rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A7**

First stage and reduced form estimates, main and placebo samples _Panel A. Main sample_ _<u>first</u> stage and reduced_ _<u>form</u> effects_

||First stage||Reduced form|||
|---|---|---|---|---|---|
||Road by 2012|Transport|Occupation (ag share)<br>Firms|Ag production|Consumption|
|Road priority|0.212***|0.095**|-0.080**<br>0.054|0.021|0.013|
||(0.017)|(0.040)|(0.034)<br>(0.035)|(0.027)|(0.030)|
|Control group mean|0.25|-0.00|0.00<br>-0.00|0.00|0.00|
|N|11474|11474|11474<br>10709|11474|11474|
|R2|0.30|0.19|0.30<br>0.31|0.55|0.50|

_Panel B. Placebo sample_ _<u>first</u> stage and reduced_ _<u>form</u> effects_

||First stage||Reduced form|||
|---|---|---|---|---|---|
||Road by 2012|Transport|Occupation (ag share)<br>Firms|Ag production|Consumption|
|Road priority|0.003|0.004|-0.025<br>-0.002|-0.021|0.004|
||(0.018)|(0.061)|(0.040)<br>(0.042)|(0.033)|(0.044)|
|Control group mean|0.27|0.47|-0.20<br>0.28|-0.24|0.35|
|N|8800|8800|8743<br>8084|8800|8800|
|R2|0.34|0.31|0.41<br>0.49|0.52|0.39|

_<u>∗ ∗∗ ∗∗∗</u> p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 Notes: This table presents a comparison of estimates of the effect of PMGSY prioritization on a village’s probability of treatment (first stage) and reduced form estimates of the effect of PMGSY prioritization on indices of the five major families of outcomes, for both the main sample (Panel A) and a placebo sample of villages close to the thresholds that were not followed (Panel B). For each regression, the outcome mean for the control group (villages with population below the threshold) is also shown. The specification includes baseline village-level controls for amenities and economic indicators, as well as district-cutoff fixed effects (see Section V for details). Heteroskedasticity robust standard errors are reported below point estimates.
26

**Table 4.16: Incidence of Poverty by Age Group of Head of Household 1985-93 (Poverty Line=N395.41)**

||||**_RURAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Age**Group|**P0**|**P,**|P2|**P0**|**P,**|**P2**|
|16 to 25|0.273|0.093|_0.054_|0.262|0.128|0.097|
|26 to 35|0.389|0.120|0.060|0.305|0.096|0.052|
|36to45|0.512|0.160|0.078|0.365|0.155|0.091|
|46to55|0.512|0.188|0.096|0.391|0.169|0.098|
|56 to 65|0.552|0.190|0.094|0.398|0.180|0.106|
|66andOver|0.570|0.207|0.108|0.358|0.162|0.100|
|_All Rural_|_0.495_|_0.169_|_0.085_|_0.364_|_0.167_|_0.098_|
||||**_URBAN_**||||
|||**1985/86**|||**1992/93**||
|Age Group|**P0**|P1|P2|**P,**|**P,**|P2|
|16 to 25|0.101|0.056|0.020|0.172|0.061|0.023|
|26 to 35|0.226|0.077|0.029|0.182|0.106|0.062|
|36 to 45|0.304|0.124|0.051|0.316|0.138|0.077|
|46to55|0.411|0.147|0.060|0.364|0.136|0.068|
|56 to 65|0.390|0.166|0.076|0.393|0.161|0.081|
|66andOver|0.406|0.135|0.052|0.344|0.170|0.101|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|

Source: NCS, 1985/86 and 1992/93.

Focusing on the urban areas, we find the largest decline (6.2 percentage points) in headcount is within the 66 & over age-group, closely followed by the 46 to 55 age-group with 4.7 points decline in headcount.
Table 3: Inequality of Opportunity Estimates

|Country|Parametric|Non-Parametric|Latent Class|Cond. Inf. Tree|Cond. Inf. Forest|
|---|---|---|---|---|---|
|AT|0.0888|0.0751|0.0796|0.0865|0.0880|
|BE|0.1108|0.0868|0.0534|0.0868|0.0912|
|BG|0.1542|0.1356|0.1148|0.1362|0.1335|
|CH|0.0917|0.0827|0.0631|0.0796|0.0901|
|CY|0.0942|0.0831|0.0738|0.0799|0.0800|
|CZ|0.0716|0.0659|0.0600|0.0569|0.0511|
|DE|0.0704|0.0588|0.0467|0.0697|0.0793|
|DK|0.0772|0.0409|0.0289|0.0212|0.0204|
|EE|0.1108|0.1020|0.0744|0.0967|0.1005|
|EL|0.1476|0.1208|0.1165|0.1264|0.1089|
|ES|0.1421|0.1201|0.0893|0.1280|0.1200|
|FI|0.0687|0.0515|0.0475|0.0197|0.0275|
|FR|0.0858|0.0863|0.0717|0.0904|0.0980|
|HR|0.1312|0.0884|0.0758|0.0822|0.0763|
|HU|0.1098|0.1033|0.0951|0.1134|0.1079|
|IE|0.1048|0.0971|0.0484|0.0843|0.0784|
|IS|0.0669|0.0321|0.0297|0.0123|0.0157|
|IT|0.1213|0.0907|0.0799|0.1078|0.0969|
|LT|0.0947|0.0674|0.0587|0.0693|0.0672|
|LU|0.1340|0.1209|0.0904|0.1326|0.1356|
|LV|0.1335|0.1099|0.0951|0.1102|0.1110|
|MT|0.0872|0.0796|0.0566|0.0710|0.0716|
|NL|0.0661|0.0529|0.0411|0.0284|0.0194|
|NO|0.0480|0.0405|0.0296|0.0202|0.0234|
|PL|0.1111|0.0973|0.0953|0.1019|0.0991|
|PT|0.1376|0.1236|0.1156|0.1362|0.1267|
|RO|0.1698|0.1040|0.1194|0.1204|0.1107|
|SE|0.1178|0.0604|0.0251|0.0247|0.0313|
|SI|0.0772|0.0730|0.0588|0.0317|0.0361|
|SK|0.0626|0.0507|0.0420|0.0495|0.0459|
|UK|0.1012|0.0896|0.0622|0.0714|0.0791|

_Note_ : Estimates of inequality of opportunity using five different estimation methods. Inequality of opportunity is measured as the Gini coefficient in the counterfactual distribution _Y_<sup>_EA_</sup> .

learn about the particular circumstance types the differences among which cause the existence of inequality of opportunity. In this section we illustrate such analyses for both trees and forests. To keep the analysis intelligible we restrict ourselves to two interesting cases: Sweden and Germany.

**Trees** As outlined in section 3.1, the analysis of opportunity structures is particularly intuitive in the case of trees as the relevant information can be directly read off their graphical illustration. Figure 3 illustrates the opportunity structure of Sweden that can be summarized by a tree with two terminal nodes. Inequality of opportunity in Sweden is due to marked differences between first-generation immigrants born outside Europe and the collective group of native residents and European immigrants. The former type accounts for about 10% of the population and on average obtains an equivalent household income that is 35% lower than the corresponding income of the latter group. Recall that each split is based on a statistical test for the existence of equality of opportunity within the respective internal node. Thus, in Sweden we can reject the null hypothesis of equal opportunities for first-generation immigrants born outside Europe and the remainder of the population. However, within these sub-groups the null hypothesis of equality of opportunity cannot be rejected.

A different picture arises when considering Germany. Parental occupation, parental educa16
# **1. Introduction and justification**

According to research carried out by the Tyndall Centre of Climate Change in the UK, Peru is one of the most vulnerable countries in the world to climate change. When the Mega-Niño of 1997-1998 occurred, cities along the Peruvian coast experienced temperature increases of 6oC in just a few months (see Figure 1 below), while the impact in most other places in the Americas was limited to a couple of degrees or less.

**Figure 1: Daily temperature anomalies in Lima, Peru, 1995-2008**

**Lima - Jorge Chavez International Airport**

<!-- Start of picture text --> 8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>-4.0<br>Year<br>average for 1910-1990 (oC)<br>Temperature anomaly compared to<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source:_ For daily temperatures: Average Daily Temperature Archive, University of Dayton, GSOD weather station no. 846280, located at 12.00oS/77.11667oW, 13 meters above sea level. For 1910-1990 average monthly temperatures: World Climate (www.worldclimate.org) station Lima-Callao/International Airport located at 12.00oS/77.09oW, 13 meters above sea level.

El Niño’s impact in Peru is not limited to temperature changes, but also causes large precipitation changes. Under normal conditions, the predominant ocean currents keep the water off the Peruvian coasts relatively cold and nutrient rich (Woodman 1998), which contributes to the success of the fishing activities in Peru. When waters are cold there is little evaporation, which explains why it rarely rains along the coast. However, when the El Niño phenomenon occurs, the increase in ocean temperatures causes increased evaporation, which in turn tends to cause excessive rainfall and flooding in the northern part of Peru.

The 1997-98 El Niño, for example, caused extensive damage in Peru. Flooding affected 120,000 homes and destroyed 50 bridges, hundreds of kilometers of paved roads, and 50,000 hectares of crops. The unusually warm water off the coast caused fish to migrate to 2
- - 73

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||||||||**Year(2 **|**000 +**|**)**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Country**|**05**|**06**|**07**|**08**|**09**|**10**<br>**11**|**12**|**13**|**14**|**15**|**16**|**17**|**18**|
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|social work|CRI||||||||||||||
||MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
|13. Other|BRA||||||||||||||
|community,|CHL||||||||||||||
|social and|COL||||||||||||||
|personal service|CRI||||||||||||||
|activities|MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
||BRA||||||||||||||
|14. Activities of<br>|CHL||||||||||||||
|private<br>|COL||||||||||||||
|households as|||||||||||||||
|<br>l|CRI||||||||||||||
|empoyers|MEX||||||||||||||
||PER||||||||||||||

Source: Own elaboration based on CAIT, OECD, and SEDLAC data.

# Green jobs: Occupations vs. sectors

By combining the sector and occupation classifications, four mutually exclusive job types (see Figure 1) are created. Each group faces two primary sources of vulnerabilities to green policies. First, workers in non-green sectors would be more vulnerable to policies that increase the cost of GHGE, such as a Carbon Border Adjustment Mechanism (CBAM).<sup>14</sup> If such a policy reduces the competitiveness of non-green sectors, they may be more likely to shrink and shed labor. Second, workers in non-green occupations would be more vulnerable in terms of lacking the skills to remain employed or find a new job if green policies create a skill-biased change in labor demand. By overlapping these two dimensions, the following four categories are created:

> 14 See <u>https://ec.europa.eu/commission/presscorner/detail/en/qanda_21_3661 the CBAM as proposed by the European</u> Commission.

15
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**Table 1 – Proportions of innovative/noninnovative companies, by country**

||**Noninnovative**|**Innovative**||
|---|---|---|---|
||||**N Ob**|
|**Country**|**Did not introduce new product/process**|**Introduce of new product/process**|**. s**|
|Turkey|0.60|0.40|513|
|Romania|0.51|0.49|365|
|Hungary|0.66|0.34|187|
|Latvia|0.43|0.57|226|
|Lithuania|0.34|0.66|238|
|Bulgaria|0.50|0.50|149|
|Total|0.52|0.49|1,678|
|**Country**|**Did not invest in R&D **|**Invested in R&D **|**N. Obs**|
|Turkey|0.70|0.30|513|
|Romania|0.77|0.23|359|
|Hungary|0.90|0.10|187|
|Latvia|0.84|0.16|225|
|Lithuania|0.86|0.14|239|
|Bulgaria|0.63|0.37|150|
|Total|0.76|0.24|1,673|

Source: EFCS 2009‐ES 2008

**Table 2 – Proportions of young/older companies (before the crisis), by country**

|**Country**|**Young**<br>**(< =5 years old)**|**Older**<br>**(>=6 years old)**|**N.obs**|
|---|---|---|---|
|Turkey|0.11|0.89|514|
|Romania|0.20|0.80|370|
|Hungary|0.11|0.89|187|
|Latvia|0.17|0.83|226|
|Lithuania|0.25|0.75|239|
|Bulgaria|0.11|0.89|150|
|Total|0.11|0.89|1,686|

Source: EFCS 2009‐ES 2008 Using the innovation and age groups defined above, we examine a particular measure of firm performance: growth in sales. We calculate the sales growth rates before and after the crisis (Table 3) for these categories of firms. For the pre‐crisis period, we use the sales value of 2007 fiscal year and three years before that, from ES 2008, and compute the annualized growth rate of sales in the 2004‐2007 period.<sup>7</sup> For the post‐crisis period, we use annual growth rate of sales in the 2008‐2009 period<sup>8</sup> , according to EFCS.<sup>9</sup>

> 7 Sales values for 2004 and 2007 were originally in local currencies and were converted to current US dollars using exchange rate from International Financial Statistics Database. We then controlled for outliers of sales

5
Table A.6: Excess female under-5 mortality: double-difference models by firstborn sex

|||**Outco**|**me: Death **|**before ag**|**e 5**||
|---|---|---|---|---|---|---|
||Firs|tborn girl fa|mily|Firstb|orn boy f|amily|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Female_|2.929***|2.893***|2.822***|-0.0338|-0.0609|0.00292|
||(0.694)|(0.691)|(0.680)|(0.618)|(0.603)|(0.614)|
|_Female × Post1_|-1.362**|-1.343**|-1.305**|0.282|0.307|0.225|
||(0.510)|(0.513)|(0.498)|(0.493)|(0.480)|(0.489)|
|_Female × Post2_|-1.882***|-1.853***|-1.928***|0.0976|0.148|0.0199|
||(0.461)|(0.466)|(0.458)|(0.577)|(0.581)|(0.587)|
|_Post1_|-1.897***|||-3.304***|||
||(0.499)|||(0.665)|||
|_Post2_|-3.967***|||-5.738***|||
||(0.796)|||(1.135)|||
|N|114,418|114,418|114,418|112,711|112,711|112,711|
|_Xijt_|||x|||x|
|Birth order FE|x|x|x|x|x|x|
|State FE|x|x|x|x|x|x|
|Birth year FE||x|x||x|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression. The outcome is an indicator for death before age 5. We drop children that are less than 5 years old to allow each child in the sample full exposure to the risk of under-5 mortality. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. *** 1%, ** 5%, * 10%.

53
# **Table 4a. Banks' Buffering Role: Financial Crises**

||Log of # of<br>Exporters|Log of Export Value<br>per Exporter|Exporter Entry<br>Rate|Exporter Exit<br>Rate|Exporter<br>Turnover Rate|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||_Panel A:_|_Banks vs. Stock M_|_arket_||
|(Bank Credit / (Bank Credit + Stock))*ExtFin|0.411***|-0.372|-0.038|-0.061**|-0.063|
||(0.121)|(0.263)|(0.024)|(0.026)|(0.042)|
|(Bank Credit /(Bank Credit + Stock))*ExtFin*Fin. Crisis|-1.521***|6.173***|-0.101|-0.003|-0.149|
||(0.340)|(1.673)|(0.144)|(0.204)|(0.290)|
|Observations|17,870|17,561|15,685|15,639|15,548|
|R-squared|0.882|0.550|0.528|0.498|0.551|
|||_Panel B: Forei_<br>|_gn Banks vs. Dome_<br>|_stic Banks_<br>||
|(Foreign Bank / Total Bank Assets)*ExtFin|-0.138|-0.648***|0.040**|0.050***|0.099***|
||(0.106)|(0.196)|(0.019)|(0.019)|(0.033)|
|(Foreign Bank / Total Bank Assets)*ExtFin*Fin. Crisis|-0.991***|1.669**|-0.172***|-0.064|-0.264**|
||(0.356)|(0.762)|(0.059)|(0.115)|(0.121)|
|Observations|17,653|17,233|15,919|15,904|15,766|
|R-squared|0.876|0.542|0.467|0.459|0.502|

Note: This table reports the effects of financial structures on the number, size and dynamics of exporters in 2-digit ISIC industries differentiating between normal times and financial crises. The financial structure is captured by the ratio of bank credit to the sum of stock market capitalization and bank credit (panel A) and the share of foreign bank assets in the total bank assets of the country (panel B). The dependent variables are the number of exporters (column 1); the average sales of exporters (column 2); the entry rate of exporters (column 3); the exit rate of exporters (column 4); the turnover (entry+exit) rate of exporters (column 5). Fin. crisis is a dummy equal to 1 if in the country a financial crisis occurs during the year (see text for details). All regressions include FinDev*ExtFin, ExtFin*Fin. Crisis, FinDev*ExtFin*Fin. Crisis, country-year fixed effects and industry-year fixed effects, where FinDev is measured as all credit over GDP to control for total financial development. Regressions in panel B also include Bank Concentration*ExtFin and Bank Concentration*ExtFin*Fin. Crisis to control for the concentration of the banking sector. Robust standard errors, clustered at the countryyear level, are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
# **4.4 Return to Innovation with patent right protection**

In this section we squarely focus on the most arguably relevant factor that affects innovation: patent protection. The index is provided in Park (2008). It is <mark>“</mark> _<mark>the unweighted sum of five separate scores for: coverage (inventions that are patentable); membership in international treaties; duration of protection; enforcement mechanisms; and restrictions (for example, compulsory licensing in the event that a patented invention is not sufficiently exploited)”</mark>_ <mark>(Park, 2008).</mark>

**Table 6. Returns to Innovation with Patent right protection**

|Variables|∆ln(|sales)|∆ln(sal|es/ labor)|
|---|---|---|---|---|
|Innovation|-2.266|-2.584*|0.0674|0.0140|
||(1.389)|(1.329)|(1.071)|(1.027)|
|PatentsRights*Innovation|0.0320|0.0349*|0.00197|0.00253|
||(0.0183)|(0.0172)|(0.0148)|(0.0141)|
|New Top Manager||-1.484***||-1.700***|
|||(0.194)||(0.184)|
|Exporter||0.0407||-0.0374|
|||(0.112)||(0.169)|
|More Competitors||-0.918***||-0.709***|
|||(0.167)||(0.0961)|
|Small Size||-1.597***<br>(0.355)||-0.598**<br>(0.222)|
|Medium Size||-1.209***||-0.598***|
|||(0.123)||(0.127)|
|Constant|1.575|3.014***|1.535|3.841**|
||(1.057)|(0.377)|(1.186)|(1.294)|
|Year*Country FE|yes|yes|yes|yes|
|Industry FE|yes|yes|yes|yes|
|No of Obs|1,198|1,193|1,189|1,189|
|R-Squared<br>*** p< 0.01, ** p< 0.05, * p< 0.1<br>Clustered robust standard errors b|0.306<br>y country in pa|0.359<br>rentheses|0.262|0.280|

17
incentives to act in the shadow economy or to evade taxes. A more legitimate and responsive state appears to be an essential precondition in generating compliance. If individual and business contracts are not enforced and productive efforts not protected, the incentive to be active illegally increases. Citizens feel cheated if corruption is widespread, their tax burden is not spent well, and that they are not protected by the rules of law. Such a situation also increases the incentive not be compliant. Discussing tax reforms in transition Owsiak (2007, p. 223) stresses that ―reforms under way in postcommunist countries should not, at the present stage of in-depth restructuring of the economy, rely exclusively on tax cuts but instead should concentrate on measures enhancing citizen trust in law and on the removal of sources of tax-related abuse. The establishment of a legal framework that would render the tax system stable, transparent, simple, friendly to taxpayers, accompanied by concurrent implementation of a rational mechanism for allocating and controlling public spending, highlighting the relation between collected taxes and the benefits gained by local communities, would ultimately determine whether society will welcome the development of tax-related civil behavioral patterns‖ (p. 223). Evidence in the tax morale literature indicates that positive attitudes towards the tax authority and tax system significantly increase tax morale. A respectful and fair treatment of taxpayers induces respect for the tax system and thus leads to cooperation. On the other hand, a higher perceived tax burden and inefficiencies and unfairness in the interaction between the tax administration and the taxpayers crowds out the intrinsic motivation to pay taxes. International evidence also shows that instead of focusing so much on compliance management, risk control, or structuring the application of enforcement discretion, it may make sense if the tax administration becomes more focused on service, customers, quality, transparency and process improvement.

Social norms or social capital are key factors in understanding the motivation for compliance in transition countries and other regions. Moreover, social capital seems to be an important determinant of economic phenomena like macroeconomic performance. For example, Knack and Keefer (1997) find, in a cross-sectional analysis, a strong and significantly positive relationship between social capital variables (civic duty) and economic growth. Schaltegger and Torgler (2007), using data for a synthetic panel of Swiss cantons over the 1981–2001 period, show that accountability enhances fiscal 54