rafmacalaba/datause-encoder-data huggingface dataset:
for the squared poverty gap and inequality calculated on the basis of the GE0 the truth always falls within the confidence intervals calculated around the estimates. On average, across all pseudo surveys the success rate is just under 95% for the mean consumption, headcount, and squared poverty gap measures, and just below 90% for the GE0 measure.

In Table 5 we consider how sensitive are our estimated standard errors to the presence of unobserved location effects. We saw in Table 1 that our preferred specifications for the different pseudosurveys were quite successful in proxying ˆ 2 σ η unobserved location effects ( ˆ 2 ranges between 0.9% and 3%). How much larger σ _u_ would standard errors be if our underlying models had not been so successful in this respect? Table 5 compares estimates and standard errors on small area estimates of the headcount rate from pseudosurvey 2 based on two models: one with our preferred specification; and the other with a specification in which no census-mean variables were included.<sup>19</sup> In the latter model the share of the variance of overall disturbance term that is attributable to the variance of the cluster component is now 11.9%, a four-fold increase over the 2.7% in the preferred model (Table 5). At the all-census level, the two models predict headcount rates of 61.9% and 61.5%, respectively, both virtually indistinguishable from the 61.1% actual headcount rate in the population. However, the standard error on the model with no location variables is now 0.024, up by more than two fifths from the standard error of 0.017 obtained with the preferred model. Part of the increase in the standard error is due to the fact that the explanatory power of the model with no location variables is lower than that of the preferred model. As a result, idiosyncratic error would be expected to be higher – see Section 2 and ELL(2002). However, at the level of the total population most of the idiosyncratic error will have cancelled out (poverty is being estimated over a population of more than 20,000 households). Thus the increase in the standard error from 0.017 to 0.024 is likely due mainly to the consequence of our failure to adequately capture unobserved location effects. At the target population level, standard errors are higher than at the level of the total population, irrespective of underlying models. Moving from the preferred specification to the model with no location variables, standard errors rise considerably,

> 19  Our calculations here are based on the numerical gradient “classical” simulation procedure.

14
position is the so-called Tobin tax (i.e., preventing socially excessive borrowing in good times), the use of capital controls in good times will inevitably make it more likely to see capital controls in bad times as well. Binding capital controls in bad times will naturally prevent the fiscal authority from keeping tax rates or government spending constant over the business cycle and lead to procyclical fiscal policy.

A more subtle, but equally disruptive, friction in a model with uncertainty is the presence of incomplete markets, which allows for only risk-free (i.e., non-statecontingent) borrowing. In other words, the absence of complete markets (in the Arrow-Debreu sense) that would allow the fiscal authority to borrow conditional on every state of the world —and thus replicate the first-best fiscal policy in a neo-classical model —renders fiscal policy procyclical. In fact, Fernandez et al. (2021) show, in the context of a DSGE model how, under certain conditions, incomplete markets is all that is needed for the optimal (in a second-best sense) fiscal policy to become procyclical. Further, procyclicality continues to be the best policy if sovereign risk is added to the picture (see Cuadra et al., 2010, Bianchi et al., 2019, and Dzhambova, 2021).<sup>21</sup> To capture the effects of capital controls and other financial frictions which, effectively, curtail borrowing in bad times, we will resort to the well-known Chinn-Ito (2006) financial openness index. This index, constructed based on information from the IMF, is normalized between 0 and 1, with higher values indicating more financially-opened economies. As of mid-2022, the Chinn-Ito index covers the period 1970-2019 for 182 countries.

# 3.2 Political economy explanations

The second major group of potential explanations for the procyclicality of EMDE relies on political economy considerations. As discussed in the Introduction, all these political economy factors would make it more diffi cult to manage the abundance triggered by positive shocks to commodity prices. In one way or another, all these leading theories predict that political distortions would make it more diffi cult to prevent wasteful spending and/or spend only permanent increases in resources.

> 21Yet another financial friction in the form of limited commitment is analyzed in Bauducco and Caprioli (2014). This friction generates procyclical tax policy for an exogenously-given path of government spending.

12
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<!-- Start of picture text --> ntryt OporJtdons<br><!-- End of picture text --> WPS i183 is part of a larger This paper-a product of the Latin America and the Caribbean, Country Department I - is part of a larger e.fort in the department to understand the social dimensions of inflation. Copies of the pa:x r are available free from the World Bank, 1818 H Steet NW, Washington, DC 20433. Please contact Tania Hollestelle, room 17-041, eIxtension 30968 (September 1993, 29 pages).

Most studies of how inflation affects income distribution focus only on wages or the inflation tax. Kane and Morisett argue that this approach could be misleading as it ignores important channels through which infladon affects income distribution.

The authors present an integrated framework that combines interest-bearing assets with labor income and cash holdings This allows them to describe clearly the conditions under which inflation will create gainers and losers.

They apply the model to Brazil, which is a prime candidate for this exercise because its 3conomy combines skewed income distribution and high inflation. They show that in Brazil infladion helped worsen i.,come distribution in the 1980s. Their major findings are as follows: In 1980-89, the inflation-induced income loss for the lowest quintile in Brazil was an estimated 19 percent a year, of which 16 percent is attributable to the erosion of eal wages and the rest to the inflation tax.

* During the same period, Braz:l's middle class, which lost close to 30 percent of its annual income, was devastated because of its limited access to indexed assets.

* But the richest quintile manag-d to insulate itself from inflation by taking advartage of high real interest on demand deposits - without losing from reduced labor income. Had real assets and subsidized credit been considered in the analysis, the regressive effects of inflation would probably lave been even worse, say Kane and Morisett.

This raises a question: Do these findings about the distributional effects of inflation help explain Brazil's delavs in adopting a stabilization program?

The Policy Research Working PaperSeriesdisseminates the flndings of work under way in the Bankl Anobjectiveof the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations. and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
|bd<br>If there were a disagreement<br>between you and [spouse] on<br>whether or not to sell or lease<br>this parcel, whose opinion do<br>you think would most likely<br>prevail?|Wife|Husband|Don’t<br>know/not<br>sure|
|---|---|---|---|
|Husan<br>Wife|25<br>(30.86%)|7<br>(8.64%)|2<br>(2.47%)|
|Husband|22<br>(27.16%)|21<br>(25.63%)|0<br>(0%)|
|Don’t know/not sure|2<br>(2.47%)|2<br>(2.47%)|0<br>(0%)|

Notes: N = 81 couples; question was only asked to respondents who defined the husband, wife, or both as decision-makers.

37
<!-- Start of picture text --> 0<br>Any crop Live: k Ni NFE: help/ Wage Ganyu<br>work own/| ge support employment<br>0.02<br>0.04<br>0.06<br>0.08<br>i Effect of IHS4 proxy vs. IHPS<br>-0.1<br>G Effect of IHS4 non-proxy vs. IHPS<br>0.12<br><!-- End of picture text -->

<!-- Start of picture text --> 0.06<br>0.04<br>0.020 \<br>Any crop Livestock NF NFE; help/ Ww Ganyu<br>-0.02 work own/m e support employment<br>-0.04<br>-0.06<br>-0.08 Effect of IHS4 proxy vs. IHPS<br>“04 © Effect of IHS4 non-proxyvs. IHPS<br>-0.12<br><!-- End of picture text --> <!-- Start of picture text --> 0.06<br>0.04<br>0.02<br>0<br>Any crop L ‘ock Ni NEE: / Wage yu<br>0.02 work own/! P| employment<br>0.04<br>0.06<br>0.08 @ Effect of IHS4 proxy vs. IHPS<br>0.1<br>Effect of IHS4non-proxy vs. IHPS<br>0.12<br><!-- End of picture text -->

<!-- Start of picture text --> 0.06<br>0.04 A<br>0.020 Z<br>Any crop work Livestoe NF NFE: Wi Ganyu<br>-0.02 own/m#hage —— sup} emplo'<br>-0.04<br>-0.06<br>-0.08 Effect of IHS4 proxy vs. IHPS<br>70.1 21 Effect of IHS4 non-proxyvs. IHPS<br>-0.12<br><!-- End of picture text -->
Figure 6. Out-of-sample performance of early warning system in Honduras <!-- Start of picture text --> 90.0<br>78.2<br>80.0 76.3<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>19.8<br>20.0 15.9<br>10.0<br>0.0<br>Sensitivity False alarm rate<br>In sample (2013-2014) Out of sample (2014-2015)<br>Percent<br><!-- End of picture text --> _Source_ : Author calculations based on USINIEH administrative data from 2013-2015 Table 5. Relative performance of the early warning system as a method to allocate limited <u>resources ($200 US per student)</u>

||**Nb f**||||**Me**<br>|**thod of al**<br>|**location**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**umer o**<br>**students**|<br>**Munic**|<br>**ipality po**|<br>**verty rates**|<br>**Sc**|<br>**ool dropo**|<br>**ut rates**||<br>**EWS**||
|||_True_<br>_positives_|_False_<br>_positives_|_Dollars_<br>_wasted_|_True_<br>_positives_|_False_<br>_positives_|_Dollars_<br>_wasted_|_True_<br>_positives_|_False_<br>_positives_|_Dollars_<br>_wasted_|
|_Guatemala_|5,000|45%|55%|$ 552,022|93%|7%|$ 72,772|99%|1%|$ 8,319|
||10,000|43%|57%|$ 1,143,022|86%|14%|$ 277,037|89%|11%|$ 210,860|
||20,000|35%|65%|$2,609,953|73%|27%|$1,086,566|78%|22%|$898,115|
|_Honduras_|5,000|58%|42%|$ 418,000|87%|13%|$ 129,000|99%|1%|$ 6,200|
||10,000|60%|40%|$ 798,200|82%|18%|$ 364,000|99%|1%|$ 12,400|
||20,000|56%|44%|$1,748,000|74%|26%|$1,046,800|95%|5%|$188,200|

_Source_ : Author calculations based on administrative data from Guatemala and Honduras.

_Notes_ : The allocation of resources by municipality poverty rates selects the students who live in the municipalities with the highest poverty rates. The allocation of resources by school dropout rates selects the students who are in the schools with highest dropout rates in the sixth to seventh grade transition. The allocation of resources using the early warning system selects the students who have the highest predicted probability of dropout according to the model in Table A6. True positives are the percentage of hypothetical beneficiaries who dropout in the transition from primary to lower secondary, and false positives are the percentage who do not dropout in this transition. Dollars wasted are computed as $200 x Number of students x False positives.

27
The second factor that could contribute to the heterogeneous effects of electrification is the duration households have been connected to the electric grid. While some basic benefits such as more efficient lighting can be achieved almost immediately upon electrification, other development impacts accrue only over the long run. For example, improved lighting may allow businesses to be open more hours in the evening. This would lead to more economic activities and contribute to income growth in the long term. With improved income, households can afford more time- and labor-saving electric devices, and to engage in more income generating activities. Once a virtuous cycle kicks in, the accumulated benefits of electrification could be positively correlated with the number of years a household has been connected to the grid during an extended period.

Using data from a panel survey of 7,018 rural households for 2005 and 2010 in Bangladesh, we examine the spatial and temporal heterogeneity of the effects of rural electrification. We use household’s distance to the nearest electric pole as an instrument variable and a two-stage propensity-score-weighted fixed effects model to address potential endogeneity of treatment which could derive from nonrandom expansion of the electricity network and nonrandom adoption of electricity by households.

We find that the length of daily power outages has a strong negative impact on almost all development outcomes considered. For example, a one hour increase in power outages per day is associated with a 5.9 percent increase in kerosene consumption and 0.3 percent reduction in annual income. When power outages last longer than 21 hours a day, there is effectively no difference between the electrified and the non-electrified households. On the other hand, the benefits of electrification increase with years of exposure to grid electricity, albeit with a declining rate. For example, each additional year of being connected to the grid is associated with a 7.8 percent reduction in kerosene consumption and a 1 percent increase in annual income. Labor market benefits of electrification seems to take the longest time to materialize.

Overall, we find that electrification in Bangladesh has brought substantial benefits to rural households as evidenced by increased income, time spent in study, and labor force participation, as well as higher women’s empowerment. Even without accounting for the benefits associated with better education and empowerment outcomes, the potential gain from providing universal access to electricity and improving reliability of electricity supply in Bangladesh could reach US$ 2.3 billion a year.

We also find that electrification disproportionally benefits women and girls. After gaining access to electricity, girls’ study time on average increases 0.47 hours a day while women’s labor force participation 3
- 27 -

FIGME 9.C <!-- Start of picture text --> Wealth distribution * Effort Chonge In Wealth dlstribution<br>at time v 4* * Capitol use +44 land tenancy 444 at. tim v+1<br>t * Distribution ot outcome<br>Change, In<br>technology<br><!-- End of picture text -->

**Change In** technology load to changes in **the** long-run distribution of wealth and tenancy arrangements.

steady-state wealth distribution, and this in turn will have real productivity effects. Virtually all changes in technology which change the transition matrix M will lead to a changed long-run distribution of wealth, landholdings, and tenancy arrangements.

Three tasks now lie before us. First, we need to be able to show how particular changes in technology lead to particular changes in the transition matrix H(t). We need to ascertain the precise conditions under which changes in technology are likely to change significantly the long-run equilibrium distributions, both positively and negatively. Our discussion has suggested that adverse distribution effects are more likely to arise from changes in technology which increase the variance of output (or more precisely, the likelihood of a serious crop failure, sufficiently serious that the farmer has to sell his land); and which increase the return to capital.

Secondly, if there is an adverse distribution effect, we need to know the magnitude of the effect on productivity.

Thirdly, the fact that the distrib'ition of landholdings (tenancy arrangements) may have a significant impact on productivity suggests that
Table A3: Correlation between platform observations data and rider reports

|Platform observations|Rider reports<br>Share of men in reserved space<br>(1)|High crowding<br>(2)|
|---|---|---|
|Average share of reserved space riders who are male|0.636***<br>(0.055)||
|High crowding||0.246***<br>(0.029)|
|Constant|0.158***|0.490***|
||(0.017)|(0.018)|
|Observations|4846|20742|
|Riders|327|363|

_Note:_ Unit of observation is one ride. Sample includes all rides and riders with matching platform observations across all study phases. Rider report variables are collected for each ride and reported only with respect to the space chosen by the rider. As a result, column (1) is restricted to reserved space rides. Platform observations are taken on different days, then aggregated by time and line segment to be merged with rides. Standard errors in parentheses, clustered at rider level.

Table A4: Response to platform survey and IAT

||Dependent variabl|e:||
|---|---|---|---|
||Responds platform survey<br>R|esponds IA|T|
||(1)<br>(2)|(3)|(4)|
|Female respondent|0.045**<br>-0.055|||
||(0.020)<br>(0.034)|||
|Usually chooses reserved space||-0.036<br>(0.050)||
|Female family members usually choose reserved space|||-0.013<br>(0.067)|
|Constant|0.955***<br>1.055***|1.000***|0.405***|
||(0.020)<br>(0.034)|(0.000)|(0.103)|
|Observations|1078<br>820|393|238|
|Sample|All<br>All|Females|Males|
|Platform FE|Yes<br>Yes|Yes|Yes|
|F-test for platform dummies (p-value)|0.000<br>0.000|0.000|0.436|
|Sample mean|0.879<br>0.406|0.380|0.435|

_Note:_ Unit of observation is one participant. Sample in column 1 includes all individuals invited to the platform survey. Sample in column 2 includes all individuals who were invited to participate in IAT. Samples in columns 3 and 4 include individuals who were invited to participate in IAT and finished the platform survey. Robust standard errors in parentheses.
PAYGO system - to estimate the impact of reforms intended to lower labor market distortions and improve incentives. Drawing from the framework, I estimate a reduced form model for changes in the size of the covered sector, proxied here by the portion of the labor force contributing to the national pension system. The two-way error component model is written where C is the number of contributors to the national social security system as a share of the labor force, in country i in year t; X is a vector of variables controlling for countries' levels of economic development, and includes per capita income and life expectancy at birth; AL is a vector of variables capturing cyclical and non-cyclical features of the labor market, including the change in the rate of unemployment,<sup>3</sup> and the number of women in labor force; 0 is the total rate of tax on pay-roll for all social insurance<sup>4</sup> ; and IRA is the share of 0) accumulating in an individual retirement account in countries that have reformed. The hypotheses corresponding to the questions above are thus

|**Ceteris paribus...**|**Null**|**Alternative**|
|---|---|---|
|**1.**Does the size of the pay-roll tax for social security<br>lower the number of contributors in the work force?|_H_<br>**_/33_**_=_0<br>°|_Hi_**_.83_**<br>_<_0|
|2.<br>Does the share of pay-roll taxes accumulating in an<br>individual retirement account increase the number<br>of contributors?|_H_<br>_A4= _0<br>°|_Hi_. _84 >_0|

However, the model presented by Edwards and Edwards (2000) is a static model that makes no specific predictions about the transition after reform. Does the labor market jump from pre to post-reform equilibrium, or are there post-reform transition dynamics in the share of workers who contribute? Does the transition follow a particular pattern? To estimate dynamic effects, I add the variable YR to the reduced form, so that where r is a vector of estimated coefficients, and YR is the number of years that have passed after the introduction of individual retirement accounts. We thus add to the previous set of hypotheses with

|**Ceteris paribus...**|**Null**|**Alternative**|
|---|---|---|
|3. Are there post-reform transition dynamics in the<br>share of contributors?|_Ho_<br>=0<br>**0**|_H:_<br>_T_• 0|

Readers should note that I use the difference in the rate of unemployment, and not the level of unemployment in the reduced form model. While the Edwards and Edwards (2000) framework is ambiguous with respect to the impact of reforms on the level of unemployment, this may be determined simultaneously with the shift of workers into the covered sector.

**4** This normally includes retirement security and public health insurance. In most countries this tax will also cover the contributor against the risk to income from disability and death (this last, the risk to dependent household members from the loss of an income provider).

8