rafmacalaba/datause-encoder-data huggingface dataset:
arbitrary share of income or expenditure as energy poverty line. Based on our findings, the energy poverty situation paints a grim picture of rural Bangladesh (Table 5). That is, the level of energy poverty is high and even higher than expenditure poverty in rural Bangladesh for almost all measures. Whereas the consumption poverty is 44.8 percent in rural Bangladesh, MEE estimate shows that energy poor households constitute 58 percent of the rural population based on end-use energy and 60 percent based on total energy.<sup>16</sup> This finding implies that a good number of expenditure non-poor households do not consume the required minimum energy.

To further compare the trends of the two energy poverty lines, they are plotted against the income deciles of the households, as shown in Figure 7 which also shows the changes in consumption poverty. Poverty decreases with an increase in income. However, the rate of such decrease is not same for all poverty measures. More precisely, a decrease in the consumption poverty is not followed by a proportionate decrease in the energy poverty measures. That is why we observe a significant share of energy poor (about 30 percent) among the highest income-decile households who have a consumption poverty of about 8 percent. The trends in the two energy poverty measures are about the same except for the highest two income deciles which show a gap between the two, that is, more households are energy poor based on the total energy than based on the enduse energy. The gap is indicative of a high degree of inefficiency in the energy consumption of rural households.

> 16 Consumption poverty has two components: food and nonfood. The poverty line expenditure for food is the cost of a specific food basket needed to maintain the per capita daily caloric requirement of 2,120 calories recommended by Food and Agricultural Organization (FAO) and the World Health Organization (WHO) (FAO/WHO 1973). For Bangladesh, the food basket contains mostly rice, and other food items including pulse, milk, meat, fish, fruits, and vegetables in specific quantities. For nonfood poverty line, a 30 percent allowance of the food poverty line is often used.

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prominently in the prevalence of bank accounts in the developing world.<sup>11</sup> While close to 50 percent of adults in cities have an account, the figure is less than 40 percent among individuals in rural areas.

# **_2.3 The Use of an Account to Save_**

In addition to account ownership, we are interested in the use of accounts to save. This information is provided in the question: “ _In the past 12 months, have you saved or set aside any money?”_ If the respondent answered yes, a follow-up question asked, _“In the past 12 months, have you saved or set aside money by: A) Using an account at a bank, credit union, microfinance institution, or another financial institution_<sup>_12_</sup> _; B) Using an informal savings club or person outside the family (e.g., Chit fund or ROSCA)?”_<sup>_13_</sup> Among those individuals who have an account (that is, conditional on having an account), 42 percent of adults, on average, used the account to save in the past year.<sup>14</sup> Unlike what we found in terms of account penetration, we find small differences between the share of individuals, on average, who use an account to save in developed countries (49 percent) and those who do so in low- and middle-income countries (40 percent) (Figure 1). In other words, globally, adults who have a formal account are on average about equally likely to use their account to save.

> 11  Gallup World Poll data contain two variables related to the urban/rural divide: municipality population data that are used to stratify the sample, and interview-coded data on area size category. We classify urban/rural based on the former, but use interview-coded data when this is not available. The correlation between the population-based and interviewer-coded urban/rural categorizations is very strong: in Sub-Saharan Africa, 94 percent of respondents in cities with populations of 500,000 or more are classified as urban and 95 percent of respondents in towns and villages under 10,000 are classified as rural.

> 12 Local examples were provided, such as cooperatives in Latin America.

> 13 The excluded category includes “in the home” (because of the sensitivity of asking this question in face-to-face interviews in the home) and other assets such as gold and livestock, as well as other formal markets, such as equity purchases.

> 14 In addition to having an account, formal saving is also conditional on an individual’s ability and willingness to save. This may be associated with cyclical macroeconomic conditions, idiosyncratic shocks (such as illness or unemployment), as well as cultural attitudes toward saving. An important caveat is that the data were collected in 2011, following the global financial crisis, which might have affected individuals’ ability to save.

9
because of differences in how male- versus female-owned assets are drawn down to cope with these events; women’s landholdings are smaller or less productive, for example, and so more likely to be sold off in the event of a negative shock.

To better understand the effects of survey approach on specific sub-groups, Table 5 presents the results from the estimation of a modified equation 1, augmented with the interactions between the business-as-usual/IHS4 identifier and a subset of controls that are closely related to household structure and land ownership, including individual headship, age (whether the individual is younger than 25, compared to all other adults), marital status, education, rural residence and variables on household composition – typically linked to norms/customs that can affect reporting around land ownership (see Desai and Barik, 2017, for a discussion using data from India, as well as Doss, 2013).

While household heads overall tend to report higher exclusive ownership and rights (Appendix Tables A4a-b), Table 5 shows that under the business-as-usual approach, economic ownership is lower among women heads as compared to reported ownership, as seen earlier across Table 2 and Figure 2 as well. For other effects, Table 5 shows relatively similar patterns across reported and economic ownership. On land ownership, across age (being younger than 25), marital status (relative to married couples, being separated/divorced or never married), and education, we see that the business-as-usual approach tends to raise exclusive ownership among men and women. On the other hand, being widowed or living in a larger household with more dependents (greater share of children and women aged 65+) reflects lower exclusive/higher joint ownership under the business-as-usual approach. Interaction effects of the business-as-usual approach also tend to be stronger for men overall, including a positive effect on both men’s exclusive reported and economic ownership in rural areas.

Regarding _SDG Owner_ , on the other hand, Table 5 shows that the interaction effects take on statistically significant coefficients more so among women. Noteworthy are the positive interaction effects associated with headship (related mainly to exclusive rights to sell/bequeath) and share of men aged 65+ in the household (related mainly to higher joint rights) as well as negative interaction effects associated with being younger, separated/divorced, and more educated (again mainly due to joint rights). For men, the only significant interaction effect is associated with the share of women aged 65+ in the household (significant negative effect, due to lower exclusive rights).<sup>30</sup> Overall, we find that compared to individual interviews, the business-as-usual approach raises exclusive ownership across reported and economic classifications, driven mainly by men household heads. For women household heads, on the other hand, the choice of survey approach matters more (higher reported ownership in the business-as-usual approach, but higher economic

> 30 The results for exclusive/joint rights are available upon request.

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- 53 -

# 4. **V1DENCE** ON FUEL **SWITCHING ELTICXTIES**

Econometric modelling produces estimates of own price and cross price elasticities which are based on country and industry level data. They represent average responses over time and over all the various plants in the aggregate. Values of these elasticities could be used for the countries concerned to simulate the effects of pollution taxes. If it were found that such elastLcities have a general pattern then this may be able to provide some guidelines for the analysis of other countries outside the sample. Although the sector which is best suited to econometric analysis is power generation, because of the product homogeneity, the larger emphasis has been on measuring substitutability ln industry. The level of aggregation for the latter is an important factor in determining the extent to which results can be generalized to other situations. Elasticities in the Electrical Power Generation Segtor Griffin (19771 refers to earlter studies which estimated inter fuel substitution elasticities for the electricity sector. These were for single countries. Griffin's study has the great advantage in that it pooled crosssections and time series data which allowed for differences in elasticities between countries. Data was for twenty O.E.C.D. countries measured at four five year intervals (lv55 to 1969). His basic model hypothesized only inter country variations in relative prices were important. This was on the basis that long standing inter-country differentials in fuel prices accounted for fuel input choices in a long run equilibrium content. Hydro and nuclear sources of electricity were excluded. The model, as explained in section 3, was a translog cost function in which fuel shares are determined independently of the share of energy in total factor input. The estimated model imposes both the equality (linear homogeneity) and symmetry restrictions and the assumption of concavity was checked at every data point and found to be satisfied. The own and cross price elasticities at mean fuel shares for each country are calculated. Table 4.1 reports the own price elasticitles between fuels, ignoring the general substitution between energy and other factor inputs. The key point to notice is that the differences in elasticities do not come from attaching separate
Finally, district-level data for the variables on resource quality based on soil and water testing were collected from the Punjab Soil Fertility Department. These variables represent values, averaged by district and year, from thousands of soil tests (organic matter, phosphorus content, pH, and soluble salts) conducted by the department for scientists and farners. While not strictly a random sample, we have no reason to believe there will be systematic biases by district or over time. There has also been considerable concern about secondary salinity and sodicity caused by use of low quality tubewell water (Siddiq, 1994; Byerlee and Siddiq, 1994). This was captured by a similar data set on tubewell water test values (residual carbonate and electroconductivity) by district and year.

Growth in TFP was analyzed for three periods corresponding to different phases of Green Revolution technical change (Byerlee, 1992): the Green Revolution period, 1966-74, when modem varieties were widely adopted with associated inputs, the input-intensification period, 1975-84, when input use increased rapidly, and a post-Green Revolution period, 1985-94, when input use leveled off. However, the cost function analysis was restricted to the whole period, 1971-94, because of the non-availability of resource quality data prior to 1971.

# **Major Trends in Punjab's Agriculture**

The major characteristics of Punjab agriculture are described in table 1. Farm size which now averages 3.9 ha has continuously declined over the past three decades, with a decreasing share of that land farmed by the tenant. At the same time, human resource investments and infrastructure have steadily improved over this period; however, rural literacy remains very low.

**Table 1. Physical and human resource base, size of holding, and land ownership type in the irrigated Pakistan's Punjab**

_By region and period (1966-94)_

||**_Period_**<br>|**_Wheat-_**<br>**_mixed_**<br>|**_Wheat-rice_**|**_Wheat-_**<br>**_cotton_**|**_Wheat-_**<br>**_mungbean_**|**_All_**<br>**_Punjab_**|
|---|---|---|---|---|---|---|
||Green Revolution<br>|4.9|4.4|_5.3_|7.0|5.3|
|Farm size (ha)|Intensification<br>|4.5<br>|4.0<br>|4.9|6.5|4.8|
||Post Green Revolution<br>|3.7|3.2|4.0|_5.2_|3.9|
||Green Revolution<br>|45.6|43.5|49.5|43.7|44.4|
|Rented land (%)|Intensification<br>|36.8<br>|38.3|42.2|37.4|37.3|
||Post Green Revolution|28.9|28.9|33.5|29.8|29.1|
||Green Revolution<br>|21.9|21.3|15.8|16.1|18.8|
|Literacy (%)|Intensification<br>|28.9<br>|28.6<br>|22.1<br>|22.1<br>|25.4|
||Post Green Revolution<br>|33.9|32.5|25.1|22.2|28.7|
|Distance from|Green Revolution<br>Intensification|6.0<br>40|6.5<br>44|7.6<br>48|9.2<br>95|7.2<br>54|
|<br>paved road (krn)|<br>Post Green Revolution|.<br>2.1|.<br>2.5|.<br>2.2|.<br>3.8|.<br>2.5|

Green Revolution =1966-74; Intensification = 1975-84; Post Green Revolution, =1985-94.

_Source:_ Pakistan Agriculture Census reports data on these parameters for the period 1971, 1981, and 1991. The values for other ye extrapolated using a constant growth rate in between the two periods. The values reported here are the average for the study period.

8
# **4 The lab setting in Peru and the nature of the sample**

To capture the logic of microfinance as accurately as possible, we implemented our game design as a “lab experiment in the field” or a “framed field experiment” which we played with owners and employees of micro-enterprises in Lima, Peru. We set up our experimental lab in an isolated room in a large consumer market, Polvos Azules, located in the center of the city. The market has approximately 1,800 stalls where vendors sell clothes, shoes, personal items, jewelry, and consumer electronics. We used two methods to recruit participants. First, we hired delegates from the local association of microentrepreneurs to round up players for specific game sessions. We then invited participants to return for subsequent sessions and to invite their neighbors from the market.

We played eleven different games an average of 27 times each over the course of seven months (from July 2004, to February 2005). Our sample includes data from 324 games played over the course of 81 days. We had 491 participants who played an average of eleven games each. Table 2 describes the allocation of players across games. 238 participants attended only one game session, while 23 participants attended more than ten sessions.

We informed participants that we would convert the points they won in each round into Peruvian currency (Nuevos Soles) at a constant rate. Payouts were made at the end of each game session, after all the games were completed. Each individual was awarded 10 Soles for showing up on time, an amount which is equal to approximately two hours worth of profits for the micro-business owners in our sample (10 Soles = $2.98 in July 2004). Earnings in a given session ranged from ten to twenty soles (including the show-up fee). The rules for each game were explained to all the participants simultaneously, and no written instructions were provided except for large poster boards explaining the rules that were posted on the wall as reminders throughout the game. Appendix A provides further details of the game administration.

Each session consisted of two or three games played in random order followed by a social networks survey. To conduct the networks survey, we asked participants to stand up one at a time. While participant _J_ was standing, we asked the other participants to 9
Nonetheless, there are significant concerns with using general perceptions measures for analysis of grand corruption in infrastructure. Where we can compare perceptions measures with audited levels of corruption at the micro level, perceptions appear to be weakly related to audited measures and to contain significant biases (Olken, 2006). Knack (2006) demonstrates how corruption perception indices correlate far better with data on petty corruption than procurement-related corruption in Eastern Europe and Central Asia, and also that petty corruption appears to be only weakly correlated to levels of grand corruption. Donchev and Ujheliyi (2008) find perceptions-based corruption indices exposed to systematic biases away from actual experience with the problem, and influenced by other governance characteristics, such as democracy, and country size. Langbein and Knack (2008) explain and prove the problem of separating between governance indicators, such as those presented by the Worldwide Governance Indicators project. The extent of corruption in a country can not be estimated without running into problems of multicollinearity. Furthermore, country-level perceptions appear to be weakly related to surveyed levels of corruption in particular sectors. For example, there is no significant relationship between Transparency International’s CPI and surveyed levels of corruption amongst construction firms in Eastern Europe and Central Asia. And finally, the lack of specificity in general corruption measures allows us to learn nothing about how the nature of corruption differs across countries within infrastructure provision. These problems with using general perceptions measures as a proxy for grand corruption in infrastructure may help to account for the limited robustness of results in the existing literature on corruption and infrastructure outcomes (Kenny, 2006).<sup>9</sup> The lack of sector-specific survey data on the extent and nature of grand corruption in infrastructure is a significant impediment to evaluating measures designed to reduce the development impact of poor governance and corruption, then. Existing policy and regulatory advice can only be based on case studies, examination of weak general proxies or global results (see for example Gulati and Rao, 2006; Cross and Plummer, 2006; Clarke and Xu, 2002, Cavill and Sohail, 2007; Lederman et. al., 2005). Whilst it is plausible to imagine, based on this work, that transparency, participation, competition, reduced regulatory and licensing discretion, deregulation, improved financial management and extended auditing may all have a role to play, the relative role of specific interventions on the level and impact of grand corruption in infrastructure cannot be evaluated (Kenny, 2006).

> 9 See Weber (2007) for a discussion about severe problems with composite perception based indices and Cooksey (2007) about the low value of this information for individual countries –Tanzania being the case.

10
risk of rising unemployment because monetary policy is initially tighter (Franzese, 2001). To counteract this, governments may weaken labor market regulations. This contracts earnings, especially among the lower skilled and poorer workers. Third, CBI curtails the ability of governments to engage in welfare spending, which also increases disparities between the rich and the poor (Bodea and Higashijima, 2017).

In sum: a government that implements CBI finds itself nudged toward adopting policies that increase inequality. Note that in this model, inequality is a side e↵ect of CBI and not a goal in itself. Note also that we are not claiming that CBI is causing inequality. Rather, we are postulating that CBI modifies policymakers’ incentives to adopt compensating policies.

Empirically, we test these hypotheses using a panel dataset covering up to 121 countries from 1980 to 2013 (exact samples di↵er depending on data availability). We find evidence in support of our main conjecture – that CBI increases inequality. An increase of CBI by one standard deviation leads, ceteris paribus, to a decline in the share of income of all income groups up to the 6th decile. The share of income earned by the bottom decile declines by 0.3 percentage points. CBI, on the other hand, has a considerable positive e↵ect on the income share of the 80%-90% and the top decile. The latter saw its share of income increase by more than 1 percentage point. CBI, in other words, shifted income from the bottom half of the population to the top earners. In several robustness tests, we rule out competing interpretations of our results. In particular, we show that the e↵ect of CBI is not driven by government ideology.

Next, we explore the three mechanisms that link CBI to inequality: financial liberalization, labor market deregulation, and welfare state retrenchment. In terms of financial reforms, governments loosen financial regulations to fuel credit growth, despite a more conservative monetary policy stance. A one-standard deviation increase in CBI leads to a more than 6 percentage point drop in the M2 growth rate while the credit growth rate increases by almost the same magnitude. With respect to labor markets, governments are more likely to initiate and agree with labor unions and employers on a social pact and enhance the legal rights of part-time and contract workers, e↵ectively paving the way for a moderation of wage growth rates. Finally, we detect alterations of social programs. According to our estimations, a one standard deviation increase in CBI is 3
In Table 10 we discuss the source of income for wage workers. It is apparent that in Accra and other urban areas, most of the workers are engaged in trading activities (39% in 1991), while in the rural areas it is predominantly agriculture. If we look at the civil service workers, most of them are males located in the urban areas, with middle or secondary level education, and most of them (83% in 1991) are non-poor.

**Table 11: The characteristics of civil service workers in Ghana.**

|**Characteristics**|**1987**|**1988**|**1991**|
|---|---|---|---|
|**Sex**||||
|male|75.22|76.95|68.97|
|female|24.22|23.05|31.03|
|**Education level**||||
|primary|4.8|6.42|5.07|
|middle|63.54|59.34|59.96|
|secondary|17.90|18.29|32.07|
|higher|13.76|15.95|2.90|
|**locality**||||
|urban|60.8|58.1|61.0|
|rural|39.2|41.9|39.0|
|Poverty||||
|poor|14.9|14.7|17.0|
|non-poor|85.1|85.3|83.0|

# **3.2 Child Labor**

Child labor is perceived to be a problem in developing countries and Ghana is no different. Child labor is especially prevalent in the rural areas where the capacity to enforce minimum age requirement for schooling and work is lacking. Children work for a variety of reasons, the most important being poverty. Though children are not well paid, they still serve as major contributors to family income in developing countries. Schooling problems also contribute to child labor. Many times children seek employment simply because there is no access to school. When there is access, the low quality of education often makes attendance a waste of time for the students. As a result parents may find no use in sending their children to school when they could be home learning a skill (trade or agriculture) and supplementing the family income.

The concept of child labor is problematic, since it can apply to a range of activities which children do. They can range from domestic work to wage work. It could **be** light artisan work to heavy physical work. A key element is whether the arrangement is exploitative- in the extreme case this can take the form of bonded labor or feudal relationships. The International Labor Organization (ILO) defines child labor as an
FIGURE 9. ESTIMATED REDUCTIONS IN INFANT AND CHILD MORTALITY FROM TETANUS AND MEASLES IMMUNIZATION: 1982-83 MATLAB COMPARISON AREA BIRTH COHORT <!-- Start of picture text --> 120 6 6<br>D<br>ea<br>100<br>h 91<br>p<br>o 80-<br>r<br>0<br>o 60<br>v 40<br>e<br>1~~~ 20<br>h<br>S<br>0<br><!-- End of picture text -->

**INFANCY**

<!-- Start of picture text --> 90<br>~~~~~~~~69<br>41~~~~~~~A<br>UNMMNIE _I\AUNZE<br>NEONATAL POST-NEONATAL 1-4 YEARS<br><!-- End of picture text --> LIIUNIMMUNIZED =IMMUNIZED