rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 15.  Commodity exporters with and

<!-- Start of picture text --> without fiscal rules<br>1.0<br>Without Fiscal Rules With Fiscal Rules<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Without Fiscal Rules = 0.48***<br>-0.2 With Fiscal Rules = 0.44***<br>-0.4<br>-0.6<br>-0.8<br>Azerbaijan<br>Ecuador Uruguay Liberia Argentina Côte d'Ivoire<br>Rwanda Congo, Rep. Timor-Leste<br>Kenya<br>Brazil Namibia Equatorial Guinea Niger<br>Chad Colombia Tanzania Central African Republic Gabon Iceland Mongolia Senegal Mali Burkina Faso<br>Paraguay Burundi Benin Turkmenistan<br>Iran, Rep. of<br>Togo<br>Cabo Verde Uganda Cameroon Indonesia<br>Russian Federation<br>Nigeria Peru South Sudan<br>Armenia<br>Botswana<br>Kazakhstan Guinea-Bissau Costa Rica<br>Chile<br>New Zealand<br>Norway<br>Australia<br>Correlation between cyclical components of real government spending and real GDP 1980-2020<br>Canada<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with and without fiscal rules. “With” fiscal rules indicates that at least one fiscal rule is being enforced; “without” means no fiscal rule is being enforced. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on Davoodi et _al._ (2022) fiscal database and WEO (IMF) data.
6 liberalization of foreign trade and foreign ownership, legal ratification of property rights, and dismantling the system of guaranteed employment and centralized pay setting. Slovenia’s reform process was slow relative to other transition economies (Svejnar, 2002). The EBRD transition indexes in Figure 1 show that Slovenia’s structural reforms have progressed steadily but unevenly across sectors. Liberalization of foreign trade and of prices was already well underway by 1991, as was privatization of small firms. Other reforms began later and with slower progress. The legal process for privatization of large state enterprises began in 1993, and started in earnest in 1994. About the same time, reforms of the banking system and of other financial institutions began. Slovenia has also taken a gradualist approach to labor market reforms, imposing many provisions to protect jobs in traditional sectors.<sup>4</sup> Riboud, SanchezParamo and Silva-Jauregui (2001) found that Slovenia's labor policies were the most restrictive of the formerly planned economies that were being targeted for accession to the European Union, and were more restrictive than all western European countries except for Portugal. Taken as a whole, Slovenia has lagged behind the most rapid reformers among other transition countries. Its pace of structural reforms was below average through 1999. The pace of reforms accelerated since then, so that Slovenia’s overall EBRD transition index reached the average of other countries.

# **c.  Policies affecting market competition**

Before transition, the system of discretionary taxes and transfers effectively insulated firms from competition—any business losses were covered by government transfers to prevent bankruptcy. Any possible competition from private firms was suppressed by limitations on firm size. After transition, numerous new avenues for competition were opened. We provide additional details on the most important of these.
# What Doesn’t Kill You Makes You Poorer: Adult Wages and the Early-Life Disease Environment in India

## Nicholas Lawson<sup>_∗_</sup>

Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS

## Dean Spears

Centre for Development Economics, Delhi School of Economics JEL Codes: I15, J24, O15

## Keywords: sanitation, disease environment, wages, India

> _∗_ nicholas.lawson@univ-amu.fr. We would like to thank Diane Coffey, Aashish Gupta, Habiba Djebbari, the participants of the International Seminar on Food Security in India at the University of Allahabad, the WASH 2014 Conference in Brisbane, and a seminar at the World Bank Water and Sanitation Program in Delhi. Any errors or omissions are the responsibility of the authors.
||All fa|ctors|Global|factor|Group|factor|
|---|---|---|---|---|---|---|
||Infows|Outfows|Infows|Outfows|Infows|Outfows|
|All countries|||||||
|Median|0.47 (0.07)|0.36 (0.09)|0.16 (0.06)|0.30 (0.07)|0.18 (0.07)|0.00 (0.00)|
|Mean|0.45(0.03)|0.42(0.03)|0.23(0.03)|0.32(0.04)|0.22(0.03)|0.10(0.02)|
|Advanced|||||||
|Median|0.69 (0.08)|0.66 (0.06)|0.24 (0.09)|0.33 (0.10)|0.27 (0.09)|0.26 (0.10)|
|Mean|0.58(0.05)|0.62(0.04)|0.31(0.05)|0.37(0.05)|0.27(0.05)|0.25(0.05)|
|Emerging|||||||
|Median|0.31 (0.09)|0.27 (0.08)|0.11 (0.06)|0.27 (0.08)|0.13 (0.08)|NA|
|Mean|0.36(0.05)|0.28(0.05)|0.17(0.04)|0.28(0.05)|0.18(0.04)|NA|
|No fnancial centers|||||||
|All countries|||||||
|Median|0.42 (0.07)|0.35 (0.09)|0.16 (0.06)|0.30 (0.07)|0.18 (0.06)|0.00 (0.00)|
|Mean|0.43(0.04)|0.40(0.04)|0.23(0.03)|0.32(0.04)|0.20(0.03)|0.07(0.02)|
|Advanced|||||||
|Median|0.66 (0.08)|0.67 (0.07)|0.27 (0.10)|0.42 (0.10)|0.22 (0.09)|0.19 (0.09)|
|Mean|0.57(0.05)|0.62(0.05)|0.34(0.06)|0.40(0.06)|0.23(0.06)|0.22(0.05)|

Table 5: Fraction of the variance explained by the estimated factors. Two-standard errors shown in brackets. The bottom part of the table shows the results excluding the main international financial centers (U.S., U.K., Switzerland, Germany and Japan) 40
<!-- Start of picture text --> Industrial Europe Rest of countries<br>2 foe ENE: Pe<br>as fccfissereieietesesciche<br>& fs:<br>33<br>36<br>-4<br>GLinear estimator<br>Non-linear estimator (median value)<br><!-- End of picture text --> <!-- Start of picture text --> Peru changed Peru changed Dominican Rep. Colombia Portugal Spain changed<br>its VAT rate its VAT rate changedits VAT changed its VAT changed its VAT _its VAT rate 5<br>from 18% to from 19% to rate from 16% rate from 16% to rate 5S times times during<br>19% in 2003. 18% in 2011. to 18% in 2013. 19% in 2017. during the 1996- the 1986-2015<br>—______ _ _ 2012 period. period.<br>2 Qe * . 3 x<br>R 4 R 4 q Ff Rg 2<br>. =<25 a&s& =s a& B£é >Rg a¢ go:sg ao$2 Rx ooBuyfs<br>@ S 8 S 8 8 ¢ § = gs a §:<br>= = 2 a ir S £ a £ aed c§ & £8<br>2 2 3 2 3 & & ® 23 ¢ 23<br>= ] 56 8 sg g 2 s 2 £ ge 33 2f<br>£&2 a5 35 25 3}OS ez fo5 &es6 &$s6 =6 65<br>x<br>oO<br>vi]<br>So<br>-5<br>-6<br>GB Linear estimator from external source<br>f Non-linear estimator<br><!-- End of picture text -->
Faso has experimented with producing biodiesel from cotton oil. The operators and managers of the cottonseed processing companies in the four countries take the view that producing biodiesel from cotton oil would not be profitable unless it were heavily subsidized. Further, because their companies face enough domes‐ tic demand for edible oils (all countries are net importers of edible oils) they see no need to diversify their operations.

In Uganda and Tanzania, all imports of refined oils are subject to a 25 per‐ cent duty, while edible oils imported in crude form enter duty free. Most indus‐ try representatives argued that while most oil in fact enters in refined form, it is declared as crude to escape the import duty. Benin introduced a short‐lived seed export ban in 2001 that was never strictly enforced and a 45 percent import duty on palm oil (the key competing product to cotton oil). Yet in Benin, as in ESA, traders report that in most cases import duties are not collected on edible oils. Some of the owners and managers who were interviewed favored stricter protec‐ tion of Benin’s edible oil industry while others did not. The views in favor of im‐ port restrictions were stronger in WCA than in ESA.

Several other issues were identified during the interviews. Overcapacity was cited as a problem in all four countries. Electricity disruptions were cited as a key constraint in Tanzania and Uganda but not in Benin or Burkina Faso. High interest rates for operating capital were an issue in Uganda only. Attracting labor (either skilled or unskilled), was not an issue.

While the surveys undertaken in the four SSA countries allow us to have a deeper understanding of the structure of the cotton by‐products industries, they do not offer enough quantitative detail for a full statistical assessment of the con‐ tribution of cotton by‐products to the value of cotton. Furthermore, the data are not directly comparable for a number of reasons. For example, as mentioned ear‐ lier, the ginning costs in the US are paid by the cotton growers who retain the ownership of both lint and by‐products. On the contrary, SSA cotton growers sell their seed cotton to ginners or traders, hence the ginning costs are accounted by ginneries. Furthermore, while there are reliable estimates of ginning costs in WCA, no such estimates exist for ESA. Nevertheless, the following conclusions can be drawn:

- In West Africa unlike in the US, the value of cotton by‐products is small com‐ pared to the costs of ginning. In 1999/2006, the revenue from cottonseed cov‐ ered only 23 percent of ginning costs in Benin and 28 percent in Burkina Faso. For comparison, consider that the value of byproducts in the US is expected to cover the ginning costs. This implies that not only cottonseed is valued much less in WCA than in the US but also that ginning costs are much higher in WCA than in the US.

**_Page 21_**
**Table 5: Average leverage by geographical area, based on all identified data (%)**

||**Levera**<br>|**ge 1 - Market**<br>|**based**<br>|**Leverage**<br>|**2 - Accountin**<br>|**g based**<br>|
|---|---|---|---|---|---|---|
|**Year**|**South**<br>**America**|**Asia**|**Other**|**South**<br>**America**|**Asia**|**Other**|
|1991|25.4|27.9|-3.6|28.2|31.1|-25.7|
|1992|27.0|26.2|-24.2|29.5|48.4|-79.4|
|1993|19.6|6.9|3.8|21.5|27.0|12.0|
|1994|13.3|9.8|7.9|17.3|25.4|1.7|
|1995|21.0|18.4|5.4|27.9|32.5|-15.1|
|1996|21.7|13.6|0.2|26.2|26.1|0.2|
|1997|23.1|31.4|0.5|21.1|33.5|0.5|
|1998|41.8|28.2|-0.1|31.4|28.9|-0.1|
|1999|38.7|33.0|8.8|31.2|33.1|8.6|
|2000|42.8|35.0|2.0|28.7|29.6|6.3|
|2001|52.1|36.1|4.7|30.4|33.0|1.4|
|2002|59.3|39.2|-10.0|37.7|37.0|-5.4|

Source: Datastream and authors’ calculations.

# **3.2 Results from the full sample of transport companies**

Table 6 reports the estimated levels of leverage across the two types of transport company in developing countries. The numbers clearly show that the average leverage levels in the transport services sector appear to have been increasing over the period. The increase seems more pronounced when the market value of equity is used in the calculations. In the transport infrastructure sector, the development has been slightly different. The leverage levels seemed to have reached their peak around 1999, after which there has been a rapid decline. A similar pattern is observed for both market and book value based measures.

9
Therefore, we postulate the following b-cneral model: increases in VAT revenue are due to increases in VAT rates and the coverage and size of the tax base, while rate differentiation z.lises costs and thus negatively affects revenue. That is:

# _REV = F (RA7E', BASE', R4NGE)_

(1) where: REV variable is defined as VAT revenue as percentage of a country's GDP; RATE Is defined as VAT rate (in percent); BASE is defined as a VAT base variable taking the value of **1** if the base comprises -lI consumer goods and services (G **+** S), and 0 otherwise; and RANGE is a measure of rate dispersion, defined as the difference between highest and lowest VAT rates. **The** signs above the dependent variables denote the expected direction of influence.

Due to missing observations, mostly on revenue, the original samnple of 49 countries had **to** be reduced to 34 countries for full sample models, 20 countries for single-late country models, and 14 countries, for multiple rate models. The choice of the year was 1988, lirgely based on the Silvani data as quoted by Tanzi (1993, p.18). We comnpiled additional data on revenue, rates and base from various World Bank and IMF sources (see Table I in the Appendix).

We estimated linear versions of the general model (1) using the Ordinary-Least Squares (OLS) technique". The estimated models are given in Table 1 below. The mos. important determinant of VAT revenue is the VAT rate, which is significant at well below one percent, and with the expected sign in all estimated versions of the model (see Table 1) indicating the robustness of the estimates. The RATE variable dominates the results, with a coefficient of determination of 0.75 for the single rate VAT model and 0.57 for all countries model (see Table 1).

# A. **The** Sample of Single VAT Rate Countries

A simplest estimated relationship between revenue and rate from the sample of 20 single VAT rate countries is depicted in Figure 1. For this group of countries, VAT generates, on average, 4.7 percent of GDP with an average rate of 11.3 percent: each I percent of VAT rate generates 0.50 percent of VAT revenue. The estimated VAT rate that will generate positive revenue, which can be interpreted as the minimum rate from the revenue perspective, is close to 2 percent (see Figure 1)". The RATE coefficient is strongly significant and, in the linear model, explains 71 percent of the variation in VAT revenues".

The estimated relationship can be used to predict _the potential_ VAT revenue in a country contemplating the introduction of a VAT, given a proposed single VAT rate. For example, following some other Eastern European countries (i.e., Hungary and former Czechoslovakia), Bulgaria is planning to introduce a VAT rate at the single rate of 18 percent. Given this rate, our model suggests

# 11/ The exponential form, also tried, does not seem to fit the data.

1/ It should be noted, however, that this is _not_ to be interpreted as the rate at which VAT, as a tax instruments, breaks even, because its costs are not taken into account.

12/ Note that this a univariate model (not reported in Table 1) which excludes the BASE variable.
# **1. Introduction**

If, in the next decades, Africa catches up with the rest of the world, then that will almost certainly coincide with intergenerational mobility out of rural into urban areas and out of agriculture into non-agricultural activities (Lewis 1954; Harris and Todaro 1970). Historically, in both rich developed countries and fast-growing developing countries, this type of migration has moved in lockstep with development and poverty reduction (Collier and Dercon 2009). Recently, China’s urban population officially surpassed its rural one: of China’s 1.35 billion people, 51 percent lived in urban areas at the end of 2011, rising from less than 20 percent in 1980 (UN, 2012). Furthermore, UNDP (2009) reports that of the one billion migrants worldwide, three-quarters are internal migrants. With international migration open to only very few Africans, we should expect massive internal migration to form a core part of the development process.

The scale of this demographic process is captured in the data that form the basis of this paper, further motivating our focus on internal migration. These data are part of an exceptional panel data set from the Kagera region in Tanzania, spanning nearly two decades of migration and development. The 2010 follow-up survey attempted to trace all 6,353 individuals listed on the baseline 1991/94 household rosters and reinterview them irrespective of their location. Once we exclude the 1,275 individuals who had died by 2010, we are left with 4,996 baseline individuals whose 2010 locations are known.<sup>1</sup> Of those, 45 percent were found residing in the baseline village, 53 percent had migrated within the country, 2 percent to another East African country (primarily Uganda) and 0.3 percent had moved outside of East Africa. This region – not atypical of remote rural Africa – is clearly on the move, with internal migration dwarfing international migration.

We attempt to understand how this powerful current of internal migration, which is part and parcel of the modernization process, interacts with a traditional institution like informal risk-sharing to shape economic mobility and vulnerability. This is a key question because, as Munshi and Rosenzweig (2006) put it (p. 1230) “[...] _a complete_

> 1 We miss location information on 82 individuals. Because this is after multiple attempts through various sources it is unlikely that these individuals have moved outside of East Africa. Information on such an important, low-occurrence event is unlikely to be hidden.

2
# **4 Data and Statistical Outline**

Our investigation covers a total of 153 countries over a 23-years period: 1993-2015.

To measure the cyclicality of fiscal policy, we compute, as aforementioned, the impact of real GDP growth on real expenditure growth using the LGWOLS. Our preference for public expenditure as fiscal policy variable rather than others like primary balance or revenue is motivated by the criticism of Kaminsky et al. (2004). The authors argue that fiscal balance or revenue are results of fiscal policy rather than instruments. In fact, if a government wants to influence economic activity, it would modify its expenditure program or change tax rates and such effects would affect revenue and fiscal balance. Moreover, the advantage of using government expenditures is that co-movements with GDP are able to clearly differentiate between pro-cyclical, a-cyclical and counter-cyclical fiscal policy (Bergman and Hutchison (2015)).

The source of the data on fiscal rules is “IMF Fiscal Rules Dataset 2016” from the Fiscal Affairs Department of the International Monetary Fund. This dataset contains information on all the types of fiscal rules (expenditure rules, debt rules, budget balance rules, revenue rules) and their characteristics for the countries in which they did exist at any time since 1985. We perform our regressions using dummy variables of different types of fiscal rules. FRD stands for fiscal rules dummy, ERD for expenditure rules dummy, DRD is debt rules dummy, BBRD is budget balance rules dummy and RRD is revenue rules dummy.

Our institutional quality variable is “Bureaucracy quality” from International Country Risk Guide (ICRG). Bureaucracy quality represents “the institutional strength and quality of the bureaucracy; a shock absorber that tends to minimize revisions of policy when governments change” (ICRG 2015). It ranges between 0 and 4, with “high points indicating countries where the bureaucracy has the strength and expertise to govern without drastic changes in policy or interruptions in government services. In these low-risk countries, the bureaucracy tends to be somewhat autonomous from political pressure and to have an established mechanism for recruitment and training” (ICRG).

11
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