rafmacalaba/datause-encoder-data huggingface dataset:
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 7. Optimal and existing fuel levy in selected countries of Sub-Saharan Africa**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Existing Optimal for maintenance<br>US cents per liter<br><!-- End of picture text --> _Source_ : Gwillliam and others 2009.

The challenge of financing road infrastructure in Benin has been made worse in recent years by the poor budgetary situation of the government. Budget allocations have been depressed by poor execution of road investment budgets in 2005 and 2006—budget execution dropped from 90 percent in 2004 to 50 percent in 2005 and 25 percent in 2006. This situation reflects a national context of deteriorating budget performance characterized by declining revenue collection owing to an economic slowdown and significant budgetary pressures. Actual expenditures for roads in Benin reached 2.6 percent of GDP in 2001, and have decreased since then. The figure for 2006 was 0.8 percent (World Bank 2007b). Benin is spending around 36 percent less than what is needed for road maintenance and only 26 percent of the amount required for rehabilitation of the network (figure 8).

**Figure 8. Spending on road maintenance and rehabilitation falls well short of rehabilitation needs**

<!-- Start of picture text --> 20%<br>10%<br>0%<br>-10% Nigeria Niger Senegal Cote d'Ivoire Liberia Ghana Benin<br>-20%<br>-30%<br>-40%<br>-50%<br>-60%<br>-70%<br>-80%<br>Maintenance Rehabilitation<br>Spending as percentage of norms<br><!-- End of picture text --> _Source_ : Gwilliam and others 2009.

_Note_ : Analysis of adequacy of road maintenance spending can only be performed for primary network under federal jurisdiction.

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with an efficiency cost: idiosyncratic shocks are not completely smoothed within the network. Finally, if there is imperfect information about the realized incomes, households may have an incentive to misreport their incomes to avoid payments or even claim transfers from other households. In hidden income models (Townsend 1982; Fafchamps 1992; Kinnan 2012), the maximization problem is augmented with truth-telling constraints that require that households will not gain from misreporting. To encourage truthful reporting, migrants are allowed to enjoy a larger share of the consumption cake. As a consequence, Pareto-efficient risk sharing is again sacrificed.

These frictions can have important implications for the degree of risk sharing.<sup>6</sup> Distinguishing which of the three models of constrained insurance explains our data best is beyond the scope of this paper.<sup>7</sup> One common feature, however, is that despite friction, reciprocity remains intact: households engage in reciprocal risk sharing but the degree of its efficiency varies. In Section 5, we study the existence of such reciprocal but partial risk-sharing arrangements by testing whether households are responsive to income shocks faced by other households in the same extended family network.

In this paper, we contrast these reciprocity-based models with models that take into account social norms. Redistributive values may have been instilled since childhood and carefully nurtured through oral transmission, rituals and ceremonies in which the importance of the kinship group is strongly emphasized (Lévi-Strauss 1969). Remittances and other forms of assistance may also buy social prestige, political power or serve to perpetuate subordination (Platteau 2012; Platteau and Sekeris 2010). In the risk-sharing literature, social norms have been seen as the glue that keeps the risk-sharing contract from breaking apart by alleviating enforcement and information problems (Stark and Lucas 1988; Fafchamps 1999; Foster and Rosenzweig 2001). Theoretically this can be modeled as subjective satisfaction that individuals receive from participation.<sup>8</sup> The satisfaction can stem from the

> 6 For example, Chandrasekhar, Kinnan and Larreguy (2011, 2012), using field experiments from Southern India find that limited commitment reduces transfers by 10 percent and hidden income by 40 percent.

> 7 See Kinnan (2012) for such exercise with data from rural Thailand.

> 8 In the context of limited commitment, we can re-write the right-hand side of Equation (3) as uA −A, where A captures such satisfaction (Fafchamps, 1999; Foster and Rosenzweig, 2001; De Weerdt and Fafchamps, 2011).

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- Incentives for cost efficiency.

- Incentives for transparency and competitive service provision.

Under such an accountable governance framework, grant-financed budget allocations support contracts and work program agreements, which are based on pre-specified outputs and performance targets. The grant recipient‘s flexibility in input selection— including hiring and firing of personnel and implementation of programs—is fully respected, but there is strict accountability for achieving results. The incentive and accountability regime created by output-based transfers is expected to create responsive, responsible, and accountable governance without undermining local autonomy. In contrast, traditional conditional (earmarked) grants with input conditionality undermine local autonomy and budgetary flexibility while reinforcing a culture of opportunism and rent seeking where such culture exists (table 8). Even where such a culture of opportunism may not be a concern as in most OECD countries, citizen empowerment engendered through such transfers would be invaluable in strengthening citizen trust and confidence in their governments.

**<u>Table 8. Features of Traditional Earmarked and Output-Based Conditional Grants</u>**

|_Feature_|_Traditional Earmarked grant_|_Output-based grant_|
|---|---|---|
|Grant objectives|Spending levels|Quality and access to public<br>services|
|Grant design and administration|Complex|Simple and transparent|
|Eligibility|Recipient government<br>departments/agencies|Recipient government provides<br>funds to all government and non-<br>government providers|
|Conditions|Expenditures on authorized<br>functions and objects|Outputs -service delivery results|
|Allocation criteria|Program or project proposals<br>approvals with expenditure<br>details|Demographic data on service<br>population (potential clients)|
|Compliance verification|Higher level inspections and<br>audits|Client feedback and redress,<br>comparison of baseline and post-<br>grant data on quality and access|
|Penalties|Audit observations on financial<br>compliance|Public censure, competitive<br>pressures, voice and exit options<br>for clients|
|Managerial flexibility|Little or none. No tolerance for<br>risk and no accountability for<br>failure.|Rewards for risks but penalties<br>only for persistent failure.|
|Local government autonomy and<br>budgetary flexibility|Little|Absolute|
|Transparency|Little|Absolute|
|Focus|Internal|External, competition, innovation<br>and benchmarking|
|Accountability|Hierarchical to higher-level<br>government, controls on inputs<br>and process with little or no<br>concern for results|<br>Results-based, bottom-up, client-<br>driven; empowers citizens to<br>hold their governments to<br>account.|

_Source_ : Shah (2007).

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19

# FIGURE 2

<!-- Start of picture text --> Path of Income, Consumption & Wealth<br>T)Vial MIuihc/ld<br>I~~* u<br>l a 20 2S 30 3S 40 45 50 as 60 65 70 75<br>-<br>I w- w _ w w<br>2 ~ 1fi 2 5 J 5 4 4 0 Sf 0 ^ o 7<br>I.9~~~~~~~~~~~~~~~~~<br>_.<br>t : 20 2S 30 35 40 40k 50 55 60 6S 70 76<br><!-- End of picture text -->

- fF

HO
_Connecting Lagging and Leading Regions: The role of labor mobility_ services is more likely to add to congestion costs in cities than to contribute to agglomeration benefits.

In the empirical analysis, we examine the relative contribution of economic opportunities and amenities in leading areas (“pull”) and lack of access to basic public services in rural/ lagging regions (“push”) on internal migration decisions of Brazilians. We use household level data that are representative samples down to the second level of sub-national administration (e.g., counties in the United States). These data record migration history over a short-term horizon (e.g., 5 years) and relative to birth location.<sup>7</sup> Our empirical approach employs repeated cross-sectional data to control for time-invariant unobserved local attributes in a utility-based model of individual migration decisions. Even the best data set will necessarily lack information about important amenities, local public goods, and geo-economic features that might motivate migration behavior. If these unobserved factors are correlated with migration determinants about which we do have information (e.g., access to piped water, sewage, electricity, or healthcare), they can bias our conclusions about the role those observed determinants play in the migration decision. Following Bayer, Keohane and Timmins (2007), we incorporate repeat cross-sectional data on migration behavior into a two-stage discrete choice model that allows us to easily overcome many of these biases, with important implications for our conclusions regarding many of these factors.

Our analysis confirms the importance of public service differentials in influencing longrun migration decisions. In particular, we find that in addition to look for better jobs, working-age men migrated from the lagging Northeast region to get better access to basic public services such as piped water, electricity and health care. How much are migrants willing to pay for public services? A full-time minimum wage worker earning Rs$7 per hour (about US$2.3 in February 2008) was willing to pay Rs$390 per year in compensating wage differentials to have access to better health services, Rs$84 for better access to sewage services, and Rs$42 for better access to electricity.

> 7 We focus on the latter, where missing observations are less of a concern.

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**Figure 2: Progress toward the health MDGs among the poorest 40 percent and richest 60 percent**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Poorest 40% Richest 60%<br>.1 .1 .15 .25<br>.2<br>.05 .05 .1<br>.15<br>0 0 .05 .1<br>0 .05<br>-.05 -.05 0<br>Av. growth p.a. Av. growth p.a. Av. growth p.a. -.05 Av. growth p.a.<br>-.1 -.1 -.05<br>-.1<br>-.1<br>-.15 -.15 -.15 -.15<br>.5 .3 .6 .7<br>.4 .25 .5 .6<br>.3.2 .2.15 .4 .4.5<br>.1 .3 .3<br>.1<br>.05 .2 .2<br>Av. growth p.a. 0 Av. growth p.a. 0 Av. growth p.a. Av. growth p.a. .1<br>-.1 -.05 .1 0<br>-.2 -.1 0 -.1<br>-.3 -.15 -.1 -.2<br>1.4 .08 .8<br>.25<br>1.2 .06<br>1 .04 .2 .6<br>.8 .02 .4<br>.6 0 .15<br>-.02 .2<br>.4 -.04 .1<br>Av. growth p.a. .2 Av. growth p.a. -.06 Av. growth p.a. Av. growth p.a. 0<br>0 -.08 .05 -.2<br>-.2 -.1<br>-.4 -.12 0 -.4<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

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<!-- Start of picture text --> Figure 6: Infant mortality (kernel density estimation)<br>a) during normal times b) during growth c) during growth<br>acceleration deceleration<br>0 50 100 150 200 25 0 50 100 150 20 0 50 100 150 200 25<br>Infant mortality (per 1,000) Infant mortality (per, 1000) Infant mortality (per 1,000)<br>Source: World Bank Africa Development Indicators 2007.<br>0 .0 1 0 .0 1 0 .0 1 5<br>0 .0 0 8 0 .0 0 8<br>0 .0 1<br>0 .0 0 6 0 .0 0 6<br>D e n s ity D e n s ity D e n s i ty<br>0 .0 0 4 0 .0 0 4<br>0 .0 0 5<br>0 .0 0 2 0 .0 0 2<br>0 0 0<br><!-- End of picture text -->

**_Explaining the probability of growth accelerations and decelerations._** Table 8 shows the conditional probabilities of a country experiencing a growth acceleration and deceleration at the aggregate level. Models 1–4 refer to growth accelerations and models 5–12 to growth decelerations. These regressions represent a further search for stylized facts about acceleration and deceleration episodes. No causality is inferred from the relationships, and no attempt has been made to control for endogeneity of some of the right-hand-side variables.

Model 1 shows that a 1 percent change in investment is on average associated with a higher probability of a growth acceleration of about 0.1 percent. So, an increase of, say, 10 percent in investment is associated with an increase of 1 percent in the probability of a growth acceleration. Voice and resource endowment (models 2 and 3) are also positively associated with a growth acceleration. However, all coefficients are significant at only the 10 percent level, and the _R_<sup>2</sup> are low. Model 4 shows a regression with all these correlates together. Only voice and resource endowment remain significant at the 10 percent level.

All estimated coefficients of models 5–11 have the expected sign and are significant at the 5 percent level. They show that more investment, higher ODA per capita, increased imports, and better governance indicators are associated with fewer growth decelerations. Model 12 shows the coefficients for all the nongovernance indicators together. In this case, only investment remains significant. The governance indicators were not regressed together because of their very high collinearity.

Only investment is significantly associated with the conditional probability of both acceleration and deceleration episodes. This finding suggests that investment is likely to be an important factor for predicting spells of growth and collapse at the 21
46 agriultural growth Thus, the needed policy-directed agncultural inveutment credit ranges from Rs. 4.2 billion to Rs. 24 billion to support agricultural growth ranging from 4 to 8 percent (table 4.1). The production credit need of sma11 dm is also calculatd at 40 percent of the production credit need for all farmers , which yields Rs. 2.0 billion and Rs. 4.2 billion respctively, for the two growth scenanos. Thus, total agncultural credit need ranges from Rs. 6.2 billion to Rs. 28.1 billion. However, the associated budgetary support would be quite small because credit would not be lent at a subsidized lending rate but would involve higher tranton costs to target credit The subsidy associated with the higher transaction costs would be around 10 percent of actual lending and would range from Rs. 620 million to Rs. 2.8 bilion.

**Table** 4.1. Smallholders Annual Need for Agricultural Investment (1993 Rs. Billion)

|Alternat|ive Assumptions of InvestmenVtAg.<br>GDP Required|
|---|---|
|Agricltu Growth|2 Percent<br>3 Percent|
|Rate<br>......................................................................................................|.........................................................................................................|
|4|4.2 billion<br>9.8 billion|
|6|9.8 billion<br>14.7 billion|
|8<br>................................................................................................|15.4 billion<br>24.0 billion<br>...............................................................................................................|

onsunmtion smoothing. The need for consumption credit, as projected by respondents in the 1985 rural credit survey, is estimated to be 24 percent of total credit. For simplicity, it is assumed that this proportion holds for institutional credit as well. According to the 1985 survey, of the total rural credit needs 56 percent is needed for agricultural purposes and 44 percent for nonagricultural purposes. It is assumed that these proportions are the same for poor rural households. The total annual rural credit needed by poor rural households for agricultural purposes is thus estimated at Rs. 11.1 billion (Rs 6.2 billionlO. 56) for the low-growth scenario. The consumption credit need of poor rural households provided by institutional sources can be estimated as Rs. 2.7 billion (24 percent of the total institutional credit need) under the low-growth scenario. Under the high-growth scenario, farm incomes would rise and the need for consumption credit would decline.

Microenterprises. The 1985 rural credit survey projects the credit need of rural enterprises at 20 percent of the total credit. Rural microenterprises' need for credit can be estimated as Rs. 2.2 billion under the low-growth scenario. Under the high-growth scenario, nonfarm business activities are likely to increase, resulting in greater neod for credit finance.
Annex Table 1: The effect of orphanhood, with no baseline controls (Dependent variable: _ever married)_

||(1)<br>All|(2)<br>Women<br>(17-23 in 2004)|(3)<br>Men<br>(17-28 in 2004)|
|---|---|---|---|
|Male prime-age adult died|-0.038|-0.152|0.077|
||(0.086)|(0.111)|(0.124)|
|Female prime-age adult died|-0.127|0.001|-0.300***|
||(0.080)|(0.118)|(0.092)|
|Father died|0.041|0.106|-0.081|
||(0.056)|(0.069)|(0.094)|
|Mother died|-0.005|0.025|-0.088|
||(0.073)|(0.098)|(0.112)|
|Observations|683|320|363|
|R-squared|0.138|0.173|0.117|

Notes: Results reported are marginal effects estimated using a Probit. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. Includes controls for age and age squared, and district fixed effects.

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operators compete on markup to some extent, there are relatively few new entrants in the sample, and newer entrants do not appear to provide downward pressure on reduce costs.<sup>**24**</sup>

**TABLE 12:   COSTS OF SERVICE DELIVERY AND PRICES ON MAIN TRADE ROUTES IN US CENTS PER TON-KM**

|**Country of Firm**<br>**Location**|**Unweighted**<br>**Mean**<br>**Average**<br>**Direct Cost**|**Median**|**Mean Direct**<br>**Cost (Using**<br>**mono versus**<br>**non- mono**<br>**weights)**|**Minimum**<br>**ADC**|**Maximum**<br>**ADC**|**Standard**<br>**Deviation**|
|---|---|---|---|---|---|---|
|Costa Rica|4.8|3.7|5.3|2.0|30.1|4.3|
|El Salvador|7.3**|6.0|5.3|3.1|25.3|5.0|
|Guatemala|5.4|5.8|5.4|2.1|9.9|2.2|
|Honduras|6.4*|4.5|5.7|1.8|37.3|6.3|
|Nicaragua|5.2|3.8|6.0|1.8|25.7|4.4|
|Panama|6.2|5.1|8.5|2.2|18.0|3.5|
|Region|5.9|4.5|5.7|1.8|37.3|4.6|

*Denotes statistically significant difference with lowest cost country at 5 percent level.   **At 10 percent level.

**TABLE 13:  CONTRIBUTION OF COST COMPONENTS TO AVERAGE DIRECT COSTS IN REGION -** **<u>(US CENTS PER TON KM)</u>**

|**Maintenance**|<br>**Fuel**|**Tires**|**Wages**|**Security**|**Insurance**|**Other**<br>**costs***|**ADC**|
|---|---|---|---|---|---|---|---|
|**0.39**|**2.74**|**0.80**|**0.98**|**0.16**|**0.10**|**0.71**|**5.87**|
|**(Percent)**<br>**6.6%**|**46.7**|**13.6%**|**16.7%**|**2.7%**|**1.7%**|**12.1%**||

*Other costs including financing and depreciation of trucks.

**FIGURE 11:  DISTRIBUTION OF AVERAGE DIRECT COSTS, REGION**

<!-- Start of picture text --> 0 10 20 30 40<br>Average Direct Costs<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> Table 15 shows mean prices and the average markups over _ADC_ by country. Average prices are high relative to _ADC_ , with average markups exceeding 100 percent of costs. Despite the lack of disparity between countries in costs, prices are markedly higher in Panama than in the rest of the region. As shown in Figure 12, the average firm-level price distribution is skewed, with most firms charging average prices

> 24As shown in Section 4, firm age is not correlated with costs, once one accounts for fleet size and firm and route characteristics. Of the 13 percent of sampled firms which were less than 5 years old, only 3 had over 20 trucks – one in El Salvador and two in Honduras.

17
2018).<sup>1</sup> The federal Head Start program in the United States also induced switching out of private preprimary schools (Kline & Walters, 2016).

The inadequacy of stimulation in their counterfactual environment and lower access to substitute services may partially explain why children from more disadvantaged backgrounds tend to benefit more from preprimary education (Cascio and Schanzenbach, 2014; Currie, 2001).

# **Preprimary education in low- and middle-income countries**

According to UNESCO’s Institute of Statistics, preprimary enrollment has increased considerably around the world over the last two decades, from an average of 30 percent of children in 2000 to 50 percent in 2018.<sup>2</sup> Access to preprimary education in low- and middleincome countries is still low, with 19 percent of preprimary-aged children in low-income countries enrolled (UIS, 2018), a coverage rate less than half of what was observed in high-income countries fifty years ago. Beyond these averages, household survey data using UNICEF’s Multiple Indicator Cluster Surveys (MICS) suggest substantial variation in preprimary enrollment across and within countries that is associated with socioeconomic status, with the largest differences in enrollment in the poorest countries (Figure 1). This unequal access to preprimary education can exacerbate learning inequalities, as children in families from lower socioeconomic groups tend to also have limited learning opportunities and stimulation at home and in their communities _(_ McCoy et al., 2018) (Figure 2).

Domestic financing for preprimary education has increased over the past decade, amounting to 6.6 percent of domestic education budgets globally. Low-income countries allocate substantially less, with less than 2 percent of their education budgets going toward preprimary education (UIS, 2018). In these countries, standards and quality assurance systems are often nonexistent or under-resourced and learning spaces often do not meet minimum safety and

> 1 In contrast to the practice of “red-shirting” in higher-income countries, in many low-income countries, parents try to enroll their children in primary school before the children are age eligible.

> 2 UIS use a variety of data sources to measure enrolment: administrative data from schools or household survey data on enrolment; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment); administrative data from ministries of education on the structure (entrance age and duration) of early childhood education. Data on provision are limited but suggest that around 38 percent of countries offer some free preprimary education, with most of these countries offering between one and three years of service. There is also wide variation of private provision across regions, ranging from 7 percent in Europe and Central Asia to 72 percent in the Middle East and North Africa (UIS, 2018).

9