rafmacalaba/datause-encoder-data huggingface dataset:
in manufacturing. Second, we obtained from the SENER fuel consumption data in each NAICS 4-digits manufacturing sector, converted fuel consumption in kWh equivalents and derived sector-specific shares.<sup>28</sup> Third, we combined these data with monthly national prices for diesel from SENER, for coal and coke from INEGI and monthly-regional prices for gas from SENER. Doing that allowed us to compute for each NAICS-4 digits sector monthly sector-specific composite price indices for fuels, weighted by sectoral consumption shares in 2009: where _αs_<sup>_d_arethesharesoffueltype</sup><sup>_d_intotalconsumptionoffuelsinsector</sup><sup>_s_</sup> and _Pm_<sup>_d_arethenationalpricesoffuel</sup><sup>_d_inmonth</sup><sup>_m_forcoal,coke,anddieseland</sup> monthly regional ( _R_ ) prices for natural gas. The final step consists of weighting these monthly indices with plant-level monthly production from EMIM using the same approach as equation 1. This generates estimates of the yearly price of fuels faced by each plant.

# **3.3 Performance Measures**

We are interested in the impact of energy prices on alternative measures of firm performance. Our main productivity indicator is an index of revenue-total factor productivity, as in Aw et al. (2001).<sup>29</sup> For each plant _f_ and year _t_ , In the above equation, _V Aft_ is the value added of the plant, _Sfjt_ its revenue share for input _j_ and _Xjft_ real value of the same input. As it is standard, we consider capital and labor as factors of production. Upper bars represent averages within a sector and year.

> 28We use the following conversion factors: 1 liter of diesel corresponds to 10 kWh; 1 kg of coal is equivalent to 8.1 kWh; 1 _m_<sup>3</sup> of natural gas is equal to 11.7 kWh, and 1 kg of coke is equal to 8.8 kWh.

> 29This methodology has been widely employed in previous studies, e.g. Topalova and Khandelwal (2011); Pavcnik (2002); Delgado et al. (2002).

19
# **On The Governance Of Public Pension Fund Management**

## Gregorio Impavidot

## The World Bank

JEL Classification Codes: G23, G28, G38.

Keywords: Public Pensions, Fund Management, Governance.

tGregorio Impavido (gimpavido(aworldbank.org is Financial Economist in the Financial Sector Development Department of the World Bank.

I an grateful to Bob Litan, Alberto Roque Musalem, Robert Palacios and Michael Pomerleano for useful comments.
The impact of ENTRE is heterogeneous, with significantly larger effects for students at higher risk of dropout, students in large schools, and boys. In particular, ITT (LATE) estimates show that the program reduced dropout by about 2.9 (6.1) percentage points for the students with the highest risk, 2.1 (4.6) percentage points for students in large schools, and approximately 1.9 (4.4) percentage points for boys.

Our multi-arm design sheds some light on the potential mechanisms driving the results. The results indicate that the bulk of the impact seems to be explained by the component providing information on _how_ to prevent dropout (as opposed to providing information on _who_ is at-risk of dropping out or small behavioral nudges to try and keep dropout prevention top-of-mind for principals). In a setting like Guatemala, where school actors have limited knowledge of and support on dropout prevention strategies, and school principals—who play a leading role in these initiatives— are usually regular teachers without any specific leadership or management preparation, the training and guidelines on simple strategies to prevent dropout appear to be highly impactful. This component was also the most intensive in terms of time and resources (representing approximately 92% of the cost of the program).

Self-reported data from school principals also helps to further unpack the mechanisms driving the results. This data shows that about two months after the training, principals in treated schools perceived higher returns to schooling, ranked dropout as a higher priority, and provided more targeted support to at-risk students. The impact on targeted support and prioritization are not only large in quantitative terms but also persist in the next academic year (with a smaller but still economically and statistically significant impact).

Finally, the results suggest that school actors have gender-biased perceptions of who is at risk of dropout (significantly underestimating the relative likelihood of dropout for girls) and that providing objective information on these probabilities seems to have corrected these biases (and distributed the benefits of the program more evenly across boys and girls). This is an important suggestive result for the dropout prevention literature that calls for further research.

37
Table 9. The impact on Government annual spending and revenues after increase in gasoline prices and <u>providing cash transfers under proposed Government scheme, trillion IRR</u>

|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Subsidies<br>1,170|731|-439|
|Average transfer per capita, daily (IRR)<br>0|13,894|13,894|
|Transfers, Government scheme<br>0|338|338|
|Net total budget<br>1,170|1,069|-101|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|Note: Transferper capita of 13,894 is the mean transfer based on thegovernment scheme.|||

The proposed mitigating measure (if perfectly targeted) would fully compensate the population from the bottom 50 percent for the direct and indirect impacts from increased gasoline prices. For example, consumption per capita of the population from the poorest decile after the price reform _and_ government transfer will be 8 percent higher than before the reform (Table 10). As a result of cash transfers, poverty might even fall below the pre-reform level. Inequality would be also reduced substantially because the proposed cash transfers play a more important role relative to the budget of poor people (Table 11).

Table 10. Ratio of consumption per capita after reform and after government transfers to pre-reform consumption per capita

||`Daily expenditure`<br>`per capita, pre-`<br>`reform (1)`|`Daily`<br>`expenditure per`<br>`capita after`<br>`reform, (2/1)`|`Daily expenditure per`<br>`capita after reform and`<br>`proposed Government`<br>`scheme(3/1)`|
|---|---|---|---|
|poorest|`100%`|`93%`|`108%`|
|2|`100%`|`93%`|`104%`|
|3|`100%`|`93%`|`102%`|
|4|`100%`|`93%`|`101%`|
|5|`100%`|`93%`|`100%`|
|6|`100%`|`93%`|`99%`|
|7|`100%`|`93%`|`98%`|
|8|`100%`|`93%`|`96%`|
|9|`100%`|`94%`|`94%`|
|richest|`100%`|`95%`|`95%`|
|`Total`|`100%`|`94%`|`97%`|

Source: Forecasted numbers from HIES 2018/19 are used.

Table 11. The total (direct and indirect) impact on poverty and inequality after increase in gasoline prices after providing cash transfers under proposed Government scheme

|Pre-Ref|Post-Ref|Post-Ref after transfers,<br>Government scheme|
|---|---|---|
|Poverty (%)<br>11.2|14.2|9.7|
|Inequality (%)<br>39.7|40.3|38.1|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|Note: Inequalityis based on welfare aggregate without spatial adjustment.|||

For illustrative purposes, we have also calculated how much it would cost just to maintain poverty at prereform levels (rather than decreasing it as the proposed GOI measure does). We do this by providing universal cash transfers. Results are shown in Table 12. The amount of daily per capita transfer will be about IRR 8,380– smaller than the GOI proposal. The overall costs of the cash transfers would be about IRR 300 trillion. This will be lower than the costs of the GOI program despite being universal. The overall net savings will be higher at about IRR 139 trillion.

Table 12. The impact on Government annual spending and revenues after increase in gasoline prices and <u>providing universal cash transfers, trillion IRR</u> 11
have significant coefficients, with the jobs total grav taxi coefficient marginally significant at the 6% level. Again, the lesson is that better job access spurs backyarding.<sup>19</sup> If the sample is restricted to just SR2 parcels, the coefficient of parcel area becomes positive. This outcome makes sense given that Census data show SR2 areas as mostly composed of black households with presumably similar incomes, weakening the assumed correlation between parcel size and income. With parcel size no longer a good proxy for income, the offsetting parcel-size effect (the ability of larger parcels to accommodate more backyarders) is able to dominate.

The marginal effects of the variables are shown in Table 8, using the regression from Table 5 with access variable jobs lowinc 60 taxi. The table shows the hypothetical change in the explanatory variable along with the percentage change in the expected number of backyarders. A 50m<sup>2</sup> reduction in the parcel size (from a mean of 302) leads to a 10% increase in the expected number of backyarders, while a 38,000 increase in the job-access variable (equal to one standard deviation from Table 3) leads to an 11% increase in the expected number of backyarders. Changing a parcel from non-SR1,SR2 status to SR1 or SR2 status raises the expected number of backyarders by 61% or 220% respectively. The marginal effects from the other regressions are similar in magnitude.

To get a sense of the meaning of these percentage changes, recall that the mean of the count variable is 0.323. The 11% increase in the expected number of backyarders due to improved job access translates almost exactly into 11% of this mean, or 0.035. The implication is that the job-access improvement leads approximately to an extra 1/30th of a backyarder per parcel, or a new backyarder for every 30th parcel. The impact of the 50m<sup>2</sup> decrease in parcel area (which yields a 10%, as opposed to 11%, increase in the expected number of backyarders) is very similar.

# 7. Conclusion

This paper explores the incentives for backyarding, an expanding category of South African land-use. In doing so, the paper provides the first treatment in the economics literature of a new category of land-use in developing countries, which represents a unique mixture of informal 20
MULTILINGUAL CHILD ASSESSMENT did not have access to the original measure. The first and second authors (HK and PK) then met with a group of translators and discussed each translation to ensure that words conveying the desired meaning were chosen over direct translation (in Swahili and Luo, several words were often possible depending on the intent of the item). The assessments were then pretested, and any additional study team concerns or discrepancies were addressed. Items for the vocabulary assessments were ordered by difficulty, as measured in a small pilot sample (between 30 and 61 respondents).

The assessors hired to administer the tests in the current study had university degrees, were from the study area, spoke Luo as their mother tongue, and were trained on the full battery of tests by the first and second study authors. On a subset of 48 children, two assessors double coded the baseline assessment to assess interrater reliability (IRR) for each of the assessments

# ( **Supplemental Table 1** ).

# **Receptive vocabulary**

We created receptive vocabulary assessments based on the British Picture Vocabulary Scale III (BPVS III) (Dunn, Dunn, & Styles, 2009), which includes 168 items for individuals 3– 16 years old (see details of translation and adaptation in **Appendix A** ). Knowledge of words is measured by asking the respondent to point to one of four pictures that corresponds to a word (object, person, or action) spoken by the assessor. The BPVS has been adapted for use in South Africa (Cockcroft, 2016) and Indonesia (Prado, Alcock, Muadz, Ullman, & Shankar, 2012) and is the British adaptation of the Peabody Picture Vocabulary Test (Dunn & Dunn, 1997), which has also been used in neighboring areas of Kenya (Ozier, 2018). As we wanted to capture young children’s knowledge of Luo, Swahili, and English words, we created three sets of 8
tionship between the local and the world prices of sugar. The stationarity of the price times series used in the model is tested and the appropriate ADF statistics are reported in Table 4. For neither London Daily Price nor the Brazilian prices the ADF test rejects the null hypothesis that the prices follow a unit root process.<sup>16</sup> However, redoing the test in terms of first differences leads to rejection of the unit root hypothesis at 1 percent level for all price time series. Thus, price differentials can be used in the error-correction model.

We then test for a long-term cointegration between the local and the international prices as described by equation 1. The test statistics, also reported in Table 4, imply that for all states except Pernambuco and Para, we should reject the hypothesis of no cointegration between the local and the world prices at the 1 percent significance level. In Pernambuco we reject the hypothesis at 10 percent. In Para, the ADF statistic for the residual is very close to the 10 percent critical value, so we estimate an error-correction model for Para along with the other states, although we can not conclude that the prices in that region are closely integrated with world prices.

The results of error-correction estimation for the 11 states are reported in Table 5. The long-term cointegration coefficient ( _γ_ ) was obtained from equation (1) and the coefficients of short-term transmission ( _δ_ ) and adjustment ( _θ_ ) were estimated using equation (2). In S ̃ao Paulo, where by far most sugar is produced and traded, a 1 percent increase in the world price of sugar leads to 1 percent increase in the local price in the long run (the elasticity is 1.01).<sup>17</sup> Perfect transmission was also found in Goi ́as and Minas Gerais,

> 16ADF tests with and without trends give similar results.

> 17See Mundlak and Larson (1992) for similar estimates of agricultural price transmission

18
affected by the financial crisis are about 20 percent more likely to say that the unemployed are the group that deserves most government support (relative to the disabled). Recent experience with the crisis is also significantly associated to supporting redistribution to the working poor, albeit to a lesser extent. Those who place themselves higher on their countries’ income scale are significantly more likely to elect the disabled as the category most deserving of government support. Finally, ideology (preference for a planned economy rather than the market economy) is only significantly associated with support for the elderly as the most deserving group for government support.

# **5. Conclusion**

There is widespread support in Europe and Central Asia for reducing the existing gap between the rich and the poor. This preference is strongest in the Western Balkans, but it is stronger still in the Western European countries included in the LiTS II sample.

Aversion for inequality and preferences for redistribution are not static: fewer people want to reduce the gap between the rich and the poor in 2010 than in 2006. Although there is some correlation between the perception of recent income shocks and attitudes to inequality and redistribution, the increased tolerance for inequality cannot be fully accounted for by the economic contraction during the crisis. Also, while perceptions of the fairness of the incomegeneration process also improved somewhat during the period, these improvements appear to be unrelated to the cross-country dynamics of tolerance for inequality in ECA.

Overall, political preferences, beliefs about institutional fairness, as well as (perceived) own personal past and future mobility, are significantly associated with redistributive preferences in Western European countries and in the new EU member states, but less so in non-EU transition economies.

Generally, there is a high degree of agreement that the disabled and the elderly deserve support from government. At the same time, there is a high level of heterogeneity across countries in average beliefs on which group(s) deserve most support from the government. Average support for reducing inequality or for redistribution benefiting certain groups is correlated (but not always) with actual redistributive policies at the country level.

At the individual level, different motives are likely to explain different redistributive preferences. Self-interest (i.e. expectation that one may directly benefit from the policy) appears to be an important motivation for support for the elderly and families with children. By contrast, values and beliefs (such as expectations about one’s future mobility, beliefs about the fairness of the income-generating process) are important drivers of support for the working poor and the unemployed.

While differences between responses to various measures of redistributive preferences indicate the importance of framing, our results are quite consistent. The effect of beliefs about fairness, 24
Only a few conditional cash transfer programs are explicitly targeted to girls. Of particular relevance to the current analysis are the programs in Cambodia, Bangladesh and Pakistan. In each of these countries, policymakers have explicitly targeted the transfer on the basis of gender – providing transfers to girls only. Using repeated cross-sectional data, Khandker et al. (2003) find that the Bangladesh Female Secondary School Assistance Program led to a substantial increase in the enrollment of girls and found no spillover effect for boys. Evidence on spillover effects from other programs is limited. Hasan (2009) examines the effects of a gender-targeted CCT program on household time allocation. Chaudhury and Parajuli (2006; 2008) use school censuses in conjunction with a district-level eligibility cut-off and find that Pakistan’s female student stipend program in Punjab between 2003 and 2005 increased enrollment by six female students per school (a nine percent increase in female enrollment in terms of relative change). Filmer and Schady (2006), exploiting the cut-off score used to determine eligibility for Cambodia’s Japan Fund for Poverty Reduction (JFPR) scholarship, find a 30 to 43 percentage point increase in the enrollment and attendance of girls.<sup>1</sup> Papers that explicitly examine how program design features influence outcomes are more recent: for example, Filmer and Schady (2009a) find evidence of diminishing returns to the size of the transfer.<sup>2</sup> Consequently, this paper has a modest goal: to build on this earlier work and to provide a more complete picture of the effects of a gender-targeted conditional cash transfer program on outcomes of interest to policymakers: girls’ enrollment and boys’ enrollment. I consider whether school infrastructure or location mediates the effects of the program. Lastly I examine whether

> 1 For additional analyses using the Cambodia data see Filmer and Schady (2009b) and Ferreira, Filmer and Schady (2009).

> 2 See also de Janvry and Sadoulet, (2006.)

5
Table 6: Disaggregated education and health aid, reduced sample

||Public edu<br>|cation exp.<br>|Public h<br>|ealth exp.<br>|
|---|---|---|---|---|
||FE|FD|FE|FD|
|Education IP|0.22|0.34<sup>_∗∗∗_</sup>|||
||(0.15)|(0.12)|||
|Education SP|0.83<sup>_∗∗_</sup>|-0.34|||
||(0.34)|(0.55)|||
|Education TC|0.024|-0.070|||
||(0.059)|(0.046)|||
|Education ONM|-0.25|-0.044|||
||(0.24)|(0.11)|||
|Health IP|||0.17|-0.19<sup>_∗_</sup>|
||||(0.19)|(0.11)|
|Health SP|||0.83<sup>_∗∗_</sup>|-0.19|
||||(0.36)|(0.24)|
|Health TC|||-0.15|-0.040|
||||(0.20)|(0.10)|
|Health ONM|||0.31<sup>_∗_</sup>|0.095|
||||(0.17)|(0.12)|
|General aid|0.027|0.00092|0.0082|-0.0074|
||(0.020)|(0.015)|(0.018)|(0.011)|
|Support to NGOs|-0.48|-0.17|-0.055|-0.025|
||(0.31)|(0.23)|(0.14)|(0.073)|
|Other non-education aid|0.00046|0.0049|||
||(0.016)|(0.010)|||
|Other non-health aid|||-0.019<sup>_∗_</sup>|0.0039|
||||(0.011)|(0.0047)|
|GDP per capita|0.22<sup>_∗_</sup>|-0.058|0.093|0.13<sup>_∗∗_</sup>|
||(0.12)|(0.088)|(0.071)|(0.063)|
|GDP per capita growth|-0.019<sup>_∗∗∗_</sup>|-0.0079<sup>_∗∗_</sup>|-0.015<sup>_∗∗∗_</sup>|-0.011<sup>_∗∗∗_</sup>|
||(0.0045)|(0.0031)|(0.0043)|(0.0031)|
|Urbanisation|0.039|0.0033|0.019|0.017|
||(0.045)|(0.064)|(0.025)|(0.026)|
|Trade|-0.0035|-0.0025|-0.0013|0.00046|
||(0.0041)|(0.0037)|(0.0023)|(0.0021)|
|PV debt|-0.0055<sup>_∗∗_</sup>|-0.0038|-0.00026|-0.00017|
||(0.0025)|(0.0038)|(0.00056)|(0.00072)|
|Public debt service|-0.059<sup>_∗∗∗_</sup>|-0.024<sup>_∗_</sup>|-0.019|-0.0031|
||(0.019)|(0.012)|(0.012)|(0.0057)|
|_R_<sup>2</sup>|0.183|0.062|0.135|0.051|
|Hausman|0.000||0.000||
|_βSP_ ⩽0|0.008|0.731|0.012|0.781|
|_βSP_ ⩾1|0.307|0.008|0.313|0.000|
|_βT C_ ⩽_−_1|0.000|0.000|0.000|0.000|
|_βT C_ ⩾0|0.658|0.066|0.239|0.347|
|Countries|94|94|103|102|
|Observations|921|819|1024|912|

Note: fixed effects (FE) and first-differenced OLS (FD) results, annual data, 1990-2003, reduced sample. All regressions include time dummies, coefficients not reported. Heteroskedasticity-robust standard errors, clustered by country, in brackets. _∗_ , _∗∗_ , and _∗∗∗_ denote significance at 10, 5 and 1%, respectively. In the case of FE estimation, _R_<sup>2</sup> refers to the within _R_<sup>2</sup> . Hausman shows the p-value of a generalized Hausman test of the null hypothesis that _ηi_ is uncorrelated with the regressors. _βSP_ ⩽ 0 ( _βSP_ ⩾ 1) and _βT C_ ⩽ _−_ 1 ( _βT C_ ⩾ 0) are p-values for the test of full (no) fungibility for sector programme aid and technical cooperation, respectively. Source: Author’s analysis based on data described in the text.

20
Table 3: Impact of expected dowry on the flow of household per capita saving

|**Dependent variable:**||**Household**|**per capita**|**saving in **|**2007**||
|---|---|---|---|---|---|---|
||Saving in f|nancial institutions|Plus cas|h saving|Plus inte|rest earned|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Firstborn girl_ * _Expected dowry_|419.24|556.25|395.89|485.14|507.69|617.54|
||[190.02]**<br>(239.26)*|[271.31]*<br>(262.68)***|[190.15]*<br>(245.13)*<br>|[251.08]*<br>(242.62)***|[231.68]**<br>(290.19)*|[304.20]*<br>(305.86)***|
|_Expected dowry_|60.86||-98.08||-113.09||
||[306.28]||[316.61]||[339.35]||
|_Firstborn girl_|-406.13|-639.85|-1044.67|-1132.86|-1942.95|-2168.65|
||[1728.71]|[3342.15]|[1584.76]|[3124.21]|[1912.26]|[3488.94]|
|N|||2,840||||
|Dep var mean for Firstborn boy||745.95|670|.20|87|4.69|
|**X**_i_|x|x|x|x|x|x|
|Caste FE|x|x|x|x|x|x|
|YOB FE|x|x|x|x|x|x|
|State FE|x|x|x|x|x|x|
|State*YOB FE|x|x|x|x|x|x|
|Caste*YOB FE|x|x|x|x|x|x|
|Caste*State FE|x|x|x|x|x|x|
|Firstborn girl*YOB FE|x|x|x|x|x|x|
|Firstborn girl*State FE|x|x|x|x|x|x|
|Firstborn girl*Caste FE|x|x|x|x|x|x|
|Caste*State*YOB FE||x||x||x|

NOTES: This table reports the coefficients from specifications (1) and (2) estimated for households where all children have the same mother. Each column is a separate regression. The dependent variable is the flow of per capita household saving in financial institutions in columns (1) and (2), plus per capita cash saving in columns (3) and (4), and plus per capita interest earning in the last two columns. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Caste refers to indicators for SC, ST, OBC, and upper castes. YOB refers to the year of birth of the firstborn child. State refers to the state of residence at the time of survey. **X** _i_ controls for parents’ age, schooling, religion, and month of survey. Standard errors in brackets are clustered by state and wild-cluster bootstrapped errors by state are in parentheses. *** 1%, ** 5%, * 10%.