rafmacalaba/datause-encoder-data huggingface dataset:
**Table 30. Reactions to political risks: Delay in permits and approvals**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.113*|-0.0975*|||-0.251**|-0.215*|||
||(0.0585)|(0.0591)|||(0.112)|(0.115)|||
|Export % of sales|2.38e-05|0.000690|1.90e-05|0.000694|-0.00107|0.000385|-0.000915|0.000546|
||(0.000821)|(0.000840)|(0.000825)|(0.000836)|(0.00154)|(0.00162)|(0.00163)|(0.00169)|
|Share of sourcing via imports|-0.00106|-0.000431|-0.00123|-0.000603|-0.00189|-0.000861|-0.00221|-0.00123|
||(0.000887)|(0.000901)|(0.000887)|(0.000894)|(0.00167)|(0.00178)|(0.00177)|(0.00187)|
|High-income source country|-0.171**|-0.149|-0.171**|-0.159*|-0.312**|-0.261|-0.330**|-0.301*|
||(0.0861)|(0.0914)|(0.0851)|(0.0880)|(0.158)|(0.168)|(0.164)|(0.172)|
|>250 employees|-0.0233|-0.0167|-0.0106|-0.0135|0.0278|0.0384|0.0440|0.0409|
||(0.0662)|(0.0688)|(0.0664)|(0.0685)|(0.127)|(0.136)|(0.130)|(0.138)|
|Cumulative inv. >US$10M|0.118*|0.0985|0.116*|0.0892|0.227*|0.190|0.224*|0.161|
||(0.0674)|(0.0683)|(0.0669)|(0.0683)|(0.131)|(0.136)|(0.133)|(0.139)|
|Years in country|-0.00136|-0.00227|-0.00122|-0.00200|-0.00321|-0.00558|-0.00290|-0.00497|
||(0.00207)|(0.00206)|(0.00209)|(0.00210)|(0.00395)|(0.00407)|(0.00410)|(0.00426)|
|Level of foreign ownership (%)|-0.00115|-0.000640|-0.00107|-0.000573|-0.00220|-0.00116|-0.00201|-0.000854|
||(0.00106)|(0.00112)|(0.00109)|(0.00114)|(0.00193)|(0.00212)|(0.00204)|(0.00220)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.048|0.040|0.072|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Delays in obtaining necessary permits and approvals to start or operate a business]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

47
business reduces the probability of being the victim of graft by 8 percentage points. Likewise, from column 3, reducing constraints in starting a business by one-standard deviation results in a 7.5 percentpoint lower probability that firms will be asked for bribes when requesting an operating license. Last, the likelihood that firms will be hit by graft when requesting construction permits is six percentage points lower following a one-standard deviation reduction in the _Doing Business_ “Dealing with licenses” measure (column 4). Therefore, our results confirm previous evidence, based on perceptions data, linking regulation and corruption, with the added benefit that we are able to focus more narrowly on specific regulations and transactions affected by corrupt practices.

Firms are also more susceptible to graft in countries where the institutional environment is weak and, in particular, where the accountability of government officials is limited. In particular, democratically-elected governments are more open to public scrutiny and hence are more likely to adopt anti-corruption efforts (Bardhan 1997; Treisman 2000; Svensson 2005). We use data from the Polity IV 11 Project to study the relationship between democracy and corruption. Our measure of democracy is based on the “polity score,” which provides a measure of competitiveness in the process of executive recruitment, constraints on the chief executive, and the competitiveness and regulation of political participation. The polity score takes values from -10 to 10, with increases in the score reflecting a more democratic political regime.

As we report in Table 6, firms in democratic countries are less likely to be asked for bribes. In column 5, the estimated coefficient for the polity score is negative and significant at the 10 percent level. When we include “Ease of doing business” as a measure of regulation (column 6), both the latter and the polity coefficient have the expected sign and are significant at the 10 percent level; the hypothesis that both of them are jointly equal to zero is rejected at the five-percent level. The fact that both coefficients are still statistically significant is of interest. Djankov et al (2002) show that democratic governments are less likely to adopt costly regulations. In Table 5 we observe that the estimated coefficient for regulation falls when we account for the level of democracy, which is consistent with the evidence in Djankov et al (2002). In addition, in our sample, even holding constant the level of democracy, regulation is still associated with more graft. Columns 7 and 8 show that the positive association between bureaucratic constraints in starting a business or obtaining a construction license, on the one hand, and the incidence of graft in obtaining and operating license or construction permits, on the other, remains statistically significant.

In order to put our previous results in perspective, let us consider how much graft in Africa would decline if both the levels of democracy and of regulation moved to those that exist in Latin America.
spouses at marriage, far more women than men experience the death of a spouse at some point in their lives; by the same token, significantly more elderly women are widows than men are widowers. The last section found that households headed by widowed women have significantly lower consumption than other households even when controlling for an extensive set of household and individual characteristics, including age. Household level data on consumption or income does not allow us to construct corresponding measures of economic welfare for specific individuals within the household.

Here I examine how, relative to other marital statuses, widowhood (including prior widowhood of currently married women) is correlated with indicators of women‘s own personal welfare and that of their dependents ─ whether they are household heads or living in male headed households. It is worth noting that I focus only on some aspects of well-being, and (for lack of data) neglect what may be important dimensions of welfare associated with widowhood ─ including bereavement, emotional loss and distress, changes in social and economic status, lifestyle and identity, and frequently, rejection and accusations of having caused the death. Widowhood rites and cleansing rituals are extremely widespread in African cultures, although I have not found references to such customary practices specific to Mali (Sossou 2002).

Demographic and Health Surveys are available for Mali for 1996 and 2006. These surveys have some drawbacks for an analysis of gender and vulnerability ─ namely, a primary focus on health and reproduction; far greater and more comprehensive coverage of women than of men; the collection of detailed information on women that is restricted to individuals aged 15 to 49; and no income or consumption expenditure data. However, the substantial attraction of the surveys is that they contain information on individuals, including a number of individual level welfare indicators, something that household surveys typically do not. Here, I use the 2006 DHS for Mali.

The 2006 survey identifies current marital status for all women aged 15 to 49, and uniquely, also whether a currently married woman was previously widowed or divorced. Having this detail on marital history information is quite unusual and allows an examination of whether remarriage provides the insurance one would expect.<sup>17</sup> However, there is no way to separately identify widows who are in a leviratic union in the DHS. Furthermore, it is not immediately clear whether such widows are categorized as currently married or as currently widowed in the survey.

> 17  Unfortunately, the 1996 DHS identifies only current marital status so that a comparison over time is not feasible.

16
_Estudios de Economia_ **38** : 8–41.

URL https://ideas.repec.org/a/udc/esteco/v38y2011i1p8-41.html

- Borooah VK, Iyer S. 2005. Vidya, veda, and varna: The influence of religion and caste on education in rural india. _The Journal of Development Studies_ **41** : 1369–1404.

- Burlando A. 2014. Transitory shocks and birth weights: Evidence from a blackout in zanzibar. _Journal of Development Economics_ **108** : 154–168.

- Duflo E. 2003. Grandmothers and granddaughters: old-age pensions and intrahousehold allocation in south africa. _The World Bank Economic Review_ **17** : 1–25.

- Edmonds EV. 2006. Child labor and schooling responses to anticipated income in south africa. _Journal of development Economics_ **81** : 386–414.

- Escobar-Loza F, Martinez-Wilde S, Mendizábal-Córdova J. 2013. El impacto de la renta dignidad: polıtica de redistribución del ingreso, consumo y reducción de la pobreza en hogares con personas adultas mayores. _Bolivia: Unidad de Análisis de Polıticas Sociales y Económicas del Ministerio de Planificación del Desarrollo, Representación en Bolivia del Fondo de Población de las Naciones Unidas & HelpAge International_ .

- Hernani-Limarino W, Mena G. 2015. Intended and unintended effects of unconditional cash transfers: The case of bolivia’s renta dignidad. Technical report, IDB Working Paper Series.

- Hoddinott J, Skoufias E. 2004. The impact of progresa on food consumption. _Economic development and cultural change_ **53** : 37–61.

- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: A guide to practice. _Journal of Econometrics_ **142** : 615–635.

- URL https://ideas.repec.org/a/eee/econom/v142y2008i2p615-635. html

16
Figure 28: Relationship between Fertilizer Use and Crop Sales, 2014/15 <!-- Start of picture text --> 100%<br>beans<br>orange banana<br>mango spice<br>80%<br>roselle other crops egypt beans onion cotton<br>arabic gum other fruits dates tomato potato<br>60%<br>sesame groundnut<br>lentils<br>wheat<br>40% maize<br>sorghum<br>20% millet<br>0%<br>0% 20% 40% 60% 80% 100%<br>Proportion of crop produce sold<br>Proportion HHs using fertilizer<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15. _Note_ : HHs = Households.

# Irrigation

Less than one-fifth of households engaged in irrigated agriculture in Sudan (Figure 29a). States on the River Nile and its tributaries, the Blue and White Nile, have higher rates of irrigation. For example, the River Nile and Al-Gezira states have cultivated lands that are nearly 100 percent irrigated. States in regions where the poverty is highest, such as Kordofan and Darfur, have nearly no irrigation of cultivated lands. Irrigation use has decreased between 2009 and 2014/15, from 23 percent to 18 percent, with the largest reduction seen in the Darfur region, where irrigation was already at very low levels.

Within states where there are significant levels of irrigation, such as in the Eastern region, Blue and White Nile regions, and Khartoum state, poor agrarian households are generally less likely to have access to irrigation for farming (Figure 29a). In these regions, expanding irrigation to the poor may be more economical than doing so in regions with very little irrigation infrastructure, such as the regions of Kordofan and Darfur. In these regions, there is little difference between irrigation access between the poor and the non-poor, because none of the lands are irrigated. Expanding irrigation may be costly, because there are few riverways in these areas, though pumping water from underground aquifers may be feasible.

31
limited model for the CB component in columns 4 and 6, which is also the case for the DR component for the digital sector in column 5.

Table 8: Panel results following equation (3) using ITPD-E data

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Business**|**(4)**<br>**Digital+**|**(5)**<br>**Digital**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**OP|-0.020|0.325|0.048|-0.083|0.176|-0.167|
||(0.971)|(0.604)|(0.940)|(0.853)|(0.776)|(0.730)|
|MODEL**CB**CT|0.476|0.009|0.235|0.304|0.026|0.187|
||(0.329)|(0.986)|(0.643)|(0.413)|(0.959)|(0.625)|
|MODEL**CB**LT|-0.973|0.311|-1.026|-1.235***|-0.435|-1.651***|
||(0.169)|(0.686)|(0.165)|(0.009)|(0.498)|(0.000)|
|MODEL**DR**OP|-0.945**|-1.066**|-0.928*|-0.437|-0.277|-0.100|
||(0.043)|(0.020)|(0.075)|(0.225)|(0.577)|(0.798)|
|MODEL**DR**CT|-0.058|0.592|-0.261|-0.177|0.406|-0.567|
||(0.901)|(0.239)|(0.559)|(0.621)|(0.440)|(0.102)|
|MODEL**DR**LT|-0.026|-1.773***|-0.294|0.255|-1.278*|0.158|
||(0.962)|(0.008)|(0.482)|(0.560)|(0.060)|(0.644)|
|DTRI CB||||-1.327*|0.578|-0.845|
|||||(0.067)|(0.501)|(0.333)|
|DTRI DR||||0.408|0.828|0.480|
|||||(0.485)|(0.235)|(0.475)|
|WEF NRI||||1.126***|1.203***|1.399***|
|||||(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp-yr + imp-yr|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|43846|35297|37823|32354|26760|28484|
|P-R2|0.994|0.994|0.990|0.995|0.995|0.992|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the ITPD-E database. Sector groupings in each column can be found in Annex Table A3. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter-year and importer-year are applied. CB refers to the cross-border transfer of data, whereas DR refers to the domestic data processing. The variables ECIPE CB and ECIPE DR are taken for the importer’s side and are interacted with intl as part of the C ∗intlo term. The WEF NRI variable is taken for the exporter’s side and interacted with intl. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A7.

would be again reduced to only a couple of years, namely 2014-2016, and therefore these results are omitted. However, results can be obtained upon request.

35
Table 4: Fraction of Households that had Another Child by Parity and Sex Composition in Albania (2002- 2012)

|**Sex of the frst child**|||||
|---|---|---|---|---|
|**in households with**|Fraction||Fraction that||
|**one or more children**|of the sample|ha|d another ch|ild|
|||All<br>(1)|Urban<br>(2)|Rural<br>(3)|
|_Panel A: _<br>_1 or more_|_Mothers aged 20 _<br> _children younge_|_to 49 years o_<br>_r than 18 yea_|_ld with_<br>_rs old_||
|(1) one girl|0.493|0.809|0.781|0.841|
|||(0.005)|(0.008)|(0.007)|
|(2) one boy|0.506|0.764|0.752|0.777|
|||(0.006)|(0.008)|(0.008)|
|Diference (2) - (1)|0.012|-0.044|-0.028|-0.063|
||(0.010)|(0.008)***|(0.011)***|(0.011)***|
|Observations|9509|9509|5049|4460|
|**Sex of frst two children**|||||
|**in households with**<br>|Fraction<br>||Fraction that<br>||
|**two or more children**|of the sample|ha<br>|d another ch<br>|ild<br>|
|||All|Urban|Rural|
|||(1)|(2)|(3)|
|_Panel B: _<br>_2 or more_|_Mothers aged 20 t_<br> _children younge_|_o 49 years o_<br>_r than 18 yea_|_ld with_<br>_rs old_||
|one boy, one girl|0.237|0.352|0.265|0.441|
|||(0.011)|(0.014)|(0.016)|
|one girl, one boy|0.260|0.343|0.247|0.449|
|||(0.010)|(0.013)|(0.016)|
|two boys|0.254|0.364|0.277|0.458|
|||(0.011)|(0.014)|(0.016)|
|two girls|0.247|0.614|0.501|0.736|
|||(0.011)|(0.016)|(0.014)|
|(1) mixed combination|0.497|0.347|0.256|0.445|
|||(0.007)|(0.009)|(0.011)|
|(2) both same sex|0.502|0.487|0.387|0.595|
|||(0.008)|(0.011)|(0.011)|
|Diference (2) - (1)|0.005|0.140|0.131|0.150|
||(0.011)|(0.011)***|(0.014)***|(0.016)***|
|Observations|7480|7480|3874|3606|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS. * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%. Standard errors are in parenthesis.

37
# **Figure S14. Drugs and medical equipment at the facility**

<!-- Start of picture text --> Medical equipment<br>Oxytocin<br>Antibiotics<br>ACT<br>ORS<br>20 40 60 80 100<br>Facilities with drugs and medicial equipment (%)<br><!-- End of picture text --> Notes: ACT stands for artemisinin-based combination therapy, a treatment for malaria. ORS stands for oral rehydration salts, a treatment for diarrhea. Dots represent country-specific means, calculated using countryspecific sampling weights, vertical bars indicate median performance across countries, and boxes delineate the interquartile range (the middle 50 percent of values). Data are from visual inspections of storage facilities and consulting rooms at the health facilities from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013). Medical equipment takes the value 1 if the health facility is equipped with a minimum set of functional medical equipment (thermometer, stethoscope, and sphygmomanometer), 0 otherwise. Drugs take the value 1 if the health facility is stocked with the drug, 0 otherwise. Data on availability of ACT was not collected for Kenya.

60
- _Lender types_ : whether the lender is a state bank, a private bank or a non-bank institution (which include microfinance institutions, credit cooperative, credit unions or finance companies). The expectation is that a non-bank institution would be less likely to require collateral because it is less formal and more likely to operate on trust and reputation.

Country characteristics:

- _GDP per capita_ : this measure captures the overall economic development of countries and is in real international dollars (PPP) based on data from the World Economic Outlook.

- _Institutional variables:_ We use _Regulatory Quality_ and _Rule of law_<sup>6</sup> data from The World Bank’s Governance Indicators.

- _Financial development measures:_ Our focus is on the availability of credit information, and our proxy is the _Depth of Credit Information_<sup>_7_</sup> (which takes a value of 1 to 6), taken from the World Bank’s Doing Business Surveys. We also use two common financial development measures. The first one is domestic credit to private sector, as a % of GDP.<sup>8</sup> This proxy is widely used in literature on the access to finance (for example, see Beck et al, 2006). The second measure is interest rate spread, to capture the efficiency of the banking sector<sup>9</sup> .

The first column in Table 1 presents the OLS regressions with country fixed effects. The dependent variable takes the value 100 when the loan is collateralized and zero otherwise. Relevant predictors of collateralized borrowing are years in operation, firm size, industry, having international management certification, export orientation, firm’s ownership and having overdraft facility. Essentially, we look for _within-country variations across firms_ . Columns 2 to 6 control for country characteristics (and remove country fixed effects). Overall, older firms are less likely to pledge collateral. One more year of operation reduces the chance of pledging collateral by about 0.1%. Small firms are 5-6% less likely to pledge collateral than large firms. One could also expect the opposite sign. However, if we consider the borrowing

> 6 Other commonly used variables such as Government Effectiveness or Control for Corruption yield similar results.

> 7 Depth of credit information index is part of Doing Business Report of the World Bank. It measures rules and practices affecting the coverage, scope and accessibility of credit information available through either a public credit registry or a private credit bureau. The index ranges from 0 to 6, with higher values indicating the availability of more credit information to facilitate lending decisions.

> 8 Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries, these claims include credit to public enterprises.

> 9 Interest rate spread is the interest rate charged by banks on loans to prime customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits.

10
Policy Research Working Paper 7750

# **Abstract**

An audit study was conducted in Ghana, Mexico and Peru to understand the quality of financial information and products offered to low-income customers. Trained auditors visited multiple financial institutions, seeking credit and savings products. Consistent with Gabaix and Laibson (2006), staff only provides information about the cost when asked, disclosing less than a third of the total cost voluntarily. In fact, the cost disclosed voluntarily is uncorrelated with the expensiveness of the product. In addition, clients are rarely offered the cheapest product, most likely because staff is incentivized to offer more expensive and thus more profitable products to the institution. This suggests that clients are not provided enough information to be able to compare among products, and that disclosure and transparency policies may be ineffective because they undermine the commercial interest of financial institutions.

This paper is a product of the Finance and Private Sector Development 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 xgine@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