rafmacalaba/datause-encoder-data huggingface dataset:
expenditure shares on commodities for eight Latin American countries: Argentina, Bolivia, Colombia, El Salvador, Mexico, Nicaragua, Panama, and Peru. There is significant cross-country variation in the average share spent on commodities, ranging from an average share of 71.3 percent in Bolivia to 31.7 percent in Mexico. A common feature is that, in all these countries, the share of commodity expenditures decreases across quintiles. This is expected due to Engel’s Law: richer households typically spend a smaller share of their total expenditures on food. In Bolivia, for example, the average commodity share is 82.9 percent in the first quintile and 54.4 percent at the top quintile. In Mexico, the average share of the bottom quintile is 40.6 percent and it is 20.8 percent at the top. In Colombia, the poorest households spend, on average, 80.1 percent on commodities; the richest households spend 41.6 percent. Clearly, differences in the populations covered by the surveys across countries (i.e., urban versus rural populations) can account for part of the differences in the average commodity expenditure shares. But, leaving this aside, the data reveal a very high commodity dependence on the consumption side, especially for the poorest households.

Table 2 reports commodity-dependence of household expenditures for a set of African countries. There is also significant variation across countries, from a minimum average share of 42.7 percent in South Africa to a maximum of 89.9 percent in Madagascar and 84.4 in Tanzania. The commodity dependence on consumption seems higher in Africa than in Latin America, however. In most countries, commodity dependence in consumption is more prevalent at the bottom of the quintile distribution. This means that Africa and Latin America are both highly dependent on commodities and, moreover, this dependence declines with household income. This pattern explains the literature’s concerns about the poverty impacts of commodity price hikes.

Tables 3 and 4 turn to dependence on the income side. The aim is to measure the share of household income derived from commodities, including income from employment (wages) and self-employment (for instance, agricultural home production). The following sectors are included in our definition of “commodities:” agriculture, forestry, and fishing; mining; food manufactures (including beverages and tobacco); and energy. Starting with Latin American data for 2004, Table 3 reports the total combined share of income derived 4
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41
_page 14_ It is likely that much of this investmnent occurred in the nontradable sector of the economy. Although data on investment by sector is simply not available for most of these countries,' and therefore this claim cannot be supported directly, much of the otber data we do have is supportive. First, there is strong evidence that the relative price of nontradables were positively associated with the terms-of-trade shocks. So there seems to have been a clear price incentive for investment in these sectors. Second, we do have evidence that in the long run output expanded in nontradable sectors such as construction and transportation. It seems reasonable to think that this rise was made possible by a higher capital stock in these sectors.

This investment effi- in nontradables can be explained formally in a variety of intertemporal optimizing models of investment (recent examples in the literue include Schmidt-Hebbel & Serven (1993) and Warner (forthcoming, 1994)). These models usually conclude that aggregate private investment is determined by the ratio of Tobin's q (the present discounted value of future marginal value products of capital in a given sector) to the relevant price deflator for investment goods. Since the countries in our sample import many capital goods, and the world prices of capital goods did not change dramatically over the period, the price of nontradables relative to physical capital probably rose in the 1970's and declined in the 1980's, causing similar changes in investment in the nontradable sector.

Tuming to the oil sector, one might think that relative price changes would also stimulate investment there. Although this would probably hold for countries experiencing exogenous changes in their export prces, it is important to remember that OPEC raised oil prices in the 1970's partly through a deliberate policy of output restriction, so that for much of our period the oil sector was engineering price changes rather than passively responding to them. In such a setting, where countries exercise their power in the world oil market, it is a _priori_ unclear which way the incentives work for investment in the oil sector. Oil production quotas may be seen as a permanent ceiling on oil production, leaving little reason to invest. On the other band, an increase in productive capacity may strengthen one's hand in the periodic quota negotiations.

X An exception is Wamer (1994), where it is shown that investment in Mexican private non-tradables did decline over 1981-85. That study also suggests that an important mechanism for the investment decline was the fall in the ratio of nontradable pnces to imported equipment prices, and that the terms-of-trade decline can explain most of the reduction in this relative price.
Policy ReseaRch WoRking PaPeR 4573

# **Abstract**

This paper provides the results of an international survey of practitioners with experience in facilitating the participation of African smallholder farmers in supply chains for higher-value and/or differentiated agricultural products. It explores their perceptions about the constraints inhibiting and the impacts associated with this supply chain participation. It also examines their perceptions about the factors affecting the success of project and policy interventions in this area, about how this success is and should be measured, and about the appropriate roles for national governments, the private sector, and development assistance entities in facilitating smallholder gains in this area. The results confirm a growing ‘consensus’ about institutional roles, yet suggest some ambiguity regarding the impacts of smallholder participation in higher-value supply chains and the appropriateness of the indicators most commonly used to gauge such impacts. The results also suggest a need to strengthen knowledge about both the ‘old’ and ‘new’ sets of constraints (and solutions) related to remunerative smallholder inclusion, in the form of the rising role of standards alongside more long-standing concerns about infrastructure and logistical links to markets.

This paper— Agriculture and Rural Development Department—is part of a larger effort in the department to promote broad-based agricultural growth and market development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sjaffee@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
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Notable are the absence of private investments in the power sector (figure 25). Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured between 1.8 and 2.5 percent of GDP, while the most successful country in this regard, Guinea-Bissau, has captured in excess of 3.0 percent of GDP.

**Figure 25. Zimbabwe needs to attract more private investment**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ World Bank’s Private Participation in Infrastructure Database, 2010. _Note:_ GDP = gross domestic product; ICT = information and communications technology.

Attracting required private sector funding is a challenge. The uncertain and difficult investment climate in Zimbabwe, where private property itself seems frequently jeopardized, makes it difficult to attract investors. Furthermore, most of the infrastructure providers are parastastals that require restructuring before any private participation can take place; poor parastastal performance currently poses too high a risk for private investors, and the regulatory framework needs to be revised and properly implemented. However, the potential scope of private participation in infrastructure (PPI) in Zimbabwe is highly promising, and therefore the country must take all possible means to tap this potential.

Given the size of the funding gap, clear principles for prioritization and sequencing of infrastructure investment decisions are essential. Since infrastructure is only a means to an end, it is important to define infrastructure development plans in close coordination with the national growth vision. Zimbabwe is currently envisaging a growth strategy based around key development corridors that anchor economic activities in a number of key sectors including agriculture, forestry, and mining. Scoping out the infrastructure packages needed to support development along these corridors would be a helpful way of prioritizing the overall national requirements identified in this report.

In any case, it will likely be necessary for Zimbabwe to consider a period longer than a decade to reach the illustrative infrastructure targets here outlined. Under business-as-usual assumptions on spending and efficiency, it would take a very long time for Zimbabwe to reach these goals. But with a combination of increased finance, improved efficiency, and cost-reducing innovations, it should be possible to catch up in a much shorter period. Simulations suggest that even if Zimbabwe is unable to raise additional finance, if at least inefficiencies can be addressed, the identified infrastructure targets could be achieved within a 14-year horizon. But without stemming inefficiencies, the existing resource envelope would not suffice to meet infrastructure targets in the medium term.

50
# **Appendix 1 (Cont): Variables**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Checking annual fee|Fees associated with maintaining a checking account expressed as percent of GDP per<br>capita in 2004|Beck, Demirgüç-Kunt., and<br>Martinez-Peria (2008)|
||Equals one if a country claims that it has pursued tax incentive savings schemes (such as||
|Tax incentives|tax incentives for retirement, education, or medical savings) to promote financial access<br>and zero otherwise.|_Financial Access Database_(2009)|
||This index is an equally weighted mean of responses to the following survey questions: (1)||
|Operations agents (savings)|Agents are allowed to receive and forward applications to open accounts; (2) Agents are<br>allowed to open accounts on behalf of the bank; (3) Agents to accept funds for deposit in<br>the client's bank account and; (4) Agents to pay withdrawals from client's bank account.|_Financial Access Database_(2009)|
|Postal Network|Equals one if financial services are offered in the post offices and these services are<br>handled by a separate private operator (typically a bank) and zero otherwise.|_Financial Access Database_(2009)|
|Contract Enforcement|Total enforcement cost, including legal fees, assessment and court fees expressed as<br>percentage of total debt in 2008|World Bank_Doing Business_<br>Indicators, 2008|
|Creditor Rights Index|Index of creditor rights following La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998)|World Bank_Doing Business_<br>Indicators, 2008|
|Minimum loan|Lowest amount of consumer loan banks make expressed as a percent of GDP per capita in<br>2004|Beck, Demirgüç-Kunt., and<br>Martinez-Peria (2008)|
|Fee consumer loan|Fee banks charge on consumer loans expressed as percent of GDP per capita in 2004|Beck, Demirgüç-Kunt., and<br>Martinez-Peria (2008)|
|Days process loan|Number of days banks take to process a typical consumer loan application in 2004<br>This index is an equally weighted mean of responses to the following survey questions: (1)|Beck, Demirgüç-Kunt., and<br>Martinez-Peria (2008)|
|Operations agents (credit)|Agents to receive and forward loan requests to the bank; (2) Agents to conduct credit<br>evaluations and to approve loans on behalf of the bank; (3) Agents to collect loan payments<br>on behalf of the bank.|_Financial Access Database_(2009)|
|Branch Approval|Equals one if the Supervisor/Regulator approval is required to open each bank branch and<br>zero otherwise in 2008.|_Financial Access Database_(2009)|

65
# **_4.3 Labor market projections and impact of crisis_**

**Labor market status in 2005.** In 2005 (according to HIES data), about 48% of the working‐age (15‐64 years of 95-99 90-94 age) population was employed.<sup>19</sup> Among those 85-89 80-84 employed, 44% was employed in agriculture, 24% in 75-79 70-74 industry, and 32% in services. 55% of those employed 65-6960-64 were salaried workers, and 75% of the labor force had 55-5950-54 45-49 less than 10 years of education (defined as “low‐ 40-44 35-39 skilled”). The employed population includes those 30-34 25-29 working for wages/salaries, the self‐employed and 20-2415-19 those contributing labor to household‐based 10-145-9 0-4 enterprises or activities that produce income for the milion-10 -8 -6 -4 -2 0 household. On average, 68% of household income comes from labor earnings and 9% comes from _Source_ : HIES 2005 remittances (from abroad and domestic), which in turn constitutes about 39% of non‐labor income (see Annex 2, Table B for detailed figures).

<!-- Start of picture text --> Women<br>Men<br><!-- End of picture text -->

**Table 2: Employment projections (% of people age 15‐64 yrs)**

|||||Impact<br>|of Crisis<br>|
|---|---|---|---|---|---|
||Baseline<br>|With<br>|Crisis<br>|_(Crisis – Be_<br>|_nchmark)_<br>|
||2005|2009|2010|2009|2010|
|_Employed (%)_|48.4|49.4|51.0|‐0.3|‐0.6|
|_Sectoral shares_|_(% of empl_|_oyed in e_|_ach sect_|_or)_||
|Agriculture|44.3|40.4|39.5|0.3|0.5|
|Industry|23.8|23.8|22.5|0|‐1.4|
|Services|32.0|35.8|38.0|‐0.3|0.8|

_Source_ : Own calculations using GDP data and HIES (2000, 2005)

**Projections for 2009 and 2010.** The _population growth rate_ between 2005 and 2010 is projected to be 7.4%, with a higher growth rate (11.9%) projected for the working age population. This is a result of the age distribution of the population in 2005. The “bulge” among the 5‐14 years age group in the 2005 population (Figure 3) indicates that the working age population will expand rapidly over the subsequent 5‐10 years,

imposing a burden on the labor market to absorb the new entrants (see Annex 2, Table A.1).

_Employment growth_ projections for 2009 and 2010 are computed using past (2000‐2005) trends in output and employment.<sup>20</sup> Given the relatively small impact of the crisis on projected GDP growth rates (particularly for 2009), the impact on overall employment is expected to be very small – employment rate would be lower by just 0.3 and 0.6 percentage points compared to the benchmark (no‐crisis) scenario in 2009 and 2010 respectively (Table 2).

> 19 The remaining 52% of the working age population comprises of those who are "inactive" or unemployed. We consider an individual to be inactive if s/he has not worked for income (wages, or contribution to family enterprise that produced income) during the last year and is not looking for work. Out of the working age population, 2% are unemployed and 50% are inactive.

> 20 See Annex 2, Tables A.2 and A.3 for detailed projections.

17
# **1. Introduction**

While almost all governments intervene in labor markets (and often for good reasons), the labor markets facing the developing world’s poorest are probably the least regulated of all. Yet the poor in much of South Asia and elsewhere depend heavily on their earnings from supplying unskilled, typically casual, labor, primarily in agriculture. The agricultural wage rate is known to be a significant predictor of the rural poverty rate in this setting.<sup>2</sup> A living wage in rural areas might then seem an obvious policy instrument for fighting poverty.

Simply legislating a minimum wage will not make it happen; the ease of evasion and costs of enforcement mean that a legislated “statutory minimum wage” may well be largely irrelevant to labor market outcomes for many low-wage workers.<sup>3</sup> While legislating a “living wage” is often advocated as an anti-poverty policy (in both developed and developing countries), limited coverage in practice has led some observers to question the efficacy of such a policy and to suggest other objectives are being served, such as protecting workers in organized sub-sectors; see, for example, Neumark (2004) in the context on minimum wage laws in the US.

This paper’s point of departure from past research on the efficacy of legislated minimum wages as an anti-poverty policy is the recognition that, in all likelihood, the only effective way to achieve a more-or-less binding minimum wage rate for the poorest in the developing world is for the government to act as the “employer of last resort.” The government commits to employ the entire excess supply of unskilled labor at the stipulated wage rate. We call this an Employment Guarantee Scheme (EGS). This is not a far fetched idea. Indeed, a coalition led by the Congress Party of India (that came to power in April 2004) developed a “Common Minimum

> 2 Evidence on this point for India can be found in Datt and Ravallion (1998).

> 3 Indeed, the classic theoretical characterization of the labor market in developing economies (following Harris and Todaro, 1970) postulates that minimum wages are only binding in the urban formal sector though there can still be (theoretically ambiguous) spillover effects to the unregulated sector.

2
and middle-income countries. The research produced 3 country studies from Tonga, India, and Indonesia (Stillman et al., 2007; Das and Das, 2007; Friedman and Thomas, 2007) and uses data from these countries as well as Mexico and Bosnia and Herzegovina (hereafter BiH) to study the correlates of mental health in a broad variety of low-income country settings.

This overview serves several purposes. First, it argues that including mental health modules in multipurpose household surveys is relatively straight-forward and informative in that they capture “real” underlying psychological morbidity. Validation studies from the Indian and BiH datasets used here show that the mental health measures collected from surveys are highly correlated with clinical diagnosis of psychiatric disorders such as depression. Additionally, across all five countries, we find similar associations between mental health and a number of socio-demographic correlates, suggesting stability in what is being measured. Secondly, this review shows that mental health status is associated with labor supply and health care utilization, conditional on self-reported physical health status and other socioeconomic variables commonly collected in household surveys.

Two additional findings suggest that mental health can be a complementary source of information to traditional welfare measures such as income, consumption, or poverty. In contrast to studies in highincome countries, we do not find a strong correlation between levels of income or consumption and mental health. While poor mental health is not a “disease of affluence” in the developing world, neither is it a disease of poverty. On the other hand, the country specific case studies in the comparative research project show that individuals’ responses to long-term or short-term shocks differ noticeably whether one looks at monetary based measures of welfare such as income and consumption, or mental health indicators.

The remainder of the paper is structured as follows. Section 2 describes the survey instruments used to measure mental health and their validation. Section 3 examines the association between mental health and potential individual and household predictors, while Section 4 considers the relationship between mental health and two behavioral outcomes: labor supply and health-care utilization. Section 5 reviews the implications of these findings and contrasts them with the happiness literature, discusses potential response biases arising from self-reported mental health measures and how these may affect our findings, and suggests probable productive paths of future research.

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