rafmacalaba/datause-encoder-data huggingface dataset:
# 4 FINANCIAL ASSETS AND FLOWS IN DEVELOPING COUNTRIES

# 4.1 Main features of th- data

The data used in this section are drawn from a variety of sources, some unpublished, some official, some based on the work of independent 30 researchers, some drawn from World Bank sources . Data for seventeen countries have been used. For most of the countries, the data are an elaboration of the national income and expenditure accounts, with savings and real investment data broken down by sector. In other cases the financial surplus figures are built up from flow of funds accounts based on balance sheet data from the financial system.

# Financial Surpluses

This section is primarily based on the data for sectoral financial' - surpluses (net lending) for four sectors32 household, business, - government and foreign for seventeen countries. State enterprises (where separately available) are included in the business sector.

Each surplus (or deficit if negative) is expressed as a percentage share of GNP. For each country there is data for at least three years; the longest time series is for Colombia (16 years). Except for four countries, the 30 See annexed list of sources. It should be noted in particular that no attempt was made to improve on the statistics obtained from other studies. The underlying methodology is not necessarily the same in all sources and it is likely that significant conceptual differences remain.

31 Henceforth the word "financial" will be omitted; except where explicitly stated, the discussion refers to financial surplus or equivalently net lending. A financial deficit (net borrowing) is treated as a negative surplus.

32 For most of the countries these four sectors are exhaustive. For a few countries a separate financial sector was identified in the data. This sector, where separately identified, was omitted from the data set being reviewed here. In other cases statistical discrepancies prevented the sectoral surpluses from adding to zero.

31
# Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?

Susan Athey Katy Bergstrom Vitor Hadad Julian C. Jamison Berk Özler Luca Parisotto Julius Dohbit Sama<sup>∗</sup>

## JEL Codes: I12; I15; J13; O12; C13; C93; D91

Keywords: Family Planning; Fertility; Long-Acting Reversible Contraceptives; Heterogenous Treatment Effects;

> ∗Athey: athey@susanathey.com, Stanford University. Bergstrom, kbergstrom@worldbank.org, The World Bank. Hadad, vitorh@stanford.edu, Stanford University. Jamison, J.Jamison@exeter.ac.uk, University of Exeter. Özler (corresponding author): bozler@worldbank.org, The World Bank. Parisotto: lparisotto@worldbank.org, The World Bank. Sama, dohbit@yahoo.com, Hôpital Gynéco-Obstétrique et Pédiatrique de Yaoundé (HGOPY), Université de Yaoundé I. The study team is grateful to the administration of HGOPY, which is the site for this experiment. In particular, we thank Ewolo Esther, the team of nurse counselors at HGOPY, led by Hildegarde Ebenye, Juliette Kegne, Denise Noupele, who are too numerous to list here. We are also grateful to Prof. Mbu Robinson Enow, Prof. Fru Buzshi Angwafo III, Prof. Pascal Foumane, Dr. Emma Woks, and Mr. Julien Ondoua, all at HGOPY, for their support in facilitating this study. The tablet-based job support tool (the _app_ ) used by nurse counselors was developed in collaboration with the Cameroon Ministry of Health’s Department of Family Health (Direction de la Santé Familiale, MinSanté): special thanks go to Prof. Mbu Robinson Enow and Dr. Jeanette Afounde for their support. Drs. Anne E. Burke (Johns Hopkins University) and Maria Steenland (Brown University) provided valuable comments throughout the development of the _app_ and Dr. Burke kindly tested early versions. Jake Robyn and Jean Claude Taptue Fotso from the World Bank were instrumental in providing initial support for this study, and Yohana Dukhan provides continued support. We thank HEREG and Dr. Njoumemi Zakariaou for assisting with project management and data collection. We are grateful for comments we received from seminar participants at UCLA, ASSA, The World Bank, FHI360 and CEGA. Funding from the World Bank’s Strategic Impact Evaluation Fund, Knowledge for Change Program, Global Financing Facility, and Research Support Budget is gratefully acknowledged. The study is registered at the AEA RCT registry (AEARCTR-0003514) and clinicaltrials.gov (NCT03733678).
water source, improved sanitation facility and secondary school attendance over four waves of DHS data.<sup>28</sup> The trend in the share of the population with access to improved sanitation and the share enrolled in secondary school – shown in Panel c and Panel d of Figure _8_ – are particularly striking. There was a remarkable improvement in all these indicators between 2003 and 2008, but virtually no change between 2008 and 2018, in line with the evidence emerging from this paper of a slowdown in household welfare improvements and poverty reduction in more recent years.

# _<u>7.4. Changes to PPP conversation factors</u>_

Changes to the PPP conversion factors used for international comparisons could alter estimates of the poverty headcount rate at international poverty lines. This paper has used PPP conversion factors based on price data collected in 2011. However, Jolliffe et al. (2022) show that using new PPP conversion factors, created using price data collected in 2017, would have a large impact on estimates of the poverty headcount rate in some countries, including Nigeria. In order to understand these implied differences in the poverty estimates, further analysis is needed to examine how and why the new 2017 PPP data affect the conversion of the welfare vector from local currency units to international US$. This is left for future work.

# 8. Conclusion

Since the last two official household consumption surveys in Nigeria cannot be compared, this paper estimates a poverty trend for the country using two alternative approaches. First, the paper uses sectoral GDP data to backcast household consumption and hence poverty from the 2018/19 NLSS to 2009. Second, the paper uses a survey-to-survey imputation approach, constructing a model for consumption using data from the 2018/19 NLSS and imputing into several waves of the GHS. Despite having very different foundations, these two approaches produce remarkably similar results. Far from poverty dropping by 17 percentage points – as making the invalid comparison between the 2009/10 HNLSS and 2018/19 NLSS would imply – it appears that poverty dropped by between 3 and 7 percentage points in the decade before the COVID-19 crisis. The results suggest that the 2010s were initially marked by gradual poverty reduction,

> 28 Similar indicators are calculated using data from the 2018/19 NLSS to calculate the multidimensional poverty measure for Nigeria (World Bank, 2020). The multidimensional poverty measure combines data on access to education, basic infrastructure and monetary welfare to better understand the different dimensions of poverty (see World Bank 2018). These non-monetary indicators are highly correlated with monetary household consumption. In fact, in Sub-Saharan Africa 40 percent of households that are multidimensionally poor are deprived under all three dimensions (World Bank, 2020). Hence, looking at trends in these non-monetary indicators can be highly informative of overall household well-being, especially in absence of frequent data on household monetary consumption.

24
# **6. Specific Features of Procedures Involving Tenders or Auctions**

_Hypothesis 4: Regions where relatively more land tenders and auctions are held have higher levels of land privatization; and land transactions that are conducted through tenders are less prone to corruption and take less time to complete than other types of procedures._ This hypothesis can be divided into three separate “sub-hypothesis”:

- 4.1. _Regions where more land tenders and auctions occur have higher levels of land privatization;_

- 4.2. _Land transactions that are conducted through tenders are less prone to corruption; and_

- 4.3. _Land transactions that are conducted through tenders take less time to complete._

These hypotheses were based on the assumption that some jurisdictions are governed in a more enlightened fashion that others. We expected that more enlightened authorities would be interested in more private ownership of land, through procedures that are relatively transparent and efficient. Unfortunately, the data do not fully support these hypotheses.

To test hypothesis 4.1 _Regions where more land tenders and auctions occur have higher incidence of land privatization;_ we used official statistics for all regions of the Russian Federation and a model with a total number of land privatizations per 100,000 residents in a region ( _Ntotal_ ) as a dependent variable _._ The equation of linear regression, describing this model, is as follows: where: Θ is a vector of control variables from a list shown in the Annex; and _ntenders_ is the number of land privatization transactions via tenders The results below show (Table 6.1) that the use of tender procedures for land privatization has a significant and positive effect on the overall number of land privatizations.

It is also worth noticing that the distance of the regional capital from Moscow has a significant negative effect on the number of land privatizations completed in a region. For example based on Model 6.1.7 in Table 6.1 below, all other things being equal, each “extra” thousand miles (1,656 kilometers) between Moscow and a given regional capital reduces the number of land privatizations per 100,000 residents by about one (0.87), or by about 15%. One might call it an “effect of provincial mentality”.

> The other two sub-hypotheses, Sub-hypothesis 4.2 _Land transactions that are conducted through tenders are less prone to corruption_ and sub-hypothesis 4.3 _Land transactions that are conducted through tenders take less time to complete,_ were tested but were not borne out by the data.

24
Table 4. Previous Unemployment Spell and the Probability of Unemployment in 2004 (Probit regression coefficients; t-statistics in parentheses; includes only those in the labor force)

||(1)<br>All|(2)<br>All men|(3)<br>Young<sup>1</sup>|(4)<br>Young men<sup>1</sup>|
|---|---|---|---|---|
|Male|-0.1694**<br>(2.51)||-0.1358<br>(1.18)||
|**Age groups**|||||
|Age 25-44|-0.4090***<br>(3.83)|-0.3870***<br>(2.95)|||
|Age 45-54|-0.6160***<br>(5.25)|-0.5460***<br>(3.97)|||
|Age 55-64|-0.8571***<br>(5.13)|-0.6471***<br>(3.13)|||
|Single|0.1741**|0.3005***|0.6423***|0.8097**|
||(2.01)|(2.86)|(2.90)|(2.46)|
|**Educational attainment**|||||
|Vocational|-0.1283|-0.2179*|-0.3027|-0.2366|
||(1.21)|(1.85)|(1.54)|(0.88)|
|High School|0.0056|-0.1082|-0.1363|-0.1596|
||(0.05)|(0.87)|(0.68)|(0.63)|
|College|-0.171|-0.1446|-0.9164**|-1.3293**|
||(1.00)|(0.66)|(2.14)|(2.11)|
|University|-0.5506***|-0.7610***|-0.8172**|-1.2790**|
||(3.09)|(3.15)|(2.25)|(2.31)|
|Ever unemployed (2001-02)|0.8449***<br>(8.05)|0.8631***<br>(7.18)|0.2959**<br>(2.29)|0.3586**<br>(2.24)|
|Constant|-0.5229***|-0.7779***|-0.7644***|-1.1434***|
||(3.24)|(4.76)|(2.79)|(2.78)|
||2975.00|1867.00|623.00|413.00|
|Entity (location) dummy|Yes|Yes|Yes|Yes|
|Observations|2,975|1,867|623|413|

* significant at 10%; ** significant at 5%; *** significant at 1% Standard errors are corrected for stratification and intra-cluster correlation.

1Those who were in the 15-24 age bracket in 2001.

13
Policy Research Working Paper 6218

# **Abstract**

This paper uses a large cross-country dataset to empirically examine factors associated with sovereign defaults on external private creditors and expropriation of foreign direct investments in developing countries since the 1970s. In the long run, sovereign defaults and expropriations are likely to occur in the same countries. In the short run, however, these events are uncorrelated. Defaults are more likely to occur following periods of rapid debt accumulation, when growth is low, and in countries with weak policy performance, and defaults are not strongly persistent over time. In contrast, expropriations are not systematically related to the level of foreign direct investment, to growth, or to policy performance. Expropriations are however less likely under right-wing governments, and are strongly persistent over time. There is also little evidence that a history of recent defaults is associated with expropriations, and vice versa. The paper discusses the implications of these findings for models that emphasize retaliation as means for sustaining sovereign borrowing and foreign investment in equilibrium, as well as the implications for political risk insurance against the two types of events.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.The authors may be contacted at meden@worldbank.org, akraay@worldbank.org, or rqian@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
analysis as they may have a different development trajectory and may thus introduce endogeneity. Figure 1 maps the distribution of refugees in 1928 at the community level.

# **4.1.2 Contemporary social cohesion at the local level**

I use three main indicators of social cohesion at the local level: participation in voluntary associations, political fragmentation, and crime.

**Sports clubs** To measure participation in voluntary associations, I use the register of sports clubs inventoried by the General Secretariat of Sports (GGA) in the early 2000s. The dataset provides the geolocation of more than 22,000 sport association across the country. The geolocation is precise enough to identify the number of association within each of the 5775 communities that existed in Greece in 2001.

**Political fragmentation** To measure political fragmentation at the local level, I follow the political science literature (Gross and Sigelman, 1984) by using an index of fragmentation of votes between different political parties:

This index simply measures the probability that two people drawn at random in the population have voted for the same party at the last election. This index is lower when people tend to vote for the same party and higher when votes are scattered between different parties. Since the Greek political landscape has many parties that sometimes hold relatively similar ideological position, I also used a fragmentation index where parties are grouped into a leftist coalition and a rightist coalition.The electoral data I use covers the legislative elections of June 2012 and January 2015 and provides the number of votes for each of the 325 municipalities of Greece at that time.<sup>17</sup> **Crime data** I draw on crime data from Vasilakis (2018) who collected the data from the Greek police. This data comprise the total number of robberies, drug arrests and burglaries in 2015 for each of the 56 departments of Greece.

**Matching places over time** Out of the 5,042 municipalities that existed in 1928, 4,845 could be geocoded and matched to the 5,775 communities that existed in Greece in 2001. I collapse the dataset at the administrative borders of 1920 and treat observations that merged after 1920 as a single unit. As a result, I obtain a panel dataset with 4,409 observations (cluster of municipalities) covering the period between 1920 and 2001, achieving a match of 97.7 percent of the 1928 population to the rest of the dataset.<sup>18</sup> Similarly, out of the 325 municipalities of Greece in the 2010s, I

> 17Between 1991 and 2010, Greece went through two major administrative reforms that sharply reduced the number of municipalities from more than 5,000 to less than 400 (known as the 1997 Kapodistrias reform and the 2010 Kallikratis Programme)

> 18See Murard and Sakalli (2018) for details of the matching procedure.

11
regressions of the form (7) or (8) above, can control only for donors’ common perceptions of corruption in recipient countries.

In equations 4-6 of Table 5 we address this problem by substituting _donor aid share_ values from 2005 for the 2007 values used in equations 1-3. Regressing use of country systems in 2007 on 2005 _donor aid share_ values should reduce, if not eliminate, any positive bias. The 2007 _donor aid share_ values are from the SMPD, while the 2005 values are from the OECDDAC’s Creditor Reporting System (CRS). Some donors included in the SMPD did not report data in the 2005 CRS, so numerous observations are lost. Results in equations 4-6 are therefore not directly comparable to those in equations 1-3. Despite the smaller sample, _donor aid share_ retains its positive and significant coefficients in equations 4-6. In the _PFM_ regressions, its coefficient drops only from .647 (equation 1) to .63 (equation 4). Coefficients decline by more than one third in the _DBS_ and _PBA_ regressions but remain highly significant.

Admittedly, measuring _donor aid share_ two years prior to use of country systems does not fully resolve the potential problem of an upward bias in coefficient estimates. Subject to this caveat, our results are consistent with the prediction that donors are more likely to rely on country systems when they have a larger reputational stake in a recipient’s development outcomes, and when they internalize more of the benefits of investing in those systems..

Recipient characteristics, including trustworthiness of country systems, are tested in Tables 6 and 7. The dependent variable in Table 6 is _PFM_ , while results for _DBS_ and _PBA_ are reported in Table 7. Donor fixed effects are included in all of these regressions, and standard errors are adjusted for non-independence within recipient clusters of observations.

As in Table 5, _donor aid share_ is positive and significant in Table 6, equation 1. So is _HIPC completion_ : use of PFM systems is 9.3 percentages points higher, other things equal, in countries completing the HIPC qualifying process.

Both _PFM Quality_ and _Transparency_ are associated with greater use of PFM systems, although _Transparency_ is only marginally significant.<sup>24</sup> Each half-point increment in _PFM Quality_ is associated with an increase of 5 percentage points in use of PFM systems. A similar increase in _Transparency_ has a slightly smaller 3.5 percentage-point effect.<sup>25</sup> Equation 2 substitutes _Strategy_ for the two CPIA indicators. Each one-grade increment in the quality of countries’ national development strategies is associated with an increase of more than 8 percentage points in use of PFM systems. This effect is only marginally significant,

> 24 These two variables are correlated at .53, and omitting either one of them increases the coefficient and significance of the other one.

> 25 These findings for _PFM_ change very little if any one of its four components is analyzed instead, as shown in the Appendix 5 table.

22
# **Table 12 Firm-Level Export Growth and the Role of Export Costs along the Firm, Market and Product Export Margins of Trade**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|gr_rupees|gr_rupees|gr_rupees|gr_rupees|gr_rupee|s gr_rupees|
|foreign demand, log-change (ldnet_imp)|0.0371***<br>(0.00703)|0.0290***<br> (0.00701)|0.0298***<br> (0.00700)|0.0290***<br> (0.00702)|-0.0509**<br>(0.0242)|-0.0466*<br>(0.0248)|
|# days customs procedures, change|-0.198|0.0109|13.29***|0.0172|0.0299|13.29***|
||(0.352)|(0.357)|(1.121)|(0.357)|(0.357)|(1.122)|
|tariff rate (hs-8 digit mfn rate)|0.0317|-0.0586|-0.0596|-0.0601|-0.0594|-0.0600|
||(0.0472)|(0.0514)|(0.0513)|(0.0505)|(0.0515)|(0.0505)|
|complexity tariff regime (sd_mfn_hs4)|-0.125**|-0.0736|-0.0721|-0.0691|-0.0734|-0.128|
||(0.0619)|(0.0505)|(0.0502)|(0.130)|(0.0504)|(0.121)|
|bilateral nominal exchange rate, change|0.0814***|0.0922***|0.0965***|0.0919***|0.0923**|*0.0962***|
||(0.00879)|(0.00883)|(0.00885)|(0.00881)|(0.00887)|(0.00887)|
|PTA|0.0151***|0.0157***|0.0152***|0.0157***|0.0160**|*0.0195***|
||(0.00262)|(0.00280)|(0.00287)|(0.00280)|(0.00283)|(0.00474)|
|Firm extensive margin||1.026***|1.277***|1.027***|1.014***|1.258***|
|||(0.0351)|(0.0367)|(0.0354)|(0.0354)|(0.0380)|
|Market extensive margin||0.886***|1.111***|0.884***|0.873***|1.094***|
|||(0.0328)|(0.0360)|(0.0333)|(0.0328)|(0.0369)|
|Product extensive margin||0.814***|1.001***|0.816***|0.803***|0.995***|
|||(0.0341)|(0.0351)|(0.0347)|(0.0337)|(0.0358)|
|FirmExt*change # days customs procedures|||-19.71***|||-19.62***|
||||(1.007)|||(1.011)|
|MktExt*change # days customs procedures|||-18.23***|||-18.22***|
||||(1.078)|||(1.081)|
|ProdExt*change # days customs procedures|||-15.52***|||-15.52***|
||||(1.325)|||(1.328)|
|FirmExt*complexity tariff regime||||-0.0672||0.00783|
|||||(0.176)||(0.157)|
|MktExt*complexity tariff regime||||0.142||0.201|
|||||(0.138)||(0.129)|
|ProdExt*complexity tariff regime||||-0.114||-0.0344|
|||||(0.149)||(0.133)|
|Firm Ext*foreigndemand|||||0.102***|0.0931**|
||||||(0.0387)|(0.0395)|
|MktExt*foreigndemand|||||0.109***|0.107***|
||||||(0.0352)|(0.0358)|
|ProdExt*foreigndemand|||||0.0988**|*0.0940***|
||||||(0.0303)|(0.0306)|
|Constant|1.978***|1.007***|0.756***|1.005***|1.049***|0.798***|
||(0.00269)|(0.0361)|(0.0385)|(0.0364)|(0.0494)|(0.0541)|
|Observations|673687|673687|673687|673687|673687|673687|
|R-squared|0.022|0.060|0.067|0.060|0.060|0.067|
|Robust standard errorsinparentheses|||||||
|***p<0.01,**p<0.05,*p<0.1|||||||

_Source: Authors’ calculations_ 46