rafmacalaba/datause-encoder-data huggingface dataset:
such as when developing global poverty scenarios for 2030 (see Jolliffe et al., 2014 for details). The results indicate a stronger correlation between growth in surveys and national accounts for longer and more comparable growth spells. For consumption means in surveys, we observe that most coefficients are significantly less than one, suggesting a lower pass through of growth from national accounts to surveys, than for income means in surveys where the coefficients are larger and the difference from unity mostly statistically insignificant. The Middle East and North Africa region stands out as a region with particularly poor correlation between national accounts and survey growth rates. We find a much stronger relationship in Eastern Europe and Central Asia than Ravallion’s (2003) assessment, which mainly used data from the transition period of the early 1990s, when both national accounts and survey data were particularly poor and periods of high inflation gave large measurement errors.

The sample of comparable growth spells available in the Global Database of Shared Prosperity, which is designed to assess growth in comparable household surveys over spells of 3 to 7 years, shows clearly how large the difference in annualized growth rates can be even for modest spells length with high quality and comparable survey data. For the most recent version of the database covering spells from approximately 2013 to 2018, the gaps between annualized real growth rate in survey mean and HFCE mean range from -8.9 to 6.7 percentage points, with mean gap of -0.27 percentage points, but not statistically significantly different from zero at 95%-confidence level. When using the no-constant regression approach to assess the gaps with the Shared Prosperity Database, the regression coefficient is 0.86 for GDP (not statistically significantly different from 1) and 0.81 for HFCE (statistically significantly different from 1).

# **4. Implications for Global Measures of Poverty and Inequality**

With the existing literature on why the gaps exist and the empirical evidence on the size of the gaps in mind, we consider two approaches for adjusting survey data to align more closely with national accounts estimates. We examine the implications of each approach for global poverty and inequality measures. One simulation uniformly scales up the welfare vector (i.e. the consumption or income vector) from survey data to match survey means with national accounts estimates. While this scaling approach has been frequently used, notably by Pinkovskiy and Sala-i-Martin (2014, 2016), we believe that the distribution-neutral adjustment is based on an untenable assumption and unsupported by the empirical evidence on measurement error in 20
Kotsadam, Andreas, and Espen Villanger. 2020. “Jobs and Intimate Partner Violence - Evidence from a Field Experiment in Ethiopia.” SSRN Scholarly Paper ID 3541457. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=3541457.

Macmillan, Ross, and Rosemary Gartner. 1999. “When She Brings Home the Bacon: Labor-Force Participation and the Risk of Spousal Violence against Women.” _Journal of Marriage and Family_ 61 (4): 947–58. https://doi.org/10.2307/354015.

Manser, Marilyn, and Murray Brown. 1980. “Marriage and Household Decision-Making: A Bargaining Analysis.” _International Economic Review_ 21 (1): 31–44. https://doi.org/10.2307/2526238.

Pillinger, Jane. 2017. “Violence and Harassment against Women and Men in the World of Work.” International Labour Organization.

Roy, Shalini, Melissa Hidrobo, John Hoddinott, Bastien Koch, and Akhter Ahmed. 2019. _Can Transfers and Behavior Change Communication Reduce Intimate Partner Violence Four Years Post-Program? Experimental Evidence from Bangladesh_ . Internatinal Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.133421.

UN Department of Economic and Social Affairs Statistical Office. 2014. _Guidelines for Producing Statistics on Violence Against Women: Statistical Surveys_ . United Nations.

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terminals, IT has fundamentally altered the way securities trading is done. Intermediaries such as stock brokers bave seen their monopoly over trading and trade information weakened as large investors and other participants gained access to real-time market information through computer networks. A<sup>high</sup> volume of trading is now done away fiom traditional stock exchange trading floors. This move away from central trading floors has forced market supervisors to consider difficult trade-offs between the efficiency benerits deriving from a larger number of diverse securities market service-providers and<sup>such</sup> problems as market segmentation which may diminish the market's price setting efficiency.

Like securities trading, clearing and settlement of transactions has changed radically in the last ten years. The physical movement of share certificates, transfer instructions and payments<sup>has</sup> been replaced in many places by "book entry" systems which rely on computer records of entitlement to securities and the automatic debiting and crediting of payment accounts. As a result, clearance and settlement can be concluded in a shorter period with consequent risk reduction and efficiency gains. On the other hand, such systems have required expanded secure custody arrangements, very accurate record keeping and changes to legal definitions of evidence of title. Market supervisors have had to develop rules and monitoring programs which attempt to protect investors' rights under the new systems.

More generally, the spread of IT in securities markets has often made the markets so dependent on automation that a systems failure is tantamount to a market failure. As a result, regulators have greater responsibility to oversee the safety and soundness of technical IT systems whose failure could have market-wide consequences. This calls for back-up and disaster recovery systems by market providers, but also improved technical capacity of the regulator to enable adequate supervision.<sup>4</sup> Emerging securities markets as well as the world's traditional financial centers are confronting these issues. Perhaps the starkest emerging market examples are found in Eastern Europe where sweeping reforms in the financial sector have ofter? included the speedy establishm-nt of a securities market infrastructure from little or no base. In some Eastern European countries the newest IT systems have been brought into operation at the very beginning of experience with the basics of market operation and supervision. Less dramatic developments are in evidence in many other emerging markets. For example, Turkey is at the first stage of a sequenced IT enhancement program from a low base, while

- 3 **Lucas** and Schwartz, **The Challenee** of Information Technoloev for the Securities Markets, p.<sup>4, call</sup><sup>**these**'second</sup> order effects' of **IT** in securities markets.

- ' **The** US SEC **release** 27445 and 29185 of November 16, 1989 and May 9, 1991 provide<sup>a good introduction to</sup><sup>**these**</sup> **issues.**
Commercial and civil disputes vary in their nature and complexity. Commercial cases include mostly payment and other contract disputes, such as sale and rent contracts involving at least one moral person (firm). Similarly, civil cases include contract and payment disputes between individuals (e.g., landlord and tenant), as well as other civil issues such as inheritance disputes. Sixty-three percent of civil and commercial disputes in our sample include a payment claim. Among these, the average claim amount is CFA franc 71,542,000 (approximately USD 135,000), ranging from CFA franc 75,000 to CFA franc 7,400,000,000 (approximately USD 142–13,962,000; Table 2).

# **3 Data**

## **3.1 Caseload data**

We digitized paper-based records of the civil and commercial chambers of the Regional First Instance Court of Dakar, Senegal, over the period January 2012 to June 2015 (Figure 1). We recorded hearing-level outcomes for each case across both pre-trial and decision phases and entered information on the minutes of the judgment. This data yields case-level information on the full civil and commercial caseload over the 2012/15 study period (5,297 cases). For each case, we recorded when it entered the court, when and to which chamber it was transferred for the pre-trial procedure (first hearing), which judge presided over its pretrial, the date and outcome of each pre-trial and decision-stage hearing, the date and nature of the final decision, some elements of the text of the decision itself (judgment minutes), as well as scant case characteristics available in the files (civil or commercial, claim amount, type and number of parties on each side).

This yields a case-hearing-level dataset that retraces the whole first instance procedure for all cases entering the court over our study period. This dataset allows us to document whether 6
**Increased Coverage of Maternal Health Services among the Poor in Western Uganda in an Output-Based Aid Voucher Scheme**

Francis Obare, Population Council Peter Okwero,* The World Bank Leslie Villegas,* The World Bank Samuel Mills, The World Bank Ben Bellows, Population Council _Key words_ : impact evaluation; vouchers; reproductive health; output-based aid; Uganda _JEL classification_ : I14 _Sector Board_ : Health Global Practice

# **----------------------------**

*Corresponding authors: Peter Okwero, The World Bank, Kampala, Uganda. Tel. +256 414 302227, Mobile: +256 772 636486, e-mail: pokwero@worldbank.org; and Leslie Villegas, The World Bank/GPOBA, Washington, DC, USA. Tel. +12024735265, e-mail: lvillegas@worldbank.org.
If both _z_<sup>X</sup> <30 and _z_<sup>M</sup> <30 then the sector is categorized as a two-way trade sector. In the sample, they are replicated, once as an import good for which _z_<sup>M</sup> is the relevant variable, and once as an export good for which _z_<sup>X</sup> is relevant. Thus, in the sample used to estimate (4), there are 94 observations, 41 for imports and 53 for exports.

Another study by Kee, Nicita and Olarreaga estimates U.S. import demand elasticities at the tariff line level using the GDP function approach.<sup>32</sup> Since the import demand elasticity variable consists of estimated values, we treat it as a variable measured with error, but with known measurement error variances (equal to the square of their standard error of estimate). A Fuller correction is applied to the variable before it is used as the denominator in the _z/_ e variable.<sup>33</sup> Corresponding estimates of export supply elasticities are unfortunately unavailable. Since most agricultural goods are relatively undifferentiated it is reasonable to assume that their export supplies are elastic. We therefore set the export supply elasticity equal to 2 for all goods.

# _Lobbying_

Agricultural PAC spending data for the three congressional election cycles between 1993-1998 were downloaded from the Federal Election Commission (FEC) website.<sup>34</sup> The main use to which the PAC spending data are put is to measure lobbying organization.<sup>35</sup> Whether a good i has political representation (Ii=1) or not (Ii=0) is determined on the basis of PAC spending summed over this period. Goods are ordered by PAC spending and sets of I are defined, each set corresponding to a percentile cutoff value. Models (4)-(6) are estimated using all these 32 Kee, Nicita and Olarreaga (2004a). See e.g. Kohli (1991) for the GDP function method. In this method imports are treated as inputs into domestic production, given exogenous world prices, productivity and endowments.

33 Fuller (1986). The idea behind this correction is to limit the influence of estimates that are large and also have large standard errors. Without the correction, these large estimates would grossly overstate the true elasticity. The correction mutes their effect.

34 <u>www.fec.gov.</u> 35 Mapping PACs into SIC-based agriculture-related sectors was done via a concordance constructed by Beaulieu and Magee (2002). In most cases many PACs mapped into one SIC code, so SIC level contributions are simply the aggregate of the mapped PACs. For one-to-many maps, political contributions from each PAC were fractionally assigned equally to each SIC code into which the PAC mapped. To check for consistency we compared our data with the data on the opensecrets.com website. For the few sectors in which opensecrets.com reports such information our data closely matched theirs.

15
# **Figure 11a.Private Capital Flows, US$ billion 11b. Net FDI, US$ billion**

<!-- Start of picture text --> Net private capital inflows,incl. errors and Direct Investment<br>omissions, US$ bn" 60<br>100<br>40<br>80<br>20<br>60<br>0<br>40<br>-20<br>20<br>-40<br>0<br>-60<br>-20<br>Net FDI DI Abroad FDI in Russia<br>-40<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> Source: World Bank.<br><!-- End of picture text -->

# **_D. But with rising dependence on oil, macro tensions reemerged in 2008_**

**_The strong recovery of oil prices since about 2000 has increased Russia’s dependence on oil and gas revenue_** (See Fig. 10a and b). The share of oil revenues in total fiscal revenue increased substantially--from about 10 to 30 percent of GDP. Instead of declared policy of diversification, Russia has, de facto, been _specializing_ in oil exports and oil fiscal revenues. While this has seemingly not been a problem during the period of high oil prices, it remains a major source of major vulnerability and shocks when oil prices begin a rapid descent from the recent levels (as was the case since mid-2008). In 2007, oil accounted for about 60 percent of total exports. In a word, higher oil revenues allow for additional spending room, but they also complicate macroeconomic management and lead to an increased dependence on a highly volatile source of income.

**_There are two problems with respect to high dependence on oil revenues: volatility of oil prices and the exchange rate consequences of spending oil revenues._** Oil revenues are highly volatile, even when quantities are relatively easy to predict: price volatility is high, making oil revenue a highly _uncertain_ source of income even when production levels are relatively stable.

**_Uncertainty of a major revenue source calls for conservatism in deciding what level of expenditure out of oil revenues is sustainable._** For example, basing fiscal, especially expenditure policy on a prediction of high oil prices while prices turn out low will cause major macroeconomic problems of adjustment because expenditure commitments are difficult to scale down in mid course and investment projects are lumpy and costly to delay or stop. By contrast, basing policy conservatively on relatively low oil prices while they turn out higher results in a much more pleasant dilemma: what to do with windfall, excess revenues; in practice, it means that expenditure benefits of such windfalls are somewhat delayed––and this may be a small price to pay for maintaining fiscal prudence and avoiding painful adjustments compared with the case when oil prices turn out much lower than planned.

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_37_ Table 4—Test Scores

|||Schoo|l level||Child level|
|---|---|---|---|---|---|
||(1)<br>Math|(2)<br>English|(3)<br>Urdu|(4)<br>Avg|(5)<br>Avg|
|High|0.16*|0.19**|0.11|0.15*|0.22**|
||(0.09)|(0.09)|(0.08)|(0.09)|(0.09)|
|Low Treated|-0.07|0.08|-0.08|-0.03|0.10|
||(0.11)|(0.11)|(0.11)|(0.10)|(0.10)|
|Low Untreated|0.03|0.06|0.01|0.03|0.01|
||(0.08)|(0.08)|(0.07)|(0.07)|(0.08)|
|Baseline|0.27**<br>|0.43***<br>|0.25**<br>|0.36***<br>|0.63***<br>|
||(0.11)|(0.08)|(0.12)|(0.12)|(0.05)|
|R-Squared|0.18|0.14|0.13|0.16|0.21|
|Observations|725|725|725|725|12,613|
|# Schools (Rounds)|725 (1)|725 (1)|725 (1)|725 (1)|719 (1)|
|Mean Depvar|-0.21|-0.18|-0.24|-0.21|-0.19|
|Test pval (H=0)|0.08|0.05|0.18|0.07|0.02|
|Test pval (_L_<sup>_t_</sup>=0)<br>|0.50|0.43|0.45|0.79|0.33|
|Test pval (_L_<sup>_t_</sup>=H)|0.03|0.33|0.07|0.07|0.24|

Notes: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 a) This table examines impacts on school and child level test scores. Columns 1-3 construct school test scores by averaging child scores for a given subject from a given school; Col 4 shows the average score (across all subjects) for the school. Col 5 shows the average (across all subjects) score at the child level. We tested two grades at endline between grades 3-6, and grade 4 at baseline. In columns 1-4, we use all available test scores, and child composition may be di↵erent between baseline and endline.

b) Regressions are weighted to adjust for sampling and include strata fixed e↵ects, with standard errors clustered at village level. We include a dummy variable for the untested sample at baseline across all columns and replace the baseline score with a constant. Since the choice of the testing sample at baseline was random, this procedure allows us to control for baseline test scores wherever available. The number of observations and schools are the same since test scores are collected once after treatment. The number of schools is lower than the full sample in round 3 due to attrition (39 schools refused surveying), closure (57 schools closed down), zero enrollment in the tested grades (9 schools), and missing values for the remaining schools. The mean of the dependent variable is the test score for those tested at random at baseline. c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.
There are about 7,000 firms where the largest shareholder is an individual or family;<sup>4</sup> however, as Table 1 shows, a number of firms are missing observation for the performance measures. Hence, our analytical sample is restricted to approximately 3,334 firms once we delete observations: a) with missing information on capital (since only 3,531 firms have information on fixed assets or capital) or sales or labor, and b) from two-digit NACE industries with fewer than five firms. The result is that the analytical sample does have somewhat higher mean sales, cost of material inputs and profits than the original sample.

We begin with three basic descriptive statistics of female-owned firms in ECA. First the higher concentration of males in business ownership is evident in this region as in most of the world. In 2004, 26.8% of all entrepreneurs were women. This share is slightly lower to that found in the global averages, where according to Reynolds et al. (2002), men are about twice more likely to be involved in entrepreneurial activity than women. However, the average in our data is higher than that found in the 2005 GEM data base for “nascent entrepreneurs” in 35 countries. There the rates of female entrepreneurship range from about 1-2% in Netherlands and Japan to nearly 20-24% in Venezuela and Thailand (Minniti et al., 2005). The country level shares of female entrepreneurs in our data, shown in Figure 1, range from above 40% in Latvia to below 15% in Armenia. Explanations for the variance in the share of female entrepreneurs across countries are left for another paper, which can exploit panel data on countries over time. However, we provide a simple plot in Table A4, which indicates a positive association between the level of female education (share with secondary level schooling in a country) and the share of female entrepreneurship.

Second, again as found in other parts of the world, women in this region own significantly smaller firms than men, when measured by the number of permanent employees. About 57% of all female-owned enterprises employ fewer than 10 permanent employees in contrast with 41% of male-owned enterprises (see Figure 2). This finding is addressed in greater detail below.

> 4 We have therefore excluded from the analysis firms where the largest shareholder is the state, domestic and foreign companies, banks, or employees or management.

10
**Figure 7: Currency Cyclicality Index and Openness of Capital Account**

<!-- Start of picture text --> Capital Openness = 1.04 - 1.57*CCI<br>(6.96) (3.17)<br>ESTCHEIRLNLDLTULVA JPNDEUUSABEL CAN PERGRC NZL URY<br>GBR<br>AUT FINSWE<br>DNK CZE<br>FRACRI ITA ESPHUNAUS<br>NORSVN IDN<br>BOL GEO PRT ROM<br>BGRCYP MLTHRV ISLMEXMYS<br>ISR ARG<br>MKD<br>SVK<br>KOR<br>RUSPOL CHL PHLTHA<br>BRA<br>COL TUR<br>MAR IRN<br>CHN ZAF IND<br>BLZ UKR<br>-.5 0 .5 1<br>CCI<br>3<br>2<br>1<br>0<br>Capital Account Openness<br>-1<br><!-- End of picture text --> Note: CCI is the correlation between the HP filtered NEER and HP filtered real GDP. The t-statistics are reported in parentheses.

One of the key observations we make is that the cyclicality of currency and that of monetary policy are positively correlated—the countries with procyclical currencies tend to pursue procyclical monetary policies, and countries with countercyclical currencies tend to pursue countercyclical monetary policies, Figure 8. While the level of development of an economy is likely to have some bearing on it, the relationships observed between currency cyclicality and policy variables point at the trilemma kind of considerations–the constraint that countries face in the policy space of capital account openness, exchange rate regime and monetary policy.<sup>16</sup> It seems that the emerging markets that are more prone to procyclical currencies resolve the trilemma by choosing limited exchange rate flexibility, a less open capital account, as well as a more procyclical monetary policy.

> 16 See, among others, Obstfeld et al. (2005), Aizenman et al. (2013), and Klein and Shambaugh (2013).

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68

# Table 63 (Cont.)

||Based<br>Value-A<br>In Domes|on<br>dded<br>tic Prices|Base<br>Value-<br>in Wor|d on<br>Added<br>ld Prices|
|---|---|---|---|---|
|In (Unsklled Workers)|039S<br>(0.052)|0368<br>(0.058)|0.43?<br>(0.055)|0.421<br>(0.060)|
|In (Skilled Workers)|1.41-4<br>(13E-4)|2.9E-4<br>(1.8E-4)|1.7E-4<br>(1.4E4)|3.5E-4*<br>(1.8E-4)|
|In (Middle Management)|13E54<br>(7.8E-5)|2.05.4<br>(9.9E-5)|S.8E-5<br>(8.1E-5)|9.3E.5<br>(1.0E-4)|
|In (Upper Management)|2.8E4'<br>(135-4)|65E4!<br>(1.85-4)|155-4<br>(13E-4)|3.7E4'<br>(1.8S-4)|
|In (Wage Bill<br>Tempora Workers)|7.7E5-<br>(6.95-5)|8.0O-5<br>(825)|6.7J.5<br>(73E.5)|_825-S_<br>(8.655)|
|In K|0.565<br>(0.043)|0.593^<br>(0.047)|0.611*<br>(0.046)|0.631^<br>(0.050)|
|nime|.0.067'<br>(0.016)|.0.070<br>(0.017)|-0.079<br>(0.017)|-0.081'<br>(0.017)|
|Industry 1itefcept|Yes|Yes|Yes|Yes|
||0.873|0.869|0.874|0.872|
|N|386|387|375|387|

* - Significant at the S percent statistical test leveL

+ - Significant at the 10 percent statistical test level. Source: 80-finn sample These results are consistent with the interpretation that upper level managerial input is important, but that the observed substantial decline in the non-African managerial input has not had a significant impact on output.

Returning to Tables 6.1 and 6.2, we observe that in most specifications the wage bill of temporary workers has a significant positive coefficient. If we assume that temporary workers are paid a competitive wage, the variation in the wage **bill** approximates