rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 5.4: TANZANIA, evaluation of TFP measures under different imputation methods

## I. Kernel<sup>1</sup> estimates of TFP densities

<!-- Start of picture text -->
-2 0 2 4 6 8<br>log-TFP<br>Complete case ICA method<br>Random ICA ICA on inputs<br>EM alg. [1] EM alg. [2]<br>EM alg [3]<br>1<br>.8<br>.6<br>.4<br>Density of TFP<br>.2<br>0<br><!-- End of picture text -->

## II. Table of descriptive statistics of TFP measures

||**# Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Complete case|313|2.53|0.87|-3.21|5.47|
|ICA meth.|661|4.79|1.30|-0.75|9.72|
|Random ICA|661|4.98|1.50|-1.47|10.03|
|ICAon inputs|505|1.81|1.14|-3.68|6.85|
|Em alg [1]|790|4.39|1.18|-1.30|8.92|
|Emalg [2]|628|2.25|0.80|-3.82|7.64|
|Em alg [3]|638|2.81|0.86|-3.21|8.35|

## III. Correlation matrix between TFP measures

||Complete<br>case|ICA meth.|Random ICA|ICA on<br>inputs|Em alg [1]<br>E|m alg [2]<br>Em alg [3]|
|---|---|---|---|---|---|---|
|Complete case|1.000||||||
|ICA meth.|0.913|1.000|||||
|Random ICA|0.877|0.991|1.000||||
|ICA on inputs|0.997|0.904|0.869|1.000|||
|Em alg [1]|0.948|0.994|0.975|0.937|1.000||
|Em alg [2]|0.981|0.829|0.779|0.971|0.884|1.000|
|Em alg [3]|0.979|0.849|0.804|0.963|0.901|0.996<br>1.000|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. Complete case: TFP measure from the sample without replacement of missing values; likewise, input-output elasticities are obtained from estimating equation (1) in the complete case (see I-O elasticities in Table 9.4). ICA method: TFP measure with inputs and output replaced by ICA method and input-output elasticities from Table 8.4. Random ICA: TFP measure with inputs and output replaced by random ICA method and input output elasticities from Table 9.4.

ICA on inputs: TFP measure when only inputs are imputed by the ICA method (not sales). The I-O elasticities and semielasticities used are in Table 9.4.

Em alg. [1]: TFP measure obtained under imputation of inputs and output by the EM algorithm described in section 5.1.1. Likewise, the I-O elasticities are in Table 9.4.

Em alg. [2]: In this case the EM algorithm used is that described in section 5.1.2. The I-O elasticities are in Table 9.4. Em alg. [3]: The description of the EM algorithm is in section 5.1.3 and the I-O elasticities in Table 9.4. Source: Authors’ estimations with ICSs.

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and derived from Genaioili et al. (2013), in place of the modeled grid-level disaggregated data. In column 1, we estimate equation 1 on the sub-sample of observed sub-national GDP per capita data. In column 2, we estimate equation 1 using the completed version of the sub-national GDP data that use national data for those countries missing in the Genaioili et al. (2013) data set. In column 3, we estimate equation 1 using the interpolated and extrapolated sub-national GDP data. Finally, in column 4, we estimate equation 1 using sub-national and national filled-in data. Results for rainfall are consistent with previous findings and indicate an increasing concave relationship between GDP per capita growth and precipitation for more than 99% of the sample.

## **3.B Heterogeneity**

For completeness, we explore the relationship between cell-level precipitation, temperature and growth patterns across grid cells with different levels of development (3.C.1) and shares of cropland (3.C.2).

## _3.B.1 Income category_

Table 2 shows that rainfall impacts growth in developing countries, but not in high-income countries. However, temperature remains significant in both high- and low-income countries. One reason could be a greater dependence on agriculture and rainfall sensitive economic activities in developing countries. One way to test for this is to explore if impacts differ across cells by levels of agricultural activity.

## _3.B.2 Agriculture_

Is the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample into quartiles based on the shares of cropland. Table 3 shows that growth in cells with more than 75% of cropland is found to be more sensitive to rainfall variations than the global average. A clear implication of this finding is that much, but not all, of the variation in GDP growth is a consequence of the effects of rainfall on agriculture.

# **4- Discussion and Concluding Remarks**

This paper has provided an explanation for an empirical paradox: The micro-econometric literature relating to agriculture, health, industry performance and conflict consistently finds that variations in rainfall causally impact economic outcomes. And yet the macro-econometric literature has failed to find a robust and statistically significant impact. In this paper we demonstrate that the consequence is that aggregation to large spatial scales masks the heterogeneity of impacts of rainfall on aggregated economic growth. Not implausibly the relationship is found to be concave, implying that in dry areas the marginal economic returns are greater than in wetter areas and that these effects are particularly pronounced in areas dependent on agriculture.

7
# **Stress-testing Africa’s recent growth and poverty performance**

Shantayanan Devarajan, Delfin S. Go, Maryla Maliszewska, Israel Osorio-Rodarte, and Hans Timmer<sup>1</sup> JEL codes: I32, F63, O55, C68 KEYWORDS: poverty, economic development, Africa, computable general equilibrium (CGE) models Sector Board: EPOL

> 1 All from the World Bank. The views expressed are those of the authors and do not necessarily reflect those of the World Bank or its affiliated organizations. The study was partly funded by a grant from the Poverty and Social Impact Analysis Trust Fund of the Africa Region, the World Bank. The authors would like to thank Punam Chuhan-Pole, Luc Christiaensen, Allen Dennis, Mark Roland Thomas, and participants of the 16<sup>th</sup> Annual Conference on Global Economic Analysis in Shanghai, China, June12-14, 2013 and other seminars for helpful comments and suggestions.
The state-owned utilities involved in the bilateral power exchanges continued to plan and execute their capacity expansion on the basis of national sufficiency rather than a regional perspective. Thus, they did not invest in new capacity that was sufficient to both meet the growing domestic demand and their contractual obligations. The generation capacity constraints that resulted in the exporting countries ultimately compromised the reliability and security of regional supply. The inadequacy of regional generating capacity has become more serious with the tightening of the demand/supply balance in South Africa (box 5).

Electricity sector reforms, including increased private sector participation and competitive restructuring (unbundling) could make a significant contribution towards reducing the inadequacy of regional generation capacity. The SAPP member countries are at different albeit mostly early stages of restructuring their electricity systems (table 10). Their utilities are still predominantly vertically integrated and state-owned. Because

# **Box 5  Koeberg Repairs Put Namibia in Energy Crunch**

ESKOM’s decision to shut down its Koeberg power plant for maintenance poses a major power threat to Namibia and has steadily pushed up supply costs.

“I don’t want to say we are in a crisis, but there’s a major challenge facing us,” NamPower MD Leake Hangala says.

Namibia could be forced to load-shed in future to deal with the decision to shut down Koeberg. Already one unit has been out of action since the end of last year and the second unit will shut down next month for repairs.

This means NamPower will have to alternate the areas that will have to go without power during peak hours. Namibia’s power demand is about 500 MW.

_Source_ : Business Day (Johannesburg), February 10, 2006.

of their financial, technical, and managerial resources, private entities could have a distinct comparative advantage relative to the monolithic, state-owned (and frequently cash-strapped) utilities in meeting the increasing power demands of the SADC economies. Moreover, the needed rebalancing of the roles of the public and private sectors in the region’s electricity systems is hindered by the continuing high degrees of vertical integration of the regional utilities. Private entities will be reluctant to enter and sink substantial amounts of money in the regional electricity markets if the essential electricity facilities (e.g. transmission) are controlled by their competitors in the provision of downstream services. Competitive neutrality and a level-playing-field, important preconditions for entry and private investment, are likely to be violated in the eyes of potential investors when the regional electricity markets are dominated by public utilities that are considered to be too important for the respective national economies and the security of energy supply.

63
it had a minimal direct impact on the welfare of survey respondents. But it may have a ff ected their expectations regarding the future, raising the possibility of omitted variable bias. At the bottom on Table 2 we report insurgency incidence fi gures based on a June 2000 classi fi cation of the Nepalese police. Some 12.5% of the surveyed households resided in areas that were seriously a ff ected by the insurgency between 1996 and 2000. These districts tend to be far from urban centers.

Ward-level variables are presented in Table 3. Using detailed information on the road distance between each ward and each of 34 towns and cities compiled by Fafchamps & Shilpi (2003), we construct a variable that represent the total urban population Pk living within 2 hours of travel distance from the ward. Population fi gures come from the 1991 census. This is our second isolation measure. Our third isolation measure is population density in the district. Other things being equal, we expect people in low population density districts to live further apart from each other, thereby raising delivery costs for private goods as well as public services.

The survey did not collect extensive price data. There is information on house rental prices, mostly on a self-assessed basis. In the next section we combine this information with house quality data to estimate a district-speci fi c house price premium. This premium is thought to capture locational advantages re fl ected in housing prices. We have information on rice prices at the household level, from which we compute a ward-level median.<sup>11</sup> We use the wage rate in the ward as an additional measure of the cost of living. We compute the median wage rate in the ward from responses of individual household members. Nearly all wage employment recorded in the survey is for low skilled manual work in farm and non-farm work. We report Gini coe ffi cients for consumption per capita computed for each ward. We use it as control. It is indeed thought

> 11 Household-level prices capture di ff erences in quality, quantity and convenience between households facing the same market. It is more reasonable to assume that all households residing in the same ward face the same prices. Because of the presence of outliers (probably due to measurement error), we use the median price in the ward rather than the mean.

13
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StataCorp., 2019. Stata Statistical Software. StataCorp.

van der Ploeg, F., 2011. Natural Resources: Curse or Blessing? Journal of Economic Literature 49 (2), 366– 420.

- Vincent, R.C., 2020. The Multi-Layer Tax Structure in Advanced, Emerging and Developing Economies: A New Dataset and Application. Mimeo. UNU-MERIT, Maastricht University; CERDI, Université Clermont-Auvergne. Further details: https://apsa2019-apsa.ipostersessions.com/default.aspx?s=7A9C-0B-63-95-59-BA-FC-F0-89-4D-5F-71-20-6F-4A.

- Zabsonré, A., Agbo, M., Somé, J., 2018. Gold exploitation and socio-economic outcomes: The case of Burkina Faso. World Development 109, 206–221. 10.1016/j.worlddev.2018.04.021.

33
20

**Table 3. Quality growth & unconditional quality convergence: panel regressions**

|Fixed effects|None|Country|Country, Product|Basic Spec. 1/|Country-Prod.|Extended Spec. 2/|
|---|---|---|---|---|---|---|
|Ln(Initial Quality)|-3.49***|-4.38***|-6.33***|-6.33***|-14.5***|-13.3***|
||(0.03)|(0.03)|(0.04)|(0.04)|(0.07)|(0.06)|
|Observations|244,742|244,742|244,742|244,742|244,742|244,742|
|R-squared|0.0551|0.0710|0.1046|0.1058|0.5494|0.7609|

Notes: All equations estimated using observations averaged of 10-year non-overlapping periods. The dependent variables is the annualized growth rate of product quality. *, **, and *** denote statistical significance at the 10 percent, 5 percent and 1 percent level, respectively. All coefficients and standard errors are multiplied by 100 for presentation purposes.

1/ Includes country, product and time fixed effects.

2/ Includes country-product and product-time fixed effects.

**Table 4. Determinants of quality growth: panel regressions**

|Basi|c specificatio|n 1/|Prefe|rred specifica|tion 2/|
|---|---|---|---|---|---|
|Manufacturing|Agriculture|Natural Res.|Manufacturing|Agriculture|Natural Res.|
|Ln(Initial Quality)<br>-7.22***|-6.62***|-5.72***|-13.9***|-13.9***|-13.4***|
|(0.07)|(0.11)|(0.20)|(0.12)|(0.17)|(0.34)|
|Ln(Initial GDP p.c.)<br>0.0508|-0.0059|-0.167|0.319***|0.355***|-0.0626|
|(0.0400)|(0.0011)|(0.190)|(0.0305)|(0.0877)|(0.1560)|
|Initial Institutional Quality<br>0.0018|0.0056*|0.0087|0.0048***|0.0077***|0.0048|
|(0.0013)|(0.0031)|(0.0058)|(0.0009)|(0.0023)|(0.0046)|
|Initial Human Capital<br>0.0000|0.0000|-0.0070|0.0059***|0.0053|-0.0071|
|(0.0027)|(0.0071)|(0.0127)|(0.0018)|(0.0050)|(0.0094)|
|Initial FDI inflows<br>0.0076***|0.0145**|-0.0131|0.0062**|0.0070|0.0596***|
|(0.0027)|(0.0071)|(0.0134)|(0.0028)|(0.0073)|(0.0152)|
|Initial Trade Lib.<br>0.2090**|0.7360***|-0.0351|0.3950***|0.8000***|0.2390|
|(0.0009)|(0.2490)|(0.4230)|(0.0657)|(0.1890)|(0.3440)|
|Initial Agric. Lib.|0.3220*<br>(0.179)|||0.0435<br>(0.1380)||
|Observations<br>98,746|29,802|8,365|98,746|29,802|8,365|
|R-squared<br>0.115<br>1/ Includes country, product and time fix<br>2/ Includes country-product and product-t<br>Notes: All equations estimated using obs<br>variables is the annualized growth rate of<br>percent, 5 percent and 1 percent level, re<br>presentation purposes.|0.144<br>ed effects.<br>ime fixed effe<br>ervations ave<br>product qualit<br>spectively. Al|0.146<br>cts.<br>raged of 10-year<br>y. *, **, and ***<br>l coefficients an|0.838<br>non-overlapping<br>denote statistical<br>d standard errors|0.839<br>periods. The<br>significance<br>are multiplied|0.834<br>dependent<br>at the 10<br>by 100 for|
- _Excessive frictions._ Excessive frictions are the distortions over and above what we would expect given the city’s actual size. As cities become larger, they become more congested, limiting a city’s population. But even among cities of the same size, not all are equal. Some cities are less efficient than others in dealing with congestion, perhaps reflecting that some city governments do not provide congestion-alleviating infrastructure as well as others. This would translate into higher local tax rates or other distortions.

Cities will be larger if they are more efficient, provide better amenities, or have lower excessive frictions. Of course, what matters for an individual city is not just its own characteristics; it is also how its characteristics compare with those of other cities. A highly productive city will have a smaller population if there are many others like it. A system of cities is in equilibrium if no one wants to (or can) move. This is not to suggest that wages should be equal across cities. Even if people can move at no cost, an individual might be willing to accept a lower wage in a city with better amenities. Many countries, like China, have internal migration restrictions. In our model, a city that restricts entry will be seen as having worse amenities. We will return to the mobility question when we discuss our findings for China.

An important advantage of the Desmet and Rossi-Hansberg (2012) framework is that it can be implemented and used for policy analysis with few data requirements. To identify efficiency, we use information on income, hours worked, and possibly (but not necessarily) capital. By using data on income, consumption, and hours worked, we can estimate a city’s distortions. By comparing a city’s actual distortions with those it would be expected to have given its size, we can identify a city’s excessive frictions. Finally, by using data on population and matching a city’s actual size to the one generated by the model, we can identify a city’s amenities.

The bottom line is that having data on population, income, consumption, hours worked, and possibly capital at the city level is enough to identify the determinants of the city-size distribution. Collecting such data is fairly straightforward. Table 1 briefly summarizes how we did this for the United States, China, and Mexico. Some metropolitan data, such as GDP for the United States and population for China and Mexico, are provided directly by national statistical offices. Other metropolitan data, such as hours worked in the United States and Mexico, need to be constructed from microdata. Even when not using microsurveys, we often need to aggregate data up to the metropolitan level. In the United States, for example, a large amount of data are provided at the county level (metropolitan statistical areas are a collection of counties), and in Mexico most variables are available at the municipal level (metropolitan areas are a collection of municipalities).
systematically overstate inequality of opportunity when sample sizes were small. For larger samples, and in particular when using household income or consumption per capita as indicators of advantage, the two estimates were numerically close and the differences between them were statistically insignificant, generating a robust lower-bound estimate of inequality of opportunity.

For labor earnings, the (parametric) estimates ranged from 17% of total inequality in Colombia, to 34% in Brazil. For household income per capita, the range was between 23% in Colombia to 35% in Guatemala. For consumption expenditures, the range was between 24% in Colombia, and 50% in Guatemala. Differences between the indices for the distribution of earnings and those for household welfare are due both to differences in the extent of measurement error, and to differences in the mechanisms through which circumstances affect outcomes (e.g. family formation, labor force participation, etc.) In all cases, family background – proxied for by parental education levels and the father’s occupation – was the largest component of the opportunity share of inequality, although ethnicity was also important in Brazil, Guatemala and Panama.

The opportunity profiles provide an X-ray of the opportunity structures in Latin America, at least for those social groups with the most limited opportunity sets in these six countries. These “opportunity-deprived” types were overwhelmingly members of ethnic minorities, and tended to hail from agricultural families with low levels of education, living in poor regions. A comparison of their income shares with those of the poorest 10% of the population in each country reveals that, as expected, many of those with limited initial opportunities do manage to move out of poverty, while others – from more advantaged backgrounds – fall into poverty. Yet, in no country did the 10% most opportunity-deprived people account for more than 5% of total consumption expenditure. In Brazil and Panama, the figure was less than 3%.

Both the scalar indices - which reveal that the lower-bound for the share of consumption inequality which is due entirely to factors beyond the individuals control is of the order of 30% to 50% - and the opportunity-deprivation profiles - which document the enduring “costs” of being born of certain races, in certain places and to certain families – suggest that unequal opportunities are an important source of the outcome 30