rafmacalaba/datause-encoder-data huggingface dataset:
often precludes supplying sex, women can only adjust their behavior after the STI has passed. To account for this, we include an indicator for the ...rst day after having experienced an STI. All dependent variables are aggregated at the day level, so there is only one observation per woman per day. Results are presented in Table 5.

For all of the shocks, total and private expenditures<sup>22</sup> seem to be relatively insensitive to the occurrence of health shocks. For instance, private expenditures actually increase by about 11 shillings (US $0.16) when another household member gets ill. Food expenditures do decline in response to household health shocks, but only by 14 Ksh, or 7.7% of food expenditures. Taken at face value, these results suggest that sex workers are relatively well insured against unexpected health shocks, though this result says nothing about how consumption smoothing is achieved. As the next few Tables will show, sex workers maintain consumption in large part by increasing their supply of unprotected sex and accepting signi...cant health costs.

# 5.2.2 Participation in the Sex Sector and the Supply of Unprotected Sex

Table 6 presents ...xed e¤ects estimates of the impact of the various shock measures on labor supply. Starting with Panel A, own sickness has the expected e¤ect for all labor supply measures: women are less likely to participate in the commercial sex market when they are sick. The more interesting result is the e¤ect of household sickness on labor supply. Women are 2.6 percentage points more likely to see a client when a household member falls ill. This amounts to a 3.1% increase in the probability of participating in the sex sector. As can be seen in Columns 3 and 4, women increase their participation by taking on more casual clients, which is notable because women usually do not know casual clients before the transaction, so they probably can be less sure of the probability that a casual client is HIV positive than they could be of a regular client. Columns 5-7 show that women adjust their labor supply in the commercial sex sector rather than in other sectors in which they work.

Panel B presents results for experiencing the symptoms of a sexually transmitted infection (STI). As expected, experiencing an STI reduces the probability of supplying transactional sex,

> 22 Private expenditures include alcohol, soda, cigarettes, meals in restaurants, clothing, health and beauty products, and other privately consumed categories such as airtime for cellular phones.

19
# 4. The Allocation of Foreign Direct Investment across China

This section examines the distribution of inward FDI in China, and how it is related to the quality of government.

Our key dependent variable is province-level inward FDI, denoted _FDI_ , taken from China’s _Statistics Yearbook_ , defined as “investment by foreign companies and individuals”. Unfortunately, “foreign companies” include Chinese firms registered abroad. Also, the “investments” include foreign loans taken out by firms undertaking State-approved investment projects. This noise is unavoidable, for these data are the best available.

Our key independent variables gauge the “quality of government” in each province. They are from surveys of firms’ experience with property rights protection and corruption.

_Private legal property right protection_ is from a city by city survey in which business executives are asked: “In disputes that your company was involved, what was the likelihood that the property and contractual rights get legal protection?” The answer can range from 0 to 100%, and our variable is the average of answers across surveyed companies sorted by the province of their headquarters’ city. City averages are aggregated to provide province-level data.

To measure corruption, we use _entertainment and travel costs_ – 2003 data on the proportion of company expenses classified as “entertainment and traveling costs” expressed as a fraction of sales. Cai, Fang and Xu (2005) show that this number is higher in cities with worse bureaucratic burdens. While these costs erode earnings, they presumably also function as bribes and bring benefits that are higher in cities with worse bureaucratic burdens and worse government services. Hence, the data capture bribery payments to “grease” a corrupt government’s “grabbing hand”. In other words, the variable could reflect a government’s discretionary power.

Figures 2 and 3 reveal that firms located in slower growing provinces spend more on entertainment and travel; and that executives in those provinces also believe legal protection to private property rights to be weaker. Figures 4 and 5 show these same measures of institutional 11
**Table 3.8: Probability that an unserved household was connected**

<u>(Proportion of unserved households receiving a connection)</u>

||Electricity|Piped water<sup>♣</sup>|Sanitaryservices<sup>♦</sup>|
|---|---|---|---|
|National|<sup>***</sup>|<sup>***</sup>|<sup>***</sup>|
|1993-1996|.19<br>|.19<br>|.31<br>|
|1997-2000|.36<br>|.34<br>|.55<br>|
|% change|89%|79%|77%|
|**Urban**<br>1993-1996|.38<sup>***</sup><br>|.31<sup>***</sup><br>|.50<sup>***</sup><br>|
|1997-2000|.70<br>|.53<br>|.82<br>|
|% change|84%|71%|64%|
|**Rural**<br>1993-1996|.13<sup>***</sup><br>|.14<sup>***</sup><br>|.22<sup>***</sup><br>|
|1997-2000|.29|.28|.48|
|% change|123%|100%|118%|
|**Extreme poor**<br>1993-1996|.06<sup>***</sup><br>|.13<sup>***</sup><br>|.21<sup>***</sup><br>|
|1997-2000|.17|.26|.37|
|% change|183%|100%|76%|
|**All Poor**|13<sup>***</sup>|15<sup>***</sup>|25<sup>***</sup>|
|1993-1996|.|.|.|
|1997-2000|.28<br>|.29<br>|.44<br>|
|% change|115%|93%|76%|
|**Non-poor**<br>1993-1996|.29<sup>***</sup>|.24<sup>***</sup><br>|.38<sup>***</sup><br>|
|1997-2000|.55<br>|.41<br>|.72<br>|
|% change|90%|71%|89%|
|**Indigenous**<br>1993-1996|.16<sup>***</sup><br>|.18<sup>***</sup><br>|.30<sup>***</sup><br>|
|1997-2000|.30|.32|.52|
|<br>% change|88%|78%|73%|
|**Non-indigenous**<br>1993-1996|.21<sup>***</sup>|.19<sup>***</sup>|.31<sup>***</sup>|
|1997-2000|.42|.35|.57|
|%change|100%|84%|84%|

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística - Guatemala _Notes_ : The null hypothesis of equality of the probability of coverage before and after Peace Accord is rejected at:<sup>***</sup> 99% level.<sup>**</sup> 95% level,<sup>*</sup> 90% level. ♣Piped water in dwelling or yard. ♦Includes toilets and latrines.

At a national level, the probability of an unserved household receiving a connection increased by approximately 80% for electricity, piped water and basic sanitation. All types of households, irrespective of location, poverty or ethnicity experienced a statistically significant increase in the probability of being connected. Moreover, traditionally disadvantaged groups gained disproportionately, increasing their probability of being connected by well over 100% in most cases. For example, the probability of being connected increased by 183% for the extreme poor, 115% for the poor, and 90% for the non-poor.

However, this disproportionate gain has not been enough to compensate for the lower initial probability of being connected for members of traditionally disadvantaged groups. Thus, notwithstanding the large percentage gains, in absolute terms the probability that a family in extreme poverty receives an electricity connection (at 0.17) is still lower than the probability for a family in poverty (at 0.28), and substantially lower than that for a non-poor household (0.55). Some indigenous groups also still have a relatively low probability of being connected, in particular the Q’eqchi (for electricity and water). However, other indigenous groups actually have a higher probability than average of receiving a connection, in particular the Ki’che and Kaqchikel (for electricity).

21
Table 8 reports the OLS and Tobit estimates. The OLS estimates are solely based on non-zero export ‡ows and are reported in columns (1) to (3). The speci...cations in columns (1) and (2) use ...rm labor productivity and employment, respectively, as proxy for ...rm e¢ ciency, while that in column (3) uses a ...rm ...xed-e¤ects estimator to account for ...rm heterogeneity. In all cases, the estimates point to a positive and statistically signi...cant relationship between export volumes and migrant networks.

Columns (4) and (5) report the Tobit estimates. In this case, the estimation sample includes both zero and positive ...rm-level bilateral trade ‡ows and the dependent variable is left-censored by the minimum value of ln xjc<sup>fobto account for sample selection.The corre-</sup> sponding marginal e¤ects con...rm the theoretical prediction that, conditional on selection into export markets, more e¢ cient ...rms obtain larger revenues. Of most interest to the present analysis, the marginal e¤ects of migrant networks on export intensity are positive and statistically signi...cant at the 1% level, but are now larger in magnitude. Therefore, conditional on a ...rm serving a market, the presence of emigration stocks appears to be an important determinant of how much it sells there.

A potential concern with the Tobit estimation is that the censoring point might be measured with error. To assess if the estimates are sensitive to the proxy for the entry threshold, in columns (6) and (7) we report marginal e¤ects from Tobit estimation, but with the dependent variable left-censored at zero. Reassuringly, the estimates remain very similar.

For robustness, Table 9 reports the results of a Heckman sample selection model. In line with Helpman et al. (2008), we use World Bank data on country-speci...c regulation costs of ...rm entry to proxy for ...xed costs of exporting. These indicators consist of the number of procedures and the number of days for an entrepreneur to legally start operating a business.<sup>14</sup> Columns (1) to (3) report the marginal e¤ects of the Probit selection equation. The results in column (1) show that export participation is signi...cantly less likely in markets characterized by a larger number of procedures to form a business, as would be expected. Column (2) shows that the number of days required to form a business also enters negatively the selection equation, but this e¤ect is not statistically signi...cant. In column (3), we specify a binary indicator for both of these costs, which takes the value of one when the sum of the number of days and procedures is above the median. Once again, the e¤ect of this indicator on the probability of exporting is negative, although statistically insigni...cant.

Columns (4) to (6) report the corresponding estimates of the Heckman selection model for the export revenue equation. The estimation can be conducted using full maximum

> 14 Data refer to 2005, come from the WDI of the World Bank, and are available for 159 countries of our sample.

15
# **Table A 8: Logit Regression Results: Household Selection Model (Propensity Score) for ATT estimates**

|**NRLP Intensive vs Non-intensive**||
|---|---|
|Age of Head of Household|-0.02<sup>***</sup>|
||(0.00)|
|SC or ST Household (Y/N)|0.91<sup>***</sup><br>014|
||(.)|
|Dependency Ratio|-0.07<br>|
||(0.06)|
|Status of Primary School in Village in 2012|0.73<br>|
||(0.40)|
|Education Level of Household Head|-0.02<br>|
||(0.01)|
|Household Type Kutcha (Y/N) - SECC 2013|0.08<br>|
||(0.13)|
|Household Owns Telephone/Mobile Phone - SECC 2013|<br>0.10<br>|
||(0.12)|
|Any Member of Household has a Salaried Job - SECC 2013|1.24<sup>***</sup><br>|
||(0.27)|
|Household Owns Any Land - SECC 2013|-0.43<sup>**</sup>|
||(0.14)|
|Percentage of Irrigated Land from Total Land in the Household - SECC 2013|-0.01<sup>**</sup>|
||(0.00)|
|Household Income Main Source: Cultivation - SECC 2013|0.53<sup>***</sup>|
||(0.14)|
|Household Income Main Source: Non-Agricultural Own Account Enterprise - SECC 2013|0.70<br>(0.74)|
|Household Owns Irrigation Equipment - SECC 2013|-0.00|
||(0.20)|
|Percentage of Households having Source of Light: Electricity - Census 2011|0.00<br>(0.00)|
|Log of Total Geographical Area (Hectares) in Village - Census 2011|-0.06<br>|
||(0.11)|
|Status of Pucca Road in the Village (A/NA) – Census 2011|<br>0.57<sup>**</sup><br>|
||(0.18)|
|Log District Head Quarter Distance (kms) from Village – Census 2011|<br>0.10<br>|
||(0.10)|
|Population Density of the Village – Census 2011|0.06<br>004|
||(.)|
|Status of Village Agricultural Marketing Society – Census 2011|0.31|
||(0.19)|
|Percentage of Households having Bicycle - Census 2011|0.00|
||(0.00)|
|Primary Health Centre (Numbers) - Census 2011|0.20|
||(0.48)|
|Worker force participation rate of females in the Village - Census 2011|-0.54|
||(0.58)|
|Constant|-1.18<br>|
||(1.01)|
|Observations|3893|
|Robust standard errors in parentheses||
|<br>***p<0.01,**p<0.05,*p<0.1||

35
**Table 4:  Results from Zero Stage BRGLM, Household Receipt of Aid**

||**Aid**||**Aid * Shoc**|**k**|
|---|---|---|---|---|
|**Weather Shock†**|1.263|***|2.549|***|
||(.238)||(.225)||
|**WFP Distribution**|||||
|WFP HH Distribution|0.077|***|-0.018||
||(.024)||(.020)||
|WFP * Weather Shock|-0.044||0.064||
||(.045)||(.044)||
|**Shock Occurred**|||||
|Limited distribution, poor district|-0.524|**|-0.667|***|
||(.230)||(.244)||
|Limited distribution, non-poor district|-1.582|***|-1.367|***|
||(.292)||(.358)||
|Moderate+ distribution, non-poor district|-0.903|***|-1.042|***|
||(.199)||(.222)||
|**Shock Did Not Occur**|||||
|Positive distribution, non-poor district|0.305|**|||
||(.150)||||
|Positive distribution, poor district|0.902|***|||
||(.194)||||
|No distribution, poor district|1.14|***|||
||(.194)||||
|**Explicit HH Targeting Characteristics**|||||
|Dummy, Female Head|0.178|***|0.283|***|
||(.059)||(.083)||
|Dummy, Children<3|-0.012||0.082||
||(.059)||(.085)||
|Dummy, All Members>59|0.603|***|0.589|***|
||(.176)||(.215)||
|Dummy, HH Member Disabled|0.066||0.04||
||(.060)||(.086)||
|**Wealth**|||||
|Wealth Index|-0.532|**|-0.415||
||(.245)||(.393)||
|Landholdings per cap.|0.365|**|0.439|**|
||(.150)||(.179)||
|**Constant**|-0.505||1.472||
||(1.036)||(1.511)||
|# of Obs.|4412||4412||
|Goodness of Fit<br>**Notes:** Standard Errors in parentheses. ***/**/* den<br>Goodness of Fit statistic is calculated as the square|0.503<br>ote statistical signifi<br>d correlation betwe|cance at 1/<br>en depende|0.605<br>5/10 percent res<br>nt variable and|pectively.<br>predicted|

**Notes:** Standard Errors in parentheses. ***/**/* denote statistical significance at 1/5/10 percent respectively. Goodness of Fit statistic is calculated as the squared correlation between dependent variable and predicted dependent variable. † **Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise.

27
autonomy than women in other parts of rural India (Dyson and Moore, 1983; Kishor and Gupta, 2009); yet even in Tamil Nadu, women’s standing is far more complex than this optimistic account would suggest.<sup>2</sup> It is not that patriarchy is less acute than elsewhere, but it is differently expressed and reinforced. For example, Mines’s (1994) ethnography of private and public identity in Tamil Nadu shows that, while men in this state value and nurture a distinct civic individuality, this is not observed among women, whose sense of self derives from their role as wives, mothers, and daughters-in-law. Similarly, Kapadia et al. (1995), in her classic ethnography of Tamil women, demonstrates that among low-caste women (who are the vast majority of the female participants in the meetings we study), the seemingly high degree of female autonomy is deceptive, as it is confined to extended family, rather than in interactions outside the family. Kapadia atributes this to practices of marital endogamy (the practice of marrying close-kin), which create an ongoing relationship between women and their birth kin afer marriage for two reasons: (a) their physical proximity, and (b) because extended families tend to have marriage ties over several generations and thus have very strong bonds. Moreover, recent improvements in education and labor market opportunities have benefited men much more than women; this change in class has caused kinship ties to break and women to be even more restricted within the home.

Tough these ethnographic accounts were writen two decades ago, recent data reinforces the distinct delineation between genders across the public and domestic spheres. Labor force participation rates for rural men in Tamil Nadu are 59.3 percent for men and 31.8 percent for women.<sup>3</sup> An analysis of survey data from the same sample as the villages we study in this paper, Khanna et al. (2015) shows that 47 percent of married women reported that they were the primary decision makers in household decisions on durable good purchases, but only 12.5 percent reported that they atended the last village assembly, or _gram sabha_ .

> 2http://rchiips.org/nfhs/a ~~s~~ ubject ~~r~~ eport ~~g~~ ender ~~f~~ or ~~w~~ ebsite.pdf

> 3Directorate of Census Operations, Government of Tamil Nadu, http://www.tn.gov.in/dear/ Employment.pdf

8
Policy Research Working Paper 7369

# **Abstract**

This article examines the effect of water pricing policies on farmers’ water saving behaviors, using original water user group (WUG) data from a reservoir irrigation system in China. The introduction of volumetric water pricing at the group level, to replace area-based pricing, induces institutional change to prevent each member’s overuse of water when the volumetric price levels are moderate. Depending on the initial conditions, the multiple pathways of change lead to new institutional arrangements, with all of them contributing to water savings. However, when the price is set high enough, many farmers exit a WUG for private irrigation. This tendency is associated with an increased probability that the remaining members do not undertake institutional change and that they do not end up saving water. This may be due to the increased management difficulties among the remaining members whose fields are separated by former members who have now opted out for private irrigation across the WUG. As a result, we do not find evidence that the reservoir water is saved at high volumetric price levels.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 authors may be contacted at k.kajisa@sipeb.aoyama.ac.jp and dongbin@whu.edu.cn.

_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
Policy Research Working Paper 10780

# **Abstract**

This paper studies the consequences of a U.S. cabotage law for Puerto Rico (PR). Data on ship arrivals in PR show that the fleet of U.S. vessels that call there lacks capacity for carrying non-containerized freight. Empirical estimation using trade data shows that PR’s imports of sea-shipped final products are biased against U.S. mainland sources. This bias is strongest for heavy products and products not typically shipped in containers. Among upstream products, a strong bias against imports of sea-shipped products applies to all sources. Estimated tariff-equivalent costs among final products imply static annual welfare losses of 1.1 percent of household consumption ($203 per person). The same tariff-equivalent cost estimates imply that the law raises the cost of investment in PR by 3.0 percent. The observed bias against sea-shipped inputs in PR’s imports may result from long-run industry location decisions that have been influenced by the law’s presence.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at rhillber@purdue.edu or jimene19@purdue.edu.

_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
# **Enforcement Capacity and the Impact of Labor Regulation: Evidence from the Russian Federation**

1 2 3 4 , Alvaro Gonzalez, Siddharth Sharma and Hari Subhash JEL: O12, D22, K31 Keywords: Firms; Labor regulation; Enforcement; Employment adjustment

> 1 The World Bank

> 2 The World Bank. Corresponding author. Email‐ ssharma1@worldbank.org

> 3 The World Bank

> 4 The authors wish to thank John T. Giles, Francesca de Nicola, Leonardo Iacovone and Luis‐Felipe Lopez‐Calva for their suggestions.
# _Figure 2: Skills and Income Per Capita_

<!-- Start of picture text -->
-<br>13<br>us<br>3<br>12<br>Ef ta<br>11-  0<br>10 -Oxl  Japan<br>Battics<br>o  9  _  aaKorea<br>OEE Russia a<br>"  8 ~-  E  EE5<br>"U7 - 0 Greece  OHong Kong<br>2  ~~~~Ukraine<br>6  - a  1  Portugal<br>MaIa.sia<br>6 -  a  China<br>4  Thaitand  1  Singapore<br>rNorth Africa (4)<br>.3  - nw  a-<br>0  4  8  12  16  20  24  28<br>(Tliousands)<br>Per capita Income In  1991  In  US$<br><!-- End of picture text -->

source: World bank (1992), UNIP<sup>(1993)</sup>