rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix 1 – Data on North-South Capital Flows

Our data on North-South capital flows is taken from the working paper version of this paper, Kraay, Loayza, Serven and Ventura (2000), and is described in detail there. In that paper, we have constructed estimates of the stocks of domestic and foreign assets for a sample of 68 countries from 1966 to 1997. In particular, we have constructed estimates of the domestic capital stock, stocks of foreign direct investment and portfolio equity investments assets and liabilities, as well as international borrowing and lending.<sup>25</sup> Domestic capital stocks are constructed by cumulating domestic investment flows. Foreign assets and liabilities are primarily constructed by cumulating corresponding flow data from the IMF Balance of Payments Statistics Yearbook, accounting for changes in the value of the underlying assets, and drawing on a variety of sources and assumptions to generate initial stocks for these series. We define the wealth of a country as the sum of its domestic capital stock and its net foreign assets.<sup>26</sup> The first panel of Table A1 shows the net foreign assets and their components for an aggregate of non-OECD countries which we refer to in the text as the “South”.<sup>27</sup> Table A2 reports the country-year observations underlying this aggregate. The data on net foreign assets and net lending are identical to those underlying Figure 1 in the text. We have however disaggregated the data on foreign investments shown in 25 We do not include accumulated net errors and omissions in our measures of foreign assets. We do so because (i) it is not clear whether errors and omissions reflect errors in the current account or the financial account of the balance of payments, and (ii) we do not know which gross balance of payments items are affected by the measurement error summarized in net errors and omissions.

26 As is well known, global balance of payments data do not balance and the sum of the current accounts of all countries in the world has been significantly negative over the past 20 years (see for example IMF (1992)). These global discrepancies in flow data are also reflected in our stock data on foreign assets. Unless we attribute the bulk of this discrepancy to net South-North flows, our stylized facts on North-South flows continue to hold.

27 Note that we do not have information on bilateral foreign asset positions. Throughout we maintain the fairly reasonable assumption that South-South investments are negligible. This allows us to estimate South’s gross foreign assets and liabilities vis-à-vis the North by simply summing the gross foreign assets and liabilities of every country in the South. To the extent that South-South flows are important, our method may overstate the size of the South’s gross positions.

26
electrification ─ both conservative (assuming such appliances were not electricity dependent) and liberal (assuming they were). In this paper we will use the more conservative definition which we believe is more likely to approximate the correct measure. However, we tested sensitivity to using the broader measure as well and found this made negligible difference to the main results.

We do not know whether the household’s electricity was obtained from a private generator or through connection to the grid. We do not include households that report just having an electricity connection for purely agricultural purposes, since we are interested specifically in the effects of the use of electricity in the home. Using this indicator on the panel, 17.3 percent of rural households have electricity for household use in 1982, while 69.6 percent do so in 1999. Our figures accord reasonably well with national trends as reported in other sources.<sup>20</sup> Table 1 presents the share of panel households with electricity in the home across the 15 states in the 1982 and 1999 rounds. There is considerable regional variation. In 1982, household electrification was less common in the East than elsewhere, especially the North (where many hydropower stations are located). By 1999, although many more households across regions had received electricity, access was still limited in the Eastern region, especially Bihar, eastern parts of Madhya Pradesh, and Uttar Pradesh.

Table 2 gives the number of households in the panel that acquired electricity over the period. 53.6% of households went from being non-electrified in 1982 to being electrified by 1999. (Very few went in the other direction.) We are also interested in the impacts of village electrification separately from those of household access. Both survey rounds asked what year the village was electrified. Using these data, we construct a variable for the years since the village has been electrified. Given discrepancies in dates across the two rounds, we rely primarily on the 1982 data which we deem likely to be more reliable given recall bias, but use information from the later survey when the information is missing in the baseline and whenever electrification occurred more recently.

We first examine the impact of electrification on real total consumption per capita

> 20 Using National Sample Survey (NSS) data for India, Pachauri and Muller (2008) estimate that 18% of rural households had electricity in 1981 (for both household and agricultural use).  Restricting their sample to just those 15 states in the REDS panel, the share is 20%.  The comparable figure in our analysis is a bit higher at 24% for households with electricity connections for agriculture and owning an electric appliance. The 55<sup>th</sup> NSS round (199900) indicates that 66% of rural households across the 15 states in the REDS panel use electricity for domestic lighting, while our figure based on the REDS 1999 is 71%.

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Policy ReseaRch WoRking PaPeR 4555

# **Abstract**

This paper studies the relationship between gender and corruption in the health sector. It uses data collected directly from health workers, during a recent public expenditure tracking survey in Tajikistan’s health sector. Using informal payments as an indicator of corruption, women seem at first significantly less corrupt than men as consistently suggested by the literature. However, once power conferred by position is controlled for, women appear in fact equally likely to take advantage of corruption opportunities as men. Female-headed facilities also are not less likely to experience informal charging than facilities managed by men. However, women are significantly less aggressive in the amount they extract from patients. The paper provides evidence that workers are more likely to engage in informal charging the farther they fall short of their perceived fair-wage, adding weight to the fair wage-corruption hypothesis. Finally, there is some evidence that health workers who feel that health care should be provided for a fee are more likely to informally charge patients. Contrary to informal charging, moonlighting behavior displays strong gender differences. Women are significantly less likely to work outside the facility on average and across types of health workers.

This paper—a product of ECA PREM and the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand corruption in public services especially coping strategies health workers adopt such as informal payments and the holding of other jobs outside the facility. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. Andrew Dabalen may be contacted at adabalen@worldbank. org and Waly Wane at wwane@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
22 data. For respondents in the VCS, who said they were victims of burglary in the five years previous to the survey, we estimate a probit regression for whether the crime was reported to the police. We use the same explanatory variables, at the police station jurisdiction level, as in our earlier analysis: inequality, per capita expenditure, unemployment, and the richest jurisdiction dummy. The results of this regression are shown in the first column of Table 9. Unemployment in the criminal catchment area is negatively correlated with the probability of a residential burglary being reported by a household, while per capita expenditure has a positive parameter estimate that is only significant at the 10% level. We use the coefficient estimates from this regression to predict, for each jurisdiction, the probability p that a burglary was reported to the police. We then calculate an adjusted count of burglaries for each police station by multiplying the reported number of crimes by I/ p. Columns 2-5 in Table 9 present the results from analysis using the adjusted burglary counts as the dependent variable for four regression models previously estimated in Tables 2-6. Column 6 redisplays earlier results from the full specification (same as column 5) using unadjusted counts as the dependent variable. Compared with the earlier results the elasticities of inequality and per capita expenditure are still positive and significant but somewhat attenuated. As underreporting is more likely in areas with higher unemployment, the adjustment causes the elasticity of unemployment to also become positive and significant -suggesting that unemployment does capture the opportunity cost of crime for potential criminals to a certain extent. This analysis also confirms that misreporting in crime may bias parameter estimates. Nonetheless, the results we derive for residential burglary remain broadly the same:
NAMIBIA: Both the IMR and life expectancy at birth are from<sup>_World Population Prospects 1990._</sup> The U.N. based its estimates on the levels of mortality<sup>in other countries within Southern Africa.</sup> NIGER: Both the IMR and life expectancy at birth are<sup>from</sup><sup>_World Population Prospects 1990._The</sup> U.N. based its estimates on a 1959-63 survey<sup>and on the 1978 post-enumeration survey.</sup> NIGERIA: Both the IMR and life expectancy at birth are<sup>from</sup><sup>_World Population Prospects 1990._</sup> REUNION: Both the IMR and life expectancy at birth are<sup>from</sup><sup>_World Population Prospects 1990._</sup> The U.N. based its estimates on vital registration<sup>data for 1986 and 1987.</sup> RWANDA: Both the IMR and life expectancy<sup>at birth are from</sup><sup>_World Population Prospects 1990._</sup> These estimates are based on the 1978 census<sup>and a 1983 survey.</sup> SAO TOME AND PRINCIPE: The IMR is a Bank estimate from official estimates<sup>from vital</sup> statistics for 1989, as published in _PVSR._ Life<sup>expectancy estimates are Bank projections based on</sup> official estimates as published in _PVSR Supplement_<sup>_1984._</sup> SENEGAL: The IMR is from _World Population Prospects 1990._<sup>It is based on the 1978 National</sup> Fertility Survey and the 1988 Demographic and Health<sup>Survey. Life expectancy is a Bank projection</sup> derived from country life tables.

SEYCHELLES: The IMR is an official estimate<sup>for 1989 from</sup><sup>_Statistical Bulletin._Life expectancy</sup> estimates are Bank projections based on the CDR<sup>and CBRR.</sup> SOMALIA: The IMR is from _World Population Prospects_<sup>_1990._This estimate is based on the 1975</sup> census and a 1980 survey. Life expectancy is a<sup>Bank projection and is consistent with the 1982-86</sup> National Development Plan.

**SOUTH** AFRICA: Both the IMR and life expectancy at birth are from<sup>_World Population Prospects_</sup> _1990._ The U.N. estimates are based on adjusted<sup>vital registration data.</sup>

SUDAN: Both the IMR and life expectancy at birth<sup>are from</sup><sup>_World Population Prospects 1990._The</sup> U.N. based its estimates on the 1979 Sudan Fertility<sup>Survey and the 1983 census.</sup> SWAZIIAND: Both IMR and life expectancy estimates are Bank<sup>projections based on the 1965,</sup> 1976, and 1986 censuses.

TOGO: Both the IMR ana life expectancy at birth<sup>are from</sup><sup>_World Population Prospects 1990._The</sup> U.N. based its estimates on the 1988 Demographic<sup>and Health Surveys, and on the 1961 and 1971</sup> censuses.

TANZANIA: Both the IMR and life expectancy at<sup>birth are Bank projections from the 1978 census.</sup> UGANDA: The IMR is from _World Population_<sup>_Prospects 1990._The U.N. based its estimate on the</sup> 1988/89 Demographic and Health Survey. Life<sup>expectancy estimates are Bank projections, based on</sup> the 1968 census.

ZAMBIA: The IMR is from _World Population Prospects 1990_<sup>The U.N. estimate is based on a</sup> projection of 1980 census data. Life expectancy<sup>at birth estimates are Bank projections from the 1980</sup> census.

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working on the household farm or in the non-farm enterprise but who do not identify themselves as being the main person running/owning the activity). When comparing the different survey assignments, one should keep in mind that the differences here will be driven in part by the inclusion/exclusion of some individuals as being in the labor force (the LFP impacts we observe in Table 4).

Comparing the short and detailed modules, we find that there are significant variations in most categories. Paid employees represent between 14 and 20 percent of working men and between 6 percent and 11 percent of working women, depending on survey assignment. Self-employment without employees is higher for men when using the short module (57 percent) compared to the detailed module (52 percent). By respondent type, while the absolute decline in being self-employed with paid employees for men is small when moving from proxy to self-report (-2.5 percentage points), the relative change is large because of the small base (-50 percent). Across survey assignments, the percentage of unpaid family workers varies dramatically for women and men. When women are directly asked about their type of employment, the percentage of unpaid family workers drops by about 13 percentage points from 73 (when proxy are used) to 60 percent. The impact is also very large for men (33 percent with proxy vs. 21 percent for self-report). Similarly, the use of the short rather than the detailed questionnaire produces a higher percentage of unpaid family workers among women, 67 compared to 62 percent. In this case, the effect is smaller for men and not statistically significant.

A summary of the results of the descriptive analysis is found in Table 7. Deviations from the detailed and self-reported survey design yield lower estimates of LFP and, in 25
with an above-median score to be “heavy overdrafters”.<sup>8</sup> Table 1 illustrates differences in baseline characteristics by overdrafter status. The first row shows that, on average, light overdrafters only took an overdraft in 6 percent of the months in the year prior to the baseline, while heavy overdrafters were in overdraft more than 50 percent of the time. Heavy overdrafters were more likely to be men, more likely to be teachers, less likely to work in the private sector, and had more children and lived in larger households. Although they had similar incomes to light overdrafters, they reported roughly GHS 400 less in savings and GHS 1,200 more in total debt and were 12 percentage points more likely to report difficulty paying bills.

# **2.2 Experimental Design**

NVRB worked with the research team to design a new savings product called Salary Susu Plus (SSP) for the purposes of the study. Similar services were not offered by other banks in the study area at the time. SSP participants specified a monthly contribution amount, which would be deducted from future salary deposits for a period of 18 months. The monthly contribution had to be equal to at least GHS 30 and could not be changed after it was set. Customers could not access their contributions during the savings cycle unless they opted out of the program at a cost of one month’s contribution. Customers who completed the commitment cycle received all their contributions plus an additional bonus payment equal to one month’s contribution (or 5.6 percent of the total contribution amount). This amounts to an annual percentage yield (APY) of 7 percent, assuming monthly compounding. By way of comparison, NVRB offered no interest on current accounts and a 3 percent APY on savings accounts at the time of the baseline. While SSP offered an attractive rate of return relative to NVRB’s other offerings, the real return on savings was still negative, since annual inflation ranged from 10-19 percent over the course of our study period (Bank of Ghana, 2014, 2017).

Half the sample in each overdrafter _×_ branch _×_ gender stratum was assigned to the treatment group, which was offered the opportunity to sign up for SSP. Appendix Table A1 confirms that baseline characteristics are balanced across treatment and control groups, both overall and when the sample is split by overdrafter status.

Appendix Figure A2 illustrates the timeline of all study activities. SSP debits began in

> 8 Overdraft scores were constructed for each person using their current account transaction records available at the time of the randomization. The score was calculated as the fraction of balance entries that were negative over the life of the account. The sample was sorted from lowest score (never had a negative balance) to highest (negative balance 66 percent of the time).

6
dimension is obtained by taking the unweighted average of all indicators in this dimension, including the indicators for the system of national accounts in use, national accounts base year, classification of national industry, CPI base year, and classification of household consumption (Appendix A, Table S1).

A score for each pillar is subsequently computed as the average score of the dimensions in that pillar. For pillars 1, 2, 4, and 5, the unweighted average of the dimensions within each pillar is taken. For pillar 3 on data products, we take a weighted average of the dimensions, where the weights are based on the number of SDGs in each dimension (6 SDGs in dimension 3.1 on social statistics, 6 SDGs in dimension 3.2 on economic statistics, 2 in dimension 3.3 on environmental statistics, and 2 in dimension 3.4 on institutional statistics. SDG 14 - Life Below Water - is omitted because land-locked countries do not report on these indicators.). This reflects a perspective that all SDGs are of equal importance, and therefore the dimensions are weighted accordingly. Additionally, for Pillar 4 on data sources, censuses and surveys are given separate weights, so that censuses, surveys, administrative data, and geospatial data each receives a weight of 1/4. While censuses and surveys are in the same pillar in the framework, and therefore each would typically only receive a weight of 1/6 in this dimension, because of their importance in producing many indicators, they are given extra weight such that each gets a weight of 1/4. (Using a weight of 1/6 for censuses and surveys provides very similar results. In particular, the correlation between the SPI overall score under the preferred approach and the alternative approach is 0.998.) The score for each pillar (S c ) is calculated as follows SS. S LL cc S c = ∑ <u>c</u> ×S c <u>c</u> (2) IIcc ωω SS.DDSSMM c is the weight for dimension _d_ SS. S in pillar LL cc _p_ , and cc=1 is the number of dimensions in pillar IIcc _p_ . Finally, the SPI overall score for country _c_ in time cc _t_ is derived by taking the average across the ωω II 5 pillars. The SPI overall score has a maximum score of 100 and a minimum of 0. A score of 100 would indicate that a country has every single element that we measure in place. A score of 0 indicates that none is in place. The SPI overall score (S c ) is calculated as follows S c = ∑ S SS. SSIIDDIIXXc <u>c</u> (3) IIcc SS.S LL where S c is the SPI pillar scores for country SS. SSIIDDIIXX cc=1 _c_ in time IIcc _t_ for the five pillars discussed above, and is the number of pillars. cc SS. S LL cc II 20
- 47 -

60. A Singapore-type area licensing scheme was actually tested and formally evaluated for Hong Kong in 1985.41 A typical supplementary fee of _HK$7_ each [US$0.90] for vehicles crossing a screenline (in either direction) which encircles the central business districts tallies to about _HK$20 [US$2.56]_ each day in AIS fees. The annual benefits of area licensing amount to HK$338.4 million in 1985 [US$64.2 million in 1990 figures].'<sup>2</sup> This benefit figure from supplementary licensing makes up slightly more than a quarter (27%) of the benefits of the theoretical optimum option and less than half (46%) of those of ERP Scheme A. Despite the fact that these dollar benefits are slightly more than those of the car ownership fiscal restraint measure, the latter policy is a sledgehammer approach; it achieves a city-wide reduction in congestion by drastically _lowering_ the level of car ownership (for four years) as well as eliminating economically worthwhile trips altogether. Further, the car ownership restraint measure is widely (and correctly) perceived to be inequitable by limiting household car ownership to the wealthiest, the top decile of the population (Hau, 1988).

61. The annualized cost of supplementary licensing as in Singapore is estimated to be in the range of _HK$10_ - $15 million in 1985 (or US$1.90 - $2.84 million in 1990 figures). The annual benefit-cost ratio for two separate zones (one on Hong Kong Island and the other in Kowloon) under an area licensing scheme is 22.5 to 33.8, even surpassing the benefit-cost ratio of 25 for the theoretical optimum of ERP - the upper bound reference level of the benefits achievable under direct road pricing.43 Suppose the benefits of time savings are ignored and sensitivity 3).

- 41 Note that the st of models used by Harison (1986) and Harrison, Pell, Jones and Ashto (1986) allows for changes in mode choice, tnp timing and destinaton, but not route choice. Most of the effects of the options tsd are due to mode choice (90%), some of it to trip time change (10%) and a very minor part is due to desiadon change. _HeRco_ the flexbility of the models used in the exhaustive Hong Kog study on road pricing does allow for receduling, which overcoms one of Wilson's 1990 criticisms of the Singapore ALS.

- a Taxis are chargd at **_HI4_** per trip [US$0.511 under supplemtary licensing but are exempt unkd&r ERP schemes A. B and C.

- 4 Thes numbers were adjusted fiom daily benefit figures (Hason, 1986, Table 3) to annual figurs by 188 days, possibly an underesimate. The factor of 188 was found by dividing the known aggregate aual benefit figure of BRP Scheme A of **_HK$734_** million [US$94.1 million] (Tanspotch, 1985, Table 8.5) by