rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 1 Introduction<br>In economics, health sciences, and other disciplines, it is common to ha e a situation where the<br>outcome ariable of interest is costly to observe while its co ariates are relati ely inexpensi e<br>to observe. or example, measurement of consumption po erty is expensi e because it in olves<br>a long questionnaire often administered 0 er an extended period of time. Howe er, its co ari-<br>ates, such as asset holdings, ater and lighting sources, and housing materials, are relati ely<br>inexpensi e to observe. In health sciences, simple oral questions and anthropometric data taken<br>by anon-in asi e device often serve as a predictor of the outcome that is expensi e to measure.<br>In industries, certain non-destructi e testing may serve as a cheaper but less accurate alternati e<br>of destructi e testing.<br>In these cases, prediction estimators offer a useful alternati e. Prediction estimators es-<br>timate the mean outcome for the population by e aluating the mean of predicted outcomes,<br>which exploits the information contained in the a ailable co ariates. The data on co ariates is<br>not utilized when estimating the population mean by the mean of observed outcomes. or appli-<br>cations of this type of estimator to po erty measurement see e.g. Elbers et al. (2003), Stifel and<br>Christiaensen (2007), Christiaensen et al. (2012), and Douidich et al. (2013), where po erty rate<br>estimates are deri ed from predicted household consumption data. In an application to health<br>measurement, Fujii (2010) estimates the pre alence of stunting and underweight of children<br>using predicted data.<br>As is shown by Matlof (1981), prediction estimators may be useful e en when the outcome<br>ariable is in act observed for all subjects, simply because the mean of predicted outcomes can<br>be a more efficient estimator of the population mean than the sample mean of the observed out-<br>come ariable. — extension, this suggests that prediction estimators might also be incorporated<br>into the design of surveys where the outcome ariable is collected for a sub-sample of subjects<br>only.<br>This study can be viewed as a generalization of Matlof (1981), who uses a weighted-least<br>square estimator for prediction, in se eral directions. First, we establish the properties of the<br>prediction estimator when prediction is based on a general class of consistent and asymptoti-<br>cally normal estimators. Second, we deri e the condition under which the prediction estimator<br>2<br><!-- End of picture text -->
**Table 3. Effects of Outcomes with Single‐Impact Evaluations**

|**Outcome**|**Intervention**|**Effect Measure**|**Effect**|**Significance**|
|---|---|---|---|---|
|Access to sewerage|Multiple|Risk difference|0.20|*|
|Behavior change|Hygiene|Risk ratio|1.11|*|
|Cholera reduction|Water quality|Risk ratio|0.48|**|
|Cognitive skills|Sanitation|% variation|0.30|**|
|Satisfaction|Water supply|Risk difference|0.13|*|
|Dengue pupae<br>reduction|Multiple|Risk ratio|0.34|**|
|Shigellosis infection<br>incidence|Hygiene|Risk difference|‐0.22|**|
|Child weightgain|Waterquality|Risk ratio|1.27|*|

_Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database. _Note:_ * p‐value<0%, ** p‐value<5%.

Table 4 summarizes the results of the meta‐analysis for the remaining outcomes. Overall, there are three outcomes (adoption of water treatment, incidence of ocular infection, and source switching) for which the heterogeneity of effects, combined with the heterogeneity effects of small sample sizes, led to statistically insignificant aggregate results. The pooled effects of WASH interventions on the remaining 10 outcomes were statistically significant. In particular, the odds of missing school were reduced by a factor of 0.69 for students who had benefited from a WASH intervention. Similarly, children who received a WASH intervention were 1.44 times as likely to use soap, 0.5 times as likely to develop _Ascaris_ infections, 0.65 times as likely to develop diarrhea (however, this particular result may be inflated due to the potential bias of the six studies from North America), and 0.91 times as likely to die than children not receiving one. Finally, child growth, handwashing, and latrine adoption increased by 26, 8, and 22 percent, respectively, and water quality improved by 20 percent.

**Table 4. Pooled Effects of Outcomes with Multiple Impact Evaluations**

|**Outcome**|**Effect Measure**|**Effect**|**Significance**|
|---|---|---|---|
|Adoption of water<br>treatment|Risk ratio|1.35|‐|
|Ocular infection|Odds ratio|0.91|‐|
|Soap usage|Risk ratio|1.44|**|
|Source switching|Risk difference|0.04|‐|
|School absenteeism|Odds ratio|0.69|**|
|_Ascaris_reduction|Risk ratio|0.47|**|
|Child mortality|Risk ratio|0.91|**|
|Child growth|Risk difference|0.26|**|
|Water access|Risk difference|0.09|**|
|Handwashing|Risk difference|0.08|**|
|Latrine adoption/ Toilet<br>access|Risk difference|0.22|**|
|Water quality|Risk difference|0.21|**|
|Diarrhea reduction|Risk ratio|0.65|**|

_Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database. _Note:_ * p‐value<10%, ** p‐value<5%.

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WPS 3364

# **CAN THE UNSOPHISTICATED MARKET PROVIDE DISCIPLINE?**

**Gerard Caprio and Patrick Honohan**

## **The World Bank**

### World Bank Policy Research Working Paper 3364, August 2004

_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._ Paper prepared for the BIS-Chicago Fed Conference on Market Discipline. This paper draws on data from a wider effort to assess bank regulatory issues; the cooperation of Jim Barth, Maria Carkovic, Cindy Lee, Ross Levine, Ying Lin and Guillermo Noguera are acknowledged.
Panama, Tanzania, and Uganda host the most foreign EMDE groups. Panama is host to banks from many different home countries <!-- Start of picture text --> 20 14<br>18 No. banks, 2013<br>12<br>16 No. home countries, 2013 (rhs)<br>14 10<br>12<br>8<br>10<br>6<br>8<br>6 4<br>4<br>2<br>2<br>0 0<br>No. of home countries<br>No. of home-country banks in host country<br>PAN TZA UGA IDN CHN GHA ZMB UKR CRI SLV KHM GTM ARM DZA EGY<br><!-- End of picture text --> EMDE groups have grown quickly in importance relative to various EMDE hosts’ local banking systems, including Panama, Uganda, El Salvador, Ghana, and, Zambia <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>PAN TZA UGA IDN<br>GHA ZMB CRI SLV<br>country's financial sector)<br>Total Assets home-banks (% Total Assets host<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Claessens and van Horen (2015); Bankscope; team calculations.

Although limited in scope, cross-border claims data from the Bank for International Settlements (BIS) suggest that banking ties between EMDEs have grown faster than elsewhere and that intraoffice exposures are sizeable (Table 2, Panel C).<sup>4</sup> The latter indicates that shocks can be more easily transmitted to other countries via the group, as was witnessed during the global crisis in Eastern Europe.

- In 2017 Q1, total outstanding cross-border claims from EMDE groups stood at $847 billion vis-à-vis all counterparties in the whole world and have grown by over 46 percent in the last 5 years.

- Of these global cross-border claims, $227 billion<sup>5</sup> were claims vis-à-vis all counterparties in overseas EMDEs, which have grown by 74 percent in the last 5 years.

- Of these cross-border claims on <u>overseas EMDEs, $126.3 billion were vis-a-vis banks</u> (including intra-group claims), which have grown by 142.6 percent.

- Of the $828.3 billion cross-border claims on all counterparties in the whole world, $515 billion was vis-à-vis <u>banks; $233.4 billion of these claims represent intra-group ties,</u>

> 4 The BIS only tracks cross-border locational claims data for banks headquartered in eight EMDEs: Brazil; Chile; South Africa; Mexico; Panama; the Republic of Korea; Taiwan, China; and Turkey. Also, these data are not nationality- but residence-based. Therefore, the data may capture the activities of AE group subsidiaries in these countries. 5 Data from Turkish banks are not included since the relevant breakdown is not available.

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**Table 1: Average 2019 score by region for each WBL indicator**

||Going<br>Places|Starting a<br>Job|Getting<br>Paid|Getting<br>Married|Having<br>Children|Running a<br>Business|Managing<br>Assets|Getting a<br>Pension|
|---|---|---|---|---|---|---|---|---|
|East Asia &|89.00|68.00|67.00|86.40|35.20|82.00|76.80|66.00|
|Pacific|(16.27)|(34.25)|(21.31)|(20.59)|(32.29)|(11.46)|(27.50)|(25.90)|
|Europe &|99.00|85.00|66.00|96.80|80.00|93.00|100.00|54.00|
|Central Asia|(5.00)|(22.82)|(31.36)|(7.48)|(16.33)|(11.46)|(0.00)|(23.58)|
|High‐income|100.00|97.66|89.84|95.00|90.00|95.31|98.75|90.63|
|OECD|(0.00)|(9.75)|(18.90)|(11.36)|(12.44)|(9.91)|(7.07)|(16.50)|
|Latin|92.19|76.56|69.53|88.75|45.00|82.81|98.13|80.47|
|America &|(13.38)|(30.41)|(20.80)|(14.31)|(26.88)|(13.38)|(5.92)|(20.80)|
|Caribbean|||||||||
|Middle East|52.50|58.75|37.50|33.00|33.00|80.00|43.00|58.75|
|& North<br>Africa|(37.96)|(39.96)|(27.51)|(26.97)|(25.36)|(10.26)|(13.42)|(28.42)|
|South Asia|90.63|84.38|46.88|70.00|30.00|71.88|55.00|50.00|
||(18.60)|(18.60)|(38.82)|(26.19)|(10.69)|(16.02)|(20.70)|(23.15)|
|Sub‐Saharan|82.29|76.04|62.50|67.50|44.58|72.40|73.33|80.21|
|Africa|(21.85)|(30.05)|(30.07)|(30.49)|(22.69)|(21.41)|(24.87)|(24.71)|

# **Stylized Fact 3:  Religious laws do not explain gendered laws.**

Looking at the results for MENA, the poorest performing region in terms of gender equality, Table 1 shows that women face the lowest levels of legal discrimination when it comes to starting and running a business (an average score of 80 out of 100), and the highest number of constraints in laws related to marriage and laws that impact their economic rights upon having children. Apart from Malta (which is an outlier with a WBL score of 88.75 in 2019), all countries in the region are majority Muslim population; thus, the high number of legal gender restrictions in the region may relate to countries’ compliance with conservative versions of Sharia (Islamic law). As discussed by Bowen (2018), during periods of colonization across Muslim Asia, Africa and the Middle East, colonial rulers applied their own Western law to criminal or commercial disputes, but were less inclined to reshape family law, which retained much of its Islamic influence.

While respecting nations’ religious beliefs and values, it is worth considering to what extent laws that discriminate by gender could be reformed in the MENA region within the confines of Islamic law. Several authors have argued that it is patriarchal cultures, and not Islamic law, that is behind gender inequality in many countries. Braunstein (2014) argues that people too readily assume that Islam is a suitable proxy for 9
5 Mattoo and Ruta, 2016). These growth rates of world trade are well below the pre-Crisis average of 7 percent (1987-2007) and lag behind the growth rate of world GDP in real terms, which has hovered around 3 percent in recent years.

What explains the slowdown in world trade? A first observation is that the relationship between world trade and income has changed over time. Figure 1 compares the average growth rates of world trade and income across three periods: from 1970 to 1985, from 1986 to 2000, and from 2001 to 2014. In a historical perspective, the middle period – the “long 1990s” – was different from the preceding and the subsequent periods, as world trade was largely racing ahead of world GDP. This evidence indicates the possibility of structural change in the trade-income relationship.

Figure 1. Average Growth Rates in Trade and GDP, selected periods, percent.

<!-- Start of picture text --> Total trade volume (goods and services)<br>8<br>GDP in constant prices<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1970-1985 1986-2000 2001-2014<br><!-- End of picture text --> _Source:_ IMF World Economic Outlook (October 2015). _Notes:_ GDP growth based on PPP rates.
**Table 2. Urban policies in China**

||**W**<br>|**elfare differences(perce**<br>|**nt)**<br>|
|---|---|---|---|
|**Urbanpolicies**|**Efficiency**|**Amenities**|**Excessive frictions**|
|Improve in worst cities<br>by20percent|4.9|13.7|0.2|
|Improve in smallest<br>cities by20percent|2.0|4.5|0.1|
|Improve in medium-<br>large cities by20percent|5.2|8.9|0.1|
|Improve in largest cities<br>by20percent|12.1|22.0|0.4|

_Source:_ Authors.

In what follows, we analyze the effects of the two types of policies mentioned above— those that benefit lagging cities and those that benefit cities of different sizes. Table 2 reports the results. The first row estimates the effect of a policy that improves conditions in backward cities. If we increase efficiency 20 percent in the bottom quartile of cities ranked by efficiency, the model predicts a 4.9 percent increase in welfare. Corresponding policies that increase amenities and lower excessive frictions would also have positive welfare effects, of 13.7 percent and 0.2 percent, respectively. The next rows estimate the effects of size-based policies that improve conditions in the smallest cities (bottom quartile), the medium-large cities (second quartile), and the largest cities (top quartile). Two results stand out. First, targeting the worst cities is more effective than targeting the smallest cities. For example, improving amenities 20 percent in the smallest cities increases welfare 4.5 percent, less than a third the 13.7 percent if the policy focuses on the cities with worst amenities. Second, when comparing the size-based policies, the welfare effects are greater, the larger the cities that are targeted. This is of course not surprising. Improving efficiency (or any of the other characteristics) in the largest cities benefits many more people than the same policy applied to the smallest cities. For example, when increasing efficiency 20 percent in the largest cities, with populations above 1.4 million, welfare rises 12.1 percent, more than six times the 2.0 percent when applying the same policy in the smallest cities (populations of less than 500,000).

From the point of view of expected benefits, this suggests that policy makers should concentrate their efforts on the country’s largest cities. But there is of course also a cost of implementing these policies, and this cost may well differ by city. Therefore, only with a cost constant across cities can we conclude that targeting the largest cities is better. For example, if we had to choose one of two isolated cities to connect by highway to the rest of the country, we would connect the larger one. But many other policies will likely have a cost increasing in the number of people. For example, building urban infrastructure or
<!-- Start of picture text --> ‘ Wealth and Occupational Exposure - Nigeria<br>5aD<br>°<br>a<br>~*~<br>Ww<br>o2<br>=]<br>©<br>©<br>fo<br>©<br>0)<br><!-- End of picture text --> <!-- Start of picture text -->
*<br>uw<br>ro)<br>oN<br>oO<br>8<br>=]<br>S|<br>O<br>0 20 40 60 80 100<br>Household Wealth Percentile<br>————_ Pr(exposed_either) e exposed_either<br><!-- End of picture text -->
# **VL CONCLUSIONS AND RECOMMENDATIONS**

6.01 The goal of a public expenditure review is to add to the Bank's<sup>understanding</sup> of how macro and sector priorities are determined and balanced<sup>over a given period of time</sup> and in accordance with explicit or implicit priorities.<sup>They are a useful vehicle for</sup> highlighting sector cost and financing issues and for analyzing<sup>them in the context of the</sup> entire economy. PERs have become a useful tool in the public<sup>expenditure reform process,</sup> but there is still a tendency to use them to fill in the<sup>gaps in sector knowledge. This</sup> process is an inefficient way to gather information and<sup>a less than optimal use of sector</sup> expertise.

6.02 Approaches and methodologies vary greatly, as does sector coverage,<sup>even by</sup> similar countries. The sector specialist will always need<sup>to rely on personal experience</sup> and intuitive judgment to some extent, but there is at present<sup>too much room for subjective</sup> interpretations, conclusions, and recommendations.<sup>These compromise the credibility of</sup> policy recommendations.

6.03 Too often the only connection between macroeconomic analysis<sup>and sector</sup> analysis is the budget constraint. The best PERs<sup>from the education sector perspective</sup> develop other macro/sector relationships. CESW and<sup>reviews of the budget and budget</sup> process precede the public expenditure review, which<sup>includes education and the social</sup> sectors in accordance with an explicit ranking of priorities.<sup>Throughout the review, the</sup> effects of changes in financing or policy at one<sup>level are taken into account when evaluating</sup> reforms at another level.

6.04 Because the budget and budget process are often deliberately<sup>opaque, the</sup> evaluation of institutional and political priorities and<sup>constraints needs to be strengthened</sup> in order to ensure the sustainability of policy reforms.<sup>Greater realism in the assessment</sup> of the political and social feasibility of proposed sector strategies<sup>is necessary. Also needed</sup> is better assessment of the institutional and managerial<sup>abilities of the agencies that will</sup> be called upon to administer reforms. The<sup>political and managerial caveats apply</sup> particularly to the intrasectoral reallocation of resources<sup>and responsibilities.</sup> 6.05 The chapter or section in a PER on education should<sup>provide an accurate</sup> picture of how funds to the sector are allocated and<sup>disbursed. Providing this picture</sup> entails an evaluation of the expenditure data and the<sup>expenditure process. In particular:</sup> a) If the budget process and the relationship of the<sup>budget to actual spending</sup> are not transparent, then a budget process review may be<sup>a prerequisite for an</sup> expenditure review.

b) The PER cannot be substituted for CESW. If the<sup>necessary data are not</sup> available, ex ante sector work is a prerequisite<sup>for any PER that attempts to</sup> make and evaluate macro and sector links.

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formality is associated with a higher wage premium and higher returns to education for formal labor in 2006.

Table 4 – Percentage of Formal vs. Informal Labor

||1998|2006|
|---|---|---|
|Informal|88.66|64.12|
|Formal|11.34|35.88|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

Table 5 - Formality and Firm Size - 2006

|Firm Size|informal Labor|Formal Labor|
|---|---|---|
|1 to 4|90.8%|9.2%|
|5 to 49|85.6%|14.4%|
|50+|35.8%|64.2%|
|dont-know/miss|15.8%|84.2%|

|Firm Size|informal|Formal|
|---|---|---|
|1to4|69.9%|12.7%|
|5to49|19.9%|6.0%|
|50+|2.5%|8.1%|
|dont-know/miss|7.7%|73.2%|

_Source_ : Authors’ calculations using ELMPS 2006.

Most of the informal labor (79.5 percent in 1998 and 70 percent in 2006) works in micro enterprises (1 to 4 employees), while most of the formal labor works in the government sector (Table 5). The majority of the informal workers are clustered in the private sector. Therefore, most of the informal labor is clustered in private micro enterprise, while most of the formal labor is in the government sector. The largest share of informal labor in 1998 and 2006 are illiterate, while the largest share of formal labor are 4-year university graduates.

Table 6 shows that: (a) informality decreases with education level; and (b) at each level of education, there was a shift toward formality between 1998 and 2006, with the exception of secondary and postsecondary education.

# **V. Results**

- A. General Regression with Full Sample, 1998-2006

Table 7 shows the results of the Heckman estimation for both periods. All the signs are the expected ones in both the participation equation and the earnings (wage) equation. The negative sign on the gender (female) variable in the participation equation is not surprising, given the observed differences in labor force participation rates (Table 1). The existence of a migrant member in the household negatively affects participation, capturing the effect of non-earned income and the corresponding substitution effect on labor force participation. This result is also reported by Assad and Binzel (2009). The other 8
non-response and thus could not examine the importance of unit non-response relative to underreporting.

The extent of item non-response to income or wealth household survey questions has not, as far as we can tell, been the subject of any cross-country research. Campos-Vazquez and Lustig (2019) showed that in Mexico the labor force survey has shown dramatic increases in item non-response to labor income questions, from around 6% in 2002 to 30% by 2017, whereas the income and expenditure survey item non-response has been roughly constant around 3%. The South African Labour Force survey item non-response rate for labor income was around 5%-8% in the 2000s. The publicly available Quarterly Labour Force Survey data from 2010 onwards does not allow for the creation of reliable item non-response rates due to imputation but non-public data shows it also increased to close to 30% in 2020. Further research on the broader trends in middle income countries especially, where item (and unit) non-response rates are much higher, seems like an important gap, as does the extent to which imputation improves the estimates of inequality and the top tail of the income distribution.

# A Research Agenda

Using Existing Data _Household surveys_ Since we have noted that there is very limited income data in the public domain for some LMICs (e.g. African countries), one crucial part of the research agenda measuring the top of the income distribution would be to harmonize and make easily and publicly available the surveys that already exist. The World Bank’s publicly available Statistics Online platform is one example of making data public and more easily available, although this currently seems to include only data on consumption and not income. It also seems to have been entirely ignored outside the World Bank.<sup>6</sup> The World Bank is well placed to lead research that harmonizes the data that does exist, and ideally makes it publicly accessible.

The Rural Income Generation Activities (RIGA) project is one example of a project that did make available and harmonize household surveys with income data across countries. It was driven by the World Bank and the Food and Agriculture Organization.<sup>7</sup> The project created harmonized and publicly available microdata, including incomes, from 35 surveys for 22 countries, most of which were the

> 6 On June 26, 2023, there were no references to Statistics Online on Twitter by anyone outside the World Bank and only three in total. I added a fourth on the June 26. 7https://www.fao.org/economic/riga/en/

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