rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Decomposition of changes in unorganized sector employment share

||Manufacturing<br>1989-2005|Manufacturing<br>1989-1994|Manufacturing<br>1994-2000|Manufacturing<br>2000-2005|Services<br>2001-2006|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|A. Full unorganized sector change||||||
|Unorganized employment share start|0.810|0.810|0.789|0.828|0.761|
|Unorganized employment share end|0.807|0.789|0.828|0.807|0.733|
|Unorganized employment share change|-0.003|-0.021|0.039|-0.022|-0.028|
|B. Balanced state-industry panel for decomp|osition|||||
|Unorganized employment share start|0.811|0.810|0.792|0.828|0.761|
|Unorganized employment share end|0.813|0.790|0.833|0.807|0.733|
|Unorganized employment share change|0.002|-0.021|0.041|-0.021|-0.028|
|C. Decomposition||||||
|Within state-industry component|-0.014|-0.023|0.007|-0.020|-0.020|
|Between state-industry component|-0.052|-0.030|-0.017|-0.027|0.001|
|Covariance term|0.068|0.033|0.051|0.026|-0.009|
|Total|0.002|-0.020|0.040|-0.021|-0.028|

Notes: Table decomposes changes in unorganized sector employment shares. Column headers indicate sectors and time periods. Decomposition technique follows Foster et al. (2001) and Baily et al. (1992). The within component represents changes in unorganized sector shares within state-industries with state-industries weighted by initial employment shares for the Indian economy. Negative values indicate that state-industries tended to have declining unorganized shares when weighted by initial employment. The between component represents changes in employment shares across state-industries interacted with the initial deviation of state-industries from the national unorganized sector share. Negative values indicate employment tended to be reallocated towards state-industries that had lower initial unorganized sector shares. The covariance component term represents the interaction of changes in unorganized sector shares for state-industries across the period with changes in employment shares for state-industries across the period. Positive values indicate that fast growing state-industries also experienced rising unorganized sector shares. The three components by definition sum to the total change in unorganized sector share for India.
# **Annex 3: Imputation procedure for Turnover**

The survey sample of formal business in South Africa was derived using SARS information on firms’ reported turnover in 2005. These turnover numbers were provided in the form of turnover bands (i.e., under R70 thousand, R70 to R300 thousand, and so on in the same fashion as being used in the analysis presented in this paper). In order to estimate turnover values for businesses that refused to provide such information to interviewers we used the following OLS model.

For the test we used the following OLS model: ln( _Turn_ )  ln( _Emp full_  _time_ )  _Dturover_  _e_ **,** where: ln(Turnfull-time) – ln total turnover, ln(Empfull-time) – ln fill-time employment, Dturnover – dummy variables for turnover bands, including DK/Refusal/Unsure.

The results are shown in the tables below.

**Table A4.1: OLS Regression Results (Dependent Variable: LN turnover (q55))**

||Coeff. (unstand.)|Std. Error|Signif.|
|---|---|---|---|
|(Constant)|10.682|0.126|***|
|LN full-time employees|0.147|0.023|***|
|70K to 300K|1.291|0.138|***|
|300K to 1,000K|2.547|0.135|***|
|1,000K to 6,000K|3.894|0.133|***|
|6,000K to 14,000K|4.947|0.147|***|
|over 14,000K|5.970|0.158|***|
|DK/Unsure/Refusal|2.961|0.311|***|
|Adj. R2|0.873|||
|N|734|||
|Mean of the dependent variable|14.442|||

## **Table A4.2: Residuals statistics**

||Minimum|Maximum|Mean|Std. Deviation|N|
|---|---|---|---|---|---|
|Predicted Value|10.682|17.637|14.442|1.672|734|
|Residual|-4.594|4.377|0.000|0.635|734|
|Std. Predicted Value|-2.248|1.911|0.000|1.000|734|
|Std. Residual|-7.202|6.862|0.000|0.995|734|

32
in market access, while areas with a more educated population benefited less from an increase in market potential. This suggests a stronger impact of accessibility in larger places with lower skilled workers.

The estimates also provide evidence that the positive impact of improved domestic market access on employment was partially attenuated after nafta came into effect (the net impact is still positive though). One explanation of this result could be that under nafta, access to international markets started playing a relatively more important role, especially the northern localities given their proximity to the u.s.. Therefore, our result may point towards a substitution effect of international market access to domestic market access, an issue we explore in further detail below, in the section describing the infrastructure regressions. Finally, it is also noticeable that improvements in market access had large beneficial and statistically significant effects on localities with oil reserves (mostly located in the South).

Table 6: The effect of market access on employment

||||Total em|ployment||
|---|---|---|---|---|---|
|Variables|maonly|ols|<br>iv (Doughnut)|<br>iv (Road count)|iv (Effcient roads)|
|Market Access (ma)|0.176***|0.149***|0.208***|0.163***|0.170***|
||(0.0153)|(0.0111)|(0.0144)|(0.0202)|(0.0219)|
|max Metropolitan dummy||0.0717***|0.0528***|0.0629***|0.0574***|
|||(0.0117)|(0.0146)|(0.0154)|(0.0164)|
|max Education||-0.0468***|-0.0486***|-0.0474***|-0.0452***|
|||(0.00334)|(0.00403)|(0.00329)|(0.00339)|
|maxnaftadummy||-0.114***|-0.119***|-0.115***|-0.117***|
|||(0.00233)|(0.00251)|(0.00213)|(0.00219)|
|max Capital city||-0.0225|-0.0294|-0.0228|-0.0237|
|||(0.0226)|(0.0390)|(0.0250)|(0.0251)|
|max Oil dummy||0.839**|0.756*|0.832**|0.809**|
|||(0.385)|(0.403)|(0.399)|(0.401)|
|Above median education||0.155***|0.124**|0.155***|0.146***|
|||(0.0581)|(0.0550)|(0.0522)|(0.0508)|
|Above median population||0.851***|0.736***|0.805***|0.779***|
|||(0.0803)|(0.0823)|(0.0918)|(0.0959)|
|Constant|6.836***|5.790***||||
||(0.0844)|(0.0755)||||
|Observations|11379|11379|9778|11251|10293|
|Adj. R-squared|0.052|0.423|0.398|0.424|0.440|

_Note_ : * denotes significance at the 10% level, ** denotes significance at the 5% level, and *** denotes significance at the 1% level. The doughnut iv is calculated by excluding all localities within a 25km circle. The road count IV is the number of roads intersecting a circle of 10km radius. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 25 and 100 km radii.

Table 7 reports estimates from the regression of employment on market potential, under ols and iv. Overall, we find a positive and significant causal effect of market potential on employment. The first column (mp only) reports our basic estimate without any controls and 21
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19
are used as instruments for the differenced equation<sup>32</sup> and by collapsing the instrument matrix, which creates an instrument for each variable and lag distance rather than for each variable, time period, and lag distance (Roodman, 2009a,b). To conserve space, I do not report the system GMM results and discuss them only briefly (the full results and a more detailed discussion are available in the working paper version of this article).

The short-term effect of SP aid in both sectors is near zero but is volatile across the different instrument configurations and is estimated imprecisely. As a result, neither the null hypothesis of full fungibility nor the null hypothesis of no fungibility can typically be rejected at conventional significance levels. This volatility and imprecision carry over to the estimate of the long-term effect of education SP aid, _β_<sup>�</sup> _SP_<sup>_LR_=</sup><sup>_β_�</sup><sup>_SP /_(1</sup><sup>_−α_�).</sup> This imprecision likely results from the lack of variation in SP aid. The effect of education TC is close to zero, and the null hypothesis of full fungibility is always strongly rejected. No fungibility cannot be rejected, and the point estimate suggests, at most, only minor displacement of public education expenditures by education TC in the short term. Given the persistence in public education expenditures, the estimate of the long-term effect of education TC is more negative (with -0.3 as the lowest estimate), but even in the long term, full fungibility is rejected and no fungibility is not rejected. In the health sector, full fungibility of TC in the short term is also rejected across the board. In fact, health TC is found to have a positive effect, although the estimate is never significantly different from zero. The average estimated LR effect is approximately 2.6 but has a large standard error. Nonetheless, in all cases except when only a single lag of the variables is used to instrument the differenced equation, full fungibility in the long term can still be rejected. Similar long-term effects are found when a lag of TC aid is added as an explanatory variable in equation (12).

# **An alternative assessment**

Finally, it is worthwhile to consider an alternative approach that allows for a broader assessment of the degree of fungibility of education and health aid while allowing for some uncertainty in the measurement of on- and off-budget aid. Beginning with (3), the estimated coefficient of health aid in (2) can be written as follows:<sup>33</sup> with weight _w_ , and with _ρ_ = _σ_<sup>_σ_</sup> _ON_<sup>_ON_</sup> _σ_<sup>_<u>,OF F</u>_</sup> _OF F_<sup>as the correlation between on- and off-budget health aid and</sup><sup>_δ_=</sup><sup>_σ_</sup> _σOF F_<sup>2</sup> _ON_<sup>2as the relative</sup> variance of off- versus on-budget health aid. If we impose that on- and off-budget health aid have the same degree of fungibility ( _β_<sup>�</sup> _OFF_ = _β_<sup>�</sup> _ON −_ 1), then we can rearrange equation (13) to express _β_<sup>�</sup> _ON_ as a function of 23
INFRASTRUCTURE AND GROWTH IN AFRICA

**Figure 7. Potential growth per capita benefits in Africa due to better growth fundamentals, using the [IK1] index of aggregate infrastructure and 2001–5 averages**

Scenario 1: Reaching the leader in Africa <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>North Africa West Africa East Africa Southern Africa Central Africa AFRICA<br>Stabilization Policies Structural Policies Infrastructure Stock Infrastructure Quality<br><!-- End of picture text --> Scenario 2: Reaching the representative country among the EAP7 <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>North Africa West Africa East Africa Southern Africa Central Africa AFRICA<br>Stabilization Policies Structural Policies Infrastructure Stock Infrastructure Quality<br><!-- End of picture text --> 36
younger and older respondents in the United States. Their findings suggest that age is not a strong predictor of environmental concern or climate change attitudes. Instead, other factors, such as political ideology and education, play more significant roles in shaping these attitudes. <u>Tranter and Booth (2015)</u> find that age poorly predicts climate change skepticism in 14 advanced industrial nations in 2010−2011. Using the World Values Survey (2010–14), which covers 51 countries, <u>Echevarria</u> (2017) finds that age is positively associated with environmental concerns.

Better-educated individuals are usually more concerned with climate change than people with less education (Poortinga et al. 2011, <u>Milfont et al. 2015, Echevarren 2017, Poortinga et al. 2019). The</u> level of education has increased considerably over the past century. In both advanced and developing countries, younger cohorts tend to be more educated than older cohorts, which may mediate the relationship between age and climate change beliefs.

Political conservatism is associated with climate change skepticism (McCright and Dunlap 2011, <u>Whitmarsh 2011, Milfont 2015), but conservative beliefs tend to be negatively associated with</u> educational attainment (Pew Research Center, 2016). <u>Poortinga et al. (2019)</u> show that individuals on the right side of the political spectrum have lower levels of education, are more likely to prioritize self-enhancement over self-transcendence, and are more likely to be skeptical of climate change or its anthropocentric links than others.<sup>4</sup> Conservatism is more prevalent among older individuals (Cornelis et al. 2009; Whitmarsh 2011).

In a meta-analysis of climate change beliefs across 25 polls and 171 academic studies spanning 56 countries, <u>Hornsey et al. (2016)</u> find ideology, world views, and political orientation to be among the strongest predictors of climate beliefs. Free-market ideology is one of the strongest predictors of climate change skepticism. In Europe and Central Asia, support for the market economy tends to be greater among younger adults, which may be related, in part, to the resistance to change of older individuals, who spent much of their lives under a socialist system (EBRD 2007).

Media is an essential source of information about climate change, and more knowledge can influence concern about climate change (Lokshin et al. 2024). Malka et al. (2009) report that in the US, increased knowledge is associated with greater concern among adults who identify as

> 4 <u>Schwartz (1992) defines self−enhancement as characterized by an emphasis on the pursuit of one’s own interests</u> and dominance over others; self−transcendence emphasizes concern for the welfare and interests of others.

5
**Table 3. Frequency of fixed effects used for in-sample predictions**

|**Region**|**Total**|**Cad. block**|**Cad. zone**|**Village **|**Community**|**Old rayon**|**Rayon**|**Oblast**|
|---|---|---|---|---|---|---|---|---|
|Center|82,133|40,955|25,298|10,162|5,640|72|6|0|
|East|10,261|4,960|557|3,134|1,486|99|23|2|
|North|38,339|19,383|10,984|5,947|1,979|27|19|0|
|South|22,710|14,284|1,937|4,015|2,349|103|22|0|
|West|17,793|7,054|2,658|3,500|4,162|363|35|21|
|Country|171,236|86,636|41,434|26,758|15,616|664|105|23|
|Percent||50.6%|24.2%|15.6%|9.1%|0.4%|0.1%|0.0%|

21
**M** _Hi_ = _0o_ + E **_O jx ij_ i=l**

5' Xi is the vector of independent variables for the i-th individual financial institution; and 0 is the vector of parameters to be estimated.

The basic equation of the logit model to be estimated can be written as:

# Pr(Yi = 1) = F[(H(Xi, 0))

We estimate three different logit models using maximum likelihood techniques. In the first model, the dependent variable takes the value of one when a financial intermediary experie-nces distress, as defined in Section 3, and zero otherwise. Here, we have 278 observations,'<sup>5</sup> of which 120 distressed and 158 non-distressed institutions. In the second model, the dependent variable takes the value of one if the institution is closed (i.e., declared failed by the chartering authority and closed down) and zero otherwise. Here, we have 215 observations,16 of which 38 closed and 177 non-closed institutions. In the third model, we only study the closure decision of distressed institutions. Here we have 104 observations,'<sup>7</sup> of which 38 closed and 66 non-closed. This model us estimates the probability of closure conditional on distress, i.e., it investigates which characteristics made a financial institution "more valuable" than others in the eyes of the authorities and was consequently left open (and possibly granted government support).'<sup>8</sup> We use various specifications for each model, but report only one specification (to conserve space). We do report, however, also other variables which were statistically significant, although not necessarily jointly with all the other variables.

# _4.2. The rationale of the explanatory variables_

Table VI summarizes each variable's rationale, along with the expected sign of their impact on the probability of a financial institution's distress and closure. Among the CAMEL-type variables, higher capitalization is expected to have a negative impact on the probability of both distress and closure, as the financial institution will be better able to absorb losses. A larger share of capitalization due to loan loss reserves is likely associated with more risky assets and can therefore be expected to increase the probability of distress. Conditional on the occurrence of distress, however, an

> **15** From the initial sample of 283 observations, five observation are discarded due to missing values.

> 16 Four observations were discarded due to missing values and 64 observations were not used due to no closure-event in Malaysia.

> **17** 104 observations are the 120 distressed institutions less the 16 Malaysian institutions that are not included due to no closure-event in Malaysia.

> **18** Note that we continue to use 1996-only data to analyze the closure and the closures conditional on distress decisions. As such, we are predicting in a sense which institutions were candidates for closure already in 1996.

10
Fig. 9 <!-- Start of picture text --> Cohort Survival Rate and Unit Cost, 1993<br>Poor<br>Guizhou T -<br>Guangxi I -.<br>Anhui<br>Henan .-.<br>Gansu<br>Sichuan<br>Jiangxi i<br>Yunnan<br>Shaanxi -<br>Hunan<br>Tibet ---. . .- . -<br>Ningxia<br>Qinghai<br>Shanxi U<br>Inner Mongolia<br>Hubei I<br>Hebei<br>Jilin ......... 3<br>X injiang -_____ _____ _____ _____ _____<br>Fujian<br>Heilongjiang<br>Shandong<br>Jiangsu .<br>Zhejiang U , .:<br>Liaoning u<br>Guangdong Tianjin '<br>Beijing<br>Shanghai<br>Rich 120 100 80 60 40 20 0 5 10 15 20 25 30 35 40<br>U CSR 1. Unit cost<br><!-- End of picture text --> Note: Provinces are ranked by per capita income.

CSR = Cohort Survival Rate at the end of primary cycle. Unit cost = per-pupil government expenditure in primary educat!r. as a percentage of provincial GNP per capita.

<!-- Start of picture text --> Fig. 10: Rate Of Return to Education<br><!-- End of picture text --> <!-- Start of picture text --> ROR (%)<br>o 5 10 15 20<br>E<br>E~~~~~~~~~~~~~~~~~ PRVT ROR<br>.0:<br>I-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br><!-- End of picture text -->
<!-- Start of picture text --> g cyP<br>=] BWA JOR<br>E 2 MLT<br>a KOR<br>g IRQ BRA nl<br>Cc cl ORE<br>=ce meNA ONT Tunpy NGA pf#HBUGe Firs PRT iy,gp =<br>Bo fe_ 86 gggctio ARREST TUR ry Ra<br>oco BFANBD RETGO ge SOAve CHERL i__ nFIRN ARG 4<br>g BGD cageMDG SEN’GHAMB MRT Ai<br>a TcD sa ay<br>a<br>Oo<br>Co)<br>a<br>s<br>Zo<br>ea<br>6 7 8 9 10 11 12<br>Log GDP per capita (constant 2015 US$)<br>——— gdp_pe_gr =-.92248+ .489 log _gdp_pc<br>Note: countries in red = BB; countries in blue = EM; countries in green = OECD<br><!-- End of picture text -->
# **References**

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- **Blattman, Christopher, Nathan Fiala, and Sebastian Martinez** , “Generating skilled self-employment in developing countries: Experimental evidence from Uganda,” _Quarterly Journal of Economics_ , 2014, _129_ (2), 697–752.

- **Brandt, Loren and Carsten Holz** , “Spatial price differences in China: Estimates and implications,” _Economic Development and Cultural Change_ , 2006, _55_ (1), 43–86.

- **, Jikun Huang, Guo Li, and Scott Rozelle** , “Land rights in rural China: Facts, fictions and issues,” _The China Journal_ , 2002, _47_ , 67–97.

- **, Scott Rozelle, and Matthew Turner** , “Local government behavior and property rights formation in rural China,” _Journal of Institutional and Theoretical Economics_ , 2004, _160_ (4), 627–662.

- **Bustos, Paula, Bruno Caprettini, and Jacopo Ponticelli** , “Agricultural productivity and structural transformation: Evidence from Brazil,” _American Economic Review_ , 2016, _106_ (6), 1320–65.

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- **, Peter D. Little, Tewodaj Mogues, and Workneh Negatu** , “Poverty traps and natural disasters in Ethiopia and Honduras,” _World Development_ , 2007, _35_ (5), 835–856.

29
Table 13: Medium-term impacts of _Mais Educação_ on educational outcomes for schools opting for the mathematics activity

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.864|–1.395|–0.770|
||(1.355)|(1.914)|(1.783)|
|_R_ <sup>2</sup>|0.701|0.808|0.807|
|Observations|1,162|1,162|1,162|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|0.342|-3.386|0.111|
||(1.375)|(2.117)|(2.199)|
|_R_ <sup>2</sup>|1,108|1,108|1,108|
|Observations|0.709|0.787|0.775|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined mathematics activity in 2008 and remained in the activity until 2011. Pedagogical monitoring must be chosen by schools as one of the fields of knowledge, but schools can choose for which subjects they offer pedagogical support (Portuguese, mathematics, sciences, history, etc.) This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.