rafmacalaba/datause-encoder-data huggingface dataset:
In addition, it is possible that differences in the extent to which the measurement error in different data sources are correlated with each other can lead to an inappropriate weighting of indicators in the UCM. Recall that the UCM assigns greater weight to data sources that are more correlated with each other. This makes perfect sense if measurement error is uncorrelated – in this case, two data sources that are highly correlated must be so because they both give more precise estimates of governance, and these data sources should receive more weight. However, this simple logic of the UCM weights breaks down when errors are correlated across data sources. If the observed correlation between two data sources mostly reflects correlated measurement error, then these data sources should receive less – not more – weight.

If it were possible to separately measure the correlation of the error terms in Equation (1) across data sources, this correlation would present no particular difficulties for the UCM – it would simply imply a different pattern of weights across data sources that appropriately discounts data sources with correlated perception errors.<sup>17</sup> Unfortunately, however, empirically assessing the extent of correlated perceptions errors is difficult. The basic problem is simple: since both governance and measurement error are unobserved, we cannot disentangle the observed correlation between two data sources into the component due to the common factor of governance and the component due to correlated measurement error. Instead, to better understand the potential importance of the problem of correlated measurement error, we perform two sets of calculations. In the following subsection, we consider the sensitivity of the WGI estimates to the precision-based weights in the UCM, by comparing our baseline estimates with an alternative method of aggregation that weights data sources equally. In the remainder of this subsection, we update our earlier analysis in Kaufmann, Kraay and Mastruzzi (2006) which provided indirect evidence suggesting that correlated perceptions errors may not be that important.

This earlier analysis began from the premise that correlated perception errors may be more likely among expert assessments from commercial business information providers but are less likely between expert assessments and cross-country firm surveys. This suggests that we can use the correlation between expert assessments and firm surveys to benchmark how much correlation we might expect to see absent correlated perceptions errors. We can then compare this benchmark with the

> 17 To see this, see the formal derivation of the UCM in Annex 3.  Everything in Equations (11) and (12) except for the very last equality holds true for any covariance matrix of the error terms Σ .  Only the final step in each expression relies on the assumption that Σ is diagonal, i.e. that measurement error is uncorrelated across data sources.

21
# **Appendix Table 3: Financial Planning - IV Specification**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Financial Planning<br>Composite Score|Q1: Have a Plan<br>to Achieve a<br>Financial Goal|Q2: Written<br>Financial Plan|Q3: Adhere to<br>Financial Plan|
|Takeup in Autonomous Trainer Selection (T1)|0.142**|0.149**<br>|0.158*|0.121*|
||(0.063)|(0.072)<br>|(0.084)|(0.072)|
|Takeup in Fixed Trainer Selection (T2)|0.022|-0.013<br>|0.053|0.026|
||(0.056)|(0.068)<br>|(0.071)|(0.064)|
|R-squared|0.024|0.010<br>|0.022|0.015|
|N|7885|7885<br>|7885|7885|
|Dependent Variable Mean in Control Group|<br>0.567|0.859<br>|0.222|0.622|
|F-test(p-value): T1 = T2|0.048|0.019|0.224|0.19|

This table presents instrumental variable analysis for financial planning using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p- values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
<!-- Start of picture text --> 0.02<br>= 2008<br>0.018<br>mer EA<br>0.016<br>0.014<br>0.012<br>0.01<br>0.008<br>0.006<br>0.004<br>0.002<br>0<br>Provinsi Kabupaten Kota<br><!-- End of picture text -->
- 12 -

London data are taken from Schwartz and Marcus" which involves the most complete examination of the effects of temperature and humidity, auEocorrelation, and functional form. Their results over the 14 irears suggest the following for all-cause mortality: Daily mortality = 2.31 * (daily average BS)" in London The standard error of the estimated regression coefficient was 0.160. During the period of study, there was an average of 280 deaths per day and a mean concentration of BS of 174 _gglm?_ in London. After taldng the derivative of the above and substituting in the mean of daily deaths, the effects of PM10 can be expressed as: % change in mortality = (0.4125 PM1Of) * change in PM1O Thus, at the mean level of pollution, a 10 yg/rn<sup>3</sup> increase in PM10 is associated with a 0.31 percent increase in mortality. Using plus or minus one standard error from the estmate, a confidence interval of 0.29 to 0.33 percent is obtained. This change is similar to that predicted from the linear models described above.

_Onrio, Canada._ Plagiannakos and Parker"' used pooled cross-sectional and timne-series data for nine counties in Southern Ontario, Canada, for the period 1976 to 1982. Their model attempted to explain mortality as a function of several socioeconomic factors (education, population over age 65, alcohol consumption), time, meteorology, and air pollution including TSP, sulfates, and sulfur dioxide. There was no correction for autocorrelation. Since mean ambient concentrations were not provided by the authors, grap1hical displays were used to estimate pollution levels. The mean for TSP appeared to be _3 3_ approxirnately 70 _g//mI_ while the mean for sulfate was approximately 12 _pLgm._ A statistically significant association was found between all-cause mortality and sulfates, and between respiratory-related mortlity and both TSP and sulfates. For all-cause mortality, the model with the highest association between air pollution and mortality, is represented by the following quantitative relationship:
||**Table 1**<br>**U.S. Tangible Capital Assets and Debt**<br>**1993**|**Instruments, Y**|**ear-End**|
|---|---|---|---|
||**Tangible Capital Assets**|$ Trillion|Percent|
|1|Residential Structures|_5.5_|29|
|2|Non-Residential Structures|3.0|16|
|3|Land|4.2|23|
|4|Total Real Estate (1+2+3)|12.7|68|
|_5_|Total Tangible Capital|18.7|100|
||**Debt Instruments**|||
|6|Mortgages|4.2|34|
|7|U.S. Government|3.4|27|
|8|Corporate Bonds|1.2|10|
|9|State and Local Government|1.2|10|
|10|Total Debt|12.4|100|
||Source: Flow of Funds, Federal Reserve Board|||

In terms _offinancial sector development,_ housing finance has remained in a primitive state compared to the rapid development of banking. This is particularly striking because, in most developed countries, the mortgage market--meaning the market for financing real estate assets--is among the largest components of the capital markets. In the United States, for example, mortgage debt is the largest component of the domestic debt markets (see Table 1).

Mortgage market development is likely to be a key factor in overall financial market development. In particular, an efficient mortgage market will act as a positive externality for the other capital markets, creating pressure for higher efficiency in these markets. On the other 3
respondents, this approach may be better suited for more focused agricultural surveys conducted on a rotating basis, that could then be compared with standard employment survey data.

# Counting women’s off-farm employment: The role of informality

Aside from own-farming, rural women are also heavily involved in informal jobs that are more difficult to pick up in surveys. Casual wage labor on farms , for example, is increasingly common amid a growing share of landless households (Mueller and Chan, 2015). Figure 2, for example, shows that poor individuals in the Malawi 2016-17 LSMS-ISA, and particularly women, reported nearly all wage work in _ganyu_ (or short-term, temporary labor). As this work is highly seasonal and typically with little pay, and has traditional nomenclature, it can often go unreported if not explicitly asked in surveys (Beegle et al, 2017). Valenzuela (2003) also discusses other contexts where day labor can be underreported for similar reasons — In Japan, for example, “laborer” is closely associated with the word _romusha_ , which implies low-skilled, menial labor, and many respondents in this work may not self-identify in these roles.

The second panel of Figure 2 also shows that among wage earners, the gender discrepancy in _ganyu_ labor widens with age; older women wage laborers are as likely as younger women to continue working in _ganyu_ , while the share of men in this work declines rapidly with age. Close attention is needed in surveys to local labor arrangements that the poor engage in — and which might get missed with standard questions on wage and self-employment, particularly for women who are often highly engaged in these seasonal forms of work, and even among more vulnerable/older groups.

## Figure 2.  Malawi LSMS-ISA, 2016-17: share of men and women in ganyu (short-term, temporary) labor

Share of men and women (total sample) that have worked in _ganyu_ and other wage labor, by poor/non-poor households and reference period Among poor men and women wage earners, share in _ganyu_ in the last 12 months, by age Within self-employment, another issue is identifying women who run their own business — and separately those in contributing family work. Many rural women who have a business do not necessarily count themselves as employed/entrepreneurs, because their operations are typically small in scale, and this work is often conducted alongside other family responsibilities (ILO, 2018b). Cognitive testing that informed the 8
<!-- Start of picture text --> 1' 8'<br>3' 5 7'<br><!-- End of picture text --> <!-- Start of picture text --> 2' 3' 5 7'<br>4' 3' 6<br><!-- End of picture text --> <!-- Start of picture text --> 4' 3' 6<br><!-- End of picture text --> <!-- Start of picture text --> HS1' HS0' M1' M0'<br><!-- End of picture text --> Figure 2: **Discriminatory Market Access Barriers** <!-- Start of picture text --> Price'<br>Domes6c'market' Import'market'<br>HS’'<br>HS'<br>FS’'<br>9' 12'<br>10' 11'<br>M’'<br>M'<br>D'<br>HS0=HS1' M1' M0'<br><!-- End of picture text --> Figure 3: **Non-Discriminatory Regulatory Barriers** 17
|tradeopen.|Total exports plus total imports of goods and services<br>as a share of GDP (%)|1964-2013|WDI|
|---|---|---|---|
|enrolment|Gross secondary school enrolment rate<br>(%)|1964-2013|WDI|
|fdi|Foreign direct investment, net inflows as a share of<br>GDP (%)|1964-2013|WDI|
|bsupervision|Banking Supervision (3: highly supervised, 0: not<br>supervised)|1973-2005|Abiad et al<br>(2008)|
|financialcrisis|The ratio of the years with banking crises to total<br>number of years in the period|1970-2011|Laven and<br>Valencia<br>(2012)|
|legalorigin|Legal origin of the company law or commercial code<br>of the country. There are five categories: (1) English<br>Common Law, (2) French Commercial Code,<br>(3)German Commercial Code, (4) Scandinavian<br>Commercial Code, (5) Socialist/ Communist Laws.|dummy|La Porta et<br>al (1999)|
|religion|Percentage of the population of country that belonged<br>to the three most widely spread religions in the world<br>in 1980. There are four categories: (1) Roman Catholic,<br>(2) Protestant, (3) Muslim, (4) Other.|dummy|La Porta et<br>al (1999)|
|inflation|Change in consumer prices (annual %)|1964-2013|WDI|
|std_exchangerate|Standard deviation of the change in real effective<br>exchange rate index (2010 = 100)|1964-2013|WDI|
|tot_change|Percentage change in net barter terms of trade index<br>(2000 = 100)|1964-2013|WDI|
|commodityexporter|=1 if the country is classified as commodity exporter<br>by WEO October 2011, =0 if otherwise.|dummy|WEO|
|HIGHINCOME|=1 if the county is classified as high income by WDI,<br>=0 if otherwise|dummy|WDI|
|ECA|=1 if the county is located in Europe and Central Asia<br>region, =0 if otherwise|dummy|WDI|

31
involves the elimination of tariffs on the non-preferential supplier. This is shown in the bottom half of Figure 1. Elimination of the tariff on remaining third-country suppliers, given the duty free access already for preferential suppliers, means that third-country exporters see their exports increase from _X_ non-LDC,1 to _X_ non-LDC,2. There is a gain in exporter surplus of area E, which may be greater or less than the original loss of exporter surplus resulting from the preferences, area B in the top part of Figure 1. The preferential supplier experiences a fall in demand for its exports from DLDC,1 to DLDC,2. This results in a partial, though generally not full, loss of the benefits from the original preference scheme. This is represented by area C, which is shown as being less than area A in the top half of Figure 1. The reason the loss is not complete is that preferences include, in part, the benefits relative to the original tariff-ridden equilibrium from a non-discriminatory tariff reduction by the importer. Preference erosion therefore generally yields a partial, not full, loss of the original benefits of the preference scheme. At the same time, third-countries recover some of the costs originally imposed by the preference scheme.

The foregoing ignores numerous important dimensions of reality.<sup>5</sup> First, preferences can only have an impact if there is a non-zero tariff in the importing market. Two-thirds of the major items Africa exports to Canada, for example, faced zero MFN tariffs even before the 2003 initiative in favor of LDCs. Similarly, 69 percent of EU imports from Africa (by value) in 2000 were in items facing zero MFN duties (Stevens and Kennan, 2004)—again, before EBA was introduced in 2001.

Second, there are general equilibrium effects to consider, especially the impact of changes in policies in other countries, both those that do and those that do not grant preferences. Such changes may affect demand and supply and thus world prices of the product concerned. Changes in overall (global) trade policies may also affect the relative returns of trading different products, create opportunities for exports that did not exist before, and so forth.

> different origin. Early studies assumed these elasticities were identical. General equilibrium studies by contrast tend to use Armington elasticities. For more discussion, see Brown (1987), Langhammer and Sapir (1987) and the references cited there.

> 5 See for example the survey by Hoekman and Ozden (2005).

5
# **Exhibit 3.5 – Number of listed firms**

_Per 1,000,000 inhabitants for selected countries, 2003-07 (left graph)_ _Per 1,000,000 inhabitants for Egypt actual versus benchmarked values, 2000-06 (right graph)_ <!-- Start of picture text --> Egypt Argentina Chile Colombia Hungary Israel Jordan Mexico Morocco Pakistan Peru Philippines Turkey<br>25<br>18<br>20<br>16<br>15 14<br>12<br>10 10<br>8<br>5 6<br>4<br>0<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006<br>2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source: EGX, World Federation of Exchanges, World Development Indicators; World Bank staff analysis Note for right graph: Benchmarked values based on a worldwide regression model that takes into account GDP per capita, population size and density, value of fuel exports to GDP, the poverty gap, and whether the country is an offshore financial center. The confidence interval is plus and minus the standard error of the prediction._

- _3.2.3 Trading activity concentration: value and volume traded concentrations and number of stocks with a high fraction of non-zero trading days have been decreasing, but went up slightly in 2007-08; many stocks however remain illiquid_

The top ten most active Egyptian companies are responsible for 45-50 percent of total value traded (Exhibit 3.6), which is in line with other lower-middle income countries. To filter out price effects, Exhibit 3.6 benchmarks Egypt’s 2008 distribution of _volume_ traded (as opposed to value traded) for the 100 most-active Bloomberg-covered stocks. Somewhat higher than reported by the WFE, in 2008 the top ten most active companies were responsible for 55-60 percent of total volume traded. This was only around 40 percent for Amman and Kuwait. Combined, Egypt’s 30 most active companies accounted for 90 percent of volume traded. Casablanca and Manama were the most concentrated markets. Exhibit 3.6 shows that the distribution has improved over the period 2005-07 but has reversed slightly in 2008.

26
implemented along with the implementation dates. The second is monthly data from January 2008 until August 2012 on the number of firms registering as formal entities in each municipality. This includes registration of firms under all registration categories except as individual microenterpreneurs (MEIs). In particular, it includes firms who register for SIMPLES, the type of registration which is the focus of the one-stop shop. The third type of administrative data is the monthly total municipal tax payments received in each municipality from January 2008 to October 2012. The tax paid is the _Imposto Sobre Serviços de Qualquer Natureza (ISS)_ – a tax on all firms in the service sector, which is paid as part of the SIMPLES payment whereby a firm makes a single payment that covers a range of different government taxes.

There are 853 municipalities in Minas Gerais. Thirty-one of these, including Belo Horizonte, the state capital, have a physical Minas Facil office and are generally much larger in size. Our dataset consists of the monthly formalization and tax data by municipality for the remaining 822 municipalities, along with the details of which of these received a Minas Fácil Expresso office.

# **4. Estimation Methodology**

Figure 2 plots the mean monthly number of registrations for the 55 municipalities that had a Minas Fácil Expresso office implemented at some point during our study period (treated municipalities), as well as the monthly mean number of registrations for the 767 municipalities in which it was not introduced. Vertical lines show the period during which the Minas Fácil Expresso offices were introduced into the treated municipalities in a staggered fashion, as shown in Figure 1.

Measuring the impact of this reform requires coming up with a counterfactual of what registration rates would have been in treated municipalities had this reform not occurred. A simple before-after comparison would use the registration rates in the period before the reform as the counterfactual for what rates would have been in the post-reform period had this reform not occurred. There are two reasons to not believe this. First, Figure 2 shows considerable month-to-month variation in the number of firms registering, suggesting seasonal factors and other changes in the economy are likely to influence the registration rate in any given month. Second, even if registration rates were completely constant over the 44 months of data pre-intervention, there is still the concern that some other change in government policy or in the economy may induce a change in the registration rate at the same time as our intervention is occurring. Figure 2 suggests this may have occurred in September 2011, where there is a large reduction in the number of firms registering. During this month the Federal Government 6
|(8)<br>Girls|-4.107*<br>(2.151)|5195<br>1950.024<br>47.598<br>1.815***|Yes<br>Yes<br>Yes<br>Yes|ved food are<br>of elderly are<br>n instrument<br>17. Standard<br>of education,<br> of children,<br>uality (Gini),<br> 1stage is the|
|---|---|---|---|---|
|activities<br>(7)<br>Boys|0.366<br>(0.970)|4985<br>583.091<br>55.647<br>1.976***|Yes<br>Yes<br>Yes<br>Yes|essing preser<br> taking care <br> is used as a<br>tween 6 and <br> age2, years <br> elderly, share<br>cy rate, ineq<br>. Coecient|
|Other household <br>(5)<br>(6)<br>Adult-Male<br>Adult-Female|-2.409<br>-7.776**<br>(2.361)<br>(3.218)|7282<br>9597<br>1055.533<br>1854.033<br>41.195<br>70.938<br>1.611***<br>1.913***|Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes|nitting, weaving, tailoring and proc<br>ing, laundry dishes, babysitting and <br>rnational migrants in a VDC in 2001<br>irls samples are restricted to ages be<br> _<_ 0_._001. Individual controls are age<br>of male and female adults, share of <br>mployment rate, poverty rate, illitera<br>lls, Western Terai, and Eastern Terai|
|(4)<br>Girls|5.541**<br>(2.354)|5195<br>1678.898<br>47.598<br>1.815***|Yes<br>Yes<br>Yes<br>Yes|making mats, k<br>king food, clean<br>le. Share of inte<br>hile boys and g<br>_p <_ 0_._01, _∗∗∗p _<br>H head, share <br>ontrols are, une<br> hills, Eastern hi|
|Household production<br>(1)<br>(2)<br>(3)<br>Adult-Male<br>Adult-Female<br>Boys|Household with migrant<br>1.034<br>8.184**<br>2.006<br>(4.456)<br>(3.810)<br>(2.326)|Observations<br>7282<br>9597<br>4985<br>Wald _χ_2<br>1805.517<br>3126.243<br>755.167<br>_F_-test 1stage<br>41.195<br>70.938<br>55.647<br>Coecient 1stage<br>1.611***<br>1.913***<br>1.976***|Controls<br>Individual characteristics<br>Yes<br>Yes<br>Yes<br>Household characteristics<br>Yes<br>Yes<br>Yes<br>VDC characteristics<br>Yes<br>Yes<br>Yes<br>Region Fixed Eect<br>Yes<br>Yes<br>Yes|Note: Fetching water, collecting rewood and dung, taking care of animals, <br>classied as household production activities. Whereas, minor house repair, coo<br>categorized as other household activities. 2SLS estimates are reported in the tab<br>for the estimations. Male and female samples are working age,18 to 60, adults w<br>errors are clustered at VDC level. Standard errors in parentheses._∗p <_ 0_._05, _∗∗_<br>marital status, and household head identier. Household controls are female H<br>amount of land-owned, house ownership and social caste. Similarly VDC level c<br>and urban/rural location. Regions are Kathmandu, other urban areas, Western <br>estimated coecient on the exogenous instrument in the rst-stage.|

31