rafmacalaba/datause-encoder-data huggingface dataset:
- 80 -

also registered a similar performance and had incremental benefit-cost ratio exceeding one in the long run for four out of seven industries studied with the r,atio being close to 1 in one of these four cases. Corporate tax rate reductions stimulated investments but resulted in revenue losses exceeding this stimulative impact in all cases and in all runs considered in this study. Note that corporate tax rate reductions apply to a larger base of pre-tax profits than the smaller base of current investments relevant for investment tax credits. In terms of the effectiveness of tnese incentives by location, investment allowances worked well in Turkey while in Mexico both the investment tax credit and accelerated capital consumption allowances met with mixed results and none of the incentives worked well in Pakistan. In Pakistan failure of the tax incentives regime in part may be attributable to political instability and economic uncertainty for a significant part of the period studied.

Shah and Baftes (forthcoming) employ a flexible accelerator type dynamic factor demand models with endogenous capacity utilization to examine the effectivess of tax incentives available to large-scale private manufacturing industries in Pakistan using data for the period 1956 to 1985. Tax incentives measures evaluated include: investment tax credit, full expensing of R&D investment and corporate income tax rate reductions. They find that while investment in physical and knowledge capital were sensitive to tax measures, the elasticity values were without exception quite small. Further, the incremental benefit-cost ratios associated with changes in investment tax credit and corporate tax rate were smaller than one in the short run. The full-expensing option for R&D was found to be cost-effective (see Table 4).

Rajagopal and Shah (forthcoming) argue that analyses of the effectiveness of tax incentives can be considerably enriched by explicitly incorporating into the analyses the industrial market structure of the industry at hand. They propose an empirical procedure to test the market power hypothesis. Such a test has important implications for the effectiveness of fiscal incentives for investment. If the producers in an industry have market power, they may be able to shift taxes forward completely so that any tax incentive would simply result in windfall gains for the firms in such an industry. On the other hand, in a competitive industry producers are not able to shift taxes forward completely so that tax incentives can stimulate investment. Rajagopal and Shah test the market power hypothesis empirically using data for selected ir iustrits: in Turkey, chemical and petroleum derivatives; in Pakistan, textiles as well as chemicals and pharmaceuticals. In addition, they also examine the impacts of investment tax credits (credits against tax liability), investment allowances (deductions against taxable income) and R&D expensing on production and investment decisions and government revenues. They introduce three
# non-linearities among R&D, initial education, and initial de jure IPR.

# 3.2.1 Two-stage rolling regressions

The rst approach entails a two-stage estimation procedure, which is purely descriptive. In the rst stage, we estimate the semi-elasticity of R&D over GDP with respect to (the natural logarithm of) initial educational attainment, while controlling for country-specic xed eects, over a moving window of observations ranked by the initial IPR index. In turn we estimate the correlation between the elasticities estimated in the rst stage and each country's level of educational attainment and IPRs. Since the dependent variable in the second stage is not a precise statistic, but rather an estimated elasticity, the standard errors of the second-stage estimations are bootstrapped. Also, it is likely that the sample size of the window of observations can aect the estimated elasticities, and thus we report results from specications with various window sizes.

More formally, the regression model to be estimated over each window of a subset of observations ranked by the level of IPRs is: _HK_ is human capital observed in the initial year of each ve-year period, as reected in its t-1 subscript, _ηi_ is the country xed eect, and _ηt_ is time-period eect.

Figure 1 shows the estimated coecients over the number of interactions corresponding to a rolling window of 60 observations.<sup>9</sup> This preliminary evidence shows that, in fact, the semi-elasticity of R&D over GDP with respect to educational attainment is generally positive, but it is clearly a non-linear function. The relationship between R&D and human capital is unstable and rising with the rank of the IPR index. Furthermore, the changes in the semi-elasticity seem to be discrete and unpredictable. It is zero in the samples with the worst levels of IPRs, then abruptly rises in the middle of sample, and stabilizes towards the end of the sample. These abrupt changes in the relevant semi-elasticity are not due to abrupt changes in the IPR index as we move up the rankings of IPRs. Considering that the each iteration involves a set of observations with increasing IPR index, the slope of the curve in Figure 1 corresponds approximately to the cross derivative of R&D share with respect to human capital and the IPR rank. Thus, we expect that this cross derivative could be positive on average for the whole sample. In any case, we discuss the results from our two-stage estimations further below.

> 9We excluded one observation from the data, namely for El Salvador in 1980, as the Lederman and Saenz data had a value of 2.27% of GDP. This data point is consistent with the RICyT data, but it is impossibly high for a poor developing economy, and there were no data points within ve years of this observation. Estimations with this observation also yielded notable unpredictable non-linearities. The corresponding graph is available from the authors upon request. We are grateful to Bill Maloney and Edwin Goni for pointing out this outlier.

14
**-3-**

It Is followed by a discussion of factors<sup>affecting farm Investment and a</sup> description of Investment patterns in the<sup>study areas.</sup> A formal model of farmers' consumption and Investment uecisions,<sup>and an econometric analysis are</sup> then presented and results are Interpreted.<sup>The last section summarizes the</sup> paper.

# I1 DESCRPTION OF STUDY AREAS AND SURVEYS

The data underlying this study were obtained through<sup>recent farm surveys</sup> organized by the authors In four countries<sup>of China: Gongzhullng county In Jilin</sup> Province (December 1987). Tal and<sup>.</sup> urong<sup>counties In Jiangsu Province (March</sup> 1988) and Xlajlang county In Jlar<sup>-,AlProvince (November 1988). The samples</sup> consist of agricultural households, Ie., households<sup>operating farms as a major</sup> activity, as distinct from other rural households<sup>which engage In commerce or are</sup> mainly employed In rural Industry.

Gongzhullng county Is located in Jilin province<sup>of northeastern China.</sup> Agro-clknatic conditlons are such that only one<sup>crop season Is feasible annually,</sup> and corn Is the major crop grown. Farm sizes<sup>In the study area In Gongzhuling</sup> are large relative to typlcal farms sizes In China,<sup>and the significant surplus of</sup> output over consumption requirements<sup>makes the county a leading corn supplier</sup> In China.

Tal and Jurong counties of Jiangsu province<sup>are within the Central East</sup> China region where a two-season wheat-rice<sup>cycle Is practiced annually. They are</sup> characterized by high population density and<sup>consequently have very small farm</sup> sizes. Many farmers In these counties supplement<sup>their Income through off-farm</sup>
not reflect individual taste for labor market status or for contractual arrangements but simply the interest of the social planner for maximizing production and productivity.

Figure 4 below shows our normative classifications and how we capture downward and upward mobility. It can be argued that, from the standpoint of the social planner who wants to maximize production and productivity, employment is better than unemployment and inactivity while private employment is better than public employment. We can also argue that services and industry are better than agriculture in that these sectors are generally characterized by higher value added and productivity. This is not always the case of course. But it is known that, during the historical and structural transformation of societies, agriculture is the first sector to shrink during periods of industrialization while the service sector is the last sector to expand as societies move from agriculture to industry and into the knowledge era. Such classification may help to capture advancements in the structural transformation of a society. Based on the same principles, we also categorize paid and unpaid employment, full-time and part-time employment and formal and informal sectors in this order.

**Figure 4 – Labor Market Mobility Across Sectors**

|**Downward**<br>**mobility**||**Sec**|**tors**|||**Upward**<br>**mobility**|
|---|---|---|---|---|---|---|
|Employed|Serv./Ind.|Private|Paid|Full-time|Formal||
|Unemployed|Agriculture|Public|Unpaid|Part-time|Informal||
|Inactive|Unemployed <br>Inactive|Unemployed <br>Inactive|Unemployed <br>Inactive|Unemployed <br>Inactive|Unemployed<br>Inactive||

It is known that labor mobility occurs mostly within sectors rather than across sectors and that net crosssector mobility tends to be close to zero as bilateral flows tend to cancel each other out (Jovanovic and Moffitt, 1990). The framework presented above will allow us to clearly see whether cross-sector mobility has occurred, to what extent and in which direction.

# **4. Labor mobility**

We follow the same structure presented in Figure 4 and look at transitions estimated as percentages of total transitions, focusing therefore on the “movers” rather than the “stayers”. However, all tables will also provide the share of “movers” over the total population in each group considered while Table A1 in the annex provides the population shares of each group considered. The analysis is carried out separately by gender and location so that we can compare outcomes for four groups: urban men, urban women, rural men and rural women. Such categorization is a natural choice that derives from the description of the Moroccan economy and labor market that we provided in section 2 where we remarked the extraordinary segmentation of the Moroccan labor market into these four groups.

9
# **5 References**

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- Bourguignon, Francois, Carolina Diaz-Bonilla, and Hans Lofgren (2007). “Aid, Service Delivery and the MDGs in an Economy-Wide Framework.” World Bank Policy Research Working Paper forthcoming.

- Bourguignon, François, and Luiz Pereira da Silva (2003). _The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools_ . Washington, DC: World Bank and Oxford University Press.

- Bourguignon, François, and Mark Sundberg (2006). “Constraints to Achieving the MDGs with Scaled-Up Aid,” UN Department of Economic and Social Affairs Working Paper No. 15.

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- Bussolo, Maurizio and Denis Medvedev (2007). “Accelerated Growth and MDG Achievement in Ghana: A General Equilibrium Analysis of Fiscal Policy Alternatives.” Presented at PEGNet conference, Berlin, Germany, September 6-7.

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- Calderon, Cesar A. and Serven, Luis (2004). "The Effects of Infrastructure Development on Growth and Income Distribution," World Bank Policy Research Working Paper No. 3400.

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- Ghana Joint Assistance Strategy, G-JAS (2007). _Commitments by Partners to Work Toward GPRSII Goals and Harmonization Principles_ .

- Government of Ghana, Ministry of Education (2003). _Education Strategic Plan 2003 to 2015_ , Accra, Ghana.

19
55

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# **Subjective Well-Being across the Lifespan in Europe and Central Asia**<sup>**1**</sup>

JAN MICHAEL BAUER<sup>1</sup> , VICTORIA LEVIN<sup>2</sup> , ANA MARIA MUNOZ BOUDET<sup>2</sup> , PENG NIE<sup>1</sup> , AND ALFONSO SOUSA-POZA<sup>1,3</sup> _1 Institute for Health Care & Public Management, University of Hohenheim, Germany 2 World Bank, Washington D.C., USA_ _3IZA, Germany_ Keywords: Age; subjective well-being; Eastern Europe; Central Asia, life satisfaction JEL codes: C23, D1, I31, J1

> 1 We thank the staff at the various institutions that provided us with the data needed for this report, including the Integrated Values Survey, the Life in Transition Survey, and the Russia Longitudinal Monitoring Survey.
||**Table 3. Cro**|**ss-Section Analysis**|**: Average PEFA Sco**|**res and Country C**|**haracteristics**||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**GDP per capita (log)**|0.2192***|0.1669***|0.2478***|0.1843***|0.1944***|0.1865***|
||(0.0449)|(0.0582)|(0.0586)|(0.0449)|(0.0446)|(0.0566)|
|**Growth (per capita)**|0.0325***|0.0472***|0.0319***|0.0317***|0.0360***|0.0372***|
||(0.0105)|(0.0144)|(0.0112)|(0.0097)|(0.0111)|(0.0103)|
|**Population (log)**|0.0556**|0.0502*|0.0648**|0.0941***|0.0558**|0.0495*|
||(0.0276)|(0.0294)|(0.0293)|(0.0315)|(0.0271)|(0.0298)|
|**SIDS**|-0.2755*|-0.2621*|-0.2667*|-0.2647*|-0.3436**|-0.2858*|
||(0.1428)|(0.1484)|(0.1440)|(0.1374)|(0.1419)|(0.1497)|
|**Resource**|-0.3816***|-0.2268**|-0.3684***|-0.3333***|-0.3233***|-0.3373***|
||(0.0914)|(0.0949)|(0.0940)|(0.0883)|(0.0939)|(0.0955)|
|**Tax**||0.0137*<br>(0.0073)|||||
|**Aid**|||0.0025<br>(0.0042)||||
|**Political Stability**||||0.0524***|||
|||||(0.0196)|||
|**Regime**|||||0.0315*<br>(0.0173)||
|**Programmatic Parties**||||||0.3573*<br>(0.2044)|
|**Observations**|112|93|111|112|112|102|
|**R-squared**|0.40|0.41|0.41|0.42|0.42|0.43|

Notes: Robust standard errors in parentheses; statistical significance is indicated as: *** p<0.01, ** p<0.05, *p<0.1 29
From the standpoint of urban and rural areas, it turns out that the tax system contributes significantly to the reduction of extreme poverty in rural areas (Figure IV-7). Thus, while urban areas see their extreme poverty levels drop by a little more than 3 percentage points, rural areas do so 3 times more, going from 40.3 to 31.9 percent. However, the decrease in poverty incidence is more moderate and not statistically significant when considering the moderate poverty line (Figure IV-7).

# **V. Progressivity and marginal contributions of taxes, transfers, and subsidies.**

A relevant question for public policy is to determine what are the policies that contribute to the reduction of poverty and inequality. Figure V-1 presents the distribution for 2017 of the impacts of direct and indirect taxes, transfers, and subsidies of the Colombian system as a percentage of market income, including pensions. Benefits are seen to represent a greater proportion of the market income of households in the first five deciles. In other words, the net balance is positive for households up to the 5<sup>th</sup> decile. However, taxes paid as a percentage of market income exceed the monetary benefits received by households from the highest 50 percent of the distribution.

<!-- Start of picture text --> FIGURE V-1 : DISTRIBUTIVE IMPACT OF THE TAX AND BENEFITS SYSTEM IN 2017<br>(BENEFITS/TAXES AS A PERCENTAGE OF MARKET INCOME + PENSIONS)<br>280%<br>230%<br>180%<br>130%<br>80%<br>30%<br>-20% 1 2 3 4 5 6 7 8 9 10<br>Direct taxes Direct transfers Indirect taxes Subsidies<br>Education Health Net balance Total Balance<br>Note: The net balance of a household is calculated as the difference between consumable income and market income plus<br>pensions and is equal to all transfers and subsidies received by the household minus taxes paid by the household.<br>Source: World Bank Group staff estimates based on ENPH 2017<br><!-- End of picture text -->

## i. Taxes and social spending

A relevant question for public policy decision-making is whether each of the tax interventions and combinations thereof helps reduce inequality. The Kakwani indicator may be used to answer this question. If the Kakwani indicator is positive, then the intervention under consideration is progressive. In case the coefficient is negative, then it is possible to state that the policy is regressive and therefore contributes to increasing inequality. It is important to analyze each of the policies individually, since, by doing so in the aggregate, it may be possible that the positive effect of another compensates the negative effect of one policy. It should be noted that the progressivity or regressivity of a policy does not imply that it is good or 33
rates for municipality _c_ , and _Environmentc_ is a dummy variable indicating whether the municipality _c_ has an operational solid waste management system<sup>5</sup> . As can be seen, _Educc_ receives, by far, the highest weight in the _QuotaICMS,c_ redistribution rule: 18% out of 25% transferred to municipalities depends on education performance. It is also important to mention that the _QuotaICMS,c_ is calculated by an independent think _−_ thank ( _Instituto de Pesquisa e Estratégia Econômica do Ceará_ - IPECE) using information collected by the state government. Municipalities do not collect or report on their _Educc_ , _Healthc_ , and _Environmentc_ data.

This paper focuses on _Educc_ because of its preeminence in the _QuotaICMS,c_ redistribution rule. It has two main components: literacy rates ( _Literacy_ ) and learning scores (Portuguese and Mathematics) in primary education ( _Learning_ ). Literacy rates and learning scores are calculated with a state-managed assessment called SPAECE that is applied to students yearly to monitor the performance in basic education in _Ceará_ . In the original _RBF_ model, proposed in 2007, literacy performance is weighted twice as much as _Learning_ performance to induce greater efforts towards improvements in basic literacy as shown in the following _Educc_ equation: where _Literacyc_ averages the _level_ and _time variation_ of literacy rates per municipality _c_ . On literacy _level_ ( _L_<sup>_w_</sup> _c_<sup>) the formula weights the average literacy rate (</sup> _Lc_ ) by SPAECE _enrollment_ rates _Enroll_ % _<u>,c</u>_ ( _Enroll_ % _,c_ ) times half the standard deviation of Portuguese scores: _L_<sup>_w_</sup> _c_<sup>=</sup> _Lc ×_ 0 _._ 5 _σL_<sup>_<u>c</u>_</sup> . Adding these two weights aims to reduce the potential negative selection of students into SPAECE and to punish municipalities with highly unequal learning performance. Based on the weighted literacy rates ( _L_<sup>_w_</sup> _c_<sup>),thefinalindexforlevelis</sup><sup>_Iliteracy,c_=</sup> _LL_<sup>_w_</sup> _max_<sup>_w_</sup> _<u>c</u>_<sup>_−−LLw_</sup> _<u>min</u>_<sup>_<u>w</u>_</sup> _min_<sup>dividedbythesumof</sup><sup>_Iliteracy,c_forall</sup> municipalities,<sup><u>�</u></sup> _c_<sup>_Iliteracy,c_.Forthetimevariationcomponent,the</sup><sup>_RBF_formulaconsidersthe</sup>

> 5The methodology for computing these indexes was established by Holanda et al. (2008a,b).

8
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