rafmacalaba/datause-encoder-data huggingface dataset:
**In this scenario, the increase in the minimum wage has a more substantial and positive impact on income inequality compared to Scenario 1.** This is evident in the reductions of the Gini index and the increases in the bottom 50% income share, as detailed in Table 8. When all dismissed workers move to unemployment or inactivity, the Gini index is expected to decrease substantially, from 0.356 to 0.344, and the bottom 50% income share could increase by 0.7 percentage points. As in Scenario 1, the lower bound of inequality suggests that reduction in inequality can be muted if dismissed workers end up not leaving the labor force but finding new employment opportunities with negligible wages.  In this case, the increase in compensation from rising wages among one group is almost fully offset by the decrease in income for those employees who lose their jobs.

**Table 8. Distributional Effect of Minimum Wage Increase on Labor Market Outcomes in Scenario 2: Convergence to Living Wage Estimate**

(16.41% of Full-Time Employees Affected by the Raise)

|**Indicator**|**Baseline**<br>Before MW increase|**Simulation**<br>After the MW increase,<br>sample of wage earners<br>andjob losers*|**Simulation**<br>After the MW increase,<br>sample of wage earners<br>only**|
|---|---|---|---|
|Gini coefficient|0.3560|0.3554|0.3444|
|Theil Index|0.2504|0.2499|0.2391|
|Mean logdeviation|0.2037|0.2030|0.1904|
|Bottom 50% income<br>share|26.43%|26.46%|27.16%|
|Middle<br>40%<br>income<br>share|44.33%|44.32%|43.93%|
|Top10%income share|29.23%|29.22%|28.90%|

_Source: Own simulations based on administrative tax data._ _*Sample includes wage earners and those who move out of formal employment (assumed to move to other employment opportunities in the formal or informal sector with zero earnings). This represents the lower bound of inequality reduction._ _**Sample restricted to wage earners, assuming all dismissed workers move to unemployment or inactivity._ _Note: The Gini coefficient and Theil Index are measured over labor income and do not include non-labor income sources._

**Overall, these policy simulations under scenarios 1 and 2 show the minimum wage increase does not result in substantial wage hikes among the formal employee population.** Nevertheless, noteworthy wage adjustments can be observed for those employees directly affected by the wage increase (individuals earning more than the 2021 minimum wage but less than the newly simulated minimum wage). However, it is crucial to note that a significant portion of this wage increase is counterbalanced by an income drop of those employees who lose their jobs after the minimum wage increase. Additionally, over the long term, the wage increase tends to be less pronounced than in the short term, primarily due to the heightened elasticity of labor demand.  This result could be sensitive to the assumption of non-compliance, as non-compliant firms could, in principle,

40
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This country report provides an overview of the key AICD findings for the Republic of Congo so that the country’s infrastructure situation may be benchmarked against that of African peers—particularly other resource-rich countries.

Due to the cross country nature of the data collection, a time lag is inevitable. The AICD covers the years 2004–07. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short term fluctuations. In addition, the report standardizes the indicators and analysis to ensure consistency and allow for comparisons across countries. For these reasons, some of the indicators may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

The infrastructure sector contributed half a percentage point to the Republic of Congo’s annual per capita growth over the past decade—considerably lower than the regional average of 0.99 percentage points. Growth in the Republic of Congo was generated primarily by increased access to mobile telephony, while the inadequacy of the power supply held back the economy.

Infrastructure’s historically small contribution to growth suggests that the Republic of Congo can achieve significant gains from improvements in these sectors. Simulations suggest that if the country’s infrastructure platform were improved to the level of the African leader—Mauritius—annual per capita growth rates could increase by 3.7 percentage points. This compares with an average of 2.92 percentage points for the continent as a whole (figure 1).

Most of the potential contribution to growth would come from improvements in the power sector, particularly through increases in generation capacity. Evidence from a 2009 World Bank/IFC enterprise survey<sup>1</sup> suggests that power is by far the greatest infrastructure constraint for Congolese firms. Surveyed firms report losing up to 16 percent of sales due to frequent power outages—almost triple the average of 6 percent for Sub-Saharan Africa.

Road improvements would also make a significant contribution to growth. For example, the cost of importing to the north of the country would fall from $313 per ton via Douala to $267 per ton via PointeNoire if road connections were used; and the development of feeder roads in the areas of Pool and Cuvettes, where land has high agricultural potential, could bring returns of around 34 percent (BriceñoGarmendia and Foster 2009).

> 1 http://www.enterprisesurveys.org /ExploreEconomies/?economyid=49&year=2009

3
In (2) the hypotheses of interest are now β _1_ < 0 and β _2_ < 0.

The previous model can be pushed forward and allow for more flexibility as in Gately and Huntington (2002) or Dargay, Gately, and Huntington (2007) who propose a model also based on asymmetry and imperfect price-reversibility. Yet, they allow for the possibility that not all price increases are the same and hence not all price increases have the same effect. In principle, it would be possible that increases that break a price record do not have the same effect as increases within what could be considered a normal range. More specifically, Gately and Huntington (2002) propose the following decomposition for the price level ln _P_ = ln _P0_ + ln _Pmax_ + ln _Pcut_ + ln _Prec_ where _P0_ is the price in the starting year; _Pmax_ is the cumulative increase in the log of maximum historical price; _Pcut_ is cumulative decreases in log of price; and _Prec_ is the cumulative sub-maximum increases in log of price. Figure 3 presents a graphical decomposition of the (logged) oil prices into the three sub components just defined. It shows that: during the 1970s _Pmax_ and _P_ go hand in hand (i.e. each observation for the price of oil was a maximum); during the 1980s and most of the 1990s, most of the variation in _P_ is due to _Pcut_ (i.e. price declines dominate the evolution of _P_ ); and finally, during the last 10 years it is _Prec_ the component that dominates (i.e. prices on the rise but without reaching the maximum level).

Using (3) to substitute for the price of oil in (1), then we obtain the following unrestricted version for the changes in oil efficiency: In (4) the hypotheses of interest are now β _1_ < 0, β _2_ < 0, and β _3_ < 0. Note that the idea in this specification is that price increases may now have different effects depending on past prices (i.e. a price increase above the historical maximum is different to a price increase in a period of more or less normal variations). In the case of oil demand, Gately and Huntington (2002) expected (and found) that β _1_ > β _2_ > β _3_ . Here our prior would be similar.

# _II.3. Results_

We now review the results of estimating equations (1), (2) and (4) for a sample of 108 countries in the world, high income OECD countries, middle income countries and LAC. Oil data and the GDP series are as above whereas energy use (in kt of oil 6
the reforms. On the other hand, the methodological change in the measurement of indirect taxes, particularly accounting for special taxes to tobacco, alcohol, sodas and mobile phones, and the addition of the indirect effect of GST would more likely contribute to an increase in poverty and inequality in consumable income. The inclusion of the indirect effect of electricity subsidies, which we found to be progressive and marginally equalising, could have counterbalanced some of the negative impacts mentioned above, at least for inequality. Finally, since Jordan did not have any major education or health reform between 2010 and 2018, we believe the difference in final income inequality across studies is more likely a product of methodological changes in the treatment of health expenditures between the two studies.

**_Table 5. Selected Revenues and Expenditures 2010 and 2018 (Percent of GDP)_**

**_Figure 14. Poverty and Inequality Change between Income Concepts in Fiscal Incidence Analysis for 2010_**

||**2010**<br>**Percent**<br>**of GDP**|**2018**<br>**Percent**<br>**of GDP**|45<br>40||
|---|---|---|---|---|
|Personal Income Tax|0.8|0.7|35||
|GST (Sales Tax on Domestic Goods|4.6|4.4|30||
|and on Services)|||nt<br>25||
|Total Cash transfers|0.7|0.9|<br><br>rce||
|NAF|0.4|0.3|Pe<br>20||
|Bread compensation scheme|0|0.6|15||
|Total in-kind transfers|7.4|5.9|10||
|Education|3.2|3.4|5||
|Health|3.1|2.5|0||
|Total residential subsidies||||Market<br>Disposable Consumable Final Income|
|Electricity<sup>61</sup>|1.0|1.5||Income<br>Income<br>Income|
|Water|N/A|1.7||Poverty<br>Inequality|
|Food and fuel|1.0|0||<br>|

Source: for 2018 budget see Table 1 and Table 2. 2010 numbers from Alam _et al._ 2017.

# 5. Potential Reforms

We have seen that Jordan’s current fiscal system, despite some improvements, does relatively little to reduce poverty and inequality. At the same time, pre-COVID fiscal pressures combined with COVID-related pressures mean that fiscal reforms are necessary. The need for such reforms presents both a challenge and an opportunity. The challenge is how to increase revenues and reduce or redirect spending in a manner which least affects the poor and vulnerable. The opportunity is that such reforms may be politically feasible in the shadow of the COVID-19 crisis and, furthermore, that they can potentially be done in a distributionally neutral or even progressive manner.

Building upon this paper, a microsimulation tool is being developed which will allow policy makers to run various fiscal reform scenarios and assess both the impact on the fiscal deficit as well as the impact on poverty and inequality. In this closing section, we do not attempt to examine all options or even the most ambitious but present three feasible reforms.

> 61 In 2011, electricity subsidies rose to 5.5 percent of GDP as political unrest disrupted the supply of natural gas from the Arab Republic of Egypt (Alam _et al_ . 2017).
|**Table 3: Impact of Training on**|**Employmen**|**t Outcomes**|**(Survey D**|**ata)**<br>|Transformed||||Aggregate|
|---|---|---|---|---|---|---|---|---|---|
||Working<br>at all|Employed <br>20 hours+|Weekly<br>Hours|Monthly<br>Income|Monthly<br>Income|Occupational <br>Status|Formal<br>Work|Formal<br>Income|Employment<br>Index|
|ITT Estimate|0.020|0.012|0.860|17.316|0.121|0.962*|0.020*|22.166*|0.039*|
||(0.013)|(0.013)|(0.680)|(12.271)|(0.093)|(0.573)|(0.012)|(11.965)|(0.023)|
|_Lee lower bound ITT_|0.011|0.003|-0.103|-4.959|0.050|0.922|0.011|-0.231|0.016|
||(0.013)|(0.013)|(0.668)|(11.720)|(0.093)|(0.573)|(0.012)|(11.378)|(0.023)|
|_Lee upper bound ITT_|0.026**|0.018|1.145*|22.592*|0.165*|1.242**|0.025**|26.460**|0.050**|
||(0.013)|(0.013)|(0.684)|(12.331)|(0.094)|(0.576)|(0.012)|(12.034)|(0.023)|
|LATE Estimate|0.029|0.019|1.294|25.989|0.182|1.452*|0.030*|33.243*|0.059*|
||(0.018)|(0.018)|(0.980)|(17.624)|(0.134)|(0.828)|(0.017)|(17.184)|(0.033)|
|Control Mean|0.420|0.361|17.922|299.109|2.541|17.128|0.293|257.887|0.001|
|Control Standard Deviation|0.494|0.480|25.545|464.600|3.516|21.763|0.455|448.160|0.871|
|Sample Size|5497|5529|5439|5396|5396|5418|5508|5464|5497|

Notes: Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5 and 1 percent levels respectively. All regressions include controls for course*gender*age group lotteries applied for. <mark>Appendix A defines the outcome variables.</mark> 36
# **1. Introduction**

Between 2007 and 2008, world prices of food commodities rose dramatically. The global food price inflation was transmitted to the domestic market of Indonesia, where the agricultural sector’s share is about 19% of GDP and 41% of total employment. The rising food prices raised fears that the spike in food expenditures could worsen households’ well-being, especially among the poor. Welfare impacts of food price inflation can be particularly large in Indonesia since the average family spends about a half of its income on food<sup>4</sup> . On the other hand, higher food prices increased agricultural profits and created large income gains to agricultural households compared to non-agricultural households (Ravallion, 1990; Yamauchi and Dewina, 2011). Interestingly, World Bank (2011) shows evidence that the positive impacts on producers seemed to outweigh the negative effects on consumers’ welfare in Indonesia.

In the literature, a large number of papers have investigated the short-term impact of food price shock on poverty and welfare in different contexts (e.g. Ivanic and Martin (2008); Ravallion (1990) for Bangladesh; Ferreira, Fruttero, Leite, and Lucchetti (2011) for Brazil; Vu and Glewwe (2011) for Vietnam; Friedman, Hong, and Hou (2011) for Pakistan). However, its long-term effects on agricultural production are still yet to be explored and understood<sup>5</sup> . If the rise in food prices creates positive net gains to farmers, it is important to understand how farmers utilize the gains and change their agricultural production activities. If farmers perceive that the

> 4 Paxson (1993) showed from Thailand that price changes significantly cause consumption fluctuation. Using the same sample from Indonesia, Yamauchi (2012a) also recently showed that recurrent seasonality of rice prices fluctuates birth weight, resulting in variations in subsequent child growth and schooling investments.

> 5 In recent years, there is an emerging academic interest in examining the long-term impact of political or economic shocks (Collins and Margo (2007) for the impact of the 1960s riots in American cities on the property value; Hornbeck (2012) for the impact of the 1930’s American Dust Bowl on the population and agricultural production also in the U.S).  However, there is no research which investigates the long-term impact of food price crisis.

2
Table B2: Association between Household Welfare and Characteristics (OLS), 2007

|Variable|Coefficient|Std.Error|t-Statistic|Prob.|
|---|---|---|---|---|
|Constant|13.56208|0.048448|279.9298|0.0000|
|Male|0.267855|0.026378|10.15431|0.0000|
|Age Head|-0.016445|0.001826|-9.004842|0.0000|
|Age Head Squared|0.000139|1.86E-05|7.481732|0.0000|
|Age <5 (% Household)|-0.008335|0.000326|-25.58600|0.0000|
|Age 5 to < 10 (% HH)|-0.011623|0.000357|-32.53275|0.0000|
|Age 10 to < 15 (% HH)|-0.011045|0.000352|-31.36243|0.0000|
|Age 15 to <20 (% HH)|-0.006998|0.000278|-25.21718|0.0000|
|Age 20 to <25 (%HH)|-0.003273|0.000244|-13.40387|0.0000|
|Schooling (years)|0.034182|0.001391|24.56746|0.0000|
|Land|0.000597|0.000248|2.403839|0.0162|
|Access to Credit|0.126968|0.020147|6.301943|0.0000|
|Has Migrant (s)|0.082713|0.011060|7.478536|0.0000|
|Distance Nearest Hospital|-0.000531|0.000468|-1.135817|0.2561|
|Distance Nearest Tarred Road|-0.000296|0.000184|-1.613524|0.1067|
|Public Sector|0.166832|0.025839|6.456619|0.0000|
|Private Sector Formal|0.131884|0.025976|5.077098|0.0000|
|Agriculture|-0.176951|0.021780|-8.124541|0.0000|
|Non-Agriculture Informal|-0.041567|0.021050|-1.974723|0.0483|
|Unemployed|-0.012119|0.037286|-0.325021|0.7452|
|Urban|0.264181|0.012585|20.99248|0.0000|
|Adamaoua|0.020208|0.025173|0.802760|0.4221|
|East|-0.012617|0.024691|-0.510984|0.6094|
|Far-North|-0.047467|0.019496|-2.434630|0.0149|
|Coast|-0.188228|0.023752|-7.924789|0.0000|
|North|-0.072705|0.022931|-3.170551|0.0015|
|North-West|-0.030746|0.018423|-1.668934|0.0952|
|West|-0.016742|0.019021|-0.880175|0.3788|
|South|0.028901|0.025313|1.141725|0.2536|
|South-West|0.079206|0.019463|4.069534|0.0000|

Source: Authors’ calculations (R-Squared :0.46) 48
16 While the underinvoicing of exports is consistent with capital flight, the systematic underinvoicing of imports after 1981 and the overinvoicing of exports before 1985 are not. Underinvoicing of imports may be because many developing countries have heavy import taxes and other value-based irnport restrictions, counteracting capital-flight incentives to overinvoice imports. Trade co.itrols, especially on imports, give incentives not to record goods. Reasons for overinvoicing exports before 1985 may be related to export subsidies and other incentives to generate legitimate foreign exchange eamings (see Eggerstedt et al. (1993). Taking the two misinvoicing measures together, there is negative capital flight until 1989 (a lower trade surplus reported by the trading partners than reported by the countries), implying unrecorded capital inflows to these 84 countries.

Assuming that these trade misinvoicing estimates are a useful approximation for actual unrecorded imports and exports, we adjust our capital flight based estimates accordingly, since funds spent on imported goods would no longer constitute assets controlled by the country.

<!-- Start of picture text --> Figure 3: Capital Flight Due to Trade Misinvoicing (Annual Flows)<br><!-- End of picture text --> <!-- Start of picture text --> 8llon of US$<br>30 T<br>1 6 -.-<br>7Z .72 73 74 76 7e 77 78 79 sO 81 82 83 84 85 8e 87 es 89 90 91<br>-16 -r<br>-30 -<br>-46 -<br>-<br>Trade Misinvoicing, Trade Misinvoicing, Trade Misinvoicing, Total<br>Exports Imports<br><!-- End of picture text --> What happens when misinvoicing is added to the World Bank residual and Dooley measures? They both show that outward capital flight flows peaked in 1988 at about $100 billion and $130 billion respectively for each measure (see Figure 4). It is also seen that capital flight was reversed in 1989 and 1990. According to anecdotal evidence, this has continued in 1991 and 1992. In the past four years, much flight capital has reportedly returned particularly to three Latin American countries-Argentina, Brazil, and Mexico."5 150ur numbers show for Mexico a return of flight capital of about $20 billion total in 1989. 1990 and 1991. For Argentina and Brazil. our data show lower levels of capital flight reversal during the same period.
# **Appendix Table 2: Probit of women's migration 2000-2006**

||**coef**|**se**|
|---|---|---|
|Aged 7-16 in 1997|0.606***|0.098|
|Aged 17-23 in 1997|0.538***|0.125|
|Aged 24-30 in 1997|0.459***|0.117|
|Aged 31-35 in 1997|0.321***|0.107|
|Aged 36-52 in 1997|0.133**|0.063|
|Years of schooling in 1997|0.020***|0.007|
|Years of schooling in 1997 squared|-0.001*|0.000|
|Age interaction with years of schooling|-0.000**|0.000|
|Was married|-0.042|0.027|
|Had child(ren) in 1997|-0.013|0.017|
|Head’s years of schooling in 1997|-0.006***|0.001|
|Number of girls less than 6 in 1997|-0.011|0.013|
|Number of boys less than 6 in 1997|0.003|0.010|
|Number of girls aged 7-15 in 1997|0.001|0.006|
|Number of boys aged 7-15 in 1997|0.017**|0.008|
|Number of working age women (16-<br>60) in 1997|-0.002|0.008|
|Number of working age men (16-60) in<br>1997|0.027***|0.007|
|Number of women 60 + in 1997|0.002|0.012|
|Number of men 60 + in 1997|-0.012|0.015|
|Father/father-in-law alive in 1997|0.015|0.013|
|Mother/mother-in-law alive in 1997|-0.028|0.019|
|Household assets per capita in 1997<br>(log)|0.045*|0.026|
|Assets squared|-0.004**|0.002|
|% of people in the village migrated in<br>1997|0.003**|0.001|
|Migration *education|0.000|0.000|
|Village had a primary school in 1997|0.024**|0.009|
|Village had a junior middle school in<br>1997|0.015|0.016|
|Village had a senior middle school in<br>1997|-0.033**|0.013|
|% villagers working in large<br>enterprises in 1997|-0.001**|0.000|
|% villagers working in small<br>enterprises in 1997|-0.001**|0.000|

46
Table 2 – Descriptive Statistics

|||**Mex**|**ico**|||**Chile**||
|---|---|---|---|---|---|---|---|
||All|12 to|15 to|18 to|All|15 to|18 to|
|**Actualpercentages**|ages|14|17|24|ages|17|24|
|Average age|17.19|13.0|16.0|20.8|18.9|15.95|20.8|
|Percent female|53.5|50.65|52.9|55.9|53.0|50.5|54.6|
|Percent indigenous|-|-|-|-|10.8|11.5|10.4|
|<br>Percent rural|24.7|28.1|26.0|21.6|13.2|13.3|13.1|
|**Behaviors and Outcomes**||||||||
|Share not completing high school|29.9|10.0|33.0|42.3|14.2|8.4|17.8|
|Share in school|45.3|85.9|61.8|27.5|60.5|91.4|41.0|
|Share idle/inactive|20.3|8.9|17.7|29.9|27.3|6.7|35.9|
|Share working|41.9|17.2|35.1|35.1|21.4|4.7|32.0|
|<br>Share having sex|52.1|-|13.3|56.0|59.1|26.9|79.4|
|Share of the sexually active using protection|50.6|-|54.0|52.5|61.9|59.9|62.3|
|Share reporting at least 1 child|19.4|-|2.5|28.9|18.6|3.9|27.9|
|Share married|15.7|0.4|4.1|32.9|4.8|0.1|7.7|
|**Risk and Protective Factors**||||||||
|Share reporting physical or psychological abuse<br>in home|-|-|-|-|6.3|6.2|6.4|
|Share reporting problems arising from substance|-|-|-|-|8.5|7.0|9.5|
|abuse in home||||||||
|Share without access to medical services|49.4|50.2|50.7|48.0|-|-|-|
|Share attending church weekly|9.3|11.3|10.3|7.3|21.6|20.9|18.1|
|Share attending church at least once in the past<br>month|66.3|72.0|65.9|62.5|-|-|-|
|Share believing in God|-|-|-|-|94.8|95.1|94.6|
|Share optimistic about the future|-|-|-|-|87.7|88.2|87.3|
|<br>Share reporting being happy (sense of well-|98.2|-|98.0|98.4|-|-|-|
|being)||||||||
|Share reporting social exclusion|53.2|66.8|53.9|43.1|17.7|10.1|22.5|
|Share with parents who have a primary degree or|56.1|56.3|58.0|55.0|38.1|38.8|37.7|
|less||||||||
|Share reporting they have felt discriminated<br>against|-|-|-|-|54.7|54.2|55.1|

26
# **Cooperative Behavior and Common Pool Resources: Experimental Evidence from Community Forest User Groups in Nepal**<sup>**1**</sup>

## **1. Introduction**

Social dilemmas are an obvious challenge for many groups and communities, including those focused on the use of natural resources. To sustain cooperation and improve how social dilemmas can be solved, we need a better understanding of these issues. As argued by Falk and Heckman (2009), there are three main ways to investigate people’s behavior: (i) using surveys, (ii) observing actual behavior and (iii) conducting experiments. Each of the methods has its pros and they complement each other. Naturally occurring data has its obvious benefit of being based on real behavior, but it is difficult to interpret, because causality is hard to identify. On the other hand, laboratory experiments can easily control for causality but external validity can be questioned.

Some research has thus tried to meet on middle ground and focused on the important question of correlation between real life cooperative behavior and cooperative behavior in experimental settings. These studies compare, for example, real life cooperative decisions of users of a common resource with the same people’s decisions in an experimental game.

Public goods experiments have been used to experimentally measure cooperative preferences (for overview see e.g., Chaudhuri, 2011; Ledyard, 1995; Zelmer, 2003). The advantage with an experimental approach, where subjects are anonymous to each other and decisions are made in privacy, is that cooperative behavior is not affected by factors such as reputation or self-image that are potentially context-specific. Such an approach potentially allows us to control for confounding concerns. However, only a few studies have investigated the

> 1 Financial support from the World Bank Knowledge for Change Program is gratefully acknowledged. Responsibility for the content of the paper is the authors’ alone and should not be attributed to their institutions, the World Bank, or its member countries.

2