rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 7. Effective tariffs across various consumption levels<br>a. Highly increasing b. Moderately increasing<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50 75 100 150 200 300 400 450 500 900 50 75 100 150 200 300 400 450 500 900<br>monthly consumption, kWh monthly consumption, kWh<br>Cape Verde Côte d'Ivoire Madagascar<br>Chad Kenya Mali Tanzania Uganda Zimbabwe<br>c. Neutral d. Decreasing<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50 75 100 150 200 300 400 450 500 900 50 75 100 150 200 300 400 450 500 900<br>monthly consumption, kWh monthly consumption, kWh<br>Cameroon Malawi Senegal<br>Burkina Faso Congo, Dem. Rep. Niger Zambia Congo, Rep. Botswana<br>US cents per kWh US cents per kWh<br>US cents per kWh US cents per kWh<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

# **b. What is the level of average effective nonresidential tariffs?**

As for residential tariffs, one commercial and one industrial schedule per country were selected for our analysis of nonresidential tariffs. Representative or typical _commercial_ customers are defined as small to medium business users with an average consumption level of 900 kWh/month. Representative or typical _industrial_ customers are medium to large business users usually associated with high-voltage, highusage tariffs. But in order to exclude very large industries with preferential tariffs from our analysis, we did not use the highest voltage bracket included in tariff schedules (box 1). In cases where tariff schedules for commercial and industrial users are not differentiated or where several tariffs apply to commercial users, tariff schedules were selected based on both voltage level and load. For example, commercial customers were associated with a tariff for medium-voltage and medium-consumption users, and industrial customers were associated with a tariff for medium- to high-end users.

17
**Table 7: Counterfactual change in labor force participation rate: Using barriers and preferences from 1995, 2005**

||Labor ma<br>|rket only<br>|Human<br>on<br>|capital<br>ly<br>|Preferen<br>|ces only<br>|A<br>|ll<br>|D<br>|ata<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|1995|2005|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Panel A: Labor for**|**ce participation**|**rate, differen**|**ce over 201**|**5: women a**|**ged 25-34 (p**|**p)**|||||
|Costa Rica|15.3|2.4|3.7|2.1|-23.4|-12.1|-15.9|-14.0|-22.8|-10.8|
|El Salvador|-6.5|-1.2|-0.3|-0.2|38.9|34.1|35.3|32.3|-3.9|0.0|
|Panama|20.1|7.9|12.1|9.8|5.4|9.7|18.8|17.2|-18.2|-8.5|
|**Panel B: Labor for**<br>|**ce participation**<br>|**rate, differen**<br>|**ce over 201**<br>|**5: men aged**<br>|**25-34 (pp)**<br>||||||
|Costa Rica|-2.8|-0.5|2.4|1.5|-0.8|-2.0|1.1|-0.1|1.9|2.2|
|El Salvador|0.3|0.1|0.2|0.0|7.3|6.3|7.4|6.3|2.7|-0.3|
|Panama|-2.5|-1.7|2.9|2.4|1.0|1.0|2.7|2.4|-0.8|-0.3|
35 hand, data on iron and vitamin A deficiency in school age children tend to be scanty.31

# The trends

Developing countries as a group enjoyed improvements (reduction) in their indices of undernutrition during the period 1965-1985. (Undernutrition tables 1 and 2). However, undernutrition remains a problem in many subSaharan African, South Asian, and low income LAC countries. The degree of underratrition worsened in over one-third of sub-Saharan African countries. Ease . -* .,ad the most impressive record with its undernutrition index impl !t .'. i 16.74 in 1965 to minus 11.5 in 1985. Note, however, that this record is largely due to improvements in the undernutrition indices of China, Indonesia and the Philippines. Finally bear in mind that these indices dc not tell us anything about the actual size of the undernourished population in Asia.

A recent UN report on the world nutrition situation confirms these trends and also reveals the staggering magnitude of the global nutritional problem.32 Based on estimates of the proportion of undernourished population by region (see table below), the report concludes that during t 1960-85, 3tFor more details on the available data in this area, refer to Rae Galloway, "The Prevalence of Malnutrition and Parasites in School-Age Children: An Annotated Bibliography," PHREE, World Bank, 1989.

32Administrative Committee on Coordination - Subcommittee on Nutrition ACC/SCN. "First Report on the World Nutrition Situation" November 1987. For country coverage and regional grouping used in the analysis, see Table 3. Members of ACC/SCN are: FAO, IBRD, ILO, UN, UNHCR, UNICEF, UNU, WFP, and WHO.
Table A1.13: Chinese Taipei; products on which SSG measures were applied, - <u>2002 2007</u>

|Product<br>|Number<br>|of tariff line<br>|s on which v<br>measures w<br>|olume-base<br>ere applied<br>|d or price-b<br> <br>|ased SSG<br>|
|---|---|---|---|---|---|---|
|Year|2002|2003|2004|2005|2006|2007|
|Betel Nuts|1|1|0|0|0|0|
|Chicken Legs and Wings|3|1|1|1|1|1|
|Dried DayLilies|1|0|0|0|0|0|
|Dried Shiitake|1|2|1|1|1|0|
|Fresh Milk|0|2|2|0|0|0|
|Garlic Bulb|2|2|2|0|2|2|
|Liquid Milk|3|8|8|8|8|8|
|Oriental Pears|1|1|1|1|1|1|
|Other Chicken Cuts|2|1|1|1|1|1|
|Peanuts|3|6|7|7|7|7|
|Persimmon|1|1|0|1|0|0|
|Pork Belly|0|0|0|4|4|4|
|Poultryoffal|0|0|0|13|0|0|
|Red Bean|4|1|1|11|1|1|
|Rice|0|10|10|0|10|10|
|Shaddock|1|1|0|0|0|1|
|Sugar|1|22|2|0|0|0|
|Total|24|59|36|48|36|36|

Source: Tabulated from Chinese Taipei notifications, WTO documents G/AGRICULTURE/N/TPKM/8, 20, 27, 36, 46, and 58.

62
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- White, H. and Lu, X. (2010). Granger Causality and Dynamic Structural Systems. _Journal of Financial Econometrics_ , 8(2):193–243.

- White, H. and Pettenuzzo, D. (2014). Granger causality, exogeneity, cointegration, and economic policy analysis. _Journal of Econometrics_ , 178:316–330.

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36
organophosphate and carbamate pesticides.<sup>7</sup> Selection criteria included symptoms of cholinesterase inhibition such as headache, nausea, drooling, fatigue, dizziness, tearing, pin-point pupils, shortness of breath, muscle weakness and convulsion. In each case, a 0.4 ml blood sample drawn from the farmer’s fingertip was used to test the level of active cholinesterase enzyme (AchE) in red blood cells and plasma. The blood samples were analyzed at the Central Medical Laboratory of Tien Giang province, following the guidelines of the technical manual on Occupational and Environmental Health of Vietnam (1993). The results, displayed in Figure 3, indicate a high prevalence of pesticide poisoning (35%), with a large proportion showing chronic poisoning (21%).

**Figure 3: Medical blood test results for the detection of acute and chronic pesticide poisoning**

<!-- Start of picture text --> Normal<br>65% 35%<br><!-- End of picture text --> <!-- Start of picture text --> poisoning1<br>9%<br>High acute<br>posioning2<br>5%<br>Chronic<br>poisoning3<br><!-- End of picture text -->

- 1 - Reduction of numerical value of AchE in red cells and in plasma >25%; 2 - Reduction of numerical value of AchE in red cells and in plasma >33%; 3 - Reduction of numerical value of AchE in red cells >66%.

We use the test results to assess the relationship between self-reported symptoms and medically-verified pesticide poisoning. First, we calculate a correlation coefficient for each self-reported symptom against the blood test results. The highest correlation in the

> 7 Epidemiological studies have linked carbamates and organophosphates with fetal death, hormonal changes, DNA damage, birth defects, and abnormal sperm, ovaries and eggs.  In addition, organophosphates, as a class of insecticides, have been linked with Non-Hodgkin’s lymphoma, leukemia, and lung cancer.  In children organophosphates have been linked to aplastic anemia, the failure of the bone marrow to produce blood cells, and leukemia.  Children with asthma may have severe reactions to organophosphates in particular (Zahm et al, 1997).  See Appendix I for details on the blood (cholinesterase) test.

8
to the question of the overall effectiveness of providing collective conditional conservation payments. While we cannot provide formal proof of additionality, it is very likely that our study’s outcomes are substantially better than absent financial incentives. As discussed in more detail below, we observe an average survival rate of about 34 percent, and this is substantially higher than the survival rates that are typically obtained in such dry agro-ecological conditions (Carey, 2020).<sup>9</sup> We assess the impact of the threshold versus the linear group payment scheme on both intermediate and ultimate measures of within-management-group cooperation. Theory suggests that, unless uncertainty is too high, threshold group payments should induce higher survival rates through stricter coordination between participants. Our endline survey included several questions that intended to measure the (self-reported) extent to which maintenance groups managed to cooperate. These measures are (i) how often members of a maintenance group organized and/or attended group meetings to discuss and plan maintenance activities, (ii) their assessment of their fellow group members’ trustworthiness, and (iii) the extent to which they feel that all group members contributed more or less equally to the maintenance effort.

These three variables constitute our intermediate measures of cooperation; the actual tree survival rate is the key output measure – and hence also our ultimate measure of cooperation. We employed independent tree survival verification teams to measure the number of trees still alive at endline. To be able to accurately determine the number (and share) of trees surviving we georeferenced all planted trees at baseline in every reforestation plot. Accurate estimation of tree survival rates was facilitated because, in line with standard reforestation practice, all saplings were planted in hand-dug holes of about 20 centimeters in diameter and about 10 centimeters deep. Still, it proved to be very challenging to systematically verify the health status of each individual sapling. Obviously, there is GPS measuring error when the newly planted trees were georeferenced, and again when we revisited the trees nine months later. We solved this issue by programming virtual bands of 10 meters wide in the independent verification teams’ GPS location devices. Within those 10-meter bands it is very easy to find back all holes, and to verify

> 9For example, Wade _et al._ (2018) report an average survival rate of only about 20 percent from the reforestation efforts conducted as part of the pan-African Great Green Wall for the Sahara and the Sahel Initiative (GGW).

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Figure 11: Rural and Urban Incomes in Sudan, 2014/15 <!-- Start of picture text --> (a) Rural<br>Agriculture Non-Agriculture Wage<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Rural Non-Poor Rural Poor Rural (All)<br><!-- End of picture text --> <!-- Start of picture text --> (b) Urban<br>Agriculture Non-Agriculture Wage<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Urban Non-Poor Urban Poor Urban (All)<br><!-- End of picture text --> _Source_ : Authors’ calculations using NHBPS 2014/15 and NBHS 2009.

Across states, there is significant variation in the dependence on agriculture (Figure 12a). In the Darfur and Kordofan regions, agriculture is the main source of income for most households. In Khartoum, the Red Sea, and Al-Gadarif, nonagricultural activities and income from wages and salaries are the main sources of household income. In states such as Al-Gezira, Sinnar, Kassala, and the River Nile, wages and salaries are important sources of income for households, but due to the importance of irrigated agriculture in these states, such wages and salaries are mostly tied to agricultural employment.

Households in states in the irrigation belt rely heavily on wages and salaries as their main source of income (Figure 12a). These are in the Blue and White Nile regions of Sudan, including the states of AlGezira, the White Nile, Sinnar, and the Blue Nile, and in the rural areas of the states of Khartoum and the River Nile. Households that earn incomes from wages and salaries are often employed in agrarian work in the rural regions of these agrarian states with their easy access to irrigation. Such agrarian work is often done on large commercial farms, including those in the large agricultural schemes. The major irrigation schemes include Al-Gezira in Al-Gezira state, the New Haifa Scheme in Kassala, and the Rahad Scheme in the Blue Nile. Pump irrigation schemes also exist in the Northern, River Nile, Blue Nile, and White Nile and in the Suki Scheme in Sinnar state (FAO 2015).

Households in Darfur and Kordofan rely more on direct incomes from agriculture, through sales of crop and livestock produce, as opposed to incomes from wages and salaries (Figure 12a). Agricultural production in these states is much less reliant on large irrigated farms and hired labor than in the irrigation belt. The disparity in poverty rates between households whose main livelihood is agriculture and those whose main livelihood is from wages and salaries therefore stems primarily from geographic differences. As shown in Section III.A, agrarian households in these regions cultivate millet primarily for subsistence needs, with increasing cultivation of sesame for sale in Kordofan.

Wages and salaries have increased as an important share of total income, at the expense of agricultural and nonagricultural incomes (Figure 12b). A vast majority of households in Al-Gezira, a state known for its large-scale irrigation scheme, earn wages and salaries primarily from agrarian work. Most states have 13
Bover (1995) and Blundell and Bond (1997), combines in a system regression model (4) in differences and levels. The regression in differences requires differencing model (4) and using instruments to deal with the likely endogeneity of the independent variables, and the fact that in the differenced model the error term is correlated with one of the explanatory variables – the lagged dependent variable. The instruments consist of previous observations of the explanatory and lagged-dependent variables. The GMM estimator assumes that the error term in model (4) is not serially correlated and the explanatory variables are weakly exogenous. The instruments for the regression in levels are the lagged differences of the corresponding variables. These are appropriate instruments assuming there is no correlation between the differences of the independent variables in model (4) and the country specific effect. This GMM estimator is consistent and efficient.<sup>22</sup> To test the robustness of the estimates Loayza _et al_ . (2005) estimate model (4) using various other estimation techniques. They use the ‘pooled’ OLS estimator, the ‘within’ OLS estimator, and the GMM levels estimator for the dynamic model of 5-year and 10-year panel datasets<sup>23</sup> (Appendix Table 1), and the OLS estimator for the pure-cross section model<sup>24</sup> (Appendix Table 2). Each of these alternative techniques has its shortcomings. The ‘pooled’ OLS estimator ignores the presence of country-specific effects and treats all variables as exogenous, while the ‘within’ OLS estimator ignores the joint endogeneity of the explanatory variables. The GMM levels estimator uses instruments to control for joint endogeneity but ignores country specific effects.

> 22 The consistency of the GMM estimators depends on whether lagged values of the dependent variables are valid instruments in the regression. Loayza _et al._ conducted a Sargent test of over-identifying restrictions, which tests the overall validity of the instruments by analyzing the sample analog of the moment conditions used in the estimation process, and tested the null hypothesis for serial correlation of the error term in model (4). The Sargan and serial correlation tests could not reject the null hypothesis of correct specification of the main model. 23 The sample based on 10-year averages consists of an unbalanced panel of 65 countries and 175 observations. 24 The sample based on 30-year averages consists of one observation for 70 countries. A country is excluded from the pure cross section sample if there is no complete information for at least 30 years during the period 1966-99.

18
where

# **Intergenerational Persistence Equation**

Combining equations (3) and (8) above, we get the following relationship between the education of the father and that of a son, determined by the optimal investment decision: where Equation (10) is consistent with the almost universally used specification for intergenerational schooling persistence in the literature, but it allows for possible differences in educational opportunities across the farm and nonfarm households. Some examples of studies that use this linear-in-levels specification are: Neidhofer et al. (2018), Narayan et al. (2018) and Hertz et al. (2007) on cross-country analysis, Azam and Bhatt (2015) and Emran and Shilpi (2015) on India, and Emran and Sun (2015a) on China. However, we are not aware of any studies on intergenerational educational mobility in developing countries that derive the estimating equation from a theoretical model.

It is standard to assume homothetic functional forms in the analysis of intergenerational income mobility, as the estimating equation is log-linear (see, for example, Solon (1999, 2004)). The reliance on the linear in levels (years of schooling) specification in the literature on intergenerational educational persistence partly reflects the fact that a substantial proportion of fathers have no schooling. This is especially relevant in the rural areas of developing countries such as India where about 40 percent of fathers have no schooling (estimate based on REDS 1999 data). The estimating equation (10) is also consistent with the common assumption in the literature that the omitted ability is captured in the error term of the intergenerational persistence regression, and can lead to ability bias in the OLS estimates of the parameters.<sup>19</sup> An important implication of equation (10) is that both the slope and the intercept of

> 19Although most of the existing studies on intergenerational mobility adopt this additively separable specification for the impact of ability, there is little evidence on the validity of this assumption. In a recent paper, Ahsan et al. (2020) use measures of ability based on Raven’s test and two memory tests in Indonesia and find evidence in favor of this assumption.

9
# **4.5 Investment incentives**

An aspect of investment promotion that typically receives high levels of attention from both policy makers and academics is investment incentives. In the Census, we collected time-varying information on five different types of investment incentives: financial incentives, tax holidays, reduced tax rates, subsidized infrastructure or services, and regulatory concessions. Our finding is that it is in general difficult to distinguish the effect of incentives from a more general effect of investment promotion, as there is a high correlation between these variables (see Table 8). When both the existence of investment promotion agency and incentives are included simultaneously, we generally find that the investment promotion dummy is significant while the incentive variable is not, as can be seen in Table 22. When the investment promotion variable is excluded, as in Table 23, we find a statistically significant positive correlation between FDI inflows and tax incentives and between FDI inflows and subsidized infrastructure or services. The magnitude is about the same as what we have typically found for the IPA existence effect in the earlier specifications.

# **4.6 Competition between countries**

Government officials are often concerned about growing competition for FDI among countries. To shed some light on this issue we extend our specification to include presence of IPAs and FDI incentives in a peer group of host countries. In other words, in each year we count the number of countries in the peer group with an existing IPA and with FDI incentives on offer.<sup>37</sup> In Table 24, the peer group refers to countries within the same geographic region, while in Table 25 to countries within the same income group.<sup>38</sup> We control for the average GDP growth in the peer group (weighted by each country’s GDP).

Two interesting findings emerge. First, FDI incentives appear to divert FDI inflows, but only within the geographic region, not within the income group. Incentive competition taking place

> 37 To be more precise the variable enters as the log(number of other countries in the group with z + 0.001), where z is an IPA or incentive provision in year t.

> 38 We use the World Bank classification of geographic regions: Latin American and the Caribbean (LAC), East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Sub-Saharan Africa (SSA), South Asia (SA) and Middle East and North Africa (MENA). We also use the World Bank classification of income groups: low income, lower middle income, upper middle income, high income.

29
# **ECONOMIC TRANSFORMATION AND THE FISCAL CRISIS**

# **A Critical Look at the Central European Experience of the 1990s**

# **I. INTRODUCTION**

An (unfortunate) stylized fact common to virtually all reforming ex-socialist economies (RESEs) is the deep fiscal crisis in which they have found themselves after the onset of the liberalization programs (Table 1).

Table 1. Selected Economies in Transition: General Government Balance <!-- Start of picture text --> (in percentage of GDP) 1989 1990 1991 1992 1993<br>(proj.)<br>Albania -8.6 .15.4 -30.6 -22.6 -19.6<br>Bulgaria .. -8.5 -4.1 -5.0 -8.0<br>former Czechoslovakia I/ -2.4 0.1 -2.0 -3.6 -2.2<br>Hungary -1.3 0.5 -2.5 -8.0 -7.6<br>Poland -6.1 2.7 -3.7 -7.1 -2.8<br>Russian Republic 2/ .-3.1 -6.3 -19.9 -7.4 -8.4<br><!-- End of picture text --> Sources: Ministries of Finance; World Bank staff estimates.

1/ The figure for 1993 is the weighted average of projected Czech and Slovak Fiscal Deficits 2/ Figures prior to 1991 refer to the USSR To a certain extent the emergence of a fiscal crisis should not be surprising, since almost all the reforming economies have also experienced, at least during 1990-91, considerable output losses, of a magnitude not previously seen in market economies. Inasmuch as automatic stabilizers were built into the structure of thf socialist public sector (at least on the revenue side), one should have expected that the recession would be accompanied by a large swing in fiscal balances. But, as