rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10: Progress of land consolidation in Orissa, 1974-98**

|**Status**|**Number of**<br>**villages**|**Share of total no.**<br>**villages in Orissa**<br>_(%)_|**Area (ha)**|**Share of total**<br>**operated area in**<br>**Orissa**(%)|
|---|---|---|---|---|
|Excluded from consolidation u/s<br>**5(1)**|686 (6.9)|1.3|132,318 (8.1)|2.5|
|Updating of records and<br>publication u/s 13 (4)|426 (4.3)|0.8|101,281 (6.2)|1.9|
|Completion of consolidation<br>operation and publication of ROR<br>and Maps u/s 22(2)|6992 (70.5)|13.7|918,020 (56.6)|17.3|
|Consolidation operation yet to<br>start|1817 (18.3)|3.6|471,594 (29.1)|8.9|
|Total (i.e. issue of notification u/s<br>3(1))|9921 (100.0)|19.4|1,623,213 (100.0)|30.6|

Note: Figures in parentheses are percentages of the total Source: Director (Consolidation), Board of Revenue, Cuttack

**Table 11: Progress and expenditure in land consolidation in Orissa, 1995-98**

|||**1995-6**|**1996-7**|**1997-8**|**Total**|
|---|---|---|---|---|---|
|Progress u/s 22(2)|No. of villages|184|257|213|654|
||Area (ha)|22,414|40,725|36,735|99,874|
|Progress u/s 13(4)|No. of villages|11|28|74|113|
||Area (ha)|3,803|8,990|14,784|27,577|
|Total expenditure<br>(in Rs. Crore, at<br>current prices)||18.67|21.33|22.61|62.61|

Source: Director (Consolidation), Board of Revenue, Cuttack

# _4.2.3 Process of consolidation_

The land consolidation process is designed to amalgamate plots into consolidated blocks while maintaining more or less constant land distribution within the village, and taking into account broad variations in land quality. The selection of villages for consolidation and broad operational procedure is as follows: _Village selection and local representation:_ Under the OCH & PFL Act, a consolidation operation may be taken up only in those villages in which a minimum of 70 percent of all landowners agree to consolidation. In addition, it is required that at least 25 percent of owners should have more than 3-4 plots per holding. If these two conditions are met then the village is said to be 'consolidable'. The Act also provides for a Consolidation Committee to be formed in the village to ensure local participation in the consolidation operation. The committee should comprise 7-15 members representing all land holding size classes, plus one landless person 44
# **5. Input Usage in Manufacturing Production Function Estimations**

We now turn to the link between input counts and the productivity of manufacturing establishments. Table 8 provides a simple analysis of whether greater input counts in plants is associated with stronger productivity in the organized sector. We estimate a simple production function with log output _Y_ of each establishment _i_ in a district _d_ and industry _j_ as the dependent variable. The specification takes the form: Our core regressor is an indicator variable for the raw input usage for each plant being greater than its industry’s median value. We later test variants on this metric design. We include a vector _Xi_ of plant inputs into the production function: log employees, log book values of capital, and log costs of materials. We exclude plants with missing values for these metrics. As noted above, the input counts measure that we are studying is quite distinct from the intensity of material inputs into a production function. We thus control for this latter intensity directly. Because we rely on revenue data to calculate productivity, we face a common limitation in the literature that we cannot separate the efficiency or productivity of plants in terms of real inputs and outputs from other factors like their mark-ups or quality (e.g., Foster, Haltiwanger, and Syverson 2008, De Loecker 2011).

> Regressions include three-digit industry fixed effects γ _j_ to capture regular differences in production techniques and spatial locations across industries. We also control for other district traits described below with a vector of district-level controls _Zd_ . We use establishment weights from the surveys to weight plants, and we report robust standard errors.

Column 1 provides a baseline estimation of the plant-level production function before district-level conditions are incorporated. These underlying parameters for the production function, emphasizing employees and materials, are very stable across estimations. Column 2 introduces the input count measure and finds that establishments with above median input counts for their industry have 0.039 higher log output than those with input counts below the median, holding everything else constant. We later show in Table 10 when using a linear metric design that an increase in one input is associated with a 0.007 increase in log output, indicative of some non-linearity in this effect. To provide additional perspective, the coefficient in the first row 16
Table 13: Executive Constraint, Access to Power and Cross Ethnic Group Economic Performance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Log (light per capita)|Log (light per capita)|Log (light per capita)|Log (light per capita)|
|excluded|-0.082***||-0.060**||
||(0.027)||(0.026)||
|excluded*high executive constraint|0.067**||0.042*||
||(0.027)||(0.023)||
|powerful||0.085***||0.052*|
|||(0.030)||(0.030)|
|powerful*high executive constraint||-0.085***||-0.053**|
|||(0.029)||(0.026)|
|Lag-Log (light per capita)|||0.381***|0.381***|
||||(0.059)|(0.059)|
|Country-Year FE<br>63|YES|YES|YES|YES|
|Group FE|YES|YES|YES|YES|
|Year FE|NO|NO|NO|NO|
|Observations|9,210|9,210|8,601|8,601|
|R-squared|0.991|0.991|0.796|0.796|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. “Log (light per capita)” is the natural log of night light per capita intensity for each ethnic group. Variable “excluded” is a binary flag indicating whether ethnic group is assigned a status of “Self-excluded”, “Powerless” or “Discriminated”. Variable “powerful” is a binary flag indicating whether an ethnic group is assigned status of “Monopoly” or “Dominant”. Variable “high executive” constraint is a dummy that takes a value of one if “exconst” (executive constraint) variable in Polity IV dataset equals 7. Each observation is weighted by group’s population size as a fraction of the country’s total population.
Table 5.1—Sample student distribution

|Grade 6 SY|Antique|Iloilo|Ifugao|Neuva<br>Viscaya|Leyte|West<br>Samar|Negros<br>Oriental|Cebu|
|---|---|---|---|---|---|---|---|---|
|1999/2000|244|143|188|139|217|148|230|144|
|2004/05|159|97|137|92|160|98|163|107|
|2005/06|177|111|145|101|139|99|148|95|
|Total|580|351|470|332|516|345|541|346|

Source: TEEP Tracking Survey. Note: Antique, Ifugao, Leyte, and Negros Oriental are TEEP divisions.

Table 5.2—Summary statistics of the dependent variables, separated by gender and cohort

||Fem<br>Mean|Non-TEE<br>ale<br>S.D.|P Cohort<br>Ma<br>Mean|le<br>S.D.|Fem<br>Mean|TEEP<br>ale<br>S.D.|Cohort<br>M<br>Mean|ale<br>S.D.|
|---|---|---|---|---|---|---|---|---|
|Years of schooling|10.866|2.279|10.310|2.450|9.604|1.370|8.935|1.654|
|High school repetitions|0.026|0.170|0.217|0.567|0.060|0.255|0.279|0.678|
|College entry|0.512|0.500|0.424|0.495|0.409|0.492|0.278|0.448|
|Migration|0.397|0.490|0.285|0.452|0.200|0.400|0.086|0.280|
|Log(salary)|8.147|0.750|8.322|0.768|7.598|0.478|7.640|0.801|

Source: TEEP Tracking Survey.

33
how misaligned fertility preferences can prevent efficient information sharing between spouses, leading to suboptimal outcomes for women (Ashraf et al. 2020). Finally, our paper also contributes to the extensive literature on the drivers of IPV (Aizer 2010; Anderberg et al. 2016; Erten and Keskin 2018; Bhalotra et al. 2021).

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 explains the empirical strategy. Section 4 presents the main results and discusses the evidence on potential mechanisms. Section 5 concludes the paper.

# **2 Data**

We use data from the 2015–16 NFHS, or NFHS-4, a nationwide household survey of both men and women. The Woman’s Questionnaire in NFHS-4 was administered to all women aged 15–49 and included a domestic violence module administered to one randomly selected eligible woman per household from 15 percent of the households (IIPS 2017).<sup>5</sup> The NFHS is the only source of information on several dimensions of IPV experienced by women in India, including physical, sexual, and psychological violence by male partners.<sup>6</sup> The Man’s Questionnaire in NFHS-4 was administered to men aged 15-54 in a subsample of households selected for the women’s surveys.

Our NFHS estimation sample consists of 65,292 ever-married women and 70,923 men from 628 districts covering 30 state election constituencies. Appendix Table A.1 presents descriptive statistics for the female and male NFHS samples for our variables of interest. The NFHS data include binary variables on whether a woman reports having experienced various forms of violent acts at the hands of her husband. Following Anderson (2008) and Erten and Keskin (2018), we construct three indices to capture physical, sexual, and psychological violence by averaging the z-scores of the underlying IPV indicators over the previous 12 months.<sup>7</sup> Based on a respondent’s state of residence at the time of the survey and the survey month and year, we identify the last statewide election year for each respondent.<sup>8</sup>

> 5Following the WHO’s guidelines, the module was not implemented if privacy could not be obtained. 6While the 2005–06 round of the NFHS implemented a domestic violence module, the dataset lacks district identifiers. The 1998–99 round of the NFHS has district identifiers, but it only asked three questions about whether the respondent has been beaten or physically mistreated by any person, who this person was, and how often this had occurred in the last twelve months.

> 7Definitions of variables are available in the Appendix.

> 8We assume that the respondent lived in the same state at the time of the last state election. This is a reasonable assumption to make in our context. Fulford et al. (2013) show that cross-state migration by females is quite small (applicable to less than 1 percent of women in most cases). Among

4
double orphans in the sample. Double-orphanhood also did not affect the magnitude of the effects of orphanhood episodes so it is not considered further.

Unpacking the results further, we explored whether there was any heterogeneity in the impact of orphanhood in other dimensions. For example, we investigated whether the impact on health and education differed with family wealth, by interacting the orphanhood variable with the baseline consumption expenditure variable. The interaction term was not significantly different from zero.

Finally, we investigated whether there was any impact of different living arrangements. In principle, understanding the impact of fostering and other living arrangements as a coping mechanisms after orphanhood is of considerable interest, not least for policy responses, but not easily investigated as the placement of orphans may well be correlated with unobservable child and household characteristics, affecting inference on the heterogeneity of the impact of orphanhood in the data.<sup>13</sup> However, we can explore the consequences of living arrangements at the baseline, before orphanhood, by interacting the orphanhood shock with whether the child was living with the respective parent that subsequently died at the time of the baseline survey. As discussed in section 2, a substantial number of our sample of the nonorphaned children at baseline where not living with their mother or father in that period (table 3). While for height, we find no significantly different effect according to living arrangements, for education, we find that the entire impact of maternal death on schooling is confined to those children living with their mother at baseline; those fostered have no significantly negative schooling effect from orphanhood.<sup>14</sup> It appears therefore that fostering can shelter children from the negative effects of orphanhood in the data. Still, placement issues may also trouble this result, for example, if only more able children are taken out of households with ill parents to continue their education, so caution is warranted.

# **5. Interpreting the Findings**

One of the key problems in assessing the impact of shocks such as orphanhood is that unobservables correlated with orphanhood may bias the results: even after controlling for observables, orphanhood is not random. Finding that orphanhood is significant may not actually determine a causal impact, but may rather capture effects of unobservable covariates. For example, if particular attitudes towards health lead to parental illness and death as well as to poor health outcomes of the child, then orphanhood in itself is not the cause of lower height attainment in the child. An alternative narrative could

> 13 For example, more able or healthier children may more easily be adopted by other families. Ksoll (2007), using a sub-sample of the KHDS data set explores the placement of double orphans based on a further survey of the extended family network.

> 14 In this case, we also find some significant effect of paternal death for those living with their father at baseline. The effect is considerably smaller than for maternal death.

11
energy consumption geographically, by income quintile groups, and across socioeconomic groups.

Table 1 reports wood and electricity expenditures as a percentage of total household expenditures for households in different income groups and regions. The regional differences in household energy consumption patterns are striking. Fuelwood expenditures are considerably higher in the North (average of 4.9% of household expenditures) and for the households in the poorest quintile in the North and Central regions (over 5% of household expenditures). Fuelwood expenditures amount to approximately half of household total energy expenditures in all income groups in the North. In contrast, households in the South spend less than 1 percent of household expenditures on fuelwood. The share of fuelwood expenditures in total energy expenditures is 20 percent or less for households in the South.

Electricity expenditure shares for the poorest quintile group in Montenegro are generally two to three times higher than the shares for the richest quintile group. A survey of household electricity expenditures in other eastern European and central Asian countries, shown in Figure 2, reveals a similar pattern of electricity expenditure shares between low and high income households. Given that electricity expenditures are a substantial component of the total budget of the poorest households, an increase in electricity tariffs will likely have a major impact on their welfare.

**_<u>Table 1.  Energy expenditures as a percentage of household total expenditures</u>_**

|||**Wood**|||**Electricity**|||**Total e**|**nergy**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Quintiles**|**North**|**Center**|**South**|**North**|**Center**|**South**|**North**|**Center**|**South**|**ALL**|
|**1**|5.9|5.5|0.5|6.2|9.2|8.9|12.1|14.7|9.4|12.9|
|**2**|5.3|3.9|1.1|4.2|6.0|6.8|9.5|9.9|7.9|9.5|
|**3**|4.4|3.3|0.6|3.6|5.7|6.3|8.0|9.0|6.9|8.1|
|**4**|3.9|2.1|0.4|3.9|4.6|5.4|7.8|6.7|5.8|6.9|
|**5**|2.8|1.1|1.0|3.6|3.7|4.1|6.4|4.8|5.1|5.2|
|**ALL**|**4.9**|**3.8**|**0.7**|**4.7**|**5.6**|**6.0**|**9.6**|**6.0**|**9.6**|**8.8**|

Source: calculated from 2004 ISSP Montenegro Household Survey.

4
-2-

# Table 1 MEASURES OF THE OVERALL DEBT BURDEN-

||1980|1981|1982|1983|1984|1985|1986|1987<br>Est.|
|---|---|---|---|---|---|---|---|---|
|Turkey:|||||||||
|Debt (US$ billion)<br>Medium/long term<br>Short term|16.3<br>13.8<br>2.5|16.9<br>14.7<br>2.2|17.6<br>15.9<br>1.8|18.2<br>16.0<br>2.3|20.8<br>17.6<br>3.2|25.5<br>20.8<br>4.8|32.5<br>25.6<br>6.9|37.3<br>28.8<br>8.5|
|Debt/GNP|28.0|28.6|32.8|35.6|41.5|47.9|55.9|57.6|
|Debt/exports|284.1|198.3|175.0|192.9|180.5|194.5|260.5|227.2|
|Current Account<br>Surplus/GNP|-5.0|-2.8|-1.6|-3.4|-2.8|-1 .u|-2.6|-1.5|
|Non-Interest Current<br>Account Surplus/GNP|-3.9|-0.8|1.2|-0.4|0.4|1_1_|1.0|2.2|
|Countries with Recent<br>Debt-Servicing Problems:|||||||||
|Debt/GDP|32.5|37.6|43.7|47.6|47.6|49.1|51.3|53.9|
|Debt/exports|151.5|186.1|240.9|254.6|246.3|266.8|309.5|313.4|
|Current Account<br>Surplus/GDP|-3.6|-5.9|-5.5|-2.0|-0.9|-0.5|-1.8|-1.5|
|Non-Interest Current<br>Account Surplus/GDP|-0.5|-1.7|-0.5|2.8|4.1|4.2|2.5|2.6|

Notes: For comparability the debt figures reported here for Turkey refer to gross debt. _In_ the rest of the chapter aet debt Ls used. See Page 4, footnote I in the text.

The debt-export ratio refers to year-end debt to exports of goods and services (and for Turkey also workers' remittances) during the year.

Countries with recent debt-servicing problems are defined as those which incurred external payment arrears in 1985 or rescheduled their debt during the period from end-1983 to ead-1986.

Source: Undersecretariat of Treasury and Foreign Trade, Central Bank and World EcoQomic Outlook (IMF), October 1987.

**7469e1** p **LO**
# **References:**

- Adamowicz, W. L., P. Boxall, M. Williams, and J. J. Louviere (1998), Stated Preference Approaches for Measuring Passive Use Values: Choice Experiments and Contingent Valuation. _American Journal of Agricultural Economics_ 80, 64-75.

- Angrist, J. D. and S.-J. Pischke (2009), _Mostly Harmless Econometrics: An Empiricist’s Companion._ Princeton, N. J.: Princeton University Press.

- Bennett, J. and E. Birol (eds.) (2011), _Choice Experiments in Developing Countries: Implementation, Challenges and Policy Implications._ Cheltenham, U.K.: Edward Elgar.

- Bennett, J. and R. Blamey (2001), _The Choice Modelling Approach to Environmental Valuation_ Cheltenham, U.K.: Edward Elgar.

- Carson, R. T. (1998), Valuation of Tropical Rainforests: Philosophical and Practical Issues in the Use of Contingent Valuation. _Ecological Economics_ , 24, 15-29.

- Carson, R. T., M. B. Conaway, and S. Navrud (2013), Preliminary Valuation of a Cultural Heritage Site of Global Significance: A Delphi Contingent Valuation Study. In I. Rizzo, and A. Mignosa (eds.) : _Handbook on the Economics of Cultural Heritage_ . Cheltenham, UK: Edward Elgar.

- Carson, R. T., Flores, N. E. and Meade, N. F. (2001), Contingent Valuation: Controversies and Evidence. _Environmental and Resource Economics_ , 19, 173-210.

- Carson, R.T., and J.J. Louviere (2011), A Common Nomenclature for Stated Preference Elicitation Approaches. _Environmental and Resource Economics_ , 49, 539-559.

Choicemetrics (2012), Ngene 1.1.1 User Manual and Reference Guide. Sydney: Choicemetrics.

Dalkey, N. C. (1967), _Delphi_ . Rand Paper P-3704. Santa Monica, CA: Rand Corporation.

- Dalkey, N. C. (1969), _The Delphi Method: An Experimental Study of Group Opinion_ . Rand Paper RM5888-PR, Santa Monica, CA: Rand Corporation.

- Dalkey, N. and O. Helmer (1963), An Experimental Application of the Delphi Method to the Use of Experts. _Management Science_ , 9 (3), 458-467.

- Ferrini, S. amd R. R. Scarpa (2007), Designs with A Priori Information for Nonmarket Valuation with Choice Experiments: A Monte Carlo Study. _Journal of Environmental Economics and Management_ , 53, 342-363.

- Flores, N. E. and R. T. Carson (1997), The Relationship Between the Income Elasticities of Demand and Willingness to Pay. _Journal of Environmental Economics and Management_ , 33, 287-295.

33
What Do Alternative Measures of Comparative Advantage Reveal About the Composition of Developing Countries' Exports?

by Alexander J. Yeats* Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|The Comparative Advantage Indices|3|
|III.|Industry Analysis of RCAs and Heckscher-Ohlin<br>Indices|6|
|IV.|Source of Variation<br>iRevealed Comparative Advantage|14|
|V.|Summary and Conclusions|21|
|Refer|ences|23|
|Appen|dix Tables|25|

* Principal Economist, International Economics Department, The World Bank, Washington, D.C. 20433. I would like to thank Bela Balassa and Paul Meo for many helpful comments and suggestions.
**Table 8**

**AFP Charges**

|**AFP**|**Commission**|**Insurance**<br>**premium**|**Total**|
|---|---|---|---|
|**Porvenir**|1.50|2.00|3.50|
|**Colfondos**|1.45|2.05|3.50|
|**Horizonte**|1.50|2.00|3.50|
|**Proteccion**|1.50|2.00|3.50|
|**Colmena**|1.70|1.80|3.50|
|**Davivir**|1.50|2.00|3.50|
|**Colpatria**|1.40|2.10|3.50|
|**Invertir**|1.50|1.70|3.50|
|**Pensionar**|0.75|1.74|2.49|

Source: Superintendencia Bancaria, 1/1997 There is a different fee structure for severance funds. In the severance business, fund management companies are allowed to charge fees on assets under management (calculated on a daily basis) corresponding to a maximum of 4 percent of assets per annum. All but one of the severance fund management companies are charging the maximum fee of 4 percent.

The sales force of the Colombian AFP system increased from about 3,000 agents at the end of 1995 to about 4,800 agents at the end of 1996. Measured by the number of affiliates per agent, the Colombian AFPs employ less agents than the Peruvian pension system. While the Peruvian AFPs employed one agent for every 360 affiliates, there were 430 affiliates per agent in Colombia at the end of 1996. In Argentina, the private pension system employs one agent for 3,600 affiliates.

# **_Fund Accumulation and Investment_**

The total funds under management of the mandatory pension system amounted to about 870 billion pesos at the end of 1996, corresponding to about US$823 million and approximately 1 percent of GDP. Voluntary pension funds had assets of an additional 74 billion pesos or about US$69 million. The assets managed by the mandatory severance funds amounted to about 720 billion pesos or US$680 million with a total of 1.7 million affiliates. Total assets administered by the pension and severance fund management companies thus amounted to US$1.57 billion and corresponded to about 1.7 percent of GDP.

The contribution rate to the individual pension account was initially set at 8 percent in 1994 and, as originally planned, was increased to 9 percent in 1995 and to 10 percent in 1996. In addition, up to 3.5 percent of salary are payable to cover disability and survivors' insurance as well as the fund management commissions. 75 percent of the contribution is payable by the employer and 25 percent by the worker. Contributions are mandatory up to a pensionable salary of 20 minimum wages. The mandatory and voluntary contributions to the AFPs are deductible from income tax.

The investment regulations are much more relaxed in Colombia than in Chile or Peru. Investment in public debt is limited to 50 percent of assets under management. Investrnent in equities 30