rafmacalaba/datause-encoder-data huggingface dataset:
_Ain_ indicates the number of the _n_ th appliance owned by the household. Based on prior empirical research that has studied households’ appliance use decisions (Parti and Parti, 1980; Reiss and White, 2005), 15 distinct appliances are modeled in the demand equation, including refrigerators, freezers, televisions, computers, air conditioners, electric space heating, and swimming pools and so on. See Table 6 for a complete list of variables entering the model.

Appliance ownership ( _Ain_ ) is also assumed to be exogenous. This is somewhat contentious because it is possible that factors affecting appliance utilization behavior also affect decisions to acquire appliances. In the short run, however, the appliance stock is fixed, and existing evidence suggests that the bias from ignoring the possible endogeneity is likely to be small. See Reiss and White (2005) and Sebold and Parris (1989) for a discussion.

Previous studies indicate that weather conditions are important predicators of electricity consumption (Reiss and White, 2003). However, HBS 2008 and 2009 do not reveal household geographic locations, making it impossible to match weather data with sample households. As an alternative way to capture the weather effects, I add two dummy variables _SUMi_ and _WINi_ which take value 1 if the household is interviewed during the summer months (June – August) and winter month (December – February), respectively. For completeness, I also include month and year fixed effects that, as it turns out, deplete the explanatory power of price, while changing the coefficients of demand parameters only slightly. See Section V and Table 3 for details. This alternative approach, although capturing the seasonality in electricity demand, does not address regional variation in weather conditions. In Section V, I also take a formal approach to validating the model using out-of-sample testing.

Finally, is the error term; , and are unknown parameters to be estimated.

To test for the robustness of the above demand estimation, I also allow household income to enter the interaction term as a continuous variable: (2) Furthermore, households may not have noticed price changes until after receiving the electricity bills. To account for delayed response to tariff increase, I also estimate the model using the electricity prices of the previous month. The results are robust to both alternative specifications. This is discussed in detail in Section V.

# _Sample Selection_

Using disaggregated data to estimate residential electricity demand is desirable as we avoid the confounding effect of misspecification arising from aggregation bias. (Blundell, 1988; Barnes, Gillingham, and Hagemann, 1981; George 1980; McFadden and Dubin 1980). However, using 8
The next obvious question to examine is the variation in PA rents as a percent of GDP across countries.

The scatter in Figure 1 shows no discernible trend or pattern, and this is not entirely surprising. While land rents per hectare will tend to rise with GDP per capita, owing to better technology, there is no obvious reason why the underlying endowment (land suited to be protected areas) should vary with income. There is conceivably a greater willingness of high income countries to establish protected areas than in developing countries, but we show below that this is not borne out by the data.

_PA rents as a percent of GDP_ Figure 2 shows the distribution of the PA rent percentage across the 146 developed and developing countries in the sample from World Bank (2011).

**Figure 2.  Distribution of PA rents as a percent of GDP, 2005**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0% 11.0% 12.0% 13.0%<br>PA rent, % of GDP<br>Number of countries<br><!-- End of picture text --> Source: Derived from World Bank (2011). Note that Ecuador (22.6%) is not shown.

12
# **INTRODUCTION**

Questions of how to effectively reform and strengthen tax systems have moved to the center of development debates. On one hand, there is an opportunity to build on the successes of tax reform efforts in recent decades. Across much of the developing world, countries have achieved significant administrative and policy modernization, and meaningful improvements in revenue mobilization. On the other hand, successes have been uneven, and major challenges remain. Tax revenue in many countries remains well below the levels needed to finance the achievement of the sustainable development goals, with 15 percent of GDP often cited as a rough annual target (Figure 1).<sup>1</sup> Meanwhile, revenue collection is frequently characterized by significant unfairness and inequity, with particularly weak compliance and enforcement among the rich, and often significant – though overlooked – formal and informal burdens on lower income groups.<sup>2</sup>

**Figure 1: Total Non-Resource Tax Collection by Region, 1990-2016 (% of GDP)**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015<br>EAP ECA LAC MENA NA SA SSA<br><!-- End of picture text --> _Source:_ International Centre for Tax and Development/UNU-WIDER Government Revenue Dataset Observers have questioned whether recent improvements in revenue collection have contributed to the construction of broader “fiscal contracts”. Revenue collection is not an end in itself. It only becomes socially desirable if it is translated into efficient and effective spending by governments. There is now extensive research documenting the ways in which the expansion of taxation may spur greater accountability and contribute to state building. Yet the research makes clear that while

> 1 Gaspar, V., Jaramillo, L and Wingender, P. (2016) Tax capacity and growth: is there a tipping point? _IMF Working paper_ WP/16/234. Washington DC: International Monetary Fund.

> 2 Moore, M., & Prichard, W. (2017). How can governments of low income countries collect more tax revenue? _ICTD Working Paper 70._ Brighton: International Centre for Tax and Development.

2
covered all 823 government and private schools that offer primary level education in these villages. In addition, the survey interviewed and followed a random sample of 1,807 households in these villages for four years.

Under the rules of the program, households in Attock and Faisalabad districts (henceforth comparison districts) are ineligible to receive the stipend while those in Rahim Yar Khan district (henceforth the treatment district) may be stipend-eligible if they have a girl enrolled in grades 6, 7 or 8 in a public school. Detailed time use data were collected on the mothers in these households in 2004, 2006 and 2007. The time use module collects information on a range of activities including time spent on housework, paid work, looking after children’s needs and sleep.<sup>4</sup> Respondents were asked to recall the amount of time they spend in various activities in a 24-hour period on a typical school day.

From among the 1807 households surveyed, I identify 603 mothers of children eligible for the stipend program, of which 541 provide time use data.<sup>5</sup> I exclude from the analysis 59 mothers who either gave reports of time use that exceeded 24-hours or were missing information on key items.<sup>6</sup> The remaining 482 mothers comprise the sample with which the main analysis in this paper is conducted. In addition to household data from the LEAPS survey, I briefly present evidence from the school surveys conducted by the LEAPS team as well as some supporting

> 4 Other activities on which data was collected include: school shopping, children’s studies, prayer, entertainment and a catch-all category – other.

> 5 A household is deemed to have an eligible child if the child is a girl who reports being enrolled in any of the following grades: grade 6, 7 or 8. Given the small size of the sample, I pool observations from both stipendineligible districts into a unitary comparison group. I run the comparisons between treatment and the two comparison districts separately in sensitivity checks.

> 6 For the purposes of the analysis presented below, I treat 2004 as a pre-program year. In what follows it is referred to as round 1 of the survey. 2005 (round 2) is the first year of the program but for this year no time use data was collected from adults. 2006 (round 3) represents the first post-program year and 2007 (round 4) represents the second post-program year. In round 1, 44 eligible households report more than 24 hours of time use. 11 households are from Attock, 15 from Faisalabad and 10 from Rahim Yar Khan. In round 3, 38 eligible households report more than 24 hours of time use. 13 are from Attock, 15 from Faisalabad and 10 from Rahim Yar Khan. In round 4, 25 eligible households report more than 24 hours of time use. 5 are from Attock, 13 are from Faisalabad and 7 are from Rahim Yar Khan.

7
- 15 -

# Table 3

# Subjects of US "301" Cases, July 1975 - July 1991

# (number of cases)

||_Mercha_<br>_Agriculture_|_ndiae_<br>_Trade_<br>_Manufactures_|_Services_<br> <br>_Trade_|<br>_Intellectual_<br>_Propry_|_Government_<br>_Procedurec5_|<br>_/Ivestment_<br>_Regulation_|_Several_<br>_sSubject_|_Total_|
|---|---|---|---|---|---|---|---|---|
|All Countries|||||||||
|Number of**cats**|40|24|11|7|2|1|1|86|
|(% of total number)|(47)|(28)|(13)|(8)|(2)|(1)|(1)|(100)|
|Devebped Countrbs|||||||||
|Number of cases|30|19|3|0|0|0|0|52|
|(% of total number)|(58)|(37)|(6)|(0)|(0)|(0)|(0)|(100)|
|Developing Countries|||||||||
|Number of cases|10|5|7|7|2|1|1|33|
|(% of total number)|(30)|(15)|(21)|(21)|(6)|(3)|(3)|(100)|
|Developing excd. Korea|||||||||
|Number of cases|7|5|5|6|2|1|1|25|
|(% of total number)|(28)|(20)|(20)|(24)|(3)|(4)|(4)|(100)|
|Korea|||||||||
|Number of cases|3|2|2|1|0|0|0|8|
|(% of total number)|(38)|(25)|(25)|(13)|(0)|(0)|(0)|(100)|

Source: Tabulated from Office of the US Trade Representative, "Section 301 Table<sup>of Cases," Washington, DC, USTR,</sup> August 15, 1991, photocopied.

a/ Customs valuation and import licensing procedures.
Table 6: Impact of Conflict Intensity on Schooling Enrollment and Attainment, 5-14 year-olds

||(1)<br>Years of<br>Education<br>Female 5-14|(2)<br>Years of<br>Education Male<br>5-14|(3)<br>Enrollment<br>Female 5-14|(4)<br>Enrollment Male<br>5-14|
|---|---|---|---|---|
|Explained Variable|Years of<br>education|Years of<br>education|Enrolled|Enrolled|
|Specification|Eq. (3)|Eq. (3)|Eq. (3)|Eq. (3)|
|District casualties until start of|0.2422**|0.1149|-0.0252|0.0221|
|survey|(0.1044)|(0.1156)|(0.0446)|(0.0321)|
|=1 if Rural|-0.3992***|-0.1979***|-0.0585**|-0.0292***|
||(0.0848)|(0.0622)|(0.0228)|(0.0109)|
|=1 if Head has primary education|0.3975***|0.2654***|0.2119***|0.1407***|
||(0.0436)|(0.0373)|(0.0163)|(0.0137)|
|=1 if Head has secondary|1.2511***|0.9885***|0.2630***|0.1745***|
|education|(0.0631)|(0.0540)|(0.0200)|(0.0167)|
|=1 if Head has higher education|1.5440***|1.1224***|0.3333***|0.1747***|
||(0.1276)|(0.1304)|(0.0233)|(0.0346)|
|Constant|0.1064|-0.0388|0.3697***|0.4528***|
||(0.0983)|(0.0696)|(0.0292)|(0.0251)|
|Panel variable|District|District|District|District|
|Age at Interview dummies|Yes|Yes|Yes|Yes|
|DHS 2006 x Region x Age<br>dummies|Yes|Yes|Yes|Yes|
|Observations|11798|12117|11798|12117|
|No. of Clusters|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|
|R-Squared|0.5158|0.6101|0.1597|0.1381|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2001 and 2006 and aged 5-14 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either, but these districts did not experience any casualty during the conflict. Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

42
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BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Hidden costs**

Percentage revenues <!-- Start of picture text --> a. Evolution of hidden costs in Benin’s water sector<br><!-- End of picture text --> <!-- Start of picture text --> a. Evolution of hidden costs in Benin’s water sector b. Hidden costs of selected water utilities in West Africa<br>160 140<br>140 120<br>120 100<br>100<br>80<br>80<br>60<br>60<br>40<br>40<br>20 20<br>0 0<br>Collection inneficiencies Losses Under-pricing Collection Inefficiencies Underpricing Losses<br>Percentage of revenues Percentage of revenues<br><!-- End of picture text --> _Source_ : Derived from Baneerjee and others (2008) and Briceño-Garmendia, Smits, and Foster (2009).

# **Challenges**

In spite of the recovery of SONEB’s financial situation, its operational performance has deteriorated over time. Nonrevenue water increased from 20 percent in 2000 to 28 percent in 2009. Collection ratios decreased from 100 percent of billings in 2000 to 85 percent in 2009 (table 10). As a consequence, the share of these inefficiencies in total hidden costs has increased versus underpricing (figure 13b). In addition, since 2000 domestic water consumption per capita has not increased—at 24 liters per day it is only one-third of consumption in comparable low-income countries.

In rural areas the expansion of access to water and sanitation has not kept pace with rural population growth, at 2.5 percent annually, worsening over time. Whereas the combined progress in access to piped water, standposts and wells and boreholes was around 3.3 points per year between 2002 and 2006, the rural population using surface water increased 1.8 points annually. On the sanitation side, the combined progress in access to septic tanks and improved and traditional latrines was around 2.5 points per year, but an additional 0.2 percent of the population per year was practicing open defecation (figure 14).

25
- Di Tella, R. and R. MacCulloch, 2005. “The Consequences of Labor Market Flexibility: Panel Evidence Based on Survey Data.” _European Economic Review_ , 49(5): 1225–1259.

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Haltiwanger, J.C., S. Scarpetta, and H. Schweiger, 2010. “Cross‐county Differences in Job Reallocation: <u>The Role of Industry, Firm Size and Regulations.” EBRD Working Paper No. 116.</u> Hamermesh, D. 1993. Labor Demand, Princeton University Press, Princeton NJ.

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18
technical details. Readers who are more familiar with these methods may also find some useful practical insights and suggestions for further research. We will discuss several existing (well-cited) studies as examples, with a focus on developing countries.

This paper consists of eight sections. We provide a simple, but new classification of poverty imputation methods in the next section, which also offers a roadmap to the other sections in the paper. This roadmap can function both as a graphical illustration that links the different methods, and as a “user’s guide” that can help readers better identify the issues of their interest. It is subsequently followed by more detailed discussion for poverty estimates on a non-monetary basis (i.e., using wealth indexes) (Section II), poverty estimates for project targeting (Section III), and monitoring poverty trends at the national level (Section IV). We then discuss poverty estimates that are disaggregated below the national level (Section V) and estimates of poverty dynamics (Section VI) before offering some further reflections (Section VII) and the conclusion (Section VIII).

# **I. A “Roadmap” of Poverty Imputation Methods I.1. Roadmap**

The need to provide imputed poverty estimates varies from context to context, and nuanced differences exist between seemingly similar imputation methods. It can thus be useful to be explicit about the outcomes of interest, as well as the desired types of analysis and their associated challenges, before starting an imputation project. We offer in Figure 1 a simple list of five common questions that can be asked about key aspects of the poverty imputation process, and the suggested strategy to address each question to be discussed in more detail (in the section listed in parentheses). Put differently, this figure proposes a checklist in the decision process to identify the relevant imputation method.

4
111 TABLE 83 Senepl: Disrbuo n Wof by Size of Baed F

|Family Size|1963|1986|
|---|---|---|
|2 to 4|14.2|5.2|
|5 to 7|25.5|12.2|
|8 to 10|27.5|22.0|
|11 to 13|17.0|21.2|
|14 to 16|7.0|15.2|
|17 or more|9.0|19.8|
|Total|100.0|100.0|

*Extended family is defined as the number of people fed regulary in the workees home.

Source: 1963 - Pfeffermann (1968), p. 169.

1986 - Worker sample, 1986 urvey of 17 industrial firms.

In both samples the proportion of those under 31 who support 10 or more people is far below the proportion of the oldest group (over 40) who support this number. In comparison, only 7 percent of the oldest group were supporting fewer than 6 people in 1986, whereas 43 percent of those under 31 were supporting this relatively small number of people.

How does this pattern--that older workers support larger extended families--help explain the finding (in Table 8.3) that the extended families were considerably larger in 1986 as compared to 1963? The answer can be seen in the last row of Table 8.4, which shows the proportion of workers in each age group for the two survey years. It is clear that workers in 1986 were on average older than workers in 1963. If this age distribution is representative of the entire population of workers in the industrial sector, then a strong presumption exits that the combined forces of government regulation and economic deterioration are halting the employment of new (young) workers
WPS3819 LOBBYING AND AGRICULTURAL TRADE POLICY IN THE UNITED STATES

**KISHORE GAWANDE*** BUSH SCHOOL OF GOVERNMENT, TEXAS A&M UNIVERSITY

# **BERNARD HOEKMAN**

WORLD BANK, GROUPE D’ECONOMIE MONDIALE, SCIENCES POLITIQUES, AND CEPR

**Abstract:** This paper studies whether political campaign contributions influence agricultural protection in the United States in the manner suggested by the political economy model of Grossman and Helpman (1994).  This is the first attempt to test this model using agricultural data. We test the model using a detailed cross-sectional data set of agricultural protection, subsidies, and PAC contributions in the late 1990s. The model is qualitatively affirmed by the data. We make a novel attempt to solve a puzzle about the model’s quantitative implications, also found in recent studies.  This solution makes the simple model consistent with the complicated decision-making process in real world government.  The results imply the underpinnings of a political economy equilibrium that will be hard to dislodge.

# World Bank Policy Research Working Paper 3819, January 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**_________________________________** *Helen and Roy Ryu Professor of International Affairs, Bush School of Government. Texas A&M University, College Station, TX 77843-4220. Email: <u>kgawande@tamu.edu.  Phone: (979) 458-8034.</u>

We thank Marcelo Olarreaga for access to valuable data on agricultural protection and subsidies. The paper has benefited greatly from the insightful comments of two anonymous referees.
# **_Rural Labor and Long Recall Loss_**

Kate Ambler Sylvan René Herskowitz Mywish K. Maredia IFPRI World Bank Michigan State University

**Keywords:** Labor, Survey Methodology, Measurement Bias, Recall Windows, Proxy Response **JEL Codes** : O1, J2, C8, Q1

This work was supported by the CGIAR Research Program on Policies, Institutions, and Markets and was approved by the IFPRI Institutional Review Board and the Malawi Committee on Social Science and Humanities, National Commission for Science and Technology. We thank the MwAPTA Institute for collecting the baseline data, GeoPoll for data collection, and Laura Leavens for research support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.