rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix:   Description of the Indian Dataset**

The data set covers 209 districts from 9 states in India, The states in the sample are: Andhra Pradesh, <mark>Karnataka, Kerala, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal. This represents just over half the districts in India in 1991 and covers approximately 60% of 1991 India GDP. Literacy rates, area of districts, population numbers, and roads come from various sources of the government of India and were collected from www.indiastats.com. Credit and Deposits come from the Reserve Bank of India and are credit and deposits of registered commercial banks. NDP are measured in 1991 and 2001 (at 1993 prices) from internal RBI records and were themselves calculated by state offices. Some districts in the sample split into two districts between the years 1991 and 2001 and so the data points were merged in the 2001 observation so that the geographic area covered is consistent with the 1991 observation. Below is a list of the merged districts.</mark>

## **<mark>Annex : Merging of Districts</mark>**

|District Merged|Merged Into|District Merged|Merged Into|
|---|---|---|---|
|Punjab||**Karanatka**||
|Nawa shahar|Hoshiarpur|Chamarajanagar|Mysore|
|Moga|<br>Faridkot|<br>Davanagere|<br>Chitradurga|
|<br>Muktsar||<br>Gadag|<br>Dharwad|
|Fateh Sahib|Patiala|<br>Haveri||
|Mansa|Bhatinda|Koppal|Raichur|
|Andhra Pradesh||Udupi|Dakshin Kannad|
|Ragareddy|Hyderabad|<br>Bagalkote|Bijapur|
|<br>**Rajasthan**||<br>**Tamilnadu**||
|Hanumangarh|Ganganagar|Kancheepuram|Chengalpattu|
|Baran|Kota|Thiruvallur||
|Rajsamand|Udaipur|Caddallore|Caddallore|
|Karauli|Sawai Madhopur|Villupuram||
|Dausa|Jaipur|Thanjavur|Thanjavur|
|Uttar Pradesh||Nagapatinam||
|Jyotiba Phule Nagar|Moradabad|<br>Thiruvarur||
|Bagpat|Meerut|Salem|Salem|
|G B Nagar|Bulandshahr|Namakkal||
|<br>Hathras|Mathura|Tiruchirapalli|Tiruchirapalli|
|Kannauj|Farrukhabad|Karur||
|<br>Aurraiya|Etawah|Perambalur||
|<br>Chitrakoot|Banda|Madurai|Madurai|
|Mahoba|Hamirpur|Theni||
|Kaushambhi|<br>Allahabad|West Bengal||
|Ambedkar Nagar|Faizabad|<br>South 24 Parganas|North 24 Parganas|
|<br>Shravasti|Bahraich|<br>Dakshin Dinapur|<br>West Dinapur|
|Balrampur|Gonda|<br>Uttar Dinapur||
|<br>Kushinagar|Deoria|<br>**Maharastra**||
|<br>Sant Kabir Nagar|Basti|Nandurbar|Dhule|
|<br>Sant Rabidas nagar|Varansi|Gondia|Bhandra|
|Chandauli||Washim|Akola|
|||Hingoli|Parbhani|

26
# Table 1. Country-level regression

P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. All models are estimated via three regression models: ordinary least squares (OLS), robust regression and instrumental variable model. In dropping outlier model, Top 5% samples on each dependent variable. The details of instrumental variable model are shown as below.

*1 IV1: 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Independence of Supervisory Authority - Political respectively

*2 IV2: 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies respectively

*3 IV3: 1st excluded IV is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies , and 2nd is Independence of Supervisory Authority - Political respectively

## <u>Panel A: Overall Score Nominal</u>

||OLS|Robust Reg<br>Depe|OLS dropping<br>top 5% of dep<br>var<br>ndent Variable: N|OLS w/ the<br>same<br>observations<br>to IV<br>umber of borrow|Instrumental<br>Variable 1*<sup>1</sup><br>ers as % ofpopu|Instrumental<br>Variable 2*<sup>2</sup><br>lation|Instrumental<br>Variable 3*<sup>3</sup>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Overall Score Nominal|0.0463|0.0912**|0.0642|0.0487|0.2319|0.1258|0.3466|
||[0.277]|[0.017]|[0.101]|[0.348]|[0.138]|[0.260]|[0.135]|
|Inflation, GDP deflator (annual %)|-0.1321|-0.1139|-0.1003|-0.1024|-0.086|-0.105|-0.1004|
||[0.273]|[0.279]|[0.357]|[0.531]|[0.624]|[0.402]|[0.649]|
|GDP growth (annual %)|0.0053|-0.061|-0.0319|-0.1687|-0.0626|-0.1284|-0.1759|
||[0.980]|[0.740]|[0.867]|[0.569]|[0.848]|[0.559]|[0.684]|
|Rule of law|-1.9015*|-1.6397*|-1.6604*|-1.6968|-0.6516|-1.4536|-0.2136|
||[0.080]|[0.084]|[0.091]|[0.184]|[0.681]|[0.212]|[0.917]|
|Bank overhead costs / Total assets|-12.2374|-3.1799|-9.4274|-10.8249|-13.5384|-18.6009|-22.7005|
||[0.397]|[0.800]|[0.470]|[0.533]|[0.469]|[0.305]|[0.414]|
|Ease of doing business index (1=most business-friendly regulations)|-0.0168|-0.0144|-0.0175|-0.0187|-0.0089|-0.0097|0|
||[0.234]|[0.241]|[0.170]|[0.272]|[0.651]|[0.531]|[1.000]|
|Constatnt|3.2903|0.4184|2.3342|3.8895|-5.2471|-0.0435|-9.6378|
||[0.267]|[0.870]|[0.384]|[0.290]|[0.526]|[0.994]|[0.399]|
|Observations (countries)|47|47|46|34|34|36|31|
|R-squared|0.159|0.276|0.2|0.179|-0.214|0.156|-0.792|
|Adjusted R-squared|0.033|0.168|0.077|-0.004|-0.484|-0.019|-1.239|
|F statistics for excluded instruments on 1st stage|||||1.849|2.502|1.172|
|Shea's adj. partial R-sq|||||0.125|0.152|0.093|
|P-values of Hansen J statistics|||||0.022|0.724|0.140|

18
Policy Research Working Paper 5830

# **Abstract**

When deciding who should receive welfare benefits with the aim to increase household well-being, it is necessary to understand the effects of the distribution of power within the households at which the aid is directed. Two primary household models have been used to study intrahousehold bargaining and decision making: the unitary model and the collective model. The unitary model seems to fit Pakistan’s context because the prevailing traditional culture positions the male head as the household decision maker. However, using a set of direct measures of decision-making power from the Pakistan Social and Living Standard Measurement Survey, this study finds that even in a country where men seem to have more power than women, the collective household bargaining model applies. This study also finds that, in Pakistan, when women have more decision-making power at home, households tend to spend more on women’s preferred goods (such as clothing and education), family members eat more non-grain food items, and children, particularly girls, are more likely to be enrolled in school.

This paper is a product of the Human Development Social Protection Unit , South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xhou@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
lowest average accumulated years of education in Latin America (Calder ́on and Urquiola, 2006).

This poor performance in education appears to be rooted in several factors. Almost 40% of children are not native Spanish speakers and tend to have poor academic performance relative to native-Spanish speakers. The long-lasting civil war had strong negative impacts on the education of the two most disadvantaged groups, namely rural Mayan males and females (Chamarbagwala and Moran, 2011). In rural areas, parental education is low and anecdotal evidence suggests that education is not greatly valued _−_ a child’s aspiration is to work in agriculture-related activities or housework (Rodr ́ıguez, 2001). Low levels of income seem to play a role when deciding whether to enroll or withdraw a child from school (Alvarez and Schieflbein, 2007). High rates of malnutrition are associated with a 50% larger probability of dropping out of school, and about 100% higher grade repetition rates (Mart ́ınez and Fern ́andez, 2007).

Pre-primary education covers children aged 4 to 6, while the first grade of primary school typically started at age 7 (though these thresholds are not strict). While primary education has been compulsory since 1985, parents have to pay a small fee (between $0.60 and $5 for tuition) to cover operational costs related to running the school (e.g., electricity bills). Pre-primary education is compulsory as well, though not enforced due to low coverage.

The signing of the Peace Accords in 1996 put an end to 36 years of harsh civil conflict. As part of the Accords, the national government agreed to expand basic education and health services in rural and indigenous areas with inadequate coverage. The government aggressively expanded access to public pre-primary education, increasing the number of public pre-primaries from approximately 5,300 in 1998 to 11,500 in 2005 (Figure 1). Preprimaries were usually constructed as annexes to primary schools.

According to government officials we interviewed, the selection of beneficiary communities can be described as a two-step process. In the first step, agents at the departmental offices of the Ministry of Education identified eligible communities as those that had enough children without access to pre-primary education. As a result, lists of eligible communities were constructed at the regional level. In the second step, final decisions were made at the central level. The procedures were _ad hoc_ and no strict guidelines were applied to make decisions on beneficiary schools.

# **3 Data**

We use school-level administrative data obtained from the Ministry of Education for all primary schools from 1995 until 2008. At the beginning of the school year, each operating 5
quarters of 2010. On the other hand, rainfall was substantially above average in the third quarter of 2011, and close to exactly average in the third quarter of 2014. As mentioned above, favorable rainfall may have contributed to the strong growth in survey consumption between 2009-10 and 2011-12, but did not continue in the summer of 2014.

# **III Empirical Methodology**

## **A Econometric Model**

The specification of the model is based on the small area estimation (SAE) methodology originally proposed by Elbers, Lanjouw, and Lanjouw (2003). This methodology generates a joint distribution of economic welfare and independent variables in the target data set, where a welfare measure is not available. The process consists of two steps. The first involves estimating the relationship between economic welfare and explanatory variables in the source data set, in which a welfare measure is available. The parameters are then used to simulate economic welfare into the target data set.

The first step in this analysis is to estimate the relationship between household per capita consumption expenditure (the measure of economic welfare) and other explanatory variables, in the three available source data sets with consumption data. The explanatory variables are those that are available in both the source and target data sets. The regression is a log linear specification relating per capita consumption expenditure to household and regional variables using the 2004-05, 2009-10 and 201112 surveys (equation 1).<sup>25</sup> The welfare measure is _ycht_ , which is household per capita expenditure for a household in cluster _c_ and household _h_ , interviewed in survey round _t_ . A cluster is a primary sampling unit (PSU) as defined in the NSSO surveys. For all surveys, _ycht_ is measured in 2011 rupees using separate CPI indices for urban and rural areas.<sup>26</sup> The _X_<sup>_h_</sup> vector in equation (1) consists of an intercept plus household level demographic variables, labor market variables and expenses on miscellaneous services. Expenses on miscellaneous services for all survey years are measured in 2011 rupees.<sup>27</sup> The vector _Z_<sup>_h_</sup> is a subset of the _X_<sup>_h_</sup> variables whose coefficients are allowed to vary linearly over time. In order to avoid introducing severe multicollinearity into

> 25NSSO61, NSSO66 AND NSSO68. The coefficients in this semi-log model are interpreted as a relative change in welfare as a result of an absolute change in the explanatory variables.

> 26For rural areas, we use the CPI for Agricultural and Rural Laborers and for urban areas the CPI for Industrial workers. Source: CEIC (from Labor Bureau Government of India).

> 27 The CPI indices are the same as those used for the welfare measure, _ycht_ .

10
<!-- Start of picture text --> Change in participation by broad sector<br>2<br>Home production<br>Manufacturing<br>1.5<br>Services<br>Agriculture<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 3:** Paths of aggregate participation for different broad sectors in the U.S. The percentage change in home production from the baseline year of 2019 is in blue, the percentage change in labor supply to the manufacturing sector since 2019 is in green, the same concept for the service sector is in red, and for the agricultural sector is in purple.

creasing participation. By contrast, the United States is a net manufacturing importer. In this sector, the expenditure-switching effect dominates and participation increases. Since manufacturing is the sector that experiences an increase in participation (and the highest increase in nominal wages) during the years when the shock is active, it is also the sector that suffers most of the unemployment when the shock dissipates.

Appendix figure C.2 depicts the evolution of relative sectoral prices (nominal sectoral prices divided by the aggregate price index). Manufacturing experiences the highest increase in relative price (up to 4%). Agriculture’s relative price increases slightly (up to 1%), while the relative price of services decreases (up to -1%).

Turning to regional results, figure 4 presents a map of the cumulative percentage 16
its business, Buenos Aires' situation appears to be distinct from those of other public provincial banks.<sup>13</sup> Therefore, Buenos Aires is treated separately in much of the analysis.

Using balance sheet data from between 1991 and 1996, Appendix 11 estimates the rate at which provincial, private, and privatized provincial banks' net worth declined relative to total liabilities (and assets). The data indicate that loss rates were, on average, much higher for public provincial banks than for privatized or private banks. Based upon this data, we derive counterfactual estimates of the cost of re-capitalizing typical public provincial banks under a variety of scenarios.

In the simulations, the ratio of net worth to total liabilities (NW/TL) of the public provincial bank is assumed to fall at the average rate that NW/TL fell for public provincial banks (except Buenos Aires) between 1991 land 1996 (i.e. 9%). It is also assumed that the province re-capitalizes the simulated bank every three years so that its ratio of net worth to total assets is 11.5%, the level dictated by Argentine prudential regulations.14 Finally, it is assumed that nominal liabilities grow at a rate of 11% per year, the average rate for public provincial banks during the period. For a bank with 25,000,000 pesos in reported net worth, a level very close to the median for public provincial banks (except Buenos Aires) from 1994-96, the nominal re-capitalization payments would total over 205.5 billion pesos if it remained in public hands.<sup>5</sup> 13

- As the flagship of the public banking system, Buenos Aires may be treated somewhat ditferently by regulators than other public provincial banks. A full discussion of how, and why, Buenos. Aires can be publicly owned and yet _apparently_ function more effectively than other state-owned banks is beyond the scope of this paper.

- 4 The qualitative results of the re-capitalization simulations presented here are not overly sensitive to the duration of the re-capitalization cycle. We get very similar results, for example, if we assume that the province recapitalizes its bank every five years.

- Is In this section, we calculate re-capitalization payments over the next hundred years. For most of the discounting scenarios presented here, the present value of payments received after that year is quite close to zero. The discounted re-capitalization payments, therefore, should be seen as reasonable estimates of the full fiscal cost of refusing to privatize a public provincial bank.

_5_
15-a Figure 6 <!-- Start of picture text --> INTEREST RATES IN LESOTHO.<br>RATES:<br>I - grWn. l_e laing<br>¢ / / _ v ede<br>sx \ > < _ / ~ ~ ~~~~/ ___ avg. lectcct<br>-C-- 't<br>Lg.,<br>Quarters. 1984o11 to 12: et<br><!-- End of picture text --> <!-- Start of picture text --> a~~~~~~~~~~~~~~~~~~~~~~~cl<br><!-- End of picture text --> <!-- Start of picture text --> tea' teas 'gao '907 ~ ~ ~ ~ ~ ~ yea<br>1g.e ., , , ,.--, ,<br>ai<br>r~~~~~~~~~~wre-rtee° ut-t<br>'98- t 9*5 X 08 t losen -87<br>Ckusart X 1 4tQ to 198A a<br><!-- End of picture text -->
gaps between exiting and continuing firms are significantly different from each other at the 1% level. The shrinking of the gap between productivity and exit accounts for an aggregate average loss of value added per worker of about 4% over the course of the crisis (since the exit rates in both 1997 and 1998 were approximately 12% and the gap narrowed by approximately 17%).

Demeaning value-added per worker by sector or by sector-year yields a similar, but slightly less dramatic, pattern. Thus, the aggregate attenuation effect is due to a combination of more productive sectors being more severely impacted by the crisis, as well as a decrease in the productivity differential between continuing and surviving firms within sectors. TFP proxies exhibit a similar pattern.

Table 2 furthermore shows that young and small firms were especially vulnerable during the crisis, whereas exporters were less likely to exit (compared to other years), perhaps because of favorable exchange rate movements. During the crisis, survivors were not, on average, more likely to operate in sectors highly dependent on external finance, whereas pre- and post-crisis they were, suggesting that the crisis hit sectors more dependent on external finance relatively harder. Similarly, firms in sectors with higher liquidity needs and lower levels of assets tangibility appear to have been particularly exposed. In short, firms‟ financial characteristics were correlated with vulnerability to the crisis.

# _6.1.2 Modeling Survival – Baseline Results_

Table 3 present our baseline firm-survival model, which models firm exit as a function of the log of the age of the firm, firm size and its square to allow for non-linearity in the sizesurvival relationship, the proportion of blue collar workers in the total workforce (the “unskilled ratio”), foreign and government ownership, whether or not the firm exports and productivity. All these variables are interacted with crisis and recovery period dummies to assess which firms were more vulnerable to the crisis. In addition, industry, year, and province dummies are included to eliminate time, industry and location effects. The first column uses value-added per 20
10 policy also raised the possibility of a quasi-fiscal deficit in the central bank. This was particularly the case in periods of capital inflow when interest rates paid by the central bank on its debt exceeded the earnings on its international reserves converted into local currency.<sup>17</sup>

# **B.   Bank Credit to Government and Crowding-out**

Banks’ credits to governments<sup>18</sup> more than doubled as a percentage of GDP from 1990 to 2000, on average, a rise in which bank crises played a major role. Bank credit to government rose from 3.6 percent of GDP in 1990 to 5.2 percent of GDP in 1993, then further rose to 8.4 percent of GDP in 2000. This 4.8 percentage point rise compares with an average rise in the deposit to GDP ratio of 16 percentage points the 1990s. In 16 of the 25 countries, net credits to government rose by over two percent of GDP.<sup>19</sup>

**Figure 4.  Net Claims on Government 1990-2000**

<!-- Start of picture text --> 35 35<br>Indonesia<br>30 30<br>Turkey<br>25 25<br>Egypt<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>1989 1991 1993 1995 1997 1999 2001<br>Net Credit to Gov. Ave Net Credit Gov.<br>Percent of GDP<br><!-- End of picture text --> The ratio of net government credit to deposits is a perhaps more important indicator of banks’ intermediation than the ratio with respect to GDP. A given ratio of net credit to government to GDP has a greater negative impact on banks’ intermediation between savers and private investors in a smaller banking system. Relative to deposits, net credit to government increased by about 65 percent in the 1990s, from 12.9 percent of

> 17  If such a quasi-fiscal deficit occurred, then the central bank either had to issue more debt simply to cover its interest and operating costs, perhaps when it was inconvenient from the standpoint of monetary targets, or else obtain a transfer from the government.

> 18 Net credit to government is defined as credit to government, less government deposits and government funds provided to the banks for on-lending .  Government funds for on-lending were used in 6 countries.

> 19 The ratio of government credit to GDP fell between 1990 and 2000 in Korea, Malaysia, Pakistan, Peru, Thailand, and Venezuela, and rose by less than one percent of GDP in Argentina (albeit from the relatively high initial level of 8 percent), Chile, and Egypt.
**Table 1:** Trip statistics

||Mean|St. dev.|1|per<br>10|centile:<br>25|50|75|90|99|
|---|---|---|---|---|---|---|---|---|---|
|Speed|22.1|7.1|11.5|14.7|17.1|20.6|25.4|31.6|45.8|
|Duration|20.0|17.6|4|7|9|14|23|40|93|
|Duration (no traffc)|17.2|14|4|6|9|13|20|33|76|
|Trip length|8.2|10|1.3|1.9|2.9|4.7|8.9|17.9|54.1|
|Effective length|5.4|7.0|1.0|1.2|1.8|2.9|5.5|11.9|39.6|

_Note:_ 22,744,156 observations. Durations are in minutes, lengths in kilometers; and speeds in kilometers/hour.

**Table 2:** Summary statistics for Indian cities

||Mean|St. dev.|Min.|Max.|
|---|---|---|---|---|
|Population (’000, Census/lights, 2011)|1,328|3,031|19|23,889|
|Population (’000; UN, 2015)|1,545|3,179|307|25,703|
|Population growth 1900-2015 (%)<br>|106|65|31|399|
|Total area (km<sup>2</sup>)|238|414|5.91|3,569|
|Total roads length (km)|1,393|3,451|10|32,513|
|Motorways (km)|43.9|64.5|0|437|
|Primary roads (km)|44.1|77.3|0|481|
|Share households with car access (%)|9.99|5.78|2.33|31.5|
|Share households with motorcycle access (%)|41.3|11.7|5.83|73.4|
|Mean dailyearnings($)|4.91|1.93|2.00|12.28|

_Notes:_ Cross-city averages not weighted by population. 153 cities except for vehicle registrations for which one city is missing.

**Table 3:** Summary statistics for travel speed in Indian cities

||Mean|St. dev.|Min.|Max.|
|---|---|---|---|---|
|All trips|24.4|3.79|16.2|34.9|
|Radial trips|22.2|3.79|14.8|32.8|
|Circumferential trips|20.6|3.23|14.3|29.5|
|Gravity trips|22.6|3.42|14.7|30.9|
|Amenity trips|26.9|6.08|16.6|42.0|
|All trips, unweighted by length|21.8|2.90|15.7|31.4|
|All trips, in absence of traffc|26.8|4.49|16.3|38.1|
|All trips,effective speed|16.4|2.77|11.6|24.0|

_Notes:_ 154 cities. Speed in kilometers per hour.

14