rafmacalaba/datause-encoder-data huggingface dataset:
Child developmental abilities were assessed using the ASQ-I, an instrument focused on developmental milestones for children aged 1-54 months across five domains: communication, gross motor, fine motor, problem solving, and personal-social. At the time of this study, the authors were given access to the unpublished ASQ-I instrument courtesy of the Ages and Stages Questionnaire (ASQ) group at the University of Oregon. The validity and reliability of the ASQ-I has since been published (Clifford, et al, 2018) and has also been adapted and validated in China (Xie, et al, 2017). To adapt the ASQ-I to Madagascar, the authors worked in an iterative process for any changes to item ordering, text adaptations, administration protocols for Madagascar, and coding of items. One of the study authors, a local Malagasy psychologist, translated and adapted it to the local context, and incorporated interviewer-observed items. The ASQ-I was then backtranslated and reviewed by the ASQ group in Oregon. Three iterations of piloting, testing, and updating occurred before the instruments were finalized. For each ASQ-I domain, a summary score was created, and a total ASQ-I score was calculated by summing across the domain-specific scores. The total, continuous score was age-standardized and controlled for interviewer. In order to compare this scale to the caregiver-perceived intelligence measure, we created seven percentilebased, rank-ordered (from 1-7) categories of total age-adjusted ASQ-I score. Given that caregivers used their own definition of intelligence on the ladder scale, their ranking is a function of individual and community characteristics. Therefore, we constructed two sets of ASQ-I ranked percentiles: within communities and across the whole analytic sample.

# <u>Under- and Over-estimation of Child Abilities</u>

A difference score was calculated between the ranks of caregiver-perceived child intelligence and the ASQ-I. This score ranged from -5 to 6 for both the within-community and 7
Government Revenue from Financial Repression by Alberto Giovannini and Martha de Melo*

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Interest-Rate<br>Distortions and Public Finances|2|
||2.1 Financial Repression as Optimal Tax Policy<br>2.2 The Financial-Repression<br>Tax and the Inflation Tax|3<br>8|
|3.|Data and Calculations|10|
|4.|Results|14|
|5.|Summary and Concluding Remarks|19|
|Appe|ndix A:<br>Data Sources|21|
|Appe|ndix B: Country Sources|24|
|Refe|rences|28|
|Tabl|es|31|
|Figu|res|37|

* We are grateful to Christine Corra for research assistance, and to Reza Firuzabadi and the staff of the World Bank Debt Reporting Service for valuable support. Alberto Giovannini's research was supported by the World Bank and a CEPR grant from the Ford Foundation and the Alfred P. Sloan Foundation. Part of this paper was written while he was visiting the Institute for International Economic Studies, University of Stockholm.
who left to work abroad during this period were from Muslim households. In contrast, Muslim households were among the least likely to see a member migrate to another Indian state.<sup>21</sup> In terms of economic status, households with members who migrated internationally between 1998 and 2003 came from relatively wealthier households (based on pre-migration land, averaging 71.5 cents,<sup>22</sup> and overall asset holdings). Domestic migrants came from households with holdings similar to non-migrant households (mean of 58.5 cents). I measured asset holdings using a composite index derived from a first principal component weighting of 6 indicators of durable assets ownership and 11 indicators of housing quality.<sup>23</sup> Further elaboration of my method for constructing the asset indices appears in Section 4.B.2.

# _B. Empirical Strategy_

# <u>B.1. Specification</u>

I examined asset gains by estimating a regression across migrant and non-migrant households, using a difference-in-difference specification with the panel of Kerala households surveyed in 1998 and 2003. The goal of the exercise is to determine the effect that the labor migration of household members between 1993 and 2003 had on the accumulation of assets, all else being equal. Measuring changes in household-level outcomes naturally yields a first-difference regression specification: where is the change in household asset holdings between survey rounds in 1998 and 2003 for household , is the number of household members who migrated overseas for employment between survey rounds, and reflects the number of household members who

> 21 Scheduled caste families were the least likely to have a domestic migrant during this period (7 percent). The largest group of households whose members moved within India for employment during this period was Hindus (38 percent). 22 Landholding is the sum of agricultural and non-agricultural land measured in cents, where 100 cents is equivalent to an acre.

> 23 I confined the indicators used in the construction of the overall asset index to a common set found in both survey rounds, in order to examine changes over time. The 1998 KMS data provided a larger set of assets indicators (21 indicators of durable assets and 15 indicators of housing quality). The pair-wise correlation between the composite index created using both sets of indicators was high ( _r=0.93_ ).

12
years completed, number of high school repetitions, and percent entering college were 10.3, 0.127, and 42.4 percent, respectively. We observe the same pattern for the TEEP and younger cohorts. Although females outperformed males in schooling and migration, the monthly salary of females was only 84 percent of males’ salaries for the non-TEEP cohort and 96 percent of males’ salaries for the TEEP cohort.

Table 5.3 compares the means of key variables between the two modes of interview: face-to-face and phone. Except for years of schooling completed and number of repetitions in high school, all the variables show statistically significant differences. The migration rate was particularly higher among phone interviewees due to the above-mentioned method of our survey. Note that most of the face-to-face interviews were conducted within division. In our analyses, we include indicators of interview mode to control for potential differences in unobserved characteristics correlated with the mode.

# **_Cohort-Specific Variables_**

As discussed earlier, we control for factors (progress in social development and high school quality) that could have induced differentiated trends between TEEP and non-TEEP divisions. Figure 5.2 plots the percentage of households below the poverty threshold in each of the sample provinces from 1997 to 2009. The trends in poverty incidence during the 12 years are similar between the paired provinces for two pairs (Ifugao and Neuva Vizcaya, and Antique and Iloilo), slightly different for one pair (Leyte and West Samar), and very different for the last pair (Negros Oriental and Cebu).

In Table 5.4, we compare measures of access to public services and infrastructure between TEEP and non-TEEP areas. The household survey asked each mother or guardian whether the household had access to 14 types of public services or infrastructure in its barangay in 2000 and 2010. The table shows that except for public elementary school, health center, and paved road, we do not confirm significant differences in 2000 and/or 2010. Interestingly, trends in the period of 2000 to 2010 are marginally significantly different with regard to paved road, access to market, and post office.

We use the hypothetical number of students per classroom to proxy for high school quality. The previous year’s number of students enrolled in the first year was multiplied by 4 to obtain the 19
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**Table** 3: Means and standard deviationc of school-level variables used in HLM analysis, for private, government and local schools in the Philippines, 1983

||Pri<br>_(N_-<br>|vate<br>70)<br>|Gover<br>(N<br> <br>|nment<br>83)<br>|Loc<br>(N-<br>|al<br>61)<br>|
|---|---|---|---|---|---|---|
|Variables|Mean|S.D.|<br>Mean|S.D.|Mean|S.D.|
|1. School social composition|||||||
|Large families|0.64<br>|0.16<br>|0.68<br>|0.12<br>|0.74<br>|0.11<br>|
|Dialect average|0.40|0.36|0.45|0.34|0.55|0.32<br>|
|English average<br>|0.11<br>|0.11<br>|0.10<br>|0.09<br>|0.05<br>|0.08<br>|
|Avorageage|189.64|4.91|191.22|5.42|197.05<br>|7.00<br>|
|Average books<br>|2.68<br>|0.63<br>|2.47<br>|0.48<br>|1.97<br>|0.32<br>|
|**Average**SES|0.18|0.53|0.02|0.51|-0.61|0.32|
|2. Material and non-material inouts|||||||
|Teacher post-secondary science|0.73|0.45|0.73|0.44|0.49|0.50|
|Teaching experience|10.60<br>|6.86<br>|10.84<br>|5.97<br>|8.26<br>|4.88<br>|
|Class size|42.03|8.93|41.49|7.89|37.25<br>|8.75<br>|
|Student practice|0.61<br>|0.49<br>|0.66<br>|0.48<br>|0.69<br>|0.47<br>|
|Laboratories|0.70|0.46|0.63|0.49|0.48|0.50|
|Textbooks|0.31|0.47<br>|0.22<br>|0.41<br>|0.43<br>|0.50<br>|
|Groups|0.33|0.47|0.37|0.49|0.16|0.37|
|3. Orderly environment|||||||
|Frequent testing|0.77|0.42|0.77<br>|0.42<br>|0.74<br>|0.44<br>|
|Instructional<br>planning|0.03|0.44|0.04|0.39|-0.11|0.54|
|4. Academic emphasis|||||||
|Student motivation (reverse)|-0.08|0.23|-0.07|0.27|0.25<br>|0.42<br>|
|Student active learning|-0.13|0.43|-0.02|0.37|0.29|0.33|
|5. School decision-making|||||||
|Teaching control|0.51|0.98|-0.38<br>|0.80<br>|-0.16<br>|0.94<br>|
|Management control|0.24|0.84|-0.03|1.00|-0.19|1.05|
|Students control|-0.11|0.93|-0.14|0.84|0.00|0.97|

46. The Unconditional Model. The first step in the<sup>HIM estimation process</sup> involves fitting an unconditional, or random regression model. We do this in two stages. In the first stage, we partition the variance<sup>in the achievement and</sup> attitude scores into their between unit (school)<sup>and within unit (individual)</sup> components. The results are presented in Table<sup>4; they show that school level</sup> factors account for approximately half of<sup>the variance in both science and</sup> mathematics achievement, but very little<sup>--</sup> only 10-20 percent -- of the variance in attitudes.
<u>STATA code for Monte Carlo Simulation: Interaction Model</u> clear all program define sim, rclass drop _all set obs 500 gen e=rnormal(0,1) gen u=rnormal(0,1) gen R=rnormal(0,1) gen I=rnormal(0,1) ***set coefficients in simultaneous system of equations ***note that there is an interaction between x and I

*(1) y=bx+cxI+dI+rR+e *(2) x=ay+u scalar a=-1 scalar b=1 scalar r=1 scalar c=-0.1 scalar d=0 ***generate y and x from (1) and (2)

gen y=(1/(1-a*b-a*c*I))*(b*u+c*u*I+d*I+r*R+e) gen x=(1/(1-a*b-a*c*I))*(a*(r*R+d*I+e)+u) gen x_I=x*I ***IV estimation of equation (2) ivreg x (y=R) ***generate instruments for equation (1) based on IV estimation of equation (2) scalar a_iv=_b[y] gen z=x-a_iv*y gen z_I=z*I

***IV estimation of equation (1) ivreg y  I  (x x_I =z z_I ) scalar b_iv=_b[x] scalar c_iv=_b[x_I] ***Pitfall I: instrument for eq (1) based on least squares estimation of eq (2) reg x y scalar a_ls=_b[y] gen z_ls=x-a_ls*y gen z_ls_I=z_ls*I

41
# to make a strong impact at the very<sup>beginning of a project.</sup>

## _(1) Resource base_

Settlement projects on good soils and<sup>in areas with adequate moisture to grow crops have</sup> often been very successful. Where<sup>soil and climate have been marginal, as in certain parts</sup> of Indonesia, Ethiopia, Zimbabwe and<sup>the Brazilian Amazon, outcomes have been poor. In</sup> marginal areas, especially when they<sup>are remote, only very strong communities are</sup> successful. For example, the Mennonites,<sup>a cohesive, motivated community who possess a</sup> long-term pewective and who can<sup>tolerate the isolation and the slow build-up to reasonable</sup> levels of living have successfufly colonized<sup>the semi-arid Chaco of Paraguay. There is also</sup> evidence from Zimbabwe that provision<sup>of irrigation in resource-poor areas is likely to</sup> amount to little more than a costly but<sup>futile attempt to circumvent the limitations of the</sup> environment (Kelly, 1972; World<sup>Bank, 1984).</sup>

# **_(im)_** _Cost per beneflclary family_

If all costs-public and private-are<sup>factored in, then the full costs of resettlement are likely to</sup> be far higher than niany studies have estimated.<sup>In spontaneous settlement particularly an</sup> enormous amount of work is done<sup>by the beneficiaries themselves. The capitalization of</sup> settler effort into land improvements, housing<sup>and infrastructure should be valued</sup> appropriately, encouraged and taken into<sup>account in settlement programs. If a large grant</sup> element is included in the allocation of land<sup>to settlers, the public cost will be especially</sup> high. The critical, and largely unresearched,<sup>issue here appears to be that of the minimum</sup> public investment needed to stimulate a<sup>private investment resronse of the beneficiaries.</sup> T'ne studies examined provide no direct evidence<sup>that higher public costs per beneficiary</sup> family are significantly associated with<sup>performance outcomes. One school of thought on</sup> this issue is encapsulated by the World Bank's<sup>1978 review of settlement which concluded</sup> that complex and costly projects are no<sup>more likely to succeed than more modest ones and</sup>

-27-
SOEs under their oversight (Huang et al. 2017). The main incentives of the local governments were to get rid of loss-making SOEs to unburden the governments. Under the central government’s mandate to completely restructure, most SOEs, invariably possessing valuable urban land, could be, and had been, sold. We can thus use, with details to be shown later, rulebased political incentives of local politicians as the instrumental variable for identifying the privatization effects.

Using a large data set that covers _all_ Chinese industrial SOEs from 1998 to 2009, we first document that the rule-based promotion incentives of local politicians (i.e., at the prefecturallevel city) affected their privatization decisions. As a result of the Chinese Communist Party’s bureaucratic rule that limited promoting local politicians within a specific age range, their promotion probability drops sharply and robustly when reaching age 58. Indeed, using a regression discontinuity framework, we find that local politicians become reluctant to privatize SOEs under their oversight when they get sufficiently “old” and the underlying political benefits disappear. To exclude the possibility that other unknown local economic factors might drive the result, we examine the privatization likelihood of SOEs under the oversight of the provincial or the central government in the same region, and we do not find it related to the local politician’s age.

We next study the impact of privatization on firm productivity. First, without dealing with time-varying selectivity, we use ordinary least squares, fixed-effects and the firm random growth specifications. We find that privatization in China is associated with productivity improvements within the range of effects found in the earlier literature on Eastern European and CIS countries that were formally affiliated with the Soviet Union, with the magnitude on the high end of the range. Since privatization is not a random event, we then address the timevarying selectivity of privatizations with the dummy variable of the local politician’s age exceeding 58. Results from the instrumental variable estimation show that privatizations lead to dramatic increases in firms’ productivity by more than 170 percent, an order of magnitude higher than that based on the workhorse model of firm fixed effects. Privatizations also have strong and positive effects on profitability. Moreover, we find the privatization effects are ``` 6 ```
Policy ReseaRch WoRking PaPeR 4886

# **Abstract**

Despite the popularity and unique nature of women’s self-help groups in India, evidence of their economic impacts is scant. Based on two rounds of a 2,400 household panel, the authors use double differences, propensity score matching, and pipeline comparison to assess economic impacts of longer (2.5-3 years) exposure of a program that promoted and strengthened self-help programs in Andhra Pradesh in India. The analysis finds that longer program exposure has positive impacts on consumption, nutritional intake, and asset accumulation. Investigating heterogeneity of the impacts suggests that even the poorest households were able to benefit from the program. Furthermore, overall benefits would exceed program cost by a significant margin even under conservative assumptions.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to better understand the impacts of decentralized governance and local development.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org or yliu3@worldbank.org. .

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Thus against this background, we also generate a third set of projections that now merge the potential positive impact of the reforms, with the historical growth rates observed in the region (i.e. the average over 1961-2006). These projections can be found in table 2 under the headings FCT1 and FDC1. Note that by construction they fall in between those in the previous scenarios.

On the whole, table 2 contains six growth scenarios with per capita growth rates for the group as a whole ranging from 1.4 percent (historical average) to 4.1 percent (dramatic reform projections) and averaging 2.5 percent. Below we will experiment with the two extreme cases as well as the two mix cases (i.e. FCT1 and FDC1).

# _III.3 The elasticity of marginal utility of consumption_

The third ingredient needed to calibrate the STP from equation (2) is the elasticity of marginal utility of consumption. Stern (1977) offers a good review of approaches to estimate this parameter and gives a plausible range for ε of between 1 and 10. However, this range is narrowed by Pearce and Ulph (1995) who explore the implications of different values of this parameter for egalitarian judgments and note that values above 5 seem totally implausible; and further narrowed by Scott (1989) who gives a plausible range of 1 to 2 on the basis of UK long run saving data. More recently, Evans and Sezer (2004) estimate it at between 1.3 and 1.7 in six developed countries whereas Evans (2005) finds an average value of ε of 1.4 in 20 OECD countries. Beyond developed countries, Kula (2004) estimates ε for India and finds a value of 1.64.

In this paper we follow Evans and Sezer (2004) and Evans (2005) who in turn follow Stern (1977) and assume that the personal income tax structure reflects the principle of equal absolute sacrifice, or for any value of _Y_ . In (3), _T(Y)_ is the personal income tax corresponding to the income level _Y_ , and _U(.)_ is a utility function. Differentiating (3), substituting (1), and simplifying it follows: Thus, equation (4) indicates that it is possible to estimate the elasticity of the marginal utility of income on the basis of two elements: (i) the effective marginal tax rate; and (ii) the average tax rate.

In practice, however, one has to face two difficulties. One is that _de jure statutory_ tax rates may be quite different from _de facto paid_ tax rates. This is likely to be the case in countries where tax evasion is a serious issue, particularly if individuals with different 10
Table A12. Regional emissions intensity index of non-CO2 emissions, Urals Federal District, SUST-RUS sectoral classification (thousands tons of emission per trillion nominal RUB of output of 2006)

|**Urals FD**|**Hydrocarbons**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Volatile organic**<br>**components**|
|---|---|---|---|---|---|---|
||**CnHm**|**CO**|**NOx**|**PM**|**SO2**|**VOC**|
|A|15,57|65,95|26,10|40,63||13,02|
|CA_col|1 328,62|56,16|37,68|89,11||8,99|
|CA_gas|72,11|191,57|6,24|20,41||50,17|
|CA_oil|472,21|2 290,71|96,69|405,09|13,35|952,26|
|CB|16,59|631,87|119,39|415,46||36,34|
|DA|6,18|99,32|49,86|36,97||23,67|
|DB|10,34|181,46|80,37|179,95||24,82|
|DC|||55,14|319,91||14,28|
|DD|3,07|433,36|75,32|221,04||39,84|
|DE|7,57|208,09|125,02|365,52||36,64|
|DF|63,56|180,86|57,44|23,59||243,25|
|DG|43,74|361,23|116,45|235,62||183,02|
|DH|20,04|37,74|42,24|||122,66|
|DI|11,97|395,11|207,14|755,17||44,44|
|DJ|2,41|747,14|86,06|229,62|657,45|13,34|

110
**Figure 13: Output growth volatility and volatility of Government spending in non-OECD East Asia**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Output Volatility Volatility of Government Spending<br>Sources: author’s own calculations using WDI data. Volatility of<br>Government spending is measured as the weighted average<br>standard deviation of detrended government consumption growth<br>using a ten year window.<br>197019721974197619781980198219841986198819901992199419961998200020022004<br>Volatility<br><!-- End of picture text --> Improved monetary policies have also played a significant role in stabilizing output fluctuations all over the world. Volatility of inflation and output growth volatility have a strong tendency to move together (Blanchard and Simon, 2001), and unsound monetary policy can strongly amplify or dampen fluctuations: for instance, the 1970s were both years of high output and inflation volatility, and of poorly performing monetary policies (Romer and Romer, 2002; Bernanke, 2004). In East Asia the association between volatility of inflation and output growth volatility is quite high (0.75), and the significant decrease in inflation volatility between the 1980s and the 2000s has contributed to lowering output growth volatility (Figure 14): at almost 20 percent, volatility of inflation in non-OECD East Asian countries was far above the world average in the 1970s, but thanks to sound monetary policies it decreased rapidly close to OECD levels by the end of the 1970s until the mid nineties, where a spike can be observed as a consequence of the 1997/98 financial crisis. Gains have been nonetheless more pronounced for high and middle income countries, while low income countries did not manage to reduce as much volatility of inflation (Figure 15).

17