rafmacalaba/datause-encoder-data huggingface dataset:
smallest) suggesting the most important factor is the size of the underlying gender gap itself, rather than other features of the economic environment.

Third, we find that the economic gains are substantially smaller in the short or medium run when capital cannot adjust, though they are still economically significant for the typical country. The biggest proportional reduction in gains when comparing the long run to gains when capital does not react are in the GEGI-SR models in resource rich countries that have the lowest labor shares (such as Qatar or the Islamic Republic of Iran). The reduction in gains are much smaller in the model with Capital-Skill complementarities (CSC), in part because that model has a much higher labor share. Indeed, imposing uniform and low capital shares of 0.25 in the GEGI models—implying a labor share of 0.75 (similar to that assumed in the CSC model in Section 4), leads to similar gains. The LTGM/LTGM-NR impacts by 2050 show that even after 25-30 years the gains still substantially smaller than those in the long run, though are large in absolute size.

Fourth, we find that long run economic gains with capital-skill complementarities and an unchanged skill distribution are generally a little smaller than those in the baseline GEGI model (5-10ppts) because existing gender gaps are larger for unskilled women, and so closing gender gaps reduces the level of skills. However, these smaller losses are more than offset if women continue to improve their educational attainment.

**Policy implications.** Our results highlight the importance of implementing reforms to facilitate female employment for long-run economic growth in MENA, especially in the countries with the largest gender gaps. While these gains are usually very large, they can also take a long time to be fully realized, so policymakers will need some patience, and should not abandon a reform path if gains are not apparent in the short term. They also suggest that a range of complementary reforms are necessary, with higher female educational attainment amplifying the gains, and greater private sector dynamism speeding the realization of gains.

20
**Polac,Pnning, and Research |**

# **World Development Report|**

When a country's planning and budgeting system is dilapidated, an important first step is to rehabilitate basic budgetary and accounting functions and to generate public expenditure data that can provide a starting point for rational planning. Other basic measurce. include: The budget process should first determine what resources are available and adopt aggregate revenue and expenditure targets. Then the entire set of public expenditure issues should be looked at simultaneously. If total expenditure is allowed to be the outcome of the aggregation of sectoral bids, the overal expenditure is almost certain to be unsustainable.

process should be made public in order to educate public opinion about economic altematives, build a consensus, and spread responsibility for public spending choices.

* Monitoring development projects and programs requires explicit information on actual expenditures of individual projects and the entire program, including simple breakdowns by implementing agency, sector, source of finance, and so on. For investment programs, it must be possible to relate expenditure infomiation to a financial plan for each project. The use of standardized project profiles for all approved projects is an invaluable technique.

- The responsibility for reconciling expenditure bids with agreed overal targets must be decentralized. Sectoral ministries should be given ceilings within which to prepare their estirdtes.

* It is vital that recurrent and development expenditure programs be considered simultaneously in each sector, since a country's ability to sustain the recurrent costs arising from new investrnent may be doubtful. In restructuring recurrent budgets, it is important to establish "norms ' for the level and distribution of expenditure to provide properly for key services.

* In the long run, the budgeting and planning

* The financial woes of developed countries increase the value of extemal aid and the leverage exercised by aid agencies. To minimize friction with aid agencies, govemments should exercise careful aid management by monitoring project preparation and implementation, working to minimize project problems, and encouraging dialogue between the govemment and aid agencies.

This is a background paper for the 1988 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rhoda Blade-Charest, room S13-060, extension 33754.

The PPR Working Paper Series disseminates the findigs of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright 0 1988 by the International Bank for Reconstruction and Developinentjhe World Bank
poor households with very high marginal utilities of income waste precious resources, overuse of vital drugs like antimalarials and antibiotics promotes disease resistance, and unnecessary consumption of government-subsidized care strains already-tight public budgets.

Doctors (and other health workers) are the first line of defense against overtreatment when care is subsidized. Yet a growing body of evidence makes it abundantly clear that providers in both the public and private sectors are imperfect gatekeepers, prescribing unnecessary treatment at very high rates. It is therefore essential to understand the factors that limit doctors’ ability to target treatments to the right patients.

The existing literature has largely focused on doctor-driven overtreatment. The implicit assumption is that doctors _prefer_ to overtreat, for example because they do not bear the cost of treatment, because it is in their financial interest to increase revenue, or because they do not care to exert effort to arrive at a better diagnosis. Our paper broadens the scope of investigation by asking whether patients’ preferences may also drive overtreatment: while the doctor would prefer not to prescribe a medication, she is willing to do so in order to avoid the “gatekeeping costs” of dealing with an unhappy or distrustful patient.

We begin by developing a model that can generate both doctor- and patient-driven demand for treatment, and delivers testable implications for both channels (albeit at different margins of substitution). We test the model’s predictions with an RCT that we conducted with 60 public health clinics in Bamako, Mali. Our context is well-suited to studying this issue, as misallocation of malaria treatment in public clinics is both verifiable (via follow-up RDTs), and rampant: 75 percent of patients with confirmed RDTs tested negative, yet 58 percent of these malaria-negative patients were prescribed some form of malaria treatment.

We find that vouchers for free malaria treatment are much more likely to be used when both patients and providers (as opposed to just providers) know about their availability, indicating that gatekeeping costs are empirically important. Patients with the lowest levels of malaria risk receive more prescriptions and buy more treatment, which is consistent with patient-driven demand for simple malaria treatment. We also find that this additional demand significantly worsens overtreatment. On the other hand, we find no evidence that physicians in the Doctor Voucher treatment exploit their informational advantage to upsell patients more expensive severe malaria treatment, or increase clinic revenues more generally.

34
Table 1: Descriptive Statistics for Program Placement

||Mean|Standard<br>Error|Min|Max|
|---|---|---|---|---|
|**Dependent Variable**|||||
|Family planning program in 1990 (ratio)|0_._19|0_._39|0_._00|1_._00|
|**Zone characteristics**|||||
|Percent with no education in zone|79_._20|5_._94|30_._07|94_._83|
|Percent with 1-6 years of education in zone|11_._62|5_._24|2_._05|37_._45|
|Percent with 7-8 years of education in zone|2_._39|1_._24|0_._20|10_._70|
|**District characteristics**|||||
|Total area (square km/100)|14_._53|9_._57|0_._00|53_._81|
|Avg. yearly rainfall (mm/100)|11_._91|4_._05|4_._46|20_._48|
|Avg. yearly rainfall squared (mm/100)<sup>2</sup>/100|1_._58|1_._02|0_._20|4_._19|
|Elevation (m/100)|19_._68|4_._25|8_._65|29_._26|
|Elevation squared (m/100)<sup>2</sup>/100|4_._05|1_._65|0_._75|8_._56|
|**Community characteristics**|||||
|Urban (rate)|0_._04|0_._19|0_._00|1_._00|
|Market in area (rate)|0_._35|0_._48|0_._00|1_._00|
|Road access - all year (rate)|0_._41|0_._49|0_._00|1_._00|
|Road access - dry season (rate)|0_._39|0_._49|0_._00|1_._00|
|Population / 1000|3_._23|5_._28|0_._35|96_._94|
|**Ranking of Zones (Nationally)**|||||
|Zone population rank|21_._76|9_._37|1_._00|36_._00|
|Zone urbanisation rank|19_._08|8_._76|1_._00|36_._00|
|Zone percent with no education rank|18_._88|9_._31|1_._00|36_._00|
|Zone Percent with 1-6 years of education rank|17_._21|9_._73|1_._00|36_._00|
|Zone Percent with 7-8 years of education rank|18_._59|9_._46|1_._00|36_._00|
|**Ranking of Communites (Within Zones)**|||||
|Community population rank|2_._27|1_._40|1_._00|10_._00|
|Number of communities||109|||

**Notes.** Estimated means and standard errors based on sample frame and weights. The ranking of zones is based on the sample, with 1 assigned to the smallest value and ties are assigned the same value, so that the sum of the ranks is preserved. For communities the ranking is based on the sample within a zone.

number of communities within a zone is ten, while for five zones there is only one community in the survey.

Although it would be advantageous to have more information at the community level, the set of possible variables is limited by the lack of information available at that level from published census reports.

# **3.3 Individual Data**

As discussed earlier, we surmise that the effect of family planning on fertility is highly dependent on a woman’s schooling. The lower a woman’s education, the more likely she is to benefit from access to family 10
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" Nicaragua (74 percent), and Pakistan (63 percent) were without either sewer connections or septic tanks.

The poorest households in each country had lower rates of coverage of sewer connections and septic tanks than the population as a whole. Bulgaria is the only exception. In Bulgaria nearly all households had either a sewer connection or a septic tank, but the poorest households were more likely to have septic tanks than sewers.

Rural households of all income levels had lower rates of coverage of all sanitation facilities than urban households (Figures 13 and 14). Very few urban households were without a toilet or latrine in their home. By contrast, approximately 30 percent or more of each rural decile was without any sort of sanitation facilities. Not surprisingly, the greatest sanitation deficit was among the poorest rural households. Between 80 percent and 90 percent of households in the poorest two deciles in the pooled rural sample had no latrine or toilet in their homes. Approximately one quarter of households in the poorest urban decile of the sample have no sanitation facilities.

6.4. Telecommunications For households without a private telephone in their home, having access to a public telephone in their community can be a real advantage. In the absence of a public phone, the presence of at least some private telephone connections in the community may still give households without a phone a means of communication. Phone owners may rent out their phones or allow others to use the phone for emergency communications. Information on such uses of private phones is not available in the LSMS surveys, but the community questionnaires in three countries (Ecuador, Kyrgyz Republic, and Panama) do ask about access to public phones.

In these three countries poor households were less likely than the population as a whole to have access to public telephones in their communities. In Panama and Nepal access to public telephone service increases with aggregate household income (as measured by our income proxy). In Ecuador access to public phones is fairly uniform across income deciles. Most of the poorest urban 18
20

# Notes to 1able S

# Calculation of formal rates

YAT. Application of sectral shares in value-added to the VAT receipts. In agriculture, VAT was applied to 68 percent of that sector's value-added to take into account exemption on livestock. For imports, application of receipts reported in IMF (1990, table 1) applying import shares to the import classification in that table. (imported raw materials among sectors 4, **_5,_** 6 according to those sectors intermediate input shares.) Exeise-tax. Application of sectoral shares in domestic sales to sectors 3 and 4 (Negligible excise tax revenues on imports omitted).

Trade taxes. Imports disaggregation of customs and fiscal duties reported in IMF (1990, table 1) according to import shares for food products from 1984 1/0 table. (Raw materials treated as above; equipment goods classified as heavy industry; non-food consumption products classified as light industry. Energy tax rate from IMF (1989, table 18).

Exports. Rate obtained by applying the export tax revenue on coffee, vanilla and cloves on total agricultural export revenue.
Table 6: Social network outcomes at endline

||**Net**|**work size**|**Peers eng**<br>Has peers in|**agement**<br> village that:|**Stigma**|
|---|---|---|---|---|---|
||Number of close<br>peers in village|Number of close<br>outside peers in village|Accompanied to<br>health facility|Advised woman<br>to use FP|Afraid of being<br>seen|
||(1)|(2)|(3)|(4)|(5)|
|Own Voucher|-0.050|-0.026|-0.064|-0.030|-0.018|
||[0.074]|[0.063]|[0.071]|[0.062]|[0.047]|
|BAF Voucher|0.133|0.102|0.114*|0.160**|-0.090**|
||[0.084]|[0.070]|[0.066]|[0.065]|[0.041]|
|Observations|516|516|339|339|505|
|Endline control mean|0.635|0.357|0.550|0.339|0.239|
|p-value: Own = BAF|0.053|0.112|0.026|0.010|0.150|

_Notes:_ All columns include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. FP denotes family planning, and BAF denotes bring-a-friend.Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01
**Table 1:  Results of the endogenous threshold regression**

||(1)|(2)|
|---|---|---|
|Ldist|0.077|-0.424|
||(4.11)**|(6.43)**|
|lgdp|0.035||
||(12.69)**||
|lpop|0.007<br>*||
||(2.03)||
|lgdpdecl|<br>-0.215<br>||
||(13.10)**||
|lpopdecl|0.279||
||(16.67)**||
|freedomidicator|<br>-0.006||
||(2.21)*||
|Frenchcolony|0.075||
||(4.14)**||
|NONfrenchcolony|0.008||
||(0.92)||
|SouthAfrica|-0.316||
||(17.57)**||
|DUMagri|0.285|0.331|
||(26.87)**|(29.26)**|
|DUMtext|0.237|0.219|
||(13.93)**|(13.04)**|
|DUMfoot|0.131|0.129|
||(5.36)**|(5.35)**|
|DUMmach|<br>-0.199|<br>-0.163|
||(21.56)**|(19.42)**|
|DUMwood|0.449|0.447|
||(22.16)**|(20.05)**|
|DUMmineral|0.047|0.114|
||(0.87)|(2.13)*|
|DUMcloth|0.243|0.219|
||(18.53)**|(16.29)**|
|dutysmall|<br>-0.455|<br>-0.450|
||(1.39)|(1.43)|
|dutyhigh|0.609|0.479|
||(8.46)**|(6.50)**|
|Hightariff|-0.252|-0.190|
||(7.38)**|(4.66)**|
|Quota||<br>-0.094|
|||(2.76)**|
|Country fixed effects||<br>included|
|LR|χ2(19) =6279.82, Prob>,0.00|χ2(57) =9706.34 Prob>,0.00|
|Pseudo R2|0.2074|0.3209|
|Loglikelihood|-12001.857|-10270.878|
|Observations|23684|23641|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 27
Mexico. In _Poverty, Inequality, and Policy in Latin America_ : The MIT Press.

- Benhassine, Najy, Florencia Devoto, Esther Duflo, Pascaline Dupas, and Victor Pouliquen. 2015. "Turning a Shove into a Nudge? A “Labeled Cash Transfer” for Education." _American Economic Journal: Economic Policy_ 7 (3):86-125.

- Bergstrom, Katy, and Berk Özler. 2023. "Improving the Well-Being of Adolescent Girls in Developing Countries." _The World Bank Research Observer_ 38 (2):179-212. doi: 10.1093/wbro/lkac007.

- Bertrand, Marianne, Bruno Crépon, Alicia Marguerie, and Patrick Premand. 2024. Do Workfare Programs Live Up to their Promises? Experimental Evidence from Côte d'Ivoire.

- Boulhane, Othmane, Claire Boxho, Désiré Kanga, Estelle Koussoubé, and Léa Rouanet. 2024. "Empowering Adolescent Girls through Safe Spaces and Accompanying Measures in Côte d’Ivoire." _(No. 10721). The World Bank._

- Brown, Gabriel, Morgan Hardy, Isaac Mbiti, Jamie McCasland, and Isabelle Salcher. 2024. "Can Financial Incentives to Firms Improve Apprenticeship Training? Experimental Evidence from Ghana." _American Economic Review: Insights_ 6 (1):120-36. doi: 10.1257/aeri.20220696.

- Bucarey, Alonso , Dante  Contreras, and Pablo  Muñoz. 2020. "Labor Market Returns to Student Loans for University: Evidence from Chile." _Journal of Labor Economics, 38(4), 959-1007_ .

- Buchmann, Nina , Erica M. Field, Rachel  Glennerster, Shahana  Nazneen, and Xiao Yu  Wang. 2023. "A Signal to End Child Marriage: Theory and Experimental Evidence from Bangladesh." _American Economic Review, 113(10), 2645-2688._

- Buchmann, Nina, Erica M. Field, Rachel Glennerster, Shahana Nazneen, and Xiao Yu Wang. 2021. "A Signal to End Child Marriage: Theory and Experimental Evidence from Bangladesh." _National Bureau of Economic Research Working Paper Series_ No. 29052. doi: 10.3386/w29052.

- Buehren, Niklas , Shubha  Chakravarty, Markus  Goldstein, Vanya  Slavchevska, and Munshi Sulaiman. 2017. "Adolescent Girls’ Empowerment in ConflictAffected Settings: Experimental Evidence from South Sudan." _Working Paper._

- Buehren, Niklas, Markus Goldstein, Selim Gulesci, Munshi Sulaiman, and Venus Yam. 2017. "Evaluation of an Adolescent Development Program for Girls in Tanzania." _World Bank Policy Research Working Paper, (7961)._

- Cahyadi, Nur, Rema Hanna, Benjamin A. Olken, Rizal Adi Prima, Elan Satriawan, and Ekki Syamsulhakim. 2020. "Cumulative Impacts of Conditional Cash Transfer Programs: Experimental Evidence from Indonesia." _American Economic Journal: Economic Policy_ 12 (4):88-110.

32
In this section we will use the debt solvency and creditworthiness conditiorns to guide cur analysis of the evolution and composition of public sector indebtedness. Although it is rare for a country to fail in meeting the debt solvenc7y condition because it is always possible to increase taxes (conventional or the noncc nventional inflation-tax) or reduce expenditures, a greater level of indebtedness is associated with worsening of economic conditions, i e., lower economic growth. It is apparent that the rate of growth is affected by the tax level, by the size of public sector expenditures and by sharp reductions in public sector investment. Therefore, increases in the level of public sector indebtedness not corresponded by increases in public sector fixed assets would result in a lowering of public sector net worth. The analysis of public sector indebtedness will be undertaken by examining the evolution in total nat Public ebt. This is a better proxy for public sector net worth than the external public sector debt used in other studies. The creditworthiness debt condition will be analyzed (in this and the following section) by examining how the external shock of the early-1980a affected the size of total public debt--i.e., the debt solvency condition--and the domestic policies that eacih country undertook to reduce the size and costs of servicing total public debt. In other words, the domestic policies undertaken by each country to regain creditworthiness.

# A. Evolution of Public Debt

In the period 1979-89, the evolution of total public sector (PS) debt for the six countries showed two very different patterns. (See Figures la and lb and Table 1.) The first pattern consisted of the total level of public indebtedness peaking in the mid-1980s and then falling in the late-1980s. The countries where this was more apparent are Morocco, Portugal, Turkey and
|**Table 3: Share of firms that**<br>**rules is “consistent and pred**|**_disagree_wit**<br>**ictable” acr**<br>|**h the stateme**<br>**oss types of fir**|**nt that imple**<br>**ms**<br>|**mentation of**<br>|
|---|---|---|---|---|
|**Type**|**Nigeria**|**Uganda**|**Ghana**|**Unweighted**<br>**average of**<br>**all firms**|
|Small(Employees<6)|63.9%|43.4%|32.2%|53.9%|
|Medium(6<employees<21)|60.2%|47.6%|34.1%|50.6%|
|Large(Employees>21)|38.6%|41.5%|31.3%|46.4%|
|Labor Intensive|59.5%|51.4%|39.0%|51.9%|
|Capital Intensive|56.2%|40.1%|24.6%|50.3%|
|Services|61.2%|41.7%|30.7%|52.6%|
|Capital City|65.9%|46.2%|28.7%|53.2%|
|Medium/large|55.5%|42.1%|40.9%|49.4%|
|Small city|75.1%|35.5%|34.1%|60.5%|
|Total<br>Source: Authors’ calculations|59.7%<br>with Enterpr|44.4%<br>ise Surveydat|32.1%<br>a.|51.90%|

A third piece of evidence that policy implementation is uncertain is that firms engage in actions aimed at influencing policy implementation, including actions, such as paying bribes, that are not a part of the _de jure_ policy implementation or the notional policy mapping. The extent of policy influence activity firms report varies across countries and across firms within a country. Table 4 shows the fraction of management time spent with government officials, the fraction of firms who report paying bribes and the proportion of revenue paid as bribes for those who do pay bribes. Obviously if the rules were followed irrespective of whether a bribe was paid the fraction paying bribes would be small, but in half of the countries half of the firms report paying bribes. The fraction reporting paying bribes varies from ubiquitous, over 80 percent, in Burkina Faso, DRC, Guinea to 20 percent or less in Rwanda and South Africa.

28
**5**

# **FIGURE 1**

<!-- Start of picture text --> I<br>Evolution of Outstanding Loans |<br>and Average Interest Rate<br>300- 5<br>274<br>250 . 1 pS -. . . .. . . 0 .14<br>200- 32 Pr.C<br>C<br>.2~~~~~~~~~~~~~~~~~-<br>100<br>50~~~~~~~~~~~~~<br>1989 2017<br>Yasfrom 1989 to 2025<br>-<br>[iPrincipal average rate<br><!-- End of picture text -->