rafmacalaba/datause-encoder-data huggingface dataset:
in agricultural services (such as sales of fertilizers and tools and farm maintenance).

How can these effects be estimated? With a household survey database containing information on expenditures and quantities of agricultural goods, a naive model to estimate the responses of wages to changes in agricultural prices would be to run a regression of wages on unit values. Let ahc be the agricultural wage income of household h living in region (cluster) c. Let ln vc<sup>gbe the log unit values ofgood greported by households in cluster c.A</sup> simple model would be where mhc is a vector of controls such as gender, age and marital status of the household head, his/her education, household size and demographic composition, and time and regional dummies.

There are three concerns with a regression model such as (1): endogeneity of unit values, bias due to proxy variables, and measurement error. Endogeneity may arise because households simultaneously choose quantity and quality. Therefore, unit values are not a perfect measure of prices. Even when unit values are a good proxy for prices, the model may estimate the vector γ consistently, but λ inconsistently (i.e., the proxy bias). Measurement error arises if there are inaccurate responses, mainly on quantities consumed. In all these cases, OLS estimation of (1) will lead to inconsistent estimates of the wage agricultural income price-elasticities. The joint estimator developed in this section takes care of all these problems.

In a general equilibrium model with wage and supply responses, there may be additional problems in the estimation of own- and cross-price elasticities using standard empirical models of demand (such as those discussed in Deaton and Muellbauer, 1980a). Concretely, when a fraction of household income responds to price changes, the elasticities will be inconsistently estimated. To see this, let household preferences over agricultural and non-agricultural goods be defined by a utility function uh = uh(ch), where ch is a vector of consumption. Utility is maximized subject to the budget constraint pch = xh. The vector 4
|||640510, 640520,<br>640590||||
|---|---|---|---|---|---|
||Footwear|640110, 640192,<br>640199|1. Canada|2. Argentina|China (1,2); Vietnam (1)|
|19|Ceramic tiles (unglazed)|690790|1. Ecuador (CSG)|2. Jordan (SG)|China (1)|
||Ceramic tiles (glazed)|690890|1. Ecuador (CSG)|2. India<br>3. Morocco (SG)|China (1,2)|
|||||4. Jordan (SG)||
|20|Tableware and kitchenware|691110, 691200|1. Ecuador (CSG)|<br>2. Argentina|China (1,2)|
|21|Wire of iron or nonalloy steel|721710|1. EC|2. Indonesia (SG)|China (1)|
|22|Flat-rolled products of stainless<br>steel|721931, 721932,<br>721933, 721934,<br>721935|1. EC|2. India|China (1,2); Korea (1,2); Taiwan (1,2); Japan (2); EC (2); South<br>Africa (2); Thailand (2); U.S. (2)|
|23|Pipes and tubes|730429|1. Canada|2. Ukraine (SG)<br>3. EC|China (1,3)|
||Pipes and tubes|730630|1. U.S.|2. EC<br>3. Canada<br>4. Australia|China (1,2,3,4); Belarus (2); Bosnia Herzegovina (2); Malaysia<br>(4); Russia (2)|
||Pipes and tubes|730661|1. U.S.|2. EC<br>3. Australia|China (1,3); Turkey (1,2); Belarus (2); Korea (1); Malaysia (3);<br>Mexico (1); Ukraine (2)|
|24|Iron or steel chain|731582|1. South Africa|2. Argentina|China (1,2)|
|25|Iron or steel nails, tacks, pins,<br>corrugated nails, staples|731700|1. U.S.|2. Indonesia (SG)|China (1); UAE (1)|
|26|Screws and bolts|731815|1. EC|2. U.S.|China (1,2)|
|27|Aluminum bars, rods and<br>profiles|760421, 760429,<br>760820|1. South Africa|2. Canada|China (1,2)|
|28|Aluminum foil|760711|1. EC|2. India (CSG)|China (1,2); Armenia (1); Brazil (1)|
|29|Heat pumps|841861|1. Argentina|2. Canada|China (2); Malaysia (1); Thailand (1)|
|30|Refrigerators|841899|1. Canada|2. U.S.|China (1,2)|
|31|Food grinders, processors and<br>mixers|850940|1. Turkey (SG)|2. Argentina|China (2); Brazil (2)|
|32|Recordable DVDs and CDs|852340|1. India|2. Brazil (SG)<br>3. Argentina|China (1); Hong Kong (1); Paraguay (3); Taiwan (1)|
|33|Articles of graphite|854511|1. U.S.|<br>2. Brazil|China (1,2)|
|34|Road wheels and parts and<br>accessories thereof|870870|1. Australia|2. Argentina|China (1,2)|
|35|Parts and accessories of motor<br>vehicles|870899|1. Argentina|2. India|China (1,2)|

Source: Compiled by the author from the _Global Antidumping Database._ The table identifies more than 70 distinct 6-digit HS codes with at least two different countries newly initiating trade remedy investigations over the same code between 1Q 2007 and 1Q 2009.

30
source of bias is related to contemporaneous, exogenous influences on health care use unrelated to the healthcard. The availability of panel data collected just prior to the introduction of the healthcard in 1997 and again in 2000 makes it possible to use a difference-in-differences estimator. The combination of propensity score matching with difference-in-differences eliminates selection bias as well as bias caused by timeinvariant unobservables from the estimated healthcard effect. This represents another source of deviation from Pradhan et al’s study, which used a post-intervention treatmentcontrol group design. They evaluated the healthcard impact as the difference between utilization outcomes of healthcard owners and observationally similar non-healthcard owners in 1999. I find that failure to control for these sources of bias significantly underestimates the program effect.

I begin by describing health care utilization trends in the aftermath of the economic crisis and provide an overview of the Indonesian healthcard program. This is followed by a review of the literature. I then lay out the hypotheses and analytical framework, explain the empirical strategy and describe the data. Thereafter, I present and discuss the results.

# **Background**

Low levels of use of modern health services in Indonesia are largely attributed to financial barriers to access. The large household out-of-pocket share in overall health financing provides justification for such claims. Households accounted for 60-70% of total health expenditures during 1995-2000 [4]. Out-of-pocket payments are incurred 4
|Type of property: individual<br>property|0.0108|-0.170***|-0.141***|
|---|---|---|---|
||(0.117)|(0.0567)|(0.0484)|
|Type of property: apartment|0.176|-0.0498|0.0111|
||(0.120)|(0.0397)|(0.0420)|
|Type of property: penthouse|0.685***||0.150**|
||(0.137)||(0.0645)|
|Type of property: loft|0.477***|0.0750|0.118**|
||(0.153)|(0.0557)|(0.0582)|
|Whether the development has a pool|0.0481|0.0489***|0.0399***|
||(0.0488)|(0.0127)|(0.0137)|
|Whether the development has<br>surveillance|0.0825***|0.00981|0.0368***|
||(0.0145)|(0.00674)|(0.00710)|
|Socioeconomic classification: 1.<br>Social 2. Affordable 3. Medium 4.<br>Residential 5. Residential plus|0.302***|0.429***|0.383***|
||(0.0178)|(0.0176)|(0.0157)|
|Average years of education of<br>neighborhood|-0.000241|-0.00181|-0.00303|
||(0.00547)|(0.00201)|(0.00190)|
|Proportion of individuals without<br>health insurance in neighborhood|0.00346<br>|-0.199***<br>|-0.179***<br>|
||(0.144)|(0.0618)|(0.0579)|
|Proportion of individuals affiliated<br>to_Seguro Popular_in neighborhood|-0.697***|-0.0318|0.0229|
||(0.241)|(0.116)|(0.109)|
|Constant|11.16***|10.27***|10.91***|
||(0.447)|(0.378)|(0.380)|
|Observations|2,762|8,329|11,091|
|R-squared|0.817|||
|Number of projects||2,916|5,678|

Clustered standard errors at the neighborhood level in parentheses. Panel data estimations (column B and C) with random effects. Type of property: townhouse, dropped to avoid collinearity. OLS estimation in column A has year fixed effects.

*** p<0.01, ** p<0.05, * p<0.1

34
POLICY RESEARCH WORKING PAPER 1727

# **Summary findings**

Loayza presents the view that informal economies arise when governments impose excessive taxes and regulations that they are unable to enforce.

Loayza studies the determinants and effects of the informal sector using an endogenous growth model whose production technology depends essentially on congestable public services. In this model, changes (in both policy parameters and the quality of government institutions) that promote an increase in the relative size of the informal economy will also generate a reduction in the rate of economic growth.

Using data from Latin American countries in the early 1990s, Loayza tests some of the model's implications and estimates the size of the informal sector in these countries - identifying the size of the informal sector to latent variable for which multiple causes and indicators exisr.

'The results suggest that:

* The size of the informal sector depends positively on proxies for tax burden and restrictions on the labor market.

* It depends negatively on a proxy for the quality of governiment institutions.

* An increase in the size of the informal sector hurts growtlh by reducing the availability of public services for everyone in tihe economy and by increasing the number of activities that use some existing public services less efficiently or not at all.

This paper - a product of the Macroeconomics and Growth Division, Policy Research Department - is part of a larger effort in the department to examine the determinants of economic growth. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC, 20433. Please contact Rebecca Martin, room NI 1-059, telephone 202473-9026, fax 202-522-3518, Internet address rmartinl(@'worldhank.org. February 1997. (52 pages) _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_ **_quic_** _kly, even if the presentations are less than fully polished. 'The papers carry the names of the authors and should_ **_be_** _cited accordingly. T7hc findings, interpretations, and conclusions expressed in this paper are entirely those of_ **_the_** _authors. They do not necessarily retnesent the Euiw of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Rese;rch D)ssenmination C:enter
Policy Research Working Paper 10190

# **Abstract**

- This paper analyzes the distributional impact of the tax-ben efit system in Bhutan. It makes two main contributions: first, this is the first substantive study of this kind in Bhutan, and second, due to limited information on incomes in the household survey, a consumption-based model is combined with Mincer-type earnings to derive estimates of incomes. The results show that the combined impact of government taxes and social spending is to reduce inequality and slightly increase poverty as of 2017. The increase in poverty is mainly due to the burden of indirect taxes and social contributions that are not offset by other transfers. Households in the bottom 80 percent are net receivers of fiscal interventions, with fiscal benefits primarily occurring through education and health benefits, which are both progressive. Most

households did not pay much into the system as of 2017, as personal income taxes have a high exemption threshold and sales taxes only apply to a selected number of goods that are mainly consumed by richer households. Due to the lack of direct transfers, the net cash position is negative for poor households, although the magnitude is very small. Simulations suggest that the recent personal income tax reduction leads direct taxes to be slightly more progressive; however, the inequality-reducing impact is dampened. The goods and services tax is expected to increase indirect taxes for households across the distribution and is less progressive than the sales tax. This could lead to a temporary increase in poverty, which could be offset through direct transfers financed by the additional revenues.

This paper is a product of the Poverty and Equity Global Practice. 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 ykim8@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
# **Figure 3: Impact of portfolio inflows to GDP on the comovement between local and US stock market returns**

Figure shows result from regressing the comovement between local and US stock returns on the share of portfolio inflows to GDP across countries.

<!-- Start of picture text --> 2.5<br>CYP<br>2<br>CZE<br>KOR<br>VNM GRC<br>POL<br>1.5 EGY IND ZAFFIN<br>TUR AUT<br>RUS DEU<br>BGR<br>HRV SWE ITA HUN FRA<br>SAUMEX BEL HKG AUS<br>ROMBRA SGP NLD ESP<br>NOR<br>NZL<br>1 IDNCANDNK GBR<br>KENCOL ISR<br>JPN<br>LVA PRT<br>CHEKWT MUSCHN<br>THA ARG KAZ<br>LTU MYS<br>NAMPER EST<br>MNGTUNMLTJOR SVN LBN<br>0.5 CRI PHL<br>BIHBGDBWAMARCHLLKA VENOMNSVKUKRJAMBHR<br>PAK<br>0<br>0 5 10 15 20 25<br>ECU<br>-0.5<br>Portfolio Inflows/GDP(%)<br>Comovement between local and US stock market returns (Period pre-Lehman)<br><!-- End of picture text --> 36
- Fang, Lei, David Peter Wyon, Geo Clausen, and Povl Ole Fanger (2004), “Impact of indoor air temperature and humidity in an office on perceived air quality, sbs symptoms and performance.” _Indoor air_ , 14, 74–81.

- Fesselmeyer, Eric (2019), “The impact of temperature on labor quality: Umpire accuracy in major league baseball.” _Available at SSRN 3421241_ .

- Fletcher, Jason M and Barbara Wolfe (2016), “The importance of family income in the formation and evolution of non-cognitive skills in childhood.” _Economics of education review_ , 54, 143–154.

- Galster, George and Patrick Sharkey (2017), “Spatial foundations of inequality: A conceptual model and empirical overview.” _RSF: The Russell Sage Foundation Journal of the Social Sciences_ , 3, 1–33.

- Garg, Teevrat, Gordon C McCord, and Aleister Montfort (2020), “Can social protection reduce environmental damages?”

- Hallegatte, Stephane, Jisung Park, Mook Bangalore, and Evan Sandhoefner (2018), “Households and heat stress: estimating the distributional consequences of climate change.” _Environment and Development Economics_ .

- Harries, Keith D, Stephen J Stadler, and R Todd Zdorkowski (1984), “Seasonality and assault: Explorations in inter-neighborhood variation, dallas 1980.” _Annals of the Association of American Geographers_ , 74, 590–604.

- Hausman, Jerry A, Bronwyn H Hall, and Zvi Griliches (1984), “Econometric models for count data with an application to the patents-r&d relationship.” Technical Report 17, National Bureau of Economic Research Cambridge.

- Heilmann, Kilian and Matthew E Kahn (2019), “The urban crime and heat gradient in high and low poverty areas.” Technical report, National Bureau of Economic Research.

- Heller, Sara B, Anuj K Shah, Jonathan Guryan, Jens Ludwig, Sendhil Mullainathan, and Harold A Pollack (2017), “Thinking, fast and slow? some field experiments to reduce crime and dropout in chicago.” _The Quarterly Journal of Economics_ , 132, 1–54.

- Heutel, Garth, Nolan H Miller, and David Molitor (2021), “Adaptation and the mortality effects of temperature across us climate regions.” _Review of Economics and Statistics_ , 103, 740–753.

- Hirshleifer, David and Tyler Shumway (2003), “Good day sunshine: Stock returns and the weather.” _The Journal of Finance_ , 58, 1009–1032.

- Hsiang, Solomon (2016), “Climate econometrics.” _Annual Review of Resource Economics_ , 8, 43–75.

- Hsiang, Solomon, Robert Kopp, Amir Jina, James Rising, Michael Delgado, Shashank Mohan, DJ Rasmussen, Robert Muir-Wood, Paul Wilson, Michael Oppenheimer, et al. (2017a), “Estimating economic damage from climate change in the united states.” _Science_ , 356, 1362–1369.

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- IPCC (2014), “Ipcc fifth assessment report—synthesis report.”

39
prevalence rate, we compared this to a similar indicator from 47 countries for which a Demographic and Health Survey was administered in the last decade, and included a GBV module based on the WHO methodology. Lifetime exposure to physical and/or sexual violence from intimate partners in these 47 countries ranges from 6.4% in the Comoros to 57.1% in Papua New Guinea. Lao PDR ranks in the 15% of countries with the lowest prevalence of IPV, with a prevalence rate similar to neighboring Philippines (12.2%), Myanmar (16.3%) and Cambodia (18.2%).

The same national survey estimates that 5.1% and 5.3% of Laotian women experience physical and sexual violence, respectively, from a non-partner. For approximately 60% of respondents who reported this violence, the violence was committed by family members. Notably, close to two-thirds of these family members were women, especially mothers and stepmothers. This suggests women in Lao PDR experience relatively low levels of violence by non-partnered men.

Qualitative work (from the Laotian National Commission for Advancement of Women, 2015) outlines four key triggers of IPV: (i) failure of a woman to meet the expectations of gender roles, which assert superiority of a husband; (ii) jealousy, mistrust or infidelity by the husband; (iii) alcohol or drug use; and (iv) financial stress. Notably, women’s participation in a PWP program could affect at least two of these triggers: it may violate gender roles and is likely to affect the financial situation of the household.

3. Road Management Group (RMG) Program and Experiment Design The RMG program emerged from an evaluation of the government’s Poverty Reduction Fund (PRF), a community-driven development program aimed at reducing poverty through infrastructure improvements in education, drinking water, irrigation, health, and transport. An evaluation carried out in 2015 showed that the PRF had been successful in improving infrastructure in rural areas but that post-completion, road quality deteriorated quickly. Under pressure from harsh rainy seasons and vehicular traffic, the access roads typically fell into extreme disrepair within a couple years and often became unusable. Aiming to provide a part-time supplementary income-earning opportunity to at-need households and to extend the lives of the access roads, a pilot Road Maintenance Group (RMG) program was established, in which female villagers from poor households would be trained and formed into work units to provide basic road maintenance services.

Two eligibility criteria were used to recruit interested RMG members from each village:

- I. Candidates must belong to a poor household, based on PRF poverty ranking, assigned in 2016 and updated by village heads on the day of the selection.<sup>4</sup> Only one woman from each such household could participate in the program.

> 4 Dervisevic et. al. (2020) describe this process and confirm targeting is effective.

5
**Table 1. The quality of property right protection and infrastructure**

|**Panel A:**|**The proportion of IP infringement cases**|**won by the plaintiff**|||
|---|---|---|---|---|
|2006|2007<br>2008<br>20|09<br>2010|2011<br>2012|<br>2013|
|0.814|0.833<br>0.855<br>0.8|65<br>0.876|0.815<br>0.875|<br>0.918|
|**Panel B:**|**The probability of getting legal protectio**|**n for property rights**|**and legal contra**|**cts**|
|Inland|0.601||||
|Coast|0.668||||
|**Panel C:**|**Road and railway access**||||
|Year|Length of highways<br>Popularization|Telephone|Length of|Length of|
||(10000 km)<br>Rate of internet|(Including Mobile)|railway|expressway|
||(%)|(sets/100 persons)|(10000 km)|(10000 km)|
|1993|3.611<br>.|2.20|0.201|0.011|
|1995|3.856<br>.|4.66|0.205|0.015|
|1997|3.956<br>.|8.11|0.220|0.020|
|1999|4.360<br>.|13.12|0.218|0.042|
|2001|5.477<br>.|26.55|0.234|0.065|
|2003|5.838<br>4.6|42.16|0.244|0.098|
|2005|6.227<br>7.3|57.22|0.252|0.137|
|2007|11.56<br>10.5|69.45|0.252|0.180|
|2009|12.46<br>22.6|79.89|0.276|0.217|
|2011|13.25<br>34.3|94.81|0.302|0.283|
|2012|13.67<br>38.3|103.10|0.315|0.320|
|2013|14.05<br>42.1|109.95|0.333|0.348|
|2014|14.40<br>47.9|112.26|0.361|0.362|
|2015<br>**Panel D:**|14.77<br>50.3<br>**Quality and stability of infrastructure a**|109.30<br>**ccess**|0.390|0.397|
||No. of power outages<br>Phone service|Internet service|Days to install a|Days needed to|
||per year<br>interrupted (per month)|interrupted (per|new telephone|gain the access to|
|||month)||Internet|
|Inland|11.43<br>0.496|1.035|2.863|3.789|
|Coast|11.15<br>0.297|0.828|2.965|3.718|

_Data sources: IP infringement cases are from Peking University legal case database (BeiDaFaBao); The probability of getting legal protection for property rights and legal contracts World Bank Business Environment Survey of 2005;_ the data on road and railway access is from Statistical Yearbook of China; data on the stability of infrastructure access is from WBES-China 2005.

37
# Table 5

|Determinants of Price Index - OLS w|ith Huber-White|Standard Errors|(Itl in Darenth|esis)|
|---|---|---|---|---|
|Variable|**(a)**|**(b)**|**(c)**|**(d)**|
|Festival Expenditures/1000 (1992 rupees)|-1.8102<br>(1.7)||-2.8529<br>(2.2)||
|Festival Expenditures x Income|**---**|**---**|0.0595<br>(1.0)||
|Festival Expenditures (Predicted)||-5.5314<br>(1.8)|---|-7.7058<br>(2.2)|
|Festival Expenditures (Predicted) x Income<br>_________________________________|**---**<br>___**_**_**_**_**_**_**_**___<br>_ __ __ __ __|____|**---**|0.1220<br>(1.8)|
|Beedu Village|5.5849<br>(2.1)|3.5493<br>(1.0)|4.4174<br>(1.5)|1.1021<br>(0.3)|
|Ooru Village|1.5151<br>(0.4)|0.0019<br>(0.0)|1.243<br>(0.4)(02|-0.7106|
|Annual Income/1000 (1992 rupees)<br>~~~~~~~~~~~~~~(0.6|-0.0591<br>)|0.0581<br>(0.6)|-0.3212<br>(1 0)|-0.5175<br>(1.3)|
|Father's Land Holdings|-0.6589<br>(4.7)|-0.6644<br>(4.1)|-0.6355<br>(4.6)|-0.6214<br>(3.8)|
|Land in Acres|0.9627<br>(0.9)|1.9351<br>(1.7L|0.6652<br>(0.6)|2.0219<br>(1.8)|
|Number of Adults<br>__|-2.6409<br>_<br>~~~~~~(1|-2.9491<br>-8)(1.8)|-2.4313<br>(1.6)|-2.4876<br>(1.5)|
|Number of Kids**10-16**years|0.7638<br>(0.4)|1.9119<br>(0.8)|1.0651<br>_(0.5)_|2.4375<br>(1.1)|
|Number of Kids < 10 years|0.6116<br>(0.6)|1.0664<br>(0.8)|0.7804<br>(0.7)|1.2709<br>(1.0)|
|Maximum Years of Schooling|0.0068<br>(0.0)|0.0383<br>(0.1)|0.0615<br>(0.2)|0.1939<br>(0.4)|
|Age of Head|0.0262<br>__0O.3)|0.0322<br>(0.3)|0.0337<br>(0.4)|0.0319<br>(0.3)|
|constant|106.2416<br>(22.0)|112.0663<br>(20.5)|109.0430<br>(20.8)|118.7900<br>(17.4)|
|Adi usted R-Sauared|0.17|0.21|0.18|0.23|

21