950's.

Extending the OECD record into the contemporary period, Saunders and Klau(1985) employ comprehensive Ge data for 23 nations (Table 3), and report that government spending has, on average, increased from 26.3% to 47.0% of GNP in the years 1960 to 1982. (Tanzi(1986) provides similar information but for more years: 1960, 1971, 1975 and 1983. (Table 4)) Saunders and Klau(1985)
by increasing competition for scarce land and water resources (McGuirk and Nunn, 2020; George _et al._ , 2021a). As agricultural seasons have shifted associated with climate change, nomadic herders have changed the timing of their migratory patterns and agricultural households have lengthened their growing seasons. Rising tensions motivated several Nigerian states to implement open-grazing bans in 2016 and 2017. These bans are associated with a sharp escalation of violence in the first half of 2018, which represents the core source of variation in herder-related violence in our study.

We construct panel data by combining information from Nigeria’s General Household Survey and the ACLED Project from 2010 through 2019 and pay particular attention to both the seasonality in herder-related conflict and how agricultural households respond to exposure throughout the agricultural season.

We find that both men and women exposed to herder-related violence and that have also been previously exposed to this violence shift toward informal work in the post-planting season and shift away from informal work, on the extensive margin, in the post-harvest season. This shift toward informal work in the post-planting season seems to act as a substitute activity for agricultural work for men in households exposed to herder-related violence and that have also been previously exposed to this violence. Agricultural work, however, does not decline for women. Rather, the total number of hours worked increases if women live within households exposed to herder-related violence and that have also been previously exposed. For these women, the increase in total hours worked is predominantly allocated to informal work. These results are consistent with the view that work in the informal sector can act as a partial ‘safety net’ for agricultural households exposed to herder-related violence in Nigeria.

Furthermore, we investigate heterogeneity across both time and space by exploiting the spike in violence just before the 2018-2019 GHS survey rounds and within Nigerian states implementing opengrazing bans, which have been attributed as a contributor to increased violence in the conflict. We find that the shift toward informal work and away from agricultural work is larger within states implementing an open-grazing ban and during the
2017), and Shelestov et al. (2020). In each year except 2022, when in-situ data were collected only for summer crops as the conflict made collection in the spring impossible, training data were gathered in two rounds of in-situ data collection along main roads following standard guidelines (Waldner et al. 2019). Optical data from Sentinel-2 and Synthetic Aperture Radar data from Sentinel-1 during the vegetation period were then used for a convolutional neural network that was run on the Amazon Web Services cloud computing platform.<sup>7</sup> Table 2 summarizes the data at the national level, showing that total area cultivated ranged from 27.3 million ha in 2019 (9.4 million and 17.9 million ha of winter and summer crops, respectively) to 24.88 million ha in 2023 (6.8 million and 18.1 million ha of winter and summer crops, respectively). A modest reduction in cultivated area (of less than 0.5 million ha) for the 2022 winter crop is consistent with the fact that this crop had been sown before the conflict. The fact that the 2023 winter crop area was 2 million ha

> 7 Appendix table 1 shows the number of training samples collected each year as well as the F1 scores for the winter and summer crop maps, which exceed 95% in each of the years. Deininger et al. (2023a) provide a more detailed description of the methodology, including maps of the roads traveled in different years and a confusion matrix.

8
Policy Research Working Paper 10480

# **Abstract**

The paper documents the evolution of territorial disparities in labor and location productivity in 14 countries in Latin America, using millions of observations from harmonized household surveys and censuses. Between the early 2000s and the late 2010s, most countries in the region experienced significant reductions in regional inequality as real labor incomes and location productivity premia converged at the first and second administrative levels. The leveling up reflected both the slowdown in productivity growth in affluent predominantly urban municipalities and the catchup of relatively poor, predominantly rural municipalities. Absolute convergence narrowed the labor income gaps with leading metropolitan areas, including the disparitites exploitable through migration, especially among the bottom 40 percent of households, as cities de-industrialized, yet continued to attract migrants. On the eve of the Covid19 pandemic, income disparities with leading metropolitan areas remained high in nearly all countries, largely due to differences in educational attainment, but in a few countries, large differences in returns to endowments indicate potentially significant returns to migration to the leading metropolitan areas, especially for residents of relatively poor, remote regions. Rather than a clear rural-urban-metropolitan divide, in most countries the paper documents substantial overlap between the location-premia distributions of different types of second-level administrative areas and small differences between the average urban and rural place productivity premia.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at odaoust@worldbank.org, vgaldo@worldbank.org, or eianchovichina@worldbank.org.

_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
Table 5. Poverty headcount, poverty gap, and poverty severity for different income concepts

||Poverty headcount<br>($3.20/day)|Poverty gap<br>($3.20/day)|Poverty severity<br>($3.20/day)|
|---|---|---|---|
|Gross market income||||
|plus pension|8.2|1.6|0.5|
|Disposable income|8.2|1.6|0.5|
|Consumableincome|9.3|2.2|1.0|

_Source_ : World Bank staff calculation using BLSS 2017.

Figure 4. The impact of fiscal systems on poverty reduction across countries (change in $3.20 poverty rate, percentage points)

|Bhutan (2017)<br>Turkey (2016)<br>Armenia (2011)|
|---|
|Moldova (2017)|
|Brazil (2008)|
|Peru (2011)|
|El Salvador (2011)|
|Dominican Republic (2006)|
|Romania (2016)|
|Jordan (2017)<br>Chile (2013)|
|Colombia (2017)|
|Mexico (2014)|
|Uruguay (2009)<br>Panama (2016)|
|-4<br>-2<br>0<br>2<br>4|

Note: A positive number indicates an increase in poverty. Pensions are treated as deferred income. Source: World Bank staff calculation using BLSS 2017.

# 4.2. Incidence, Progressivity, and Marginal Contribution of Taxes and Social Spending

Besides the overall impact of the net fiscal system, a relevant policy question could be which fiscal instrument is equalizing or not, and to what extent. For this purpose, we examine how the individual fiscal interventions contribute to the observed changes in poverty and inequality
growth in mean household consumption based on national accounts data.<sup>3</sup> Bhalla, Bhasin and Virmani (2022) build predictions using night-time lights and changes in state gross domestic product data. Desai (2020) estimates poverty using consumption data obtained from a sub-round of the India Human Development Survey conducted in 2017<sup>4,5</sup> . All studies report a reduction in headcount poverty in India in the years

> 1Given that approximately 18 percent of the worlds population lives in India, its poverty and inequality numbers are also crucial for any efforts to track global poverty, see e.g. Chen and Ravallion (2010).

> 2The government raised concerns about the quality of the NSS-2017 household expenditure data according to the following press release: https://pib.gov.in/Pressreleaseshare.aspx?PRID=1591792.

> 3The relationship between poverty reduction and growth in India has been studied earlier in Datt and Ravallion (2011). See also the cross-country study by Ravallion (2012) on the intricate relationship between poverty and growth.

> 4Desai (2020) is limited to only three states in India, namely Uttarakhand, Bihar and Rajasthan.

> 5More recently, Gupta, Malani and Woda (2021b) use consumption data from the Consumer Pyramid Household Survey to directly estimate poverty for 2019. However, the paper makes no attempt to make newly obtained estimates of poverty comparable to estimates for 2011, preventing assessments of how poverty evolved after 2011

2
**Figure 4** : Cumulative middle-class bounds for LAC countries in 2017 PPP ordered by country GDP

<!-- Start of picture text --> 15 85<br>80<br>14<br>75<br>13<br>70<br>12 High income (H) 65<br>11 60<br>Upper middle income<br>(UM) 55<br>10<br>Lower middle 50<br>income (LM)<br>9<br>45<br>8 40<br>hnd bol slv ecu per pry col bra dom mex cri ury arg chl pan<br>Country<br>Vulnerability line Middle-class line (right axis)<br>Vulnerability line Middle-class line<br><!-- End of picture text --> Source: Own estimations based on SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 92 synthetic panels built with available surveys from 2010 to 2019 and 25 bootstrap repetitions.

# Robustness check: Implementing other methodologies

For completeness, the analysis estimates the middle-class lines using previous methodologies. If the Lopez-Calva and Ortiz-Juarez (2014) methodology is applied only to Chile, Mexico, and Peru -as in the original study- or to Argentina, Colombia, and Costa Rica -as in Ferreira et al. (2013)-, the lower bound would be $12 and $16.1, respectively, and the upper bound $85.8 and $109.3, correspondingly. If this method is expanded to all countries over the same period, the lower bound would end up being $13.6 and the upper bound $89.1. Moreover, replicating the simple method applied, the lower and upper bounds would be $14.7 and $79.3, respectively.

# Identifying the drivers of change in the thresholds

While the update of PPPs from 2011 to 2017 contributed to