rafmacalaba/datause-encoder-data huggingface dataset:
3 _Fiscal Crisis in Central Europe_ above. We also analyze data on tax payments from the private sector in Poland, providing the first comprehensive evidence available on the subject for any RESE.

The main thesis of this paper is that traditional explanations fail to overlook crucial interconnections between key components of the deficit, particularly between the reduction in expenditures and the decrease in revenues. We therefni try to provide a somewhat different perspective. We argue that the impact on the fiscal buc of the crisis of the SOEs performance - which is stressed by almost all the studies of the fiscal aspects of transition - has been largely everestimated. Indeed, the net contribution to the government budget from the, enterprise sector - defined as _profit taxes net o,f cross-subsidization_ - has _increased_ during the transition in Polaned and Czechoslovakia, and has not changed substantially in Hungary. We posit that this net measure is the mreaningful variable to consider in order to assess the "fiscal performance" of the enterprise sector in a socialist economy. After reexamining the data, we conclude that - while it is undoubtable that the fiscal crisis in RESEs should be considered of a structural nature - its main cause lies with the explosion of expenditures, rather than in the crisis in revenues. Within our 3nalyticai framiework, we can provide a consistent explanation of the outstanding fiscal performance of Czechoslovakia vis-a-vis Poladlu aa.d Hungary. **fln** particular, the increase of social insurance expenditure - a typical development during transition - has been moderate in the former Czechoslovakia, while it has been dramatic in the other two reforming economies.

The paper is organized as follows. In the next section, we briefly review the economic mission of the public sector under socialist and market economies, and present a flow-chart model of the resulting fiscal flows among main sectors of society. We also review traditional theories regarding fiscal crisis during transition. In the third section, we present evidence - from Poland, Hungary and Czechoslovakia - supporting to a varying degree an explanation of the fiscal crisis consistent with the traditional theories (mainly revenue-based). We also provide a different perspective, and reach quite a different conclusion - that the explanation of the fiscal crisis is mainly to be found so far on the expenditure side. In section four we review the reasons for the
Politically unstable countries have less institutional capacity to carry a project, and more politically stable countries may in turn have little interest in cooperative ventures with those. Similarly, investments are not secure and property rights poorly defined in unstable countries characterized by political turmoil (Deacon 1994). Participating in an agreement requires both competence (also in terms of appropriate investment climate and supporting regulation) and stability inherent in a particular polity, which will in turn be able to honor the signed project agreement (Young 1989:365; Young 1982:287).

# _Categories of Variables to Be Used in the Analysis_

Based on the discussion in the literature, for the right hand side of the estimated relationship we will construct variables that represent variants of the level of economic activity; variables that represent the energy dependency; variables that measure the level of renewable energy endowments; variables that measure the cost of transactions; variables that measure vulnerability to climate change; variables that measure governance level; and variables that measure international trade among the host and investor countries. In addition, for level of cooperation we will calculate measures CDM incident and variables related to the CDM activities.

# **3. DATA DESCRIPTION, VARIABLE CONSTRUCTION, AND EMPIRICAL SPECIFICATION**

Data used in this study are derived from several different sources. Collected data are then combined and transformed for the purpose of empirical analysis. In particular, individual CDM project-level data are combined with corresponding host and investor country-specific macroeconomic and environmental variables, and alternative empirical models are specified.

# **_Description of Data and Variable Construction for the Empirical Analysis_**

A dataset consisting of all CDM projects that have been sent to UNFCCC for validation up until December 31, 2007 is obtained from the CDM/JI Pipeline Analysis and Database of the United Nations Environment Programme (UNEP) Risoe Center (2007). From December 2003 to December 2007, 2,966 CDM projects were sent to UNFCCC for validation. The dataset provides detailed information about each individual CDM project. That information includes project name, type, and current status, host country, expected emission reduction (ktCO2 per year and total CO2 reduction up until 2012 and 2030), credit buyers, potential energy outputs, etc. The projects in the CDM pipeline are at various stages in the projects cycle. Of the nearly 3,000 8
|8|Yi, Lu, and Zhou<br>(2016)|China|Input subsidy via<br>cash transfer (10%<br>agriculture income)|Median<br>regression|Income|
|---|---|---|---|---|---|
|9|Karamba and<br>Winters(2015)|Malawi|Input subsidy via<br>voucher|PSW, FE|Output|
|10|Beaman et al.<br>(2013)|Mali|Input subsidy via<br>voucher|RCT|Output value|
|11|Ricker-Gilbert<br>and Jayne<br>(2017)|Malawi|Input subsidy via<br>voucher|OLS|Increase in yield for<br>every kg/ha increase<br>of fertilizer used|
|12|Abman and<br>Carney (2020)|Malawi|Input subsidy via<br>voucher|IV|District-level increase<br>in yield for increase of<br>subsidized fertilizer|

Notes: IPWRA: inverse propensity weighting adjusted regression; IV: instrumental variable; MC: MundlakChamberlain device or CRE: Conditional Random Effects; MC CF: Mundlak-Chamberlain device and control function approach; MC IV: Mundlak-Chamberlain device and IV approach; OLS: ordinary least squares; PSM: propensity score matching; PSW: propensity score weighting; RCT: randomized control trial.

# **3.2 Estimated effect of input subsidy programs on yield and income**

Figure 2 summarizes the effects of input subsidy programs on yield and income or expenditure. Panel A presents the estimated effect of input subsidies on yield. There are 32 estimates from 11 studies; some of them come from different methods within a study, some are long-run effects (more than one year post subsidy), and one study estimates spillover effects. The spillover effect is defined in the study as the effect of being "more connected" to a treatment group member, based on the indicator of having above-median number of people who received subsidies in one’s social networks, regardless of whether that person is treated or not (Carter, Laajaj, and Yang 2021). It means that the spillover effect is calculated based on both beneficiaries and non-beneficiaries of the subsidy program. Except for study 4 (Ragasa and Mazunda 2018) and one estimate in study 11 (Ricker-Gilbert and Jayne 2017), all of the estimated effects on yields are positive. The largest effect measured is in study 1 (Carter, Laajaj, and Yang 2021), which is the spillover effect post subsidy. Based on these 12 studies, the meta-analysis shows an average effect of receiving input subsidies on yield is 18%, with a 95% confidence interval between 13% and 23%.

Panel B presents the effects of subsidy programs on income or expenditure using 23 estimates from 5 studies. The outcome variables include household income in all seasons or only in the main season, income per capita per day, agricultural income, and consumption expenditure. All the point estimates are positive, ranging from 1% to 46% increase in income or expenditure. The average effect across 5 studies is 16%, with a 95% confidence interval between 11% and 21%. The effect of input subsidies on income is similar to the effect of subsidy programs on productivity, which suggests that the increase in crop yields translate into higher living standards for beneficiary households.

_Figure 2 Summary of the effects of input subsidies_

# **Panel A. Effect on yield**

9
Figure 5.4Water Pollution Intensity for Selected Sectors <!-- Start of picture text --> 14000 BOD Intensity |<br>12000<br>E 10000 |<br>o<br>po]<br>tom 8000 |<br>=»<br>3 6000 |<br>35 |<br>& 4000<br>20000 4 -}- 4 -t: 4 : feI ,8,8 8 8 w= ww eww ew eee, |<br>So Ww = = So eo = ws to} oa a “n oO o a a a - —_ ~<br>- - “ — ON - ise] vr o = - o - = o un <~ = = —_<br>35>» & 8 & & 8 &® 8B & S&S SB Bb 8 lU8lUKlUCBlUCBlUS<br>| ISIC Codes<br>Total Suspended Solids intensity<br>20C000.00 -<br>180000.00<br>160000.00<br>. &€_ 140000.00<br>* 120000.00<br>3° 100000.00<br>~ i<br>a<br>; 80000.00 - |<br>| = 60000.00<br>40000.00 | |<br>| 0.00 : ' . ' +! nF || 4 a aa. a= | i jn ef ee_}. ee] oe ©] ef eet<br>! x ¥ &F & 8&8 & & & &B B SF B&B RB SF FE Bese B A CE<br>t' o “> Pod 4 “ “ toe] ~ oa ISICoO CodesoO oo oO ~ o ive] te] oO o fo?J |<br><!-- End of picture text -->
18

**Tabl-** 8: AGGREGATE WELFAR **E** FFECTS OF TRADE LIBERALIZATION

|Column|CRTS<br>(1)|Ua<br>Pri<br>(2)|rk-up<br>cing<br>(a)|Co<br>No Entr<br> <br>(4)|njectural<br><br>y/Exit<br>(5)|Variations<br>Entry<br>(a)|/Exit<br>(7)|
|---|---|---|---|---|---|---|---|
|Cost Disadvantage<br>Ratio*|0.0|0.10|0.20|<br>0.10|0.20|0.10|0.20|
||(X of|**BaseYear**|National|Income)||||
|Equivalent<br>Variation (EV)b|1.1|||||||
|_wu_0<br>||2.6|6.3|<br>2.1|4.7|-.6|22|
|w_=_loX||4.9|10.2|<br>2.5|6.2|1.6|$JD|
|Scale Efficiency<br>Gain (SE)c|0.0|||||||
|_wu_<br>0<br>||1.8<br>|8.4|0.8|3.0|-1.4|15|
|wa10X||2.0|5.8|0.7|2.6|-.4|29|

- a. CDR a 1 - MC/AC.

- b. EV _=_ C tIU (Pl, Y19), P1 ] - C EIU (PO, Yo0 **where** C To the cost function associated with tho Indirect utility function (IU) corresponding to the LES utility function describing consumer Choice.

- c. SE = (TC (PO, X0) - TC (Po, X1 ))/GDPO is a vector of product and factor prices, and GDPO is real CDP prior to the removal of protection.
13 "competencies" that is now much in favor in the business strategy literature.<sup>2</sup> Understanding the processes of change in firm capabilities is the main task, but the quest for this understanding ranges widely across levels of analysis and encounters a multitude of individual issues -- from learning and retention of skills by individual workers and the character of the knowledge embedded in organizations through the "life cycle" patterns of industries and product markets to the roles of government laboratories and universities.

A number of different dynamic mechanisms are involved in the change of firm capabilities. In the large, and on historical time scales, there is a readily discernable trend toward an expansion of the capabilities of firms in general. It can be argued -- as Schumpeter (1934) in fact did -- that this is what "economic development" is all about. The underlying mechanisms, however, admit decline as well as advance. As the phenomena are studied on shorter time scales and in more detail; it becomes very easy to identify episodes, locales, and particular firms or sectors where capabilities have declined. This fact may be puzzling if one is accustomed to thinking of technology in very abstract terms -- as "information" that is non-rivalrous in use, storable and transferrable at zero cost, and a catalyst rather than a constituent of conventional inputs. It is not at all puzzling if one thinks of capability in more 2 The intensity of this interest is measured by the reception accorded Prahalad and Hamel (1990) -- now the all-time most requested reprint of the Harvard Business Review.
# Figure S1. Long-term interest rates

<!-- Start of picture text --> Percent<br>15<br>World<br>Advanced economies<br>12<br>EMDEs<br>9<br>6<br>3<br>0<br>2000 2005 2010 2015 2020<br><!-- End of picture text --> Source: Kose et al. (2017).

Note: Average long-term nominal government bond yields (with maturity of 10 years or close) computed with current U.S. dollar GDP as weights.

Figure S2. Interest payments and interest rates in commodity-exporting EMDEs <!-- Start of picture text --> Percent of GDP Percent<br>2.1 12<br>1.9 11<br>1.7 10<br>1.5 9<br>Interest payments<br>Interest rates (RHS)<br>1.3 8<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Sources: International Monetary Fund, Kose et al. (2017).

Note: Interest payments refer to net interest payments, which are computed as differences between primary balances and overall fiscal balances. Interest rates refer to long-term nominal interest rates (i.e., government bond yields with maturity of 10 years or close). Aggregates are weighted averages using current GDP in U.S. dollars as weights. The classification of commodity-exporting EMDEs follows World Bank (2021).

38
PTA is present, (5), we can determine if t > t<sup> ̃�</sup> . That will be so if the following expression is positive: where the inequality in the fi rst line re fl ects the use of (4). If import subsidies were allowed then t<sup>p</sup> could continue to be lowered below zero and the inequality above would disappear since an interior solution could be found. But otherwise it is possible to obtain a corner solution in the PTA, that is a situation where, at t<sup>p</sup> = 0, L would like S to increase e. In this case, (4) holds with a strict inequality. The second line of (7) is zero in the case we consider where −e<sup>b</sup> t<sup>p= eb</sup> t<sup>, i.e.when an increase in the MFN</sup> tariff has the same effect as a decrease in the preferential tariff, both simply increase the preferential margin. This implies that t > t<sup>ˆ�</sup> if and only if [Wt<sup>Lp+ W</sup> e<sup>Leb</sup> t<sup>p]tp=0< 0.So,importsubsidiescompletely</sup> eliminate the need to distort the MFN tariff to maintain a preference margin.

# 2.2 Evidence

Before estimating the costs of the stumbling block effect that arises in the theoretical model we provide some direct evidence for it, which we subsequently use in our quanti fi cation. The theoretical model generates speci fi c testable predictions. At an extreme if the MFN tariff is zero no tariff preference can be offered and so no preferential agreement is possible in the absence of subsidies. Therefore, to the extent that the U.S. and E.U. value the cooperation with the side conditions in those agreements, reductions in MFN tariffs on products imported from the preferential partner are more costly than reductions in similar products.

Limão (2005) estimates the direct effect of the U.S.’s PTAs on its MFN tariffs by exploring the fact that this effect will be weaker for goods it imports only from non-PTA countries. Using tariff data for over 5000 products he fi nds that the U.S.’s PTAs generated a stumbling block effect in the Uruguay Round. After controlling for several determinants of U.S. tariff changes during the Uruguay Round he estimates that the U.S.’s average cut for goods exported by any of its PTAs was about half that of other goods. The effect is stronger for products that are exported under all PTAs or constitute relatively larger shares of a given PTA’s exports to the U.S. The average cut for GSP goods on their own was on average only 74% relative to non-PTA goods. Interestingly the estimates also allow a calculation of the direct effect on the U.S. MFN tariffs for all its goods: it would have been about 1 percentage point 9
Irwin, Tim (1997); 'Price Structures, Cross-subsidies and Competition in Infrastructure', _Puiblic Policyfor the Private Sector_ Irwin, Tim; _Free Entry Competition in Infrastructure as a Response to Poor Governance,_ (Preliminary Draft) Jacobson, Charles D. and Tarr, Joel A (1996); Private Initiative and the Public Good, _Infrastructure Delivery,_ IBRD Kay, John (1993); _Foundations of Corporate Success,_ Oxford University Press Kerf, Michel and Smith, Warwick (1996); _Privatising Africa's Infrastructure: Promise and Challenge,_ IBRD World Bank Klein, Daniel and Moore, Adrian; 'Schedule jockeying and route swamping: bus markets in Britain need kerb rights', _Institute of Economic Affairs_ (June 1997) Klein, Michael (1996); _Competition in Network Industries,_ Policy Research Working Paper # 1591, World Bank Krueger, Anne (1993); 'Virtuous and Vicious Circles in Economic Development', _Papers and proceedings of the American Economics Association_ LXXXIII (1993); cited in Mauro (1995) Lee, Kyu Sik and Anas, Alex (1992); 'Costs of Deficient Infrastructure: The Case of Nigerian Manufacturing', _Urban Studies,_ Vol. 29, 1992 Lewis, Maureen and Miller, Ted R. (1997); 'Public - Private Partnership in Water Supply and Sanitation in Sub-Saharan Africa', _Health Policy and Planning,_ (2) 1 Liebowitz, S.J & Margolis, S.E (1994); 'Network Externality: An Uncommon Tragedy', _Journal of Economic Perspectives Vol._ 8, No. 2, Spring 1994 p. 133-150 Mauro, P (1995); 'Corruption and Growth', _Quarterly Journal of Economics,_ August 1995 p.<sup>6 8 1</sup> -<sup>7 1 2</sup> McCarthy, Stephen (1994); _Africa: The Challenge of Transformation,_ St Martin's Press, New York, New York Milgrom and Roberts (1992); _Economics Organisation and Management_ Negroponte; 'RJ- l1', _Wired,_ Issue 6.04 April 1998.

OECD Pricing of Water Services, (1987) Reuter News Service - Far East Reuter Textline (Q2:60) 31 March 1998 Schumpeter, J.A. (1942); _Capitalism, Socialism and Democracy,_ New York, Harper Schumpeter, J.A. (1961); _The Theory of Economic Development,_ Oxford University Press
**Figure 3: More Scheduled Tribe children are severely stunted and wasted within the first 10 months of their birth**

<!-- Start of picture text --> Weight-for-height ( children 0-59 months old), 2005-2006<br>Height-for-age ( children 0-59 months old), 2005-2006<br>45% 20%<br>40%<br>35% 15%<br>30%<br>25% 10%<br>20%<br>15%<br>5%<br>10%<br>5%<br>0% 0%<br>0 5 10 15 20 25 30 35 40 45 50 55 1<br>Age group (in months) Age group (in months)<br>Non-ST ST #REF!<br>#REF!<br><!-- End of picture text --> _Source_ : Das et al, 2010 based on NFHS One argument for entrenched malnutrition among the ranks of the tribals could simply be poverty. However, while an important cause of both malnutrition and higher incidence of deaths among tribal children, poverty is clearly not the only cause. Malnutrition in India is not just a problem of the poor: even among children of well-off households (i.e. wealthier in terms of assets, more educated), 22 percent are stunted (Tarozzi 2007).

At the same time, malnutrition is much higher in states with larger tribal populations. Even within states, much of the underweight children are concentrated in relatively small number of districts. A mere 10 percent of villages and districts account for 27-28 percent of all underweight children in the country, and a quarter of districts and villages account for more than half of all underweight children (World Bank 2004). Most of these are remote, tribal districts and/or villages.

Child deaths on account of malnutrition and hunger have led to frequent public outcry. Public interest law suits have been filed on behalf of families that lost their children<sup>14</sup> , and state governments have been repeatedly directed by the courts to take remedial action. Governments have undoubtedly become more vigilant on this issue than they were before, but solutions are still ad hoc and in crisis-mode. For instance, during an emergency large numbers of medical personnel are deployed to the vulnerable areas, but normally absenteeism of doctors is endemic in rural and especially tribal areas. Additionally, poor registration of births and deaths has meant frequent haggling over the real numbers of deaths/children affected.

Defining a “hunger-related death” is not easy. Those who are severely malnourished tend to contract disease quite easily. When they succumb, the cause can always be attributed to the specific disease - rather than the hunger that brought about the collapse of the victim's resistance.

> 14 See for instance, Sheela Barse v/s State of Maharashtra 1993

12
India (1991).<sup>16</sup> These controls further guard against the possibility of our main variables spuriously picking up the effect of overall development and income on competition. Literacy rate is a useful proxy for wage rate and income levels. Some care in required in interpreting what sex ratio captures. In addition to higher income levels and greater gender parity (favoring women), higher values of sex ratio may be associated with less involvement of women in traditional roles including household shopping. If this is indeed true, then we might expect a negative relationship between sex ratio and competition. While our empirical results confirm such a negative relationship, we treat our suggested interpretation with extreme caution fully realizing that more work is required to ascertain its validity.

Regression results controlling for literacy and sex ratio are reported in column 1 of Table 5. Sex ratio shows a strong negative effect on competition which is significant at less than 5% level. Like for expenditure and population, we do not find any significant effect of literacy on competition. Lastly, the estimated coefficients of non-workers and children maintain their signs and significance levels with little change in their values.

As mentioned above, stores reported irregular power supply as the single most important obstacle to doing business followed by access to finance. We control for two measures of power supply using data from the Enterprise survey. These are the duration of power outage reported by a store on an average per day during 2005-06 ( _Outage_ – _own store_ ) and the average duration of power outage reported by all _other_ stores in the city ( _Outage_ ). The motivation here is that power outage in one’s own store is likely to deflect customers to the neighboring stores implying greater competitive pressure for the store in

> 16 We use 1991 values because data for non-workers and children are also for the same year. However, 1991 and 2001 values of these variables are almost perfectly correlated (correlation of over .90) and our results do not change much if we use 2001 values instead.

14
many disaster-prone Caribbean countries (e.g., St. Lucia, which in 1988 experienced the largest ever loss as compared to national income). This lack of data for countries expected to be particularly vulnerable to natural hazards may have lead to an important bias in the analysis. Consequently, we might expect our findings to improve if more observations are added. Constructing a more comprehensive and detailed database of disaster losses and savings measures may help to some extent with better addressing this problem, yet, by definition, the study of extreme events will always be constrained by scarce and imprecise data.

22
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ames Modelled incidence (%), adults 15-49, from Spectrum software Prevalence (%), females 15-24 (source: national DHS/AIS reports) mms Prevalence (9), females 15-24 (source: national ANC reports) om ART coverage ()(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013