rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9. Health Shocks, Perceived HIV Status, and Transactional Sex**

|**Panel A. Participation in Sex Sector**|(1)<br>Saw Any<br>Clients|(2)<br># of Clients|(3)<br># of Regular<br>Clients|(4)<br># of Casual<br>Clients|(5)<br>Sex Work<br>Income|
|---|---|---|---|---|---|
|Somebody in Household (other than respondent ) Sick|0.021|0.115|0.013|0.095|9.107|
||(0.028)|(0.067)*|(0.038)|(0.052)*|(39.383)|
|Respondent Sick|-0.097|-0.198|-0.068|-0.133|-114.527|
||(0.034)***|(0.076)**|(0.043)|(0.048)***|(31.116)***|
|Observations|3981|3981|3981|3981|3981|
|Number of Women|68|68|68|68|68|
||(1)|(2)|(3)|(4)|(5)|
||Had Unprotected|# Unprotected|Had Vaginal|Had Anal|Had Oral|
|**Panel B. Sexual Activities**|Sex|Sex|Sex|Sex|Sex|
|Somebody in Household (other than respondent ) Sick|0.014|0.030|0.019|0.032|0.038|
||(0.021)|(0.052)|(0.027)|(0.023)|(0.020)*|
|Respondent Sick|-0.011|-0.038|-0.093|-0.052|-0.013|
||(0.017)|(0.035)|(0.034)***|(0.020)**|(0.015)|
|Observations|3981|3958|3981|3981|3981|
|Number of Women|68|68|68|68|68|

Note: All regressions are fixed effects regressions with controls for the month and the day of the week. In a survey, we asked respondents if they had taken an HIV test and, if yes, what they thought the chance was that they were HIV positive. This table is restricted to women who said that the probability that they were infected was less than 50%. We have beliefs for 102 women (53% of the sample). Of these, 66.7% thought they were at less than a 50% chance of being infected.

Clustered standard errors (at the individual level) in parentheses. All regressions include controls for the round of data collection. Sickness is an indicator variable equal to 1 if respondent reports a cough, fever, malaria, typhoid, diarrhea, cuts, burns, or other illnesses.

* significant at 10%; ** significant at 5%; *** significant at 1%
Regional, sector, and size variations on major problems<sup>7</sup> Aleppo stands out as a region in Syria where firms face particularly challenging circumstances. The large majority of firms (over 70 percent of firms) in Aleppo report nearly _all factors_ as major or severe problems. In comparison, firms in the rest of Syria only report major or severe problems at high rates (over 50 percent of firms) for electricity or fuel. Aleppo’s results were so much more severe that for all 25 problems the difference between Aleppo’s results and the rest of Syria was statistically significant.<sup>8</sup>

**Figure 2: Single biggest constraint in Aleppo vs the Rest of Syria**

<!-- Start of picture text --> 80.0%<br>70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>‐10.0%<br>Aleppo Rest of Syria<br><!-- End of picture text --> Contrary to the rest of the country, in Aleppo, the single greatest category of obstacles cited by firms was physical damage and insecurity, which was cited by 73 percent of firms when asked for their single biggest category of issues. In the rest of Syria only 9.1 percent of firms cited physical damage and insecurity as their biggest single constraint. Instead, as already noted, service interruptions are the most cited constraint for the rest of the country with 40.6 percent of firms (i.e. excluding Aleppo).<sup>9</sup> Figure 2 provides the full break down of the single biggest constraint for Aleppo compared to the rest of Syria.

Across several indicators, manufacturing firms report being affected by problems at higher rates than service firms, with significant differences on fuel (63 percent of manufacturing firms facing problems vs. 41 percent of service firms), loss of unskilled labor (35 percent to 14 percent), looting (31 percent vs. 13 percent), inventory losses (28 percent vs. 13 percent), physical insecurity (27 percent to 10 percent), and others.

There were relatively few differences in responses on constraints by firm size. Service interruptions were the biggest single constraint for all three size categories (small, medium, and large) while loss of employees and physical damage were also in the top three for all size firms with only slightly varying percentages of firms citing them. In the more detailed

> 7 All regional, sector, and size comparison statistics reported in the text are statistically significant at the 10% level or better, but footnotes are included with the specific statistical test results for each comparison.

> 8 At the 5 percent level or above.

> 9 Statistically significant differences at the 1% level.

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# **4 Results**

## **4.1 Unconditional convergence**

Considering all the sectors in the aggregate, Figure 1 plots the average annualized growth rates in labor productivity during 2002-10 and 2010-18 against the initial labor productivity levels in 2002 and 2010, respectively. The pattern clearly shows evidence of the unconditional convergence of productivity across regions. States with initial lower productivity levels in 2002 experienced higher growth rates annually, catching up with the leading states. The convergence rate is particularly faster for the 2002-10 period compared with the 2010-18 period. Furthermore, all the sectors have experienced unconditional productivity convergence with the regional convergence pattern looking stronger in non-agriculture sectors (Figure 2). We see that initial levels of labor productivity are relatively lower in agriculture compared with the other sectors.

Considering industry-level labor productivity within each sector, the coefficient estimates in Table 1 provide strong support for unconditional convergence across regions.<sup>12</sup> Looking at the entire sample period (2002-18) and in the aggregate, the estimate is negative (-0.023) and statistically significant, representing a coverage rate of about 2.3 percent. The estimates for individual sectors are also negative and significant; entire sample period (2002-18), the estimates are -0.023, -0.031 (agriculture), -0.030 (extractives), -0.028 (manufacturing), and -0.020 (services). These results imply that states with lower levels of labor productivity in 2002, on average, experienced faster productivity growth over the sample period. This regional convergence holds when considering both the regional economies and disaggregated by sectors.

> 12 In the appendix, we report the estimation results based on labor productivity computed at different levels of aggregations. Except for agriculture and services, regression results at broader levels of aggregation are comparable to estimates at the industry levels within each of the four sectors. The aggregation level seems to have an influence on the significance and size of the estimate. These features suggest substantial variation in productivity growth across industries within these sectors. Some industries may have experienced rapid growth but their shares in the sectoral economy are probably small.

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SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE rural areas (figure 13). Sudan, meanwhile, has only a 10 percent open defecation rate. Almost no one in South Sudan has access to flush toilets compared to 5 percent of the population in Sudan and other African low-income countries. Access to improved latrines in South Sudan is more or less equal to lowincome and East African countries but significantly lags Sudan and all other comparator benchmarks. This situation is partially explained by the fact that the rural population in South Sudan accounts for 83 percent of the population, making high-end or midrange solutions simply unaffordable.

**Table 9. Benchmarking South Sudan’s water infrastructure**

||Unit|South<br>Sudan|East Africa|Low-<br>income,<br>nonfragile<br>countries|Resource-<br>rich<br>countries|Middle-<br>income<br>countries|
|---|---|---|---|---|---|---|
|Access to piped water|% pop|—|11|9|13|61|
|Access to stand posts|% pop|2|14|17|12|22|
|Access to wells/boreholes|% pop|64|32|39|47|5|
|Access to surface water|% pop|34|42|34|27|11|
|Access to flush toilets|% pop|0|4|5|13|48|
|Access to improved latrines|% pop|19|17|18|37|34|
|Access to traditional latrines|% pop|6|43|38|22|7|
|Open defecation|% pop|75|36|38|28|11|
|Domestic water consumption|liter/capita/day|20|64|51|115|196|
|Revenue collection|% sales|83|80|94|60|99|
|Distribution losses|% production|29|38|35|40|29|
|Cost recovery|% total costs|40|102|89|67|86|
|Operating cost recovery|% operating costs|56|143|125|94|121|
|Labor costs|connections per employee|107|103|176|96|203|
|Total hidden costs|% of revenue||||||
|Average effective tariff|U.S. cents per m3|81|76|**77**|**45**|—|

_Source:_ Demographic and Health Survey (DHS) and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures calculated by the AICD based on the 2000 and 2006 DHS figures published by the Joint Monitoring Program (WHO 2010). — = Not available.

While the urban-rural disparities in South Sudan are not as glaring as in other parts of Africa, this is for the wrong reason: an overall limited level of access. Similarly, there are no glaring disparities between various income levels. Estimations from the recent poverty assessments suggest that a staggering 64 percent of the wealthiest quintile of South Sudanese lacks access to any type of toilet facility. In stark contrast, in Sudan, only 12 percent of the wealthiest quintile of the population has no access to toilets compared to 50 percent of the poorest. Around 2 percent of the wealthiest quintile of South Sudanese had flush toilets and a further 22 percent had access to pit latrines. In terms of water supply, the wealthiest and poorest quintiles of the South Sudanese population relies equally on deep wells and boreholes for water. There is also not much variation in the usage of an open source of water (that is, surface water) across income levels—over 25 percent of the wealthiest quintile relies on open sources of water (World Bank 2010b).

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– – of all Indian households and 69.3 percent of rural households “usually” do not use any kind of toilet or latrine. In the 2005-6 National Family Health Survey, India’s version of the DHS, 55.3 percent of all Indian households reported defecating openly, a number which rose to 74 percent among rural households.

These statistics give several reasons to be especially concerned about open defecation in India. First, open defecation is much more common in India than it is in many countries in Africa where, on average, poorer people live.<sup>1</sup> Second, despite accelerated GDP growth in India, open defecation has not rapidly declined in India over the past two decades, not even during the rapid growth period since the early 1990s. In the DHS, where 55.3 percent of Indian households defecated openly in 2005-06, 63.7 did in the earlier 1998 survey round, and 69.7 did in 1992. In 2010, 86 percent of the poorest quintile of South Asians usually defecated openly.

# **1.2 Non-experimental evidence for effects of sanitation on health**

We report the first, to our knowledge, econometric analysis of a randomized controlled experiment about the effects of a village-level<sup>2</sup> community sanitation program on child health.<sup>3</sup> In a review of evidence on rural water and sanitation interventions, Zwane and Kremer (2007) conclude that “many of the studies that find health effects for water and sanitation infrastructure improvements short of piped water and sewerage suffer from critical methodological problems” (10). Importantly, however, two existing literatures indicate that a large effect of sanitation is plausible.

First, medical and epidemiological literatures have documented the mechanisms linking open defecation to poor health and early life human capital accumulation. Checkley

> 1Spears (2013) notes that population density is also much greater in India than in Africa, providing more opportunities for contact with other people’s fecal pathogens.

> 2Some prior evaluations of rural sanitation have focused on differences between households that do and do not have latrines (Daniels et al., 1990; Esrey et al., 1992; Lee et al., 1997; Cheung, 1999; Kumar and Vollmer, 2012). Such an approach would ignore negative externalities of open defecation.

> 3Lisa Cameron, Paul Gertler and Manisha Shah have presented preliminary results from an excellent sanitation experiment in progress in Indonesia that are quite complementary to our findings.

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# **Determinants of Property Tax Revenue: Lessons from Empirical Analysis**

## **I. Introduction**

Property tax revenue plays an important role in tax collection in developed countries. It becomes revealing, theoretically and empirically, that property tax is the most efficient tax, encompassing a number of other virtues of a tax instrument: transparency, equity, and direct linkage to benefits.<sup>1</sup> The tax has become even more relevant in the evolving trends of urbanization and fiscal decentralization.<sup>2</sup> In some of the high-performing countries, the immovable property tax brings in as much as 3 percent of GDP in revenues. At the same time, many developing countries have struggled with realizing sufficient revenues from property tax. However, as developing countries experience economic growth they are also seeing property values rising, providing a bigger tax base from which to realize revenues. Technology has made tax administration easier and more effective and developing country governments have been improving their quality of governance and have been considering introducing or enhancing property tax revenue collection to diversify their tax and fiscal revenues. Taxation of immovable properties not only improves the tax revenue collections but also works as an instrument for income redistribution, given that it is progressive by nature.

Exploring the determinants of property tax revenue using data from developed countries gives us important insights into understanding the extent to which these factors impact property tax collection. This research provides useful experiences for developing countries that are considering or planning to enforce the property tax collection.

In this research, we review literature that has focused on the determinants of property tax revenue throughout the world. We then collect state/provincial-level data for the United States, Canada, Australia, and Chile, as well as country-level data for all the OECD countries. Our data allow us to build up a panel data set for sophisticated econometrics regressions and estimations. We use OLS, Fixed Effects Model, and Random Effects Models to estimate the impact of various determinants on the property tax revenue. Our econometric analysis allows us to measure the impact of key economic and demographic factors that influence property tax revenue.

According to our research results (see Tables 5 and 6), we find that every 1 percent increase in GDP and population will increase the property tax revenue by around 0.13% and 0.76% on

> 1 OECD (2010), for example, ranks the order of efficiency (or least economic distortion, growth-impeding) tax types: property tax, VAT, personal income tax and corporate income tax.

> 2 Bahl (1999) reflects the criticality of property taxes in its direct linkage to efficiency in service delivery at the local government level.

2
Finally, would a similar program be equally protective in another context and type of aggregate crisis? Preliminary analysis from an impact evaluation of the ELA program in South Sudan (also implemented by BRAC) suggests similar crisis-offsetting effects, even though the nature of the aggregate shock, conflict, is different, and the conflict occurred after a more sustained period of club implementation [Buehren _et al._ 2018]. Nonetheless, the analysis indicates that many of the pernicious effects of conflict on young women are offset by having participated in ELA clubs.

Understanding all these issues, and designing effective low-cost interventions to ameliorate the root causes of disadvantages faced by women, can help protect slow but steady gains in women’s empowerment to external aggregate shocks. This will become increasingly important as nearly all low-income states are susceptible to such shocks in one form or another, be they through commodity price fluctuations, conflict, viral epidemics or climate change.

# **A Appendix**

## **A.1 Attrition**

Attrition is low given the circumstances: 83% of our respondents were tracked from baseline to endline (4790). Among tracked women, 81% (3865) resided in the same village, while others were tracked to a different (but typically nearby) village. Hence although geographic mobility is high, it does not lead to severe attrition. For those originally in a treated village and then tracked to another village, we can use the approximate date of their move to understand the extent to which they were exposed to ELA clubs. At least 60% of tracked movers have been partially exposed to ELA clubs in their original village. Finally, 922 (16%) of girls attrited out of the sample (only 12 were due to death).<sup>48</sup> Table A2 presents correlates of attrition. Columns 1 and 2 show that treatment assignment, and exposure to high disruption do not separately predict attrition. Column 3 shows this continues to hold when controlling for both together, and their interaction. This continues to hold when: (i) we additionally control for characteristics of girls, households and villages (Column 4); (ii) allow there to be differential attrition between treatment, Ebola disruption, and their interaction with baseline characteristics such as enrollment, employment, age and household poverty.<sup>49</sup>

> 48Attriters were replaced by a refresher sample of 1415 girls surveyed only at endline. Around 44% of this sample resided in the same village at baseline and endline.

> 49Of course individual characteristics of girls jointly predict attrition (such as baseline economic activities engaged in, marital status and household size), but not differentially so in treated villages, in high disruption villages, and villages with both (the F-statistics at the foot of each Column report this to be the case).

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market research are estimated as twice revenues of the market and consumer research industry as reported in SAS, and purchased ―organizational‖ capital is estimated using SAS data on the revenues of the management consulting industry.

Finally, due to the absence of direct data, some intangible expenditures are ―crudely‖ derived from the closest available proxy. For example, new product development costs in the financial services industry are assumed to be 20 percent of intermediate purchases in that industry. Spending on organizational change is also difficult to measure, because there is no broad consensus on the scope of these assets and little hard data on spending. A substantial portion of these activities are handled in-house and there are no available data on these activities; CHS propose a rudimentary estimate based on the value of executive time, estimated from BLS data on employment and wages in executive occupations.

In general, economic competencies, which is the largest category in the CHS framework, presents the most conceptual and measurement challenges (Aizcorbe et al. 2009). For instance, there are questions about whether brand equity should be treated as investment in a national account. Some argue that advertising and marketing expenditures are in some sense unproductive, because they are thought to affect the demand function instead of the production function. In contrast, spending on other intangibles directly affects the production function by either creating a better output or the same output using fewer inputs or better inputs.

Appropriate price deflators and depreciation rates are other outstanding questions in the measurement of intangible capital. CHS and some other recent studies use the goods price deflator associated with the final industry (that is, the industry purchasing the intangible asset) or the economy as a whole (CHS 2009, Marrano et al. 2007). The problem here is that this is not the price index for the intangible per se, but rather the price index for the good that used the intangible asset as an input. An alternative solution has been to develop a price index for the costs involved in producing the intangible asset. Since most intangible are produced in the services industry, the major component of these input price indices are wages, such as wages of engineers and scientists (van Rooijen-Horsten and others 2008). However, this ignores any productivity gains in the production of the asset. Unsurprisingly, there is no consensus yet on this issue.

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into unemployment suggests that the idea of the sector as the flex-wage sector that can absorb excess labor is probably importantly incomplete.

Clearly, caution is advised in generalizing to the developing world from two middle-income Latin countries. However, the similarity of these often counterintuitive results across two data sets and national contexts suggests that neither they nor their implications can be easily dismissed. The right modeling of both inflows and outflows of employment is critical to understanding how developing country labor markets adjust and for making quantitative predictions about the unemployment rate and the relative employment shares over the cycle.

In addition, the insights into the nature of the informal sector- something that has been subject of thirty years of discussion<sup>8</sup> - has important policy implications. While the sector is comprised of heterogeneous actors corresponding to both disguised unemployed and voluntarily entering “entrepreneurs”<sup>9</sup> , its exaggerated size in developing countries raises the stakes surrounding the relative proportions dramatically. If the often 50% percent of developing country workers found in the informal sector show dynamics similar to those of the unemployed, then the labor market distortions in the formal sector are indeed large and the case for massive reform, compelling. As we suggest that this is not the case, then the policy focus shifts to understanding the costbenefit analysis that agents undertake in choosing among sectors. Finally, although beyond the scope of this paper, the differing dynamics of the regulated and unregulated sectors –due to wage or other rigidities, particular characteristics of the micro firm sector, or characteristics of people who choose to enter the sector- may cast light on the factors driving cyclical dynamics more generally.

The next section discusses the data and methodology for estimating the continuous time transition rates underlying the analysis. Section III presents evidence on the cyclical properties of the stocks of workers. Section IV analyzes the gross flows of workers by sector and speculates on what may explain the differing patterns

> 8 For a review of the literature and early work on transition matrices in developing country see Maloney (1999, 2005).

> 9 For a survey on the methods estimating the size of the informal sector see Schneider and Enste (2000), Perry et. al (2007)

5