rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Matooke prices Input Costs (UG shs/kg)<br>700 16,000<br>600<br>14,000<br>500<br>12,000<br>400<br>10,000<br>300<br>8,000<br>200<br>6,000<br>100<br>4,000<br>0<br>0 20 40 60 80 100 2,000<br>Percentile 0<br>2010 2011 2012<br>Central East North West<br>Inorganic Fertilizer Pesticide<br>Source: Staff calculations using wholesale prices from UBOS, 2000 ‐ 2012 and UNPS 2005/06‐2013/14. Prices are averaged<br>across the 1 st and 2 nd season.<br>Real price per kg (2005 Shillings)<br><!-- End of picture text --> Using the range of output prices shown in Figure 3.4, we first calculate the return to applying nitrogen (inorganic fertilizer) for the main maize growing region, the Eastern region. Figure 3.5.1 plots the distribution of the net financial return per hectare to using maize hybrid seeds and applying nitrogen to maize across the range of prices. It uses the average yield return estimated in Figure 3.1 assuming reasonable rainfall conditions, and the average cost of inputs in 2013, as specified in Bold et al. (2017). The estimates show that when crop prices are at their lowest, returns to fertilizer use are almost 0 (bottom 5 percent of prices) and at median prices returns are 74 percent. At the highest crop prices (top 5 percent of prices), returns are a substantial 196 percent.

While the use of hybrid seeds enhances crop yields, their cost often exceeds the extra revenue gained from using them. As a result, the rate of return to using hybrid seeds alone is negative except when output prices are very high. The returns are positive for the top 50 percent of maize prices, but at median prices the rate of return to using hybrid seeds is zero. As a result, yield gains are highest when using hybrid seeds and nitrogen together; the return to their joint use is lower than the rate of return to using fertilizer alone. The rate of return is negative until the 20<sup>th</sup> percentile of the price distribution, and is 34 percent at median prices. Figure 3.5.2 plots the return to using a package of fertilizer and seeds in different regions. Returns are similar for farmers in all regions, but lowest in the Eastern region.

**Figure 3.5. The returns to nitrogen use are high across a range of prices, but not so for hybrid seeds.**

**1. Rate of return to fertilizer and hybrid 2. Rate of return to fertilizer and hybrid seeds seeds for Maize in the Eastern Region for Maize across regions**

13
15 Table 4. Estimated Effects of Population, Per Capita Income, and Population Density on Road Length

|||_C_|_oefficient estimate_|_s_a|
|---|---|---|---|---|
|_Dependent variable_|_Functional_<br>_specification_|_Population_|_Per capita_<br>_income_|_Population_<br>_density_|
|_At the national level_|||||
|Length of total road|Cross section|1.0 *|0.5*|-0.3*|
||20-year first differences|0.4|0.5|n.a.|
|Length of paved road|Cross section|1.0 *|1.0*|0.0|
||20-year first differences|1.3 *|0.8*|n.a.|
|_At the urban level_|||||
|Lengti of road|Cross section|0.8 *|variesb|-1.0*|
||First differences|0.5 *|0.1*|-0.4 *|

* Statistically significant at the 0.05 level.

a. All variables are in natural logarithm, and elasticities are jointly estimated.

b. The income elasticity increases with income level.

Source: estimated in Ingram and Liu (1997,1998).

network,<sup>3</sup> ' and gasoline prices have little influence on the length of either paved or unpaved roads at the national level. These estimates were based mainly on the crosssectional variation in panel data, but estimates based on first differences over time in the same panel data produced generally similar results (except for the relation of total road length with population), as shown in table 4.

The major implication of these findings is that at the national level paved road length increases with per capita income at roughly the same rate as vehicle ownership. As a result, congestion does not appear to be a growing problem for the national road network in most countries.

Paved road density (length of paved road per unit of area) has an elasticity of 1 with respect to both population density and income density (income per unit of area), whereas total road density has an elasticity of about 0.7 with the same two variables. Paving roads is an efficient way to increase the quality of the national road network and costs less than constructing new roads because existing rights of way are used. The percentage of roads paved increases with per capita income and population density, and in the fifty countries studied it had an elasticity of approximately 1 with respect to both variables from 1970 to 1990. Paving occurs most intensively in low- to middle-income countries. Developing countries with average population densities (100 persons per square kilometer) and average per capita incomes ($550 a year in 1987 prices) have about a third of their roads paved, a share that rises to more than three quarters when annual per capita incomes reach $1000.

31 Ingram and Liu, "Motorization and the Provision of Roads," find that the length of the rail network is strongly related to per capita income (elasticity of 0.5) and population density (elasticity of -0.4), results similar to those for total roads.
_Social Capital, Household Welfare and lPoverty in Indonesia_ 27 model results suggest that human capi.tal as well as social capital each have a significant positive effect on household welfare. However, one of the remarkable findings of the Narayan-Pritchett study was the large magnitude of the social capital effect: depending upon the specification, the social capital effect in Tanzania was found to be 4-10 times larger than the human capital effect. Although the results for Indonesia imply a larger effect from social capital than from humani capital, the difference is not as large as in the case of Tanzania.

**Table** 4: **Household Welfare and Social Capital: The Aggregate Model**

||**Naraya**<br>**Specif**|**n-Plritch**<br>**ication**|**ett**<br>**Mod**<br>**Specifi**|**ified**<br>**cation**|**Specif**<br>**with**<br>**Social C**|**ication**<br>**out**<br>**apital**|
|---|---|---|---|---|---|---|
|Intercept|13.3158|(181.04)|12.7948|(69.65)|12.6782|(67.59)|
|<br>Social Capital Index|0.0066|<br>(6.29)|0.0069|(6.52)|-|_|
|<br>Household Size|-0.0998|<br>(11.01)|-0.0972|<br>(10.23)|-0.0923|(9.59)|
|Years of Education per Adult|0.0144|<br>(1.96)|0.0343|(4.49)|0.0454|<br>(6.11)|
|<br>Female Head of Household|-0.0004|<br> <br>(0.01)|-0.0463|<br>(0.67)|_-0.0551_|(0.81)|
|Age of Head of Household|-|<br>_|0.0309|<br> <br>(3.75)|0.0354|(4.20)|
|<br>Age of Head of Household Squared|<br>_<br>-||-0.0003|<br>(3.30)|-0.0003|<br>(3.71)|
|Household<br>Asset Score|0.3144|<br>(9.32)|-||-||
|Farmer Household|-0.1249|<br>(3.03)|-0.2311|(5.73)|-0.2417|(5.89)|
|Jawa Tengah|-0.0681|(1.73)|-0.1630|(3.90)|-0.0987|(2.40)|
|<br>Nusa Tenggara Timur|-0.1307|<br>(2.72)|-0.3271|(7.24)|-0.2201|(5.21)|
|<br>Number of Observations|1137||1137||1137||
|R-squared|0.28||0.24||0.21||
|<br>F-statistic|48.4||33.6||31.3||

Notes: 1. Dependent variable = ln (household expenditure per capita) 2. t-statistics are in parentheses and are based on robust standard errors (Hubert-White estimator for non-identically distributed residuals) Furthermore, the results may be unduly influenced by the presence in Narayan and Pritchett's equation of an asset variable which includes several consumer durable goods. This is arguably endogenous in a model where total expenditure is the dependent variable. The asset variable is also correlated positively with education. This is readily apparent if we drop the asset variable fiom the RHS of the equation (column 2 of 18 Means and standard deviations of regression variables are reported in Annex Table 9.
**Table 1. Net ODA and Technical Cooperation as shares of GNP by Region and Decade** _Median of decadal country averages by region_

|_(1_|_)_<br>_(')_|~~<br>~|~<br>_~~~~(3)_<br>_(4)_|
|---|---|---|---|
||_Net ODA_|||
|_Region and decade_<br>_Net ODA/GNP_|_(eC/GNPl_|_TC/GNP_|_TC/Gross ODA_|
|_Latin America (22)_||||
|1960s<br>1.29|1.07|0.26|15.24|
|1970s<br>1.31|0.92|0.37|27.06|
|1980s<br>1.38|0.87|0.39|30.58|
|1990s<br>1.58|0.81|0.64|38.04|
|_High-Performing Asian Economies (7)_||||
|1960s<br>1.14|0.80|0.12|13.30|
|1970s<br>0.74|0.46|0.19|39.34|
|1980s<br>0.15|0.03|0.13|33.95|
|1990s<br>0.02|-0.00|0.05|42.65|
|_South Asia (5)_||||
|1960s<br>1.96|1.59|0.11|7.27|
|1970s<br>3.84|2.41|0.72|14.20|
|1980s<br>8.53|7.04|1.14|16.52|
|1990s<br>6.52|5.41|1.11|16.24|
|_Pacific (5)_||||
|1960s<br>7.23|5.84|1.11|24.99|
|1970s<br>19.49|13.41|1.98|19.53|
|1980s<br>8.10|4.11|0.78|32.15|
|1990s<br>9.21|7.49|1.72|24.94|
|_Other developing countries (14)_||||
|1960s<br>2.54|2.42|0.26|11.20|
|1970s<br>3.00|2.60|0.22|22.77|
|1980s<br>2.93|2.21|0.13|20.39|
|1990s<br>1.86|0.99|0.32|24.85|
|_Sub-Saharan Africa (34)_||||
|1960s<br>3.00|2.08|0.72|26.64|
|1970s<br>7.53|4.31|2.33|34.59|
|1980s<br>11.24|7.38|3.30|27.68|
|1990s<br>15.30|11.39|3.44|22.72|
|_All 87 developing countries_||||
|1960s<br>2.09|1.83|0.35|18.00|
|1970s<br>3.03|2.03|0.72|30.49|
|1980s<br>5.04|4.11|0.92|27.76|
|1990s<br>6.02|4.19|1.47|25.17|

_Note:_ The number in parentheses represents the number of countries in the data. The table includes all developing countries with 1990 populations exceeding 800,000 and for which more than one-half the observations are available for all four decades ("11990s" refers to 1990-96). The latter criterion elimninates the economies in transition.

_Source and definitions: Net ODA_ is the sum of development assistance grants and net disbursements of concessional development assistance loans. TC is technical cooperation grants, a sub-category of development assistance grants. The data are from the DAC98 CD-ROM (OECD Development Assistance Committee, _Geographical Distribution of Financial Flows to Developing Countries, 1960-96)._ Development assistance loans are classified by the DAC as concessional if their grant element exceeds 25 percent; see DAC98 for details. _GNP_ in US dollars is ftom the World Bank as reported on DAC98.

2
supplies.

- Special surveys, including the assembly and analysis of data from existing surveys and the collection of primary information. Data should be gathered in a variety of different geographical areas and at varying levels of demand and program scale so that the full cost function can be derived.

- Sentinel site monitoring -- or monitoring pilot projects to use as a guide for the probable costs of larger scale work -- can be used to get quick results.

A number of possible data sources were identified during the workshop. The Columbia University group is involved in a number of projects in Africa that could provide relevant data. The World Bank itself has several Safe Motherhood projects in various stages of approval and implementation that could incorporate a cost data component. A major Bank project in India, for example, has recently been approved and could provide an ideal opportunity. The MotherCare Project and Family Care International are also interested in gathering cost data as part of ongoing Safe Motherhood program development and evaluation in a variety of countries and regions. There are several countries with a variety of activities underway on the government, NGO and donor side; potential sites for rigorous data collection efforts include Bangladesh, Tanzania, Nigeria, Indonesia and Gambia. Indeed, since the conclusion of the workshop, costing studies have evolved in Bangladesh, Indonesia, and the Gambia.

# V. COST MEASUREMEENT: FURTHER METHODOLOGICAL **ISSUES**

There are a number of precautions that must be taken if cost analysis activities are to be successful. Among the most important of these precautions is the way in which costs are measured. One common pitfall is to rely solely on the use of total cash outlays on program inputs, rather than attempt to analyze the costs associated with specific outputs. Using measures of expenditure rather than cost does not allow for inefficiency or unused resources; that is, it assumes that the expenditure did achieve the desired outcome. Nor does this method allow one the ability to project the costs of a different set of services. An alternative is to derive estimates from institutional and government records, reconstructing costs using a step-down methodology. This methodology is based on scrutiny of the production process, which then enables attribution of all hospital expenditures, for example, to specific departments ("cost centers") and the distribution of all costs (including overhead and the costs of intermediate outputs) to final service categories using allocational criteria, such as time use.

An advantage of this step-down methodology is that it more closely approximates economic costs of resources than do accounting costs. Economic costs, often referred to as opportunity costs, are the benefits foregone by not using a resource in its next best alternative. Unless the true costs of a resource are used, tne true minimum cost solution cannot be determined. Often, private cost is a good measure of the opportunity cost of a resource. At times, however, market prices do not reflect the opportunity costs of a resource, for a variety 13
- 48 -

# **REFERENCE**

Amemiya, Takeshi. 1979. The estimation of a simultaneous equation tobit model. _International Economic Review_ , Vol. 20, pp. 169-181.

- Andrew, Clinton. 1989. Anticipating air conditioning’s impact on the world’s electricity producers. _Energy Journal_ , Vol. 10, pp. 107-120.

- Basani, Marcello, Jonathan Isham, Barry Reilly. 2008. The determinants of water connection and water consumption: Empiirical evidence from a Cambodian household survey. _World Development_ , Vol. 36, pp. 953-968.

- Briceno-Garmendia, Cecilia, Karlis Smits, Vivien Foster. 2008. Financing public infrastructure in Sub-Saharan Africa: Patterns and emerging issuers. Africa Infrastructure Country Diagnostic, Background Paper No. 15. The World Bank.

- Davis, Lucas. 2008. Durable goods and residential demand for energy and water: evidence from a field trial. _RAND Journal of Economics_ , Vol. 39, pp. 530-546.

- Deller, Steven, David Chicoine, Ganapathi Ramamurthy. 1986. Instrumental variable approach to rural water service demand. _Southern Economic Journal_ , Vol. 53, pp. 333-346.

- Dubin, Jeffrey, Daniel McFadden. 1984. An econometric analysis of residential electric appliance holdings and consumption. _Econometrica_ , Vol. 52, pp. 345-362.

- ESMAP. 2009. _Climate Vulnerability Assessments: An Assessment of Climate Change Vulnerability, Risk, and Adaptation in Albania’s Energy Sector_ . Energy Sector Management Assistance Program.

- Fernandez, Viviana. 2000. Decisions to replace consumer durables goods: An econometric application of wiener and renewal processes. _The Review of Economics and Statistics_ , Vol. 82, pp. 452-461.

- Filippini, Massimo, Shanali Pachauri. 2004. Elasticities of electricity demand in urban Indian households. _Energy Policy_ , Vol. 32, pp. 429-436.

- Frondel, Manuel, Jorg Peters, Colin Vance. 2008. Identifying the rebound: Evidence from a German household panel. _The Energy Journal_ , Vol. 29, pp. 145-163.

- Goldstein, Markus, Antonio Estache. 2009. The scope and limits of subsidies, in _Stuck in the Middle: Is Fiscal Policy Failing the Middle Class?_ eds. by Antonio Estache and Danny Leipziger, pp. 75-96. The Brookings Institution.

- Hausman, Jerry, David Wise. 1976. The evaluation of results from truncated samples: The New Jersey income maintenance experiment. _Annals of Economic and Social Measurement_ , Vol. 5, pp. 421-445.

- Hausman, J.A., M. Kinnucan, D. McFadden. 1979. A two-level electricity demand model: Evaluation of the Connecticut time-of-day pricing test. _Journal of Econometrics_ , Vol. 10, pp. 263-289.
it respects and protects their religious, ethnic, economic, linguistic, and other rights, including gender and family rights, personal freedoms, and freedoms of the press, belief, and association. The _Survey_ divides the world into three broad categories: "Free" (countries whose ratings average 1-3); "Partly Free" (countries whose ratings average 3-5.5); and "Not Free" (countries whose ratings average 5.5-7). {See Karatnycky, Adrian (1999)}

**Figure 3 Country Classification: Political Rights and Financial Openness**

|||Financial<br>Low|Openness<br>High|
|---|---|---|---|
||Low<br>Low|~<br>23<br>(8.86)|9<br>(9.09)|
|Democracy||||
||High<br>High|<br>32<br>(18.24)|37<br>_(25.59)_|

Drawing on the above measures of democracy and financial openness, Figure 3 provides a 2 by 2 classification of sample countries along the democracy and financial openness axis. The figure shows the number of countries in each category, as well as the mean value for the countries in each quadrant of average government expenditures on health, education and transfer payments as a percentage of GDP for the period 1991-1997 (in parenthesis). A high level democracy means _democracy index >_ 0.6. A "High" level of financial openness is defined by a score of >1.6 in the Financial Openness Index.<sup>3 0</sup> From the matrix, it could be argued that the difference in means between the two groups (High-Democracy & High Financial Openness group versus Low Democracy &-Low Financial Openness) was highly significant at 0.1% level.

Table 2 reports the statistical summary of the key variables classified by countries' degree of openness. Of interest here is the wide difference in countries' degree of redistribution as measured by the ratio of government social expenditure to GDP. Thus, countries classified as financially open spend on the average 22.3% of their GDP on social expenditures as compared to 6.7% among financially closed countries. Furthermore, the view that financial openness, democracy, and government social spending go hand in hand is confirmed by the econometric results shown in Figure 3. The cross-country data, with sample size ranging from 70 to 140, shows statistically significant results for all three relationships based on a z-test, with the highest correlation between political liberty and government social expenditures.

> 30 An.alternate measure of openness of the economic nature is the Fraser Intitute's Economic Freedom of the World rankings. These rankings are strongly correlated with the Financial Openness Index (0.70) and show a correlation of 0.49 with the Democracy Index Thethe 95% confidence interval forthe difference inmeans is 11.15 21.74

18
Policy Research Working Paper 8236

# **Abstract**

Small firms are an important source of income for the poor in developing countries, and the target of many interventions designed to help them grow. But there is no systematic information on the failure or death of such firms. The paper puts together 16 panel surveys from 12 different developing countries to develop stylized facts from over 14,000 firms on how much firm death there is; on which types of these firms are most likely to die; and on why they die, paying careful attention to issues of measurement and attrition. The authors find small firms die at an average rate of 8.3 percent per year over the first five years of following them, so that half of all firms observed to be operating at a given point in time are dead within 6 years. Death rates are higher for small firms in richer countries, younger firms, retail firms, less productive and less profitable firms, and those whose owners are female and not middle-aged. The paper proposes three theories of why small firms die: firm competition and firm shocks, occupational choice, and non-separability from the household. It finds the cause of firm death to be heterogeneous, with different subgroups of firms more likely to die for reasons consistent with each of these theories.

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 dmckenzie@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
The rationale of this test is the following: for international risk pooling to be in place, it must be the case that aggregate per capita consumption growth retains some explanatory power over and above what is implicit in the aggregate component of domestic income growth, which should bring about the statistical significance of the corresponding coefficient.

A second interesting question (always relevant for the interpretation of the coefficients of (10)) concerns the effective relevance of idiosyncratic vs. aggregate risk in determining consumption dynamics. We cannot possibly assess this relevance by simply looking at the significance and/or magnitude of β 1 . We might, in fact, obtain a large and significant estimate for β 1 , but the corresponding variable might possibly explain a very tiny portion of consumption variability, in which case we should conclude that the country in question is little affected by idiosyncratic risk. Hence, the idea is to measure the relevance of idiosyncratic and aggregate shocks (what we will term “vulnerability”) by computing the decrease (it might actually be an increase) in adjusted _R_ 2 obtained by alternatively removing the idiosyncratic component or the aggregate component of domestic income growth.

# **4. Consumption insurance in Latin American and Caribbean countries: some evidence from macro data**

We are now going to actually estimate equation (10) using data from Latin American and Caribbean countries, and carry out some additional analyses. Before that, however, a brief introduction to data sources and actual variables used in the empirical analysis is in order.

# **4.1 Data**

Data for the empirical analyses are obtained from the most recent World Bank database “World Development Indicators”, and from the International Monetary Fund database “International Financial Statistics”, June 2005 edition.

Data from the “World Development Indicators” database include: final consumption expenditure, household final consumption expenditure, GDP, the three of which constitute the main variables in the following analysis, as well as additional economic and structural variables, such as foreign direct investments, receipts from international tourism, liquid liabilities (M3), net domestic credit (both as a total and stemming from specific institutions), market capitalization of listed companies, official and real exchange rates, total value of stocks traded, long term debt, internet users, net current transfers from abroad, net foreign assets, population, and others. Most of these additional variables are measured in terms of percentage of GDP.

10