rafmacalaba/datause-encoder-data huggingface dataset:
these countries would have occurred at the start of the transition process. In contrast, the less developed countries, which are normally also slow reformers, postponed many reforms that drive enterprise restructuring; so one would expect that the peak of restructuring activity in less advanced countries occurs later than in more advanced countries. A possible explanation for this observed pattern in the BEEPS data is as follows. Most of the firms in the survey are SMEs from the new private sector. The pattern in the extent of deep restructuring in the BEEPS data is therefore largely driven by the extent of this activity among these firms. The pattern we observe suggests that this activity peaked in new private sector SMEs about 10-12 years after the start of transition. This could be either because of a standard pattern in new firm development, since the start of transition and the beginning of new private sector growth was synchronized across TEs, or because of the largely common timing of the transformational recession and the resumption of growth in TEs.

We turn now to regression analysis, and explaining the relationship between observed restructuring and the economic environment in particular. The early waves of BEEPS (1999 and 2002) are not very well suited to investigate the effect of competition in particular. A fundamental problem is that most measures of competition in the survey are contemporaneous, while restructuring refers to the period spanning three years before the survey. This is true of the market structure variables (number of competitors), price elasticity of demand, competition from imports, and indicators of pressure from competitors and customers for developing new products. In other words, competition is measured after restructuring activities have been undertaken, so it is difficult to establish a causal link from competition to restructuring (unless market structure is more or less stable). One may proceed under the assumption that market structure does not change much over three year periods, but the assumption is questionable at least. Attempts to estimate simple regressions with restructuring measures on the LHS and ownership and competition variables on the right-hand side (plus controls such as firm size, industry, location) show that it is very difficult to establish any robust result with respect to competition. Fortunately, the latest wave of BEEPS (2005) suffers less from this problem, because firms were also asked retrospective questions about number of 39
natural capital share of total wealth below its human capital share, while this share increased in Niger. A large part of this was due to countries replacing their depleting reserves with new discoveries, plus if prices rise enough the value of those depleting reserves also rises even as their stock might decrease, reflected in increased natural capital wealth. The boom did not translate in a significant improvement of the asset portfolio in resource-rich Africa. Despite resource-rich countries benefited from additional income from their abundant natural capital, they did not translate into significant higher investment in produced or human capital compared to non-resource-rich African countries. Therefore, the concentration of wealth in resource-rich Africa remain as twice as much as in non-resource-rich African countries (Figure 25).

Figure 25. Sub-Saharan Africa wealth composition, 2004 vs 2014.

<!-- Start of picture text --> 16 100%<br>14 90%<br>80%<br>12<br>70%<br>10<br>60%<br>8 50%<br>6 40%<br>30%<br>4<br>20%<br>2<br>10%<br>0 0%<br>2004 2014 2004 2014 2004 2014 2004 2014<br>nonrr nonrr rr rr nonrr nonrr rr rr<br>Natural capital Produced capital Human capital Natural capital Produced capital Human capital<br>Thousand constant 2018 US$<br><!-- End of picture text --> _Note: the left panel shows the average composition of wealth per capita of resource-rich and non-resource rich Sub-Saharan Africa in thousand US dollars. The right panel shows the component of wealth share of total wealth, excluding net foreign assets. Source: World Bank staff estimates based on the Changing Wealth of Nations 2021._ When we limit this to just nonrenewable natural assets, seven countries had the largest share (more than 50%) of total wealth in assets including oil, natural gas, coal or metals and minerals. By 2014, five of these countries still had most of its total wealth concentrated in nonrenewable natural resources. These countries were: Central African Republic, Democratic Republic of Congo, Gabon, Guinea, Liberia, Mali and Chad.

According to the Growth Commission: “Strong enduring growth requires high rates of investment. If the sustained, high-growth cases are any guide, it appears overall investment rates of 25 percent GDP or above are needed, counting both public and private investment.

We can examine this in terms of investment in physical capital (gross fixed capital formation), human capital (such as educational expenditures). First, we examine the investment rate over the period, defining investment as the sum of physical capital investment (the traditional definition of the investment rate) and the investments made into human capital (i.e. health and education spending).

From the 22 resource-rich countries with non-missing data, only 10 exceeded 25% during the boom. However, this measure excludes health and education as it does in national accounts. The Growth Commission report notes than health and education spending should 36
0 . Unauthorized or spontaneous settlement. Some indication of the global importance of spontaneous settlement is given by a World Bank (1978) study which showed that almost 75 percent of new rural land settlement in the late 1970s was spontaneous settlement. There is far greater flexibility in spontaneous settlement, and a range of arrangements is possible for land acquisition. It is often the case that spontaneous settlers negotiate for land with existing farmers as in Indonesia, where land may be purchased, rented or acquired under traditional arrangements. Settlement is often community-based, as in the Eastemn Lowlands of Bolivia, where the Mennonites purchase lands and then parcel it out to their members, and where highland Indian communities establish settlement outposts for some of their members in the unoccupied lowland forests (Solem _et al.,_ 1985). A case of communities moving as a whole is provided by the Tangwena people in eastern Zimbabwe.

Pre-existing, self-selected settler groups tend to have strong cohesion and resilience, but they have by and large been prohibited from participating in official programs. In the case of Bolivia, for example, the approach was changed to admit such groups only after administered settlement failed. In Brazil, where the Mennonites carried out a resettlement program entirely on their own, their projects, as well as many private schemes, have been generally far more successful than public ones.

There have been numerous administrative responses to spontaneous settlers, or squatters, as they are often called. One has been the perpetuation of paternalistic systems in almost all cases except perhaps Kenya and Indonesia. In Costa Rica and Honduras, where hundreds of farms were taken over by synd-ates of settlers, the administration's response was to incorporate these groups into the land reforr.. prograrn, expropriate and (partly) compensate the former landowners, and incorporate the settlements into the land reform agencies' paternalistic programs. Settlers were then prohibited from selling or renting their 5-to-10hectare parcels. At the other extreme from such forms of ex-post regularization and conversion to official programs are overtly hostile responses, often practiced alice by colonial and post-independence govemments, of buming and bulldozing settlers into at least a temporary withdrawal. A middle road is taken in Zimbabwe, where spontaneous settlers

-15-
Burger, Martijn J., Elena I. Ianchovichina, Prottoy A. Akbar. 2022. “Heterogeneous Agglomeration Economies in Developing Countries: The Roles of Firm Characteristics, Sector Tradability, and Urban Mobility”. World Bank Policy Research Working Paper, forthcoming.

Chauvin, Juan Pablo, Edward Glaeser, Yueran Ma, Kristina Tobio, 2017. What is different about urbanization in rich and poor countries? Cities in Brazil, China, India and the United States. Journal of Urban Economics, 98, 17-49 Ciccone, A., Hall, R.E., 1996. Productivity and the density of economic activity. American Economic Review 86 (1), 54–70.

Combes, P.-P., G. Duranton, and L. Gobillon 2008: Spatial Wage Disparities: Sorting Matters! Journal of Urban Economics, 63 (2), 723--742.

Combes, Pierre-Philippe, Gilles Duranton, Laurent Gobillon, 2013. The Costs of Agglomeration: Land Prices in French Cities. PSE Working Papers halshs-00849078, HAL.

Combes, P.P., Duranton, G., Gobillon, L., Puga, D., Roux, S., 2010. Estimating Agglomeration Effects With History, Geology, and Worker Fixed-Effects,” in Agglomeration Economics, Vol. 1, ed. by E. L. Glaeser. Chicago, IL: Chicago University Press, 15–65.

Combes, P.-P., and L. Gobillon. 2015. “The Empirics of Agglomeration Economies.” In Handbook of Regional and Urban Economics, Vol. 5, edited by G. Duranton, J. V. Henderson, and W. C. Strange, 247–348. Amsterdam: North-Holland.

Combes, P.-P., G. Duranton, and L. Gobillon. 2019. “The Costs of Agglomeration: House and Land Prices in French Cities.” Review of Economic Studies 86 (4, July): 1556–89.

De la Roca, Jorge, and Diego Puga. 2017. Learning by Working in Big Cities. Review of Economic Studies 84 (1): 106–42.

De Loecker, J., Goldberg, P.K., Khandelwal, A.K. and Pavcnik, N., 2016. Prices, markups, and trade reform. Econometrica, 84(2), pp.445-510.

De Loecker, J., C. Fuss, and J. Van Biesebroeck. 2014. International competition and firm performance: Evidence from Belgium. NBB Working Paper Series, N. 269.

Dhyne, Emmanuel, Amil Petrin, Valerie Smeets, and Frederic Warzynski. Multi product firms, import competition, and the evolution of firm-product technical efficiencies. No. w23637. National Bureau of Economic Research, 2017.

Diamond, R. 2016. “The Determinants and Welfare Implications of US Workers’ Diverging Location Choices by Skill: 1980–2000.” American Economic Review 106 (3): 479–524.

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Duranton, G. 2016. “Agglomeration Effects in Colombia.” Journal of Regional Science 56: 210–38.

14
by rural Indian children, the TSC also improved the cognitive skills that they subsequently attain. This paper estimates an effect of the TSC on Indian children’s cognitive skills by matching test score data on six year olds’ academic achievement with government administrative data on TSC program intensity early in their life, focusing on the first three years of the TSC, 2001 to 2003. The contribution of the paper is to show that, during the period studied, the TSC caused Indian children to be more likely to recognize letters and to recognize simple numbers.

This finding is important for three reasons. First, open defecation and poor sanitation are leading threats to global health, especially in South Asia, where open defecation is common and high population density contributes to negative externalities of disease. This paper adds evidence of a loss of cognitive ability to the health consequences of unsafe excreta disposal that are already documented in the literature. Second, because childhood cognitive skills predict adult cognitive skills, these results imply a detrimental effect of widespread open defecation in developing countries on adult human capital and labor productivity (Hanushek and Woessmann, 2008).

Third, and more optimistically, unlike some other program evaluations in the literature ( _cf._ Ravallion, 2012), in studying India’s Total Sanitation Campaign, this paper is studying large-scale and imperfect implementation of a program by the Indian government. This means that the estimated average treatment effects incorporate whatever heterogeneity, administrative costs, and losses to corruption that exist in real implementation. Nevertheless, this analysis finds a positive average effect of sanitation on children’s cognitive achievement. This suggests that low-cost rural sanitation strategies that can be implemented even by limited capacity governments can importantly support children’s cognitive development.

# **1.1 Early life health and cognitive skills**

Grantham-McGregor et al. (2007) estimate that, in developing countries, “over 200 million children under 5 years are not fulfilling their developmental potential” due to malnutri3
In this simulation experiment a census data set of _N_ = 20 _,_ 000 observations is created, where observations are allocated among 40 areas ( _a_ = 1 _, . . . , A_ ). Within each area, observations are uniformly spread over 10 clusters ( _c_ = 1 _, . . . , Ca_ ). Each cluster, _c_ , consists of _Nac_ = 50 observations, and each cluster is labeled from 1 to 10. The assumed model contains both cluster and area effects. Cluster effects are simulated as _ηac iid∼ N_ �0 _,_ 0 _._ 12�, area effects as _ηa iid∼ N_ �0 _,_ 0 _._ 052� and household specific residuals as _eachiid∼N_ �0 _,_ 0 _._ 52�, where _h_ = 1 _, . . . , Nac_ ; _c_ = 1 _, . . . , Ca_ ; _a_ = 1 _, . . . , A._ Covariates are simulated as follows:<sup>11</sup>

1. _x_ 1 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 3 + 0 _._ 5 40<sup>_<u>a</u>_+ 0</sup><sup>_._2</sup> 10<sup>_<u>c</u>_.</sup>

2. _x_ 2 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 2.

3. _x_ 3 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 1 + 0 _._ 2 40<sup>_<u>a</u>_.</sup> 4. _x_ 4 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 5 + 0 _._ 3 40<sup>_<u>a</u>_+ 0</sup><sup>_._1</sup> 10<sup>_<u>c</u>_</sup> 5. _x_ 5 is a discrete variable, simulated as the rounded integer value of the maximum between 1 and a random Poisson variable with mean _λ_ = 3 �1 _−_ 0 _._ 1 80 _<u>a</u>_ �.

6. _x_ 6 is a binary variable, taking value 1 when a random uniform value between 0 and 1 is less than or equal to 0.4. Note that the values of _x_ 6 are not related to the area’s label.

7. _x_ 7 is a binary variable, taking value 1 when a random uniform number between 0 and 1 is greater than or equal to 0 _._ 2 + 0 _._ 4 40<sup>_<u>a</u>_+ 0</sup><sup>_._1</sup> 10<sup>_<u>c</u>_</sup> The welfare vector for each cluster within an area is created from the model with these covariates, as follows: The dependent variable, _yach_ , is in the log scale. The poverty line in this scenario is fixed at _z_ = 12. From the created “census,” 20% of the observations are sampled in each of the clusters using simple random sampling without replacement;<sup>12</sup> this yields our “survey” data. The generation and sampling processes are repeated _L_ = 10 _,_ 000 times. In each simulation replicate, the following quantities are computed for the poverty rates and gaps in each area:

> 11 Covariates are simulated following the approach from Molina and Rao (2010) and Marhuenda et al. (2017), with slight modifications.

> 12The same units are sampled in every simulation and the values of _x_ 1 to _x_ 7 for all the census units are also kept fixed; this implies that the values of these covariates for the sample units are always the same across simulations.

8
**. 44.-**

**In 1968** and 1989 WHO's funds for maternal health will amount to about 6 percent of **the ageWs** total expenditures. This estimate excludes other programs, such as primary health cae, nutrition, training, malaria, and health education, all of which also contribute to maternal health. Constitutionally, WHO does not serve as a major resource for financial transfers. The major resource is a global network of technical personnel who work on behalf of developed and devloping countries.

# C. _Sof#_ **_MVwootld Fiwwu.g by Deloping CoNeda_**

The following list describes the current status of data collection on expenditures for maternal healh.

- WHO's Maternal and Child Health OMces do not maintain global data on local currency expenditures for maternal health at the country level. At the World Bank's request, WHO is distributing a questionnaire to selected WHO country representatives to solicit this information. In Latin America and the Caribbean, PAHO estimates that about a sixth of national health expenditures may be attributed to maternal care, including hospital expenditures.

- One of the few studies available was conducted by Sri Lanka's Marga Institute (1985). Based on 1982 data, health expenditures accounted for 3.7 percent of total government public sector expenditures and 1.4 percent of GNP. In the health sector, Rs. 976 million, or 20.7 percent, was directed to family health, of which Rs.72 million (64 percent) was spent on maternal health care. The study suggests that government expenditures for MCH are doubled by household expenditures. Approximately ; '%ird of all maternal and child health expenditures are related to transport. The stud) .oes not state the criteria for attribution.

- UNICEF Country Situation Analysis reports do not specify maternal health costs.

- Detailed WHO-sponsored health care financing studies on Jamaica, Costa Rica, and Mali do not contain t.aformation on maternal care.

- WHO Country Resource Utilization reports give only limited details on maternal health expenditures. The accounting categories are general -- salaries, transport, and drugs -- rather than by programs.

- A report on Malawi (1983) attributes about 4 percent of government health expenditures to *maternities and *dispensary/maternities."

- A study on Liberia (1988) indicates that 90 percent of the health budget funds curative medicine,' of which the major cost is salaries. Within these costs, however, approximately 30 percent of hospital beds are used for pregnancy and its complications (40 percent in the John F. Kennedy Hospital in Monrovia). In addition, the study documented an extensive nonsalaried network of trained traditional birth attendants (approximately 2,500).
**Table 4: Comparison of the total tax burden on imports in Tunisia and Libya**

||Total consum|ption tax bur|den in Tunisi<br>Adane|a|**Total consumt**|**ion**||**Total**<br>**tax**|
|---|---|---|---|---|---|---|---|---|
||||vc||**p**|<br>**Total cons**|**tion**<br>**Total tax brden on**|<br>|
|Product<br>Designation|Value-<br>added Tax|Consumpti<br>on Tax|over<br>Income<br>Tax|Other|**tax**<br>**burden**<br>**Tunisia**|**in**<br>**u**<br>**tax burden in L**|**p**<br>**ibya**<br>**u**<br>**imports into Tunisia**<sup>21</sup>|**burden**<br>**on**<br>**imports**<br>**into**<br>**Libya **|
|Bananas|22.5%|0.0%|10.0%|2.0%|34.5%|0.0%|> 87%|6% + 100TND<sup>22</sup>|
|Apples|18.0%|0.0%|10.0%|2.0%|30.0%|0.0%|> 81%|6% + 100TND|
|Cheese|22.5%|0.0%|10.0%|0.0%|32.5%|0.0%|> 84%|6% + 100TND|
|Tea|18.0%|25.0%|0.0%|0.0%|43.0%|0.0%|99%|6% + 100TND|
|Roasted coffee|18.0%|25.0%|0.0%|0.0%|43.0%|0.0%|99%|6% + 100TND|
|Juice|22.5%|0.0%|10.0%|1.0%|33.5%|0.0%|86%|6% + 100TND|
|Chocolate|22.5%|0.0%|10.0%|0.0%|32.5%|0.0%|> 84%|6% + 100TND|
|Tires|22.5%|30.0%|0.0%|1.0%|53.5%|0.0%|99.6%|6% + 100TND|
|Carpets*|22.5%|0.0%|10.0%|1.0%|33.5%|0.0%|77.6%|6% + 100TND|
|Clothing|22.5%|0.0%|10.0%|1.0%|33.5%|0.0%|77.6%|6% + 100TND|
|Shoes|22.5%|0.0%|10.0%|1.0%|33.5%|0.0%|77.6%|6% + 100TND|
|Refrigerator|22.5%|0.0%|10.0%|0.0%|32.5%|0.0%|76.2%|6% + 100TND|
|Air conditioning|22.5%|10.0%|0.0%|0.0%|32.5%|0.0%|> 76%|6% + 100TND|
|TVs|22.5%|0.0%|10.0%|1.0%|33.5%|0.0%|77.6%|6% + 100TND|

Sources: Authors' calculations, WTO, OTEXA.

> 21 These figures are approximate as several taxes are specific to individual products and are sometimes levied at a flat rate. The figures are provided to give an indication of the difference between the two countries.

> 22 Data are equivalent in TND.

14
<!-- Start of picture text --> Output, BRA Consumption, BRA Investment, BRA<br>4 10 15<br>2<br>0 5<br>−2 0 0 510 −5<br>−4 −10<br>−6 −5 −15<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Output, FJI Consumption, FJI Investment, FJI<br>30<br>20<br>5<br>0 246 −2 0 0 10 −10<br>−4−6 −5 −20−30<br>−8<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Output, TUR Consumption, TUR Investment, TUR<br>5 30<br>20<br>0 246 −2 0 0 10<br>−4−6 −5 −10−20<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Output, USA Consumption, USA Investment, USA<br>2 15<br>1 10<br>0 5<br>0 123 0<br>−1 −1 −5<br>−2−3 −2 −10<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Output, ZWE Consumption, ZWE Investment, ZWE<br>15 20 40<br>10 20<br>0 510 0 0<br>−5 −10 −20<br>−10<br>−15 −20 −40<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>3 3 3<br>1 1 1<br>3 3 3<br>1 1 1<br>3 3 3<br>1 1 1<br>3 3 3<br>1 1 1<br>3 3 3<br>1 1 1<br><!-- End of picture text --> Figure 7: MODWT scalogram for changes in Output, Consumption, and Investment growth for BRA, FJI, TUR, USA, and ZWE. The color scales are adjusted for the first four countries for Output, Consumption, and Investment growth.

acceleration of the economy. It picks up the indicators of upcoming economic decline, as well as shows how long it takes for the economy to start its way to recovery. For example, 17
2

# **1. Introduction**

Fuel subsidies consumed $4.7 trillion worldwide—or 6.3% of global GDP—in 2015 and are projected to remain large in the near future (Coady et al., 2019). Fuel subsidies occur when the government imposes that fuels or electricity be sold at prices below the marginal and/or average cost of producing and distributing them to customers, or when a preferential tax rate is applied to a specific fuel. For example, diesel is in many countries subject to a different tax rate per liter than gasoline (Coady et al., 2018).<sup>2</sup> The fuel subsidies are usually motivated by the desire to help poorer households, achieve full electrification, and protect the standard of living and health of the population. A large body of research, for example, has linked insufficiently heated dwellings to higher wintertime morbidity and mortality for their occupants (e.g., Clinch and Healy, 2000; Aylin et al., 2001; World Health Organization, 2018; Huebner et al., 2019).

Subsidizing energy inputs however leads to excessive consumption, which has adverse environmental consequences, and insufficient revenues, which in turn lead to substandard supply, service interruptions (which hurts the entire economy; Timilsina et al., 2018), and inadequate investment in production, transmission and distribution infrastructure (McRae, 2015; Hancevic et al., 2016). Subsidies contribute to inequality, foment social unrest (Goncharuk and Cirella, 2020), reduce welfare (Hahn and Metcalfe, 2021), and limit fiscal space for other public sector interventions (Coady et al., 2015).

In recent years, several countries have undertaken energy pricing reforms in an effort to reduce or eliminate fuel subsidies. For example, starting in 2016, Argentina gradually removed

> 2 Kotchen (2021) examines the subsidies implied by the fact that fossil fuel prices do not include the environmental public health and transportation externalities associated with their use. He calculates that US suppliers of natural gas receive subsidies equivalent to some 18% of the net income from continuing domestic operations for the median natural gas and oil producer in 2017-2018, and that they exceed net income for the majority of coal producers.