rafmacalaba/datause-encoder-data huggingface dataset:
Column (1) shows the total number of firms that started to export each new product. Column (2) presents the exporters with previous experience as a share of the total number of entrants. In 68 percent of the cases, entrants are exporters with previous experience. The entrants in products belonging to the primarily for export ( _X_ ) category are on average, more experienced than the entrants in goods that are produced for domestic consumption ( _D)_ —specifically, 76 percent of X exporters have experience, as compared with 56 percent of D exporters.<sup>24</sup> Column (3) shows average value of the exports in the main product in the previous period relative to the exports of the average exporter in that product (mainly to observe the size of the entrants relative to their main competitors). Exporters that start new products tend to be larger on average by about 24 percent. However, if we decompose this in terms of the products that belong to each categories ( _D_ and _X_ ), we observe that the entrants in the _X_ category are on average 59 percent larger than their main competitors, while entrants in the _D_ group are not necessarily the larger exporters in the year previous to their entry. This last observation could be due to the fact that the exporters in this group compete mainly with producers in the domestic sector, which are likely to be smaller but are not taken into account in the average calculated for exporters since we do not have information on their size. Column (4) shows the average ratio of the count of products exported by the entrants over the average number of products exported by all the firms whose main product exported was the same as the entrant’s main product during the year of entry into the new product. The

> 24 The _D_ category refers to the new export goods for which there is a demand in the domestic market. The _X_ category includes new export goods that are mainly produced for exportation. The grouping of new goods in terms of these two categories was based on information on domestic demand obtained from the Ministry of Agriculture (mainly for fresh produce), the National Institute of Statistics and the website of a supermarket (E.Wong).

30
Health expenditure levels are significantly higher in large cities than in any other country region. On the other hand, rural areas have the lowest health expenditure levels. The highest drop in health expenditure levels are also for households in large cities. This drop in large cities is of about 10.4 lei per capita. However, in terns of budget shares, it is outside large cities were the higher drops in health expenditure are found. Small towns and rural regions have drops in health expenditures shares of about 4 and 3 percentage points (drops of 33% and 34%), respectively, while in large cities this drop is only of (a not statistically significant) 1 percentage point (a drop of 10%).

Table 4.17. Health expenditures by country region

||||Shares|||Level||
|---|---|---|---|---|---|---|---|
|||Pre-crisis|Post-crisis|Change|Pre-cnsis|Post-crisis|Change|
|_Largecities_||||||||
||Mean|0.134|0.120|-0.014|24.819|14.430|-10.389|
||Std.err.|0.013|0.015|0.018|3.584|1.932|3.840|
||Obs.|368||||||
|_Othertowns:_||||||||
||Mean|0.123|0.082|-0.041|16.670|8.821|-7.849|
||Std.err.|0.016|0.017|0.023|2.870|2.325|3.620|
||Obs.|317||||||
|_Rural_||||||||
||Mean|0.089|0.059|-0.030|12.334|6.047|-6.288|
||Std.err.|0.009|0.007|0.010|1.605|0.822|1.668|
||Obs.|1081||||||

Table 4.18. Health expenditures by household size.

||||Shares|||Level||
|---|---|---|---|---|---|---|---|
|||Pre-crisis|Post-crisis|Change|Pre-cnsis|Post-crisis|Change|
|_Hh size I_||||||||
||Mean|0.031|0.026|-0.005|7.166|3.356|-3.809|
||Std.err.|0.006|0.006|0.007|1.640|0.680|1.566|
||Obs.|325||||||
|_Hhsize2:_||||||||
||Mean|0.119|0.082|-0.037|21.125|9.311|-11.814|
||Std.err.|0.013|0.010|0.015|3.099|1.522|3.209|
||Obs.|446||||||
|_Hhsize 3_||||||||
||Mean|0.154|0.081|-0.073|25.418|9.644|-15.774|
||Std.err.|0.022|0.012|0.021|4.826|1700|4.680|
||Obs.|328||||||
|_Hh size 4_||||||||
||Mean|0.105|0.093|-0.012|12.113|10.319|-1.793|
||Std.err|0.014|0.017|0.021|2.039|2.233|3.029|
||Obs.|390||||||
|_Hhsize5+_||||||||
||Mean|0.109|0.092|-0.017|10.421|7.809|-2.612|
||Std.err.<br>Obs.|0.017<br>277|0.019|0.024|1.826|1.783|2.455|

31
11 survey, we construct the asset index based on the same categories of assets captured in every survey wave, ensuring full comparability over time. Most of these assets are recorded as dichotomous variables, taking 0/1 values, while the few categorical variables with multiple categories are manually reorganized along an ordinal scale according to costs. The asset variables are first normalized and then transformed into a single wealth index through principal component analysis, following an approach proposed by Kolenikov and Angeles (2009). Scree plots indicate that the first principal component is highly significant in every wave, while further components carry little information, as desired. The wealth-index is likely to indicate the long-term economic well-being, as many durables captured are typically held by households for many years and are not frequently replaced (Sahn and Stifel 2000).

In between the first and second survey waves an administrative reform took place in which the definition of urban areas was revised (Megill 2004), among other things. Some communities previously considered rural were now coded as towns, which, to some extent, explains the sharp increase in the proportion of urban population. As a consequence, results for urban areas are not comparable across the three RDHS waves in a strict sense.

# **4.2 Conflict proxies**

One obvious predictor of being a genocide victim in 1994 is ethnicity (that is, being Tutsi). Yet, only the 1992 pre-genocide RDHS wave records ethnicity, as self-reported by respondents. In an effort to suppress further ethnic tensions, the post-genocide government of Rwanda forbids the usage and identification of ethnic categories. Hence, RDHS collected after 1994 does not record respondents’ ethnicity.

In response to this challenge, we construct several ‘conflict proxies’ measuring likely exposure to conflict. These proxies allow us to differentiate two channels through which exposure to mass violence may influence fertility: replacement effects (where women choose to have children in the post-conflict period to compensate their lost children from during the conflict period) and marriage market effects (where a relative shortage of men to women creates a ‘bottleneck’ for women to get married). It is important to note that the conflict proxies do not necessarily identify victims of targeted genocidal violence. Rather, these proxies indicate individuals and age groups that were likely to be exposed to conflict-related violence. Given that the 1994 genocide occurred within a time span of just about 100 days, the duration or timing of conflict exposure is of less importance in the Rwandan genocide.
namely 10-digit HS products. Although unit values are a commonly used proxy for product quality, they may be contaminated by high markups.<sup>8</sup> Therefore, I also use another proxy, a measure suggested by Khandelwal et al. (2013), which builds on the work of Khandelwal (2010). The latter paper combines information on prices and physical quantities to infer quality, based on the insight that the ability of an exporter to sell a larger quantity at a given price should imply higher quality. Khandelwal et al. (2013) identify the relationship between quantity and price by assuming specific elasticities of substitution.

The quality measure is inferred from the OLS residual obtained by estimating: where _qipct_ and _pipct_ are the quantity and the price, respectively, of exports of product _p_ by firm _i_ to destination country _c_ in year _t_ , _σ_ represents the assumed elasticity of substitution and _α_ captures time-invariant product characteristics. I refer to this quality estimate as _Q_<sup>ˆ</sup> in all the tables.<sup>9</sup> Both proxies are defined at the firm-product-destination-year level. The two measures of quality are closely related. Across the sample, their correlation is 0.90 in levels and 0.78 in differences. The relationship between the two measures is also illustrated in Figure 1 (see the left panel for levels and the right panel for first differences). Table A1 lists the top 20 and the bottom 20 products in terms of changes in unit values.

Figure 2 plots the distribution of unit values and the alternative quality measure among domestic and foreign exporters. It is clearly visible from the plots that foreign affiliates tend to export products with higher unit values and higher quality measure.<sup>10</sup>

> 8In other words, high unit values may reflect high markups rather than superior quality.

> 9Following the literature, I assume _σ_ to be equal to 5.

> 10Differences in distribution of unit values could be driven by differences in the composition of exports across products. This is, however, not true when the alternative measure is used, as it has been purged of product fixed effects.

6
guments have been advanced by noted scholars as well. Krugman argued that “... there’s the march of the meat‐eating Chinese—that is, the growing number of people in emerging economies who are, for the first time, rich enough to start eating like Westerners” ( _New York Times_ editorial, April 7, 2008). Likewise, Wolf asked “So why have prices of food risen so strongly?” and then answered “... strong rises in incomes per head in China, India, and other emerging countries have raised demand for food, notably meat and the related animal feeds” ( _Finan‐ cial Times_ , April 29, 2008). Indeed, the size of China and India, which together ac‐ count for 27 percent of the world’s population, implies that even a minor change in their pattern of demand growth has a major effect on world market prices.

But a closer look at the growth trends of population and income over the past decades, coupled with those of demand for food commodities, shows no evidence that food demand growth accelerated either in China and India or in the world as a whole. Table 1 summarizes demand growth patterns for a number of key food commodities since 1961 for four 12‐year periods roughly correspond‐ ing to four price cycles: the period of the “green revolution” (1961‐72); the after‐ math of the two energy shocks (1973‐84), the recovery of agricultural prices until their mid‐1990s price spike (1985‐96), and the last period until the recent price peak (1997‐08). The data clearly show that demand growth has slowed for most grains—including those used for feed, reflecting a slowdown in the growth of demand for meat.

During the most recent decade, despite a clear acceleration of GDP growth since 2003, stronger demand for agricultural products both at world level and in China and India has been the exception—it occurred in maize and in soybeans (driven by demand for edible oils), and was rather mixed in grains, but certainly did not occur in meats or dairy products (see Table 2 with a breakdown of the 1997‐2008 period for the world, China, and India). Similar findings on the role (or, the non‐role) of China and India have been discussed in Alexandratos (2008: 673) who emphatically stated that “... their [China’s and India’s] combined aver‐ age annual increment in consumption (both growth rates and absolute incre‐ ments) was lower in the years of the price surges, 2002‐08, than in the preceding period 1995‐2001.” FAO (2009) arrived at nearly identical conclusions.

These developments reflect the huge gap that existed during the price boom between the fundamentals of agricultural markets and the corresponding price levels. No other example demonstrates this better than the fact that the highest price increases took place in two commodities—wheat and rice—where food demand was stagnating and yet were widely explained as being driven by strong food demand. Thus, while supply shocks may explain some of the price pressures in certain food commodities, by contrast demand growth accelerated in recent years in commodities used for biofuels, such as maize and edible oils.

**_Page 10_**
# **3.  Data**

The objective of the rest of this paper is to use the analytic framework discussed above to decompose observed changes in poverty into the three terms discussed above: (a) growth in average incomes; (b) the sensitivity of poverty to growth in average incomes; and (c) changes in relative incomes. After constructing these three terms for a large sample of developing countries, I use them to identify the relative importance of, and factors correlated with, these various sources of pro-poor growth.

I use household survey data on average incomes and 10 points on the Lorenz curve for a large number of surveys, as compiled by Martin Ravallion and Shaohua Chen at the World Bank. Their data come directly from primary sources, have been meticulously cleaned, and are available at http://www.worldbank.org/research/povmonitor .<sup>6</sup> Depending on the country, the surveys measure either the distribution of income or the distribution of consumption. Average income or consumption is measured in 1993 dollars and is adjusted for cross-country differences in purchasing power parity. Since I am interested in changes in poverty over time, I take only countries with at least two household surveys. This results in a total of 285 surveys covering 80 developing countries. Most of the survey dates are in the 1990s, with some countries extending back to the 1980s. I use the World Bank’s “dollar-a-day” poverty line, which in 1993 dollars is $1.08 per day, or $393 per year.

Using these surveys, I construct two datasets of spells of changes in poverty. In the first dataset, I consider all possible spells for each country, discarding only those few cases where the survey changes from an income to an expenditure survey or vice versa. This results in 205 spells of poverty changes. The length of these spells is quite short, averaging 3.5 years and ranging from one to 13 years. In order to be able to look at changes over longer horizons, I also construct a dataset consisting of one spell per country, where the initial and final years are chosen so as to maximize the length of the spell given available data. This results in a set of 80 spells, with an average length of 8.2 years, and ranging from two to 19 years. Finally I eliminate all spells where the headcount measure of poverty is negligible in either the initial or final period, i.e. below 2

> 6 I am grateful to Shaohua Chen for kindly providing key data from all of the household surveys, including some that are not available on the poverty monitoring website.

12
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 5. Evolution of hidden costs in Cape Verde’s power sector**

<!-- Start of picture text --> 2.75<br>2.50<br>2.25<br>2.00<br>1.75<br>1.50<br>1.25<br>1.00<br>0.75<br>0.50<br>0.25<br>0.00<br>2001 2002 2003 2004 2005 2006 2007 2008 2009<br>distributional losses underpricing undercollection<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from ELECTRA Annual Reports 2001–09.

# Water supply and sanitation

## **Achievements**

Water and sewerage services in Cape Verde are also provided by the multiutility ELECTRA to the islands of São Vicente, Sal, Boavista, and the city of Praia. As noted above, this utility provides power service throughout the archipelago. In the rest of the country, water service is provided directly by local municipalities. (See the power section, above, for more on the institutional and operational issues affecting ELECTRA.)

## **Challenges**

Cape Verde has relatively good access to utility water but relies heavily on standposts. About 70 percent of the population has access to utility water, comparable to the benchmark for African middleincome countries. In contrast to other middle-income countries, however, about half of those accessing utility water do so through standposts. Relative to low-income countries, access to both private taps and standposts is substantially higher, and there is noticeably less reliance on wells and boreholes.

Access patterns to sanitation are split, more or less, in two: half the population enjoys high-end solutions while the other half receives little service of any kind. On the one hand, coverage of flush toilets, at almost 50 percent, is slightly above the benchmark for middle-income countries in Africa. On the other hand, 50 percent of the population has no access to sanitation of any kind, which is even worse than the benchmark for low-income countries in Africa. Less than 10 percent of the population has access to latrines. Given Cape Verde’s relatively good performance on household service coverage, these figures suggest that sanitation has been neglected relative to other household services. Clearly, the country has failed to develop a second-best sanitation alternative for citizens without access to a flush toilet.

Cape Verde has by far the most expensive water tariffs in Africa—and among the most expensive in the world—at over $3/m<sup>3</sup> . These high prices reflect the scarcity of water resources, which has forced the country to rely on desalination for approximately 85 percent of production. Moreover, the cost of the energy-intensive desalination process is particularly high due to its dependence on power generation, whose high costs in turn reflect reliance on small-scale diesel generation and expensive imported oil. The 14
**Djankov, Simeon, and Peter Murrell.** 2002. “Enterprise Restructuring in Transition: A Quantitative Survey.” _Journal of Economic Literature_ , 40(3): 739-92

**Dong, Xiao-Yuan, Louis Putterman, and Bulent Unel.** 2006. “Enterprise Restructuring and Firm Performance: A Comparison of Rural and Urban Enterprises in Jiangsu Province.” _Journal of Comparative Economics_ , 34(3): 608-633.

**Earle, John S., and Saul Estrin.** 1996. “Employee Ownership in Transition.” In _Corporate Governance in Central Europe and Russia. Insiders and the State_ , ed. Roman Frydman, Cheryl W. Gray, and Andrzej Rapaczynski. Budapest: Central European University Press.

**Earle John S., and Klara Sabirianova.** 2002. “How Late to Pay? Understanding Wage Arrears in Russia” _Journal of Labor Economics_ , 20(3): 661-707.

**EBRD.** various annual issues. _Transition Report_ . London: EBRD.

**Estrin, Saul.** 1994. _Privatization in Central and Eastern Europe_ . London, U.K.: Longman.

**Estrin, Saul.** 2002. “Competition and Corporate Governance in Transition.” _Journal of Economic Perspectives_ , 16(1): 101-24.

**Estrin, Saul, Jan Hanousek, Evžen Kočenda, and Jan Svejnar** . 2007. “Effects of Privatization and Ownership in Transition Economies.” IPC Working Paper No. 30. International Policy Center, Gerald R. Ford School of Public Policy, University of Michigan.

**Estrin, Saul, and Virginie Perotin.** 1991. “Does Ownership Always Matter?” _International Journal of Industrial Organization_ . 9(1): 55-72.

**Frydman, Roman, Marek Hessel, and Andrzej Rapaczynski.** 2000. “Why Ownership Matters? Entrepreneurship and the Restructuring of Enterprises in Central Europe.” C.V. Starr Center for Applied Economics, New York University Working Paper 00-03.

**Frydman, Roman, Cheryl W. Gray, Marek Hessel, and Andrzej Rapaczynski.** 1999. “When does Privatization Work? The Impact of Private Ownership on Corporate Performance in Transition Economies.” _Quarterly Journal of Economics_ 114(4) (1999): 1153-91.

**Frydman, Roman, Cheryl W. Gray, Marek Hessel, and Andrzej Rapaczynski.** 2000. “The Limits of Discipline: Ownership and Hard Budget Constraints in the Transition Economies.” _Economics of Transition,_ 8(3): 577-601.

34
- 49 -

(striking in the case of Argentina) and the sustained efforts by the governments of Brazil, Colombia and Mexico to maintain a substantial volume of transfers even during the recession.

Table 4,4.: REVENUE STRUCTURE OF SUBRATIONAL GOVERNMENTS -In **Percentage-**

||ARGENTINA<br>**1975-84**<br>-|BRAZIL<br>1975-84<br>-_|COLOMBIA<br>1975-83<br>_-a|MEXICO<br>1975-84<br>__Cs|
|---|---|---|---|---|
|TOTAL REVENUE & GRANTS|10C.0|100.0|100.0|100.0|
|I. TOTAL REVENUE|74.6|70.6|49.3|40.1|
|1. CURRENT REVENUE|74.1|70.6|49|40.1|
|1.1 TAX REVENUE|62.4|60.2|39.8|21.4|
|TX ON INC.,PROFITS & K GAINS|<br>21.5|2.1|2.2|3.4|
|TAXES ON PROPERTY|12.5|3.9|5.3|6.2|
|DOMESTIC TAXES ON GDS+SERVS|24.4|52.1|23.9|9|
|1.2 NONTAX REVENUE|13.4|10.9|9.2|18.7|
|ADM.FEES+C8GS NONIND SALES|<br>NA|1.9|0.4|18.7|
|OPERATING SURPLUS OF ENTERP B|<br>NA|2.9|NA|NA|
|2. CAPITAL REVENUE|0.6|0.2|0.4|NA|
|II. TRANSFERS|25.4|29.4|50.7|59.9|
|1. TX COLLECTED BY CENTRAL GOVT|NA|NA|NA|52.3|
|2. GRANTS|NA|29.4|50.7|7.6|
|GRANTS FROM OTHER NATNL GOVT|25.4|29.4|47.9|5.8|

Source. IMF, Government Finance Statistics Yearbook, various years.

4.05 Taxes on Goods and Services.. These may be levied by regional and local governments within their jurisdictions and include general sales, turnover or value added taxes; excises on goods, vehicles and passengers entering or leaving the jurisdictions; profits on fiscal monopolies (such as tobacco or alcoholic beverages); taxes imposed on the use or the permission to use specific goods (such as motor vehicles) or to perform business and professional activities; also in this broad category fall the taxes on entertainment, gambling or catering activities. In general, this group of taxes represents the main source of independent income for subnational governments in Latin America. In Brazil, for instance, the value added tax (ICM) accounts for 68 percent of the States revenues after sharing one fifth of its yield with the municipalities. In Colombia, taxes on goods and services (especially coffee, alcoholic beverages, tobacco and gasoline) constitute 55 percent of the Departments' income. The