rafmacalaba/datause-encoder-data huggingface dataset:
92% in Sri Lanka and Tanzania as opposed to 84% and 75% for the household model. The differences were more muted in Mexico, although unfortunately no coverage statistics were provided for the design-based simulation. The moderate underestimation of uncertainty in the household model may be due to the omission of a random effect at the sub-area level, although the direction of this bias due to omitting this sub-area random effect depends on the structure of the data (Marhuenda et al, 2018). One way to get a sense of the magnitude of this downward bias in estimated uncertainty is to compare coverage rates with those of direct estimates. The standard cluster-robust variance estimator for surveys also underestimates uncertainty because it fails to account for the correlation in poverty status across enumeration areas within target areas. This leads to similarly low coverage rates for direct estimates in Sri Lanka and Tanzania, and much lower coverage rates in Mexico. When compared with the downward bias in standard direct variance estimates, the magnitude of the downward bias in the household model MSE estimates does not seem large enough to warrant serious concern.

_Table 4: Coverage rates, relative to validation data_

|Country|Indicator|Sample|Validation<br>data|In-sam|ple||Out-of-|sample|
|---|---|---|---|---|---|---|---|---|
|Model||||Hh-<br>level|Area-level|Direct|Hh-<br>level|Area-level|
|Sri<br>Lanka|Non-<br>monetary<br>poverty|Single<br>survey|Census|84%|92%|76%|N/A|N/A|
|Tanzania|Non-<br>monetary<br>poverty|Single<br>survey|Census|75%|92%|76%|N/A|N/A|
|Mexico|Headcount<br>poverty|Single<br>sample|Census-<br>based EBP<br>estimates<br>(in-sample)|77%|82%|39%|83%|80%|

Source: Edochie et al. (forthcoming), Masaki et al. (2022), Newhouse et al. (2022)

- c. Tree-based machine learning methods appear to predict more accurately than linear models

One of the key differences across the different studies listed in Table 1 is the choice of prediction method. Many of the studies used linear mixed models, drawing on the traditional methods popular in small area estimation. On the other hand, others use more sophisticated tree-based machine learning approaches such as Regression Forests or Gradient Boosting. Regression Forests are a generalization of decision trees. The take the average predictions of a continuous dependent variable over many decision trees, which are each derived from repeated random samples of the data and of candidate variables, a process known as bagging (Breiman, 2001). Bagging helps make random forests much more robust to small perturbations in the data than single decision trees. Extreme Gradient Boosting (XGboost), meanwhile, is a generalization of random forests (Chen and Guestrin, 2016). This method generates 19
18 negative price effects of the OMA. In fact, such a conclusion is probably too extreme, for the figures in the top part of table 5 are strongly influenced by one particular commodity: outdoor sports shoes of artificial leather. Over the sample period the prices of Korean exports of these shoes to the USA, FRG and Japar. grew fairly rapidly and by broadly similar amounts, and in each case displayed one year of very substantial increase (over 40X). Unfortunately, however, the years concerned differed -- 1976, 1978 and 1980 respectively, so that whereas for the US the spurt fell in the non-OMA period, for the others it fell in the OMA period. Moreover, these footwear are generally held to have benefited from a large shift in fashion in the late 19709 (jogging, etc.), so that the weight attached to the large price increase was 11 for the US but 91 and 511 for the FRG and Japan respectively. When the comparisons are made for 20 commodity categories excluding outdoors sports shoes of artificial leather (figures in parentheses in table 5), the differences between countries are considerably narrowed. The increase in the rate of US price increases in the OMA period exceeds that of Japan and equals that of Germany, while the decline in the rate of increase in quality in US falls both absolutely and relatively to the other countries.

Table 6 shows the considerable coherence between our results and those of Aw and Roberts (1986). They were able to distinguish seventy commodity categories in the US import data and so considered the two restricted sub-groups separately: non-athletic leather footwear (401 of relevant imports in 1976/77) and other footwear (60Z). On price increases, the two studies both find average increases of around 121 to 13Z per annum during the VER and of around 41 over the non-VER years. Our later data
# **Abstract**

In 2002, David McGee wrote a comprehensive report on two important elements in the system of public financial accountability, namely the office of the Auditor General and the parliamentary oversight committee commonly referred to as the Public Accounts Committee (PAC). The purpose of the present paper is to deepen McGee’s analysis of PACs. In particular, we define PAC success and identify those factors that impact on PAC peformance. We use data that were collected by the World Bank Institute in 2002, when a survey questionnaire was sent to 51 national and state/ provincial parliaments in Commonwealth countries in Asia and Australasia and Canada and the United Kingdom. We find that the institutional factors which most account for the success of the PACs are the focus on government’s financial activity rather than its policies, the power to investigate all past and present government expenses, the power to follow up on government action in response to its recommendations, and its relationship with the Auditors General.
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governance, (7) liquidity and diversification requirements, (8) depositor (savings) protection schemes, (9) asset classification, provisioning and write-offs, (10) accounting and information disclosure, (11) discipline/problem institutions/exit, (12) supervision, (13) banking sector characteristics, and (14) consumer protection. For reasons of comparability with the previous three rounds of the survey, about a half of the questions in the 2011–12 survey are similar to those in previous rounds. To result in more precise answers, a few questions have been reformulated. Also, almost half of the questions added were in particular on issues highlighted by the crisis (e.g., macro-prudential regulation and consumer protection) and on matters related to the implementation of the new Basel rules. Some 58 percent of the survey consists of binary questions that could be answered “Yes” or “No”. About 5 percent of the questions are categorical questions, wherein respondents are required to choose one or more options from a set of alternatives. The remaining 37 percent are quantitative questions, which include various questions about ratios, currency amounts, and other numerical indicators.

The survey was generally addressed to the head of banking supervision in the central bank or to the head of a separate banking supervision agency. In some countries, the responses were provided directly by the agency head. In other countries, the agency head delegated the completion of the questionnaire to the relevant senior-level staff.

The design and implementation of the survey entailed a significant and lengthy effort to ensure the consistency and comprehensiveness of responses across countries. First, in order to make questions clearer and answers more comparable across countries, detailed guidelines were drafted with the help of a senior banking regulator. Also, in each of the survey’s sections, the respondents had the option of entering other relevant country-specific information that was not captured by any of the existing questions in the survey that they thought needed to be included in 5
Policy Research Working Paper 6116

# **Abstract**

This paper explores the possibility that universal health coverage may inadvertently result in distorted labor market choices, with workers preferring informal employment over formal employment, leading to negative effects on investment and growth, as well as reduced protection against non-health risks and the income risks associated with ill health. It explores this hypothesis in the context of the Thai universal coverage scheme, which was rolled out in four waves over a 12-month period starting in April 2001. It identifies the effects of universal coverage through the staggered rollout, and gains statistical power by using no less than 68 consecutive labor force surveys, each containing an average of 62,000 respondents. The analysis finds that universal coverage appears to have encouraged employment especially among married women, to have reduced formal-sector employment among married men but not among other groups, and to have increased informal-sector employment especially among married women. The largest positive informal-sector employment effects are found in the agricultural sector.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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 awagstaff@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 5: List of studies focused on the health impact of water provision interruption and dysfunction (continued)**

|**Country,**<br>**year**|**Population**|**Exposure/Control**|**Outcome**|**Measure of**<br>**effect**|**Source**|
|---|---|---|---|---|---|
|Taiwan,<br>China, 2004-<br>2006|Insurance claims<br>(n=1,000,000<br>insured persons)|Number of insurance claims<br>during outages**/Number<br>of insurance claims during a<br>normal period|(1)Gastroenteritis<br>(2)Skin diseases<br>(3) Eye diseases|(1)RR =1.31<br>(1.26 – 1.37)<br>(2)RR =1.36<br>(1.30 – 1.42)<br>(3)RR =1.34<br>(1.26 –1.44)|(Huang et<br>al., 2011)|
|Zambia, 2017|District month<br>(n=1230)|Impact of the number of<br>outstanding water supply<br>complaints per month on<br>the number of health clinic<br>admissions|(1)Diarrhea<br>(2)Typhoid fever<br>(3)Respiratory<br>infection<br>(4)Measles|(1)1.00<br>(0.42)***<br>(2)0.002 (0.001)<br>(3)2.4 (0.91)<br>(4)0.035(0.01)|(Ashraf et<br>al., 2017)|

_Source: Bivins et al., (2017) and modified by authors._

- _95% confidence intervals within parenthesis._

- _** Water outages because of typhoons and floods were excluded._

_*** Number of additional cases of waterborne illness per additional day of outstanding complaint (standard error of estimate within parenthesis)_ _OR = Odds Ratio, RR = Risk Ratio, CIR = Cumulative incidence ratio._ _In Mexico (Cifuentes et al., 2002) and Palestine (Abu Mourad, 2004), reported odd ratios were transformed in risk ratios. The diarrhea prevalence is not available in the control population (e.g. not exposed to IWS). Therefore, we use RR = OR / (1 – p + (p x OR)), where p is the diarrhea prevalence in the treated population (e.g. exposed to IWS)._ _When the incidence of an outcome is low (<10%), the odds ratio is very similar to the risk ratio. However, the odds ratio becomes exponentially more different from the risk ratio as the incidence increases, which here, will tend to exaggerate the risk effect._ 31
attempt at distinguishing the currently available methods in statistics and economics as well as incorporating the advances from the former into the latter. This is consistent with similar ongoing efforts in other disciplines that build on the multiple imputation method in statistics to better address their own disciplinary needs.<sup>4</sup> Empirically, we illustrate our method with an application to Jordan, a particularly interesting case for analysis. Not much is known about poverty trends since Jordan’s Department of Statistics (DOS) last conducted its Household Expenditure and Income Survey (HEIS) in 2010. In the meantime, this country’s economy has experienced several major events such as the introduction of new poverty-reduction policies by the government (e.g., in accordance with its recent Poverty Reduction Strategy), economic reforms (e.g., reducing its petroleum subsidies and implementing a targeted cash transfer), and shocks due to higher energy prices. Socio-political change and unrest in neighboring Syria and Egypt also add further uncertainty to the economy. Given this fast evolving context, policy makers are keenly interested in tracking poverty trends on a more frequent and timely basis. In contrast with the HEIS survey which was last conducted in 2010, DOS administers the Employment-Unemployment Survey, a labor force survey (LFS) with wide geographical coverage, on a quarterly basis. We exploit the LFS, which does not collect consumption data and has a different design from the HEIS, to fill the missing poverty data problem in Jordan for the years the HEIS is absent.

We validate our imputation-based estimates of poverty against those obtained from the actual consumption data (or design-based estimates) for the two years 2008 and 2010 when consumption data are available, before imputing estimates for other years when consumption

> 4 See, for example, King et al. (2001) and Honaker and King (2010) for examples of adaptation of multiple imputation methods in the field of political science.

6
regulars (22% of the time, compared to 14% with casuals). This may be because women are more aware of the risk pro...le of regular clients than they are of casual clients.

Finally, Panel B presents the percentages of women that engaged in various sexual activities at least once during the sample period. Interestingly, 82% of women engaged in anal sex and 71% engaged in unprotected vaginal or anal sex. The anal sex ...gures are particularly interesting, because they are much higher than those found elsewhere.<sup>16</sup> Again, the averages are lower in round 2. Even with the lower ...gures, however, 26% of women had unprotected anal sex at least once in the sample period (which is of interest due to the substantial risk of contracting HIV from unprotected anal sex).

As mentioned previously, roughly 44% of the women in this sample participated in peer groups sponsored by the Strengthening STD / HIV Control Project in Kenya and received education about HIV and other STIs. This is one reason why women may have scored so highly on the test of HIV knowledge presented in Table 2. Compared to the marginal sex worker, these women should be more aware of the risks of unprotected sex, so the ...gures in Table 3 are likely to be lower bounds on the frequency of unprotected sex among sex workers.

# 4 Risk Premium

## 4.1 Estimation

We will estimate a risk premium by performing a ...xed e¤ects regression of the price paid by the client on the activities performed, and whether a condom was used. Ideally, this regression should control for client characteristics. However, we only have client information for a small portion of our data, so we will have to assume homogeneity on the demand side for most of what

> 16 For instance, Brody and Potterat (2003) review a wide variety of public health and anthropological studies and ...nd a maximum anal sex prevalence ...gure of 42.8% in self-reported recall data. The authors argue, however, that most anal sex ...gures are likely underestimates, as respondents are much more likely to admit to having anal sex in a diary or in a computer questionnaire, neither of which are commonly used in Africa. Among a very similar group of sex workers in Kenya, Ferguson and Morris (2003) ...nd that only 20% of CSWs in the Kenyan Highlands responded that they had ever had anal sex. We thank Damien de Walque for pointing this out to us.

14
scenarios considered here, there are welfare costs associated with the FUNPI. Finally, note also that these results depend on the assumption of an integrated national market for cashews. With segmented regional markets, the role of competition policies can become much more relevant. Also, there might be collusion between exporters and traders, and this can lead to more severe poverty impacts.

**Table 16. Welfare Effect in Guinea-Bissau: Processors and Farmer Assistance**

|**Policy**|**(II) = (I) +**<br>**FUNPI**|**(XIV) = (I) + FUNPI**<br>**+ 4 cashew**<br>**processors (200**<br>**ton each)**|**(XV) = (I) + FUNPI**<br>**+ 1 cashew**<br>**processor (20,000**<br>**ton)**|**(XVI) = (I) + FUNPI**<br>**+ 10% reduction in**<br>**cost of farmer**<br>**services**|
|---|---|---|---|---|
|_All Households_|-5.30|-5.27|-4.58|-5.46|
|_Urban Households_|-1.65|-1.64|-1.42|-1.70|
|_Rural Households_|-8.46|-8.41|-7.31|-8.71|
|_Poor Households_|-7.89|-7.84|-6.82|-8.12|
|_Non-Poor Households_|-4.40|-4.38|-3.81|-4.54|
|_Urban Poor Households_|-3.24|-3.22|-2.80|-3.34|
|_Rural Poor Households_|-9.39|-9.33|-8.12|-9.67|
|_Urban Non-Poor Households_|-1.39|-1.38|-1.20|-1.43|
|_Rural Non-Poor Households_|-7.92|-7.88|-6.85|-8.16|
|_Cashew Producers_|-11.03|-10.97|-9.53|-11.36|
|_Poor Cashew Producers_|-12.28|-12.21|-10.62|-12.65|
|_Non-Poor Cashew Producers_|-10.38|-10.32|-8.97|-10.69|

Source: QUIBB 2010 and price changes from the simulations of Section 4. The numbers are expressed in percent.

The last column of Table 16 shows the impacts of complementing FUNPI with assistance to farmers. This assistance is modeled via a decline in the cost of producing cashews. The decline in costs implies, ceteris paribus, an increase in the supply of raw cashews and, consequently, a reduction in equilibrium prices. This would translate into a further reduction welfare (compared to the baseline). These differences are very small. For instance, the welfare effect for the average household would be 5.46 percent while the welfare effect for the average cashew producer would be 11.36 percent. Note, however, that these welfare effects do not take into account the welfare impact of the reduction in costs. These impacts can be (crudely) included as follows. Our experiment assumes a subsidy equivalent to a 10 percent reduction in the costs of subsidized inputs. In turn, these inputs account for around 60 percent of the total cost of cashew production. Finally, noting that the cost-price margin implied by our supply function specification is 0.333 (the price is roughly three times higher than the average cost), we can conclude that the subsidy to inputs creates a welfare effect similar to the welfare effect that would be created by an increase of cashew prices of 2 percent. Under these assumptions, we find it implausible to believe that the subsidy can overturn the welfare costs of the FUNPI contribution. Indeed, the FUNPI contribution implies, in the baseline scenario, a decline in cashew prices of 20 percent. If a subsidy of 10 percent is approximately equivalent to a price increase of 2 percent, it follows that the total 31
29 use of local suppliers, the extent and nature of technology dissemination by MNC affiliates, and the materialization of productivity spillovers among domestic firms.

The findings from our survey show that various characteristics of MNC affiliates and domestic firms, host economy conditions, and several mediating factors are important. Considering the characteristics of MNC affiliates, research finds that the investment motive has mixed effects. Whereas market-seeking FDI tends to source more inputs from local suppliers and generates larger productivity spillovers, efficiency-seeking FDI is found to be more supportive to local suppliers. Findings on the mode of investment and the percentage of foreign ownership are more consistent: MNC affiliates with some level of host economy participation/investment (such as joint ventures) use more local suppliers, engage more in technology dissemination, and generate larger productivity spillovers. Nationality or country/economy of origin is also important, as cultural and institutional proximity exert positive effects on the level of local sourcing and the creation of productivity spillovers. A large distance between home and host economies also fosters the extent of backward linkages and productivity spillovers. MNC affiliates that operate with a certain level of autonomy are reported to create more extensive and supportive backward linkages.

Findings on the effects of characteristics of domestic firms on local sourcing show that MNC affiliates identify local suppliers’ main area of underperformance to be the unavailability of inputs. Other constraints include low product quality, uncompetitive pricing, and unreliable delivery systems. As for technology dissemination, MNC affiliates target their support to local firms based on their (potential) capacity to use this support to provide higher-quality and more cost-effective inputs. Similar selection processes occur among domestic firms, with only the more capable and motivated domestic firms trying to become suppliers to MNC affiliates. Characteristics of these firms include firm size, level of human capital, involvement in R&D activities, having previous experience with supplying to MNC affiliates, and being familiar with producing for international markets. Similar characteristics of domestic suppliers—perceived to be related to their level of absorptive capacity—are found to be positively associated with the occurrence of productivity spillovers.

Most of the host economy conditions that the survey identifies as influencing the extent and impact of backward linkages relate to the level of development of host economies. The level of GDP and human capital have positive effects on backward linkages and productivity spillovers, reflecting the availability and the quality of local supplier bases. High-quality legal systems and low levels of corruption and bureaucracy foster the creation of inter-firm linkages. Financial markets that function adequately are important, especially because domestic suppliers need to make independent investments to establish supply linkages with MNC affiliates and benefit from technology