rafmacalaba/datause-encoder-data huggingface dataset:
percentile of the distribution in per capita consumption). See Appendix D for welfare differences for other regions. The graphs in figure 3.3 suggest that decompositions of the welfare differences at the mean may yield a misleading picture about the relative role of characteristics and their returns in explaining these differences.

# **_Quantile Decompositions Within Regions (Southeast and Northeast)_**

In figure 3.4, we present the decompositions of the metro-urban and urban-rural differences within the Southeast region, and within the Northeast region (see Appendix D for decompositions using alternative counterfactual distributions). As revealed by the simple Oaxaca decompositions, we find that both returns and covariate effects play a role, in explaining the large differential in living standards between metro and urban areas.

# **Figure 3.4:  Quantile decomposition of welfare differences within Southeast and Northeast regions**

# (a) Between metro and urban areas of SE

<!-- Start of picture text --> Returns and Covariate Effects<br>Counterfactual: Metro SE returns and Urban SE attributes<br>Brazil 2002/2003<br>0 10 20 30 40 50 60 70 80 90 100<br>percentile<br>returns effect covariate effect<br>(c) Between metro and urban areas of NE<br>Returns and Covariate Effects<br>Counterfactual: Metro NE returns and Urban NE attributes<br>Brazil 2002/2003<br>0 10 20 30 40 50 60 70 80 90 100<br>percentile<br>returns effect covariate effect<br>.4<br>.3<br>.2<br>.1<br>Difference in log welfare ratio<br>0<br>.4<br>.3<br>.2<br>.1<br>Difference in log welfare ratio<br>0<br><!-- End of picture text -->

- (c) Between metro and urban areas of NE

# (b) Between urban and rural areas of SE

<!-- Start of picture text --> Returns and Covariate Effects<br>Counterfactual: Urban SE returns and Rural SE attributes<br>Brazil 2002/2003<br>0 10 20 30 40 50 60 70 80 90 100<br>percentile<br>returns effect covariate effect<br>(d) Between urban and rural areas of NE<br>Ret urn s a nd Covariate Effects<br>Cou nterfa ctual: Urb an NE re turns an d Ru ral NE attrib utes<br>Brazil 2002/2003<br>0 1 0 2 0 3 0 40 50 60 70 8 0 9 0 1 00<br>perce ntile<br>re turn s e ffect covari ate effe ct<br>.8<br>.6<br>.4<br>.2<br>0<br>Difference in log welfare ratio<br>-.2<br>.6<br>.4<br>.2<br>0<br>Di fference in log welfare rati o<br>-.2<br><!-- End of picture text -->

- (d) Between urban and rural areas of NE

18
such a shalish sits in the UP office it is called a Shalishi Adalot and the Chairman is the main arbitrator. The secretary writes the decisions of the shalish. Few cases come to the village court (as told in a focus group discussion comprising elite men in Mymensingh). Another Chairman said ―We prefer _Shalishi Adalot_ to a village court. (The) Village court is not functioning well....(it has) a comparatively long procedure‖ (UP Chairman in Sunamgonj).

**Table 3:  Dispute resolution is seen as a priority of the Union Parishad**

|**Priority 1**||**Priority 2**||
|---|---|---|---|
|Road repair and maintenance|100%|Canal/pond re-excavation|31%|
|Water and sanitation program|92%|Tax assessment and collection|31%|
|Culvert/pipe culvert repair &<br>maintenance|90%|Sinking/installation of tubewell|21%|
|Education awareness program|83%|Agricultural development|21%|
|Early marriage discouragement|73%|Law and order|21%|
|Dowry discouragement|73%|Religious festivals|17%|
|Plantation|69%|Repair of mosque and temple|10%|
|Birth registration|63%|Irrigation maintenance|6%|
|Celebrations of national days|52%|Fish culture|4%|
|Village court (salish)|42%|Dam construction|4%|
|Women and children welfare|42%|Disaster management|4%|

_Source: SDC, 2006 based on responses of chairpersons and members from 48 UPs_ The institution of _salish_ constitutes a means through which the Chairman establishes and /or reinforces his network of potential electoral supporters among (secondary or tertiary) local leaders. Generally, the (perceived) aggrieved party files a case with the Union Parishad to request a _salish_ . Initially, these cases are referred to local influential people who reside in or near the location of the crime or incident. Often the Chairman sends a UP member to oversee the ruling. In the context of Chairman‘s overall stature in the union, his acceptance and approval of a local leader‘s role and ruling in _salish_ , affirms that individuals leadership position within the _para_ / village. (Bode, 2002)

**Multiple mediators -  competition and assertion:** A number of processes, particularly the rising importance of elected representatives of the Union Parishad, the increasing penetration of NGO sponsored dispute resolution, combined with the movement towards legal empowerment of the poor has thrown up new social and political dynamics.  Elites and institutions are competing for spaces and spheres of influence.  Traditional elites are re-imagining themselves and new elites are carving out their roles.  Mediation of local disputes provides each set of elites with a powerful arena within which to exercise influence.  Thus, from an institutional perspective, the entire spectrum of local justice – a spectrum with the traditional shalish at one end and the village courts at the other, and the NGO-sponsored shalish in between – also signifies a social and political churning in Bangladesh.

14
_Table 1: Impact on Access to Extension_

||Received ad<br>(ye<br>|vice from FTC<br>s=1)<br>|
|---|---|---|
||Men|Women|
|round 3|-0.028|0.028*|
||(0.033)|(0.015)|
|FIF*round 2|0.017|0.031***|
||(0.027)|(0.010)|
|FIF*round 3|0.018|0.051**|
||(0.037)|(0.020)|
|WIF*round2|-0.041|0.049|
||(0.038)|(0.033)|
|WIF*round3|0.085|0.142**|
||(0.060)|(0.057)|
|WIF*FIF*round2|0.035|0.037|
||(0.065)|(0.047)|
|WIF*FIF*round3|-0.064|0.053|
||(0.080)|(0.080)|
|Observations|2,428|2,428|
|R-squared|0.018|0.059|
|Estimates: FIF2 + WIF2 + FIF_WIF2|0.011|0.117|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.827|0.004***|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.039|0.246|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.503|0.000***|
|Control Mean(BL)|0.576|0.308|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household and region. FTC stands for Farmer Training Center. ***, **, and * indicate significance at the one, five and 10 percent critical level.

Next, we turn our attention to the impact of these two interventions on technology adoption ( Table 2). Through easing both information and financial constraints for households, we would expect that the interventions decrease the associated barriers to trying new technologies. We find that for technology adoption—unlike the impact on access to extension services—who was targeted does matter. Targeting the male spouse via FIF significantly increased men’s take-up of new technology overall, with an increase of 5.5 percentage points by round 3 (significant at 10 percent). This result is driven by innovation in livestock. Targeting the male spouse also increases women’s take-up of new technology overall in round 3 (significant at 10 percent), and it is driven by innovations in livestock, crops and farming methods. This strong impact contributed to the positive joint effect 15
49 Though inter-regional variations exist, on the whole developing countries have enjoyed progress in social indicators over the past three decades. Similarly, these countries as a group benefitted from growth in per capita private consumption throughout the period 1960-88. The following trends stand out. Health indicators such as child mortality, immunization coverage, life expectancy, have shown continuous and stable improvements in all regions. This period has also seen the global improvements in gross primary school enrollment ratios with the notable exception of the record in some African countries where progress has actually being reversed during the late eighties. The predicament of Africa is also revealed by increasing problems of undernutrition in a majority of the countries in the region. For instance, the degree of undernutrition, as measured by the ratio of the shortfall in per capita caloric consumption, worsened in over two-thirds of the sub-saharan African countries. The proportion of the undernourished population in this region increased from 60 million at the end of the sixties to 100 million by mid-eighties. The two regions characterized by economic difficulties in the eighties, Africa and LAC, also saw declines in average per capita private consumption during this period. Finally, whilot the share of total government expenditures on health remained stables in all regions, that of education declined in Africa, South Asia and LAC.

The renewed focu) on poverty reduction in World Bank operations has given high visibility to the issues of quality and reliability of data on social indicators and social sector financing. Similarly, most bilateral and
exchange rate appreciation may limit growth prospects by squeezing profit margins, especially in export-oriented industries, and ultimately lead to economic contraction with direct implications on loans performance [Kaminsky and Reinhart (1996)].<sup>31</sup> The direction of the association between real exchange rate appreciation and nonperforming loans is not consistent throughout the sample, however. This relationship is ambiguous for the sub-panel of non-CFA countries. In spite of its magnitude, the coefficient associated with these countries has a negative sign. Clearly, while the sign and direction of the association with banking crisis in the period preceding the crises may be anticipated, the response of monetary authorities is likely to affect the nature of this relationship over the crisis period, and may partly explain the ambiguous nature of the relationship in this sub-panel.

**Table II: Correlation Analysis between Nonperforming Loans (in % of loan loss provision) and Selected Banking and Macroeconomic Variables**

|**Variables**|**All Banks**|**State Banks**|**Private Banks**|
|---|---|---|---|
|**MACROECONOMIC VARIABLES**||||
|Real GDP growth rate|0.19|-0.32|0.09|
|Real GDP per capita|-0.22|-0.63|-0.22|
|M2 in % of GDP|-0.03|0.07|-0.07|
|CPI inflation rate|-0.13|0.11|-0.17|
|GDP deflator inflation rate|0.01|0.19|-0.28|
|Domestic credit provided by banks (in % of GDP)|0.11|0.42|-0.11|
|Real interest rate|-0.14|0.00|0.09|
|Interest rate spread|-0.20|0.29|-0.14|
|Real GDP growth rate 2|0.24|-0.33|0.16|
|Real GDP per capita 2|-0.22|-0.60|-0.20|
|CPI inflation rate 2|-0.13|0.11|-0.17|
|**BANKING VARIABLES**||||
|Equity (in % of total asset)|-0.01|-0.49|0.13|
|Return on asset|-0.14|-0.59|-0.28|
|Total deposit (in % of total assets)|-0.12|-0.18|0.11|
|Total deposit (in % of total liabilities)|0.01|0.28|-0.08|
|Return on equity|-0.29|-0.70|-0.17|
|Net interest margin|-0.26|-0.60|-0.13|
|Net income(in%of total revenue)|-0.11|-0.47|-0.27|

Equally interesting is the relationship between broad money multipliers (M2) and nonperforming loans, and the potential association between banking crisis and domestic credit to the private sector. Theoretically, credit to the private sector is expected to grow more rapidly in the periods preceding the crises. However, the lending boom in the pre-crisis period is generally followed by a fall in domestic credit in the outbreak of the crisis [Gavin and Hausman (1995)]. With falling domestic credit, financial intermediation, measured by the ratio of broad money (M2) to GDP, is expected to be low, suggesting a negative

> 31 There are other indirect channels through which the economy and the banking sector can be affected: the exchange rate regime can undermine bank soundness through its impact on vulnerability to speculative attacks, a downward adjustment in the real value of bank capital, and the ability of the central bank to act as lender of last resort.

12
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 16. Underpricing in the power and water sectors, 2006**

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>power water<br>Tanzania low-income countries<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia and others 2009.

These shortfalls in power and water tariffs are ultimately met by state subsidy. However, due to highly inequitable access to these services, the distributional incidence of these subsidies has tended to be highly regressive. Of households with access to piped water in the year 2000, 69.4 percent belonged to the top quintile of the income distribution. Similarly, 87.6 percent of households with access to power in the year 2000 belonged to the richest quintile (figure 17).

**Figure 17. Infrastructure and income, 2000**

a. Power <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br>income quintile<br>% of population<br><!-- End of picture text --> 30
- 30 -

Finally, infrastructure data are still problematic. There is no comprehensive infrastructure data specific to a particular industry. The above analysis may suffer from the measurement error problem. Efforts to correct this data paucity may be well justified. Availability of such date could facilitate evidence based decisions concerning infrastructure development that specifically targets rural growth using agriculture as the vehicle. This remains a weak area of most development strategies.

# **VI. CONCLUSION**

Agricultural development is one of the key for growth in Africa. Aggregate agricultural growth is expected to be accelerated by public infrastructure provision. However, the potential infrastructure impact may vary across commodities.

The paper investigates two different types of farm products: coffee and cocoa, representing high-value export products; and milk, representing a domestic agricultural commodity. The available infrastructure data are quite limited. Some agriculture- or rural-related infrastructure proxies are used: rural teledensity, rural water access and irrigation penetration rate.

The estimation results indicate that agricultural production could be promoted by different infrastructures, depending on commodity. Roads and irrigation facilities could strengthen production efficiency in the coffee and cocoa industries. Telecommunications infrastructures are also important for branding these commodities. Conversely, dairy production requires more water in rural areas.

One of the policy implications is that African countries might have to invest more in communication technologies as well as water and transport infrastructures to improve their agricultural marketing competitiveness. Another is that, to maximize their bargaining power
# **Working on the Streets**

23. Most street activities, both legal and illegal, are consideted the worst forms of<sup>child labor in</sup> Brazil. This paper treats street children as a subset of child labor<sup>while recognizingthat children</sup> working in the street represent a different set of issues and concerns.<sup>The risksand exposuresthat</sup> street children confront daily and the very public nature of their work<sup>and theirhardshipshave</sup> heightened concerns about this particular aspect of child labor,<sup>as wellashavingattractedlocaland</sup> international attention (Freeman, 1997, Scheper-Hughes<sup>and</sup> Hoffman,<sup>1997,UNICEF,1993).</sup> 24. According to PNAD 1995, about 161,000 children or 6.2 percent of all working<sup>children,</sup> work in roadways or public areas. Table 7 shows the number of<sup>childrenworking inthe streetsin</sup> three Brazilian cities. In the city of Salvador, an estimated<sup>16,000childrenwork inthestreets;</sup> however, only 3 percent live and sleep in the street. In fact, in the three Brazilian<sup>citiesthe</sup> percentages of children living or sleeping on the streets are relatively low if compared<sup>to Central</sup> America where an estimated 25 percent of street children sleep or live on<sup>the streets(Takahashiand</sup> Cederlof, 2000).

**Table 7: Estimate of Children in the Street in Selected Brazilian**<sup>**Cities**</sup>

|Ciies'_in_Braid<br>.-<br>'<br>Workinginthe;street-'-`Nunb`er'd.proportionsleeping.i.<br>C<br>"**'r.-..ihe**street-<br>**-**|
|---|
|SaoPaulo<br>4,520<br>895(19.8percent)|
|Fortaleza<br>5,962<br>184(3.1percent)|
|Salvador<br>15,743<br>468(3.0percent)|

Source: Rizzini, Barker, and Cassaniga, 1998 25. Though findings cannot be generalized, a study conducted in<sup>Brasiliaon streetchildren and</sup> adolescents under 18 years of age provides an insight into the population's<sup>gender composition.</sup> The 1996 -1997 survey indicated that although the absolute number of children<sup>dropped</sup> dramatically in the second year, the gender composition remained the<sup>same:89percent of the street</sup> children were boys and 11 percent were girls. Overrepresentation of<sup>boys among street children isa</sup> pattern that is also witnessed in other countries in Latin America.<sup>Boyscomprised 72 percentof the</sup> working youth population in the streets of Mexico (UNICEF, 1993), and<sup>70 percent of those in</sup> Honduras (Takahashi and Cederlof, 2000). However, the Brasilia study found no gender<sup>differences</sup> 10
This “aggregate” finding across the four main categories of trade policy actions is unpacked in Table 3, which reports results for seven different types of instruments that are most frequently observed in the GTA data set. This more disaggregated focus reveals large countries focused more on import tariffs as well as export bans and export licensing requirements.<sup>18</sup> Distance to markets is positively associated with restrictive export measures and inversely correlated with liberalizing export measures on medical products; the former finding driven by measures still in force (see Table 2, column 4). Conversely, if the focus is on measures that were removed during the period, this variable is strongly correlated with re-imposition of import restrictions, with a coefficient estimate that is statistically significant at the 1% level (Table 2, column 6). Distance has a very similar pattern as country size does on the export side (Table 3).

Government effectiveness is positively correlated with all categories of trade policy measures, especially export licensing requirements (Table 3), except for imposition of import barriers, especially when it comes to measures still in force at the end of the sample period (Table 2). Higher initial tariffs are positively correlated with liberalization of imports (Table 1), which is expected as the higher the pre-crisis tariffs the greater the expected impact of removal of the tax on prices (or more realistically, attenuation of price rises for essential products). The result seems driven by liberalization of domestic taxation on imported medical products (Table 3). Conversely, when it comes to measures that are removed during the sample period, pre-crisis tariffs are strongly associated with re-imposition of import barriers – which may reflect a return to the initial level of protection for the products concerned (Table 2, column 6). This can also be inferred from the large positive correlation between pre-crisis tariff levels and the duration of import policy measures imposed on medical products (Table 4).

Supply-side capacity (proxied by a revealed comparative advantage in exporting medical products) is positively correlated with both import restrictive and export liberalizing measures (Table 1). Conversely, greater reliance on imports of medical products is not correlated with the number of import or export measures imposed on medical products, in either aggregate or disaggregated analysis, which is a counter-intuitive result (Table 1). However, import reliance is strongly negatively associated with re-imposition of import barriers (Table 2), consistent with higher import dependence disincentivizing imposition of import barriers.<sup>19</sup> This variable is also found to be negatively correlated with the duration of restrictive measures imposed (Table 4, columns 2-3).

> 18 A possible rationale for (re-)imposing import tariffs is to protect upstream industries that produce medical inputs and/or domestic producers of medical products.

> 19 This will depend on government objectives and domestic political economy forces – a subject that is beyond the scope of the present analysis.

15
explanatory variables. Unless stated otherwise, all data are obtained from the World Bank’s World Development Indicators.

The set of explanatory variables can be divided into four groups. The first consists of the initial level of per capita GDP and is included to capture “transitional convergence,” that is, the tendency of economies to grow slower as they become richer and converge to their steady state. The second set of variables accounts for the role of external conditions related to international prices and global economic conditions. To capture changes in international prices, we use the rate of change of the terms of trade; and to account for global conditions, we use a period specific dummy variable. The third group focuses on macroeconomic stability in both aggregate domestic prices and output. It includes the (log of 1 plus) the CPI inflation rate and a measure of “crisis” volatility based on negative deviations of trend beyond a certain threshold (see Hnatkovska and Loayza, 2004, for details).

The fourth group consists of variables that measure structural conditions in areas such as educational investment, financial depth, trade openness, government burden, and infrastructure, the variables of particular interest for this study. Educational investment is measured as (the log of) the gross rate of enrollment in secondary school. Financial depth is proxied by the ratio of private domestic credit by private financial institutions to GDP. The outward orientation of the economy is proxied by (the log of) the volume of trade (exports and imports) to GDP. Government burden is measured as (the log of) the ratio of general government expenditure to GDP. And infrastructure is measured by the indices of electric power generation, transportation, and telecommunications, presented in detail in the previous section.

Except for the variables measuring external conditions, all variables are potentially jointly endogenous with economic growth (that is, caused by previous and current innovations in per capita GDP growth, the dependent variable).

# **B. Estimation Results**

We now present and discuss the estimation results. We present different variations dealing with how the measures of infrastructure are included in the growth regression. Table 4 presents the estimation results when we include the infrastructures 26