rafmacalaba/datause-encoder-data huggingface dataset:
The differences that signal a change in intervention policy are not large, at least for the balance sheet indicator: if these numbers are interpreted as estimates of thresholds, there is only a 122 per cent increase in liabilities relative to assets between pre and post floating on the one hand, and sustained fixed and floating on the other. Since external asset stock measures are cumulated from asset flows reported in the balance of payments, which are underestimates at best, the assets are largely central bank reserves. As these reserves fall, even a small increase in external debt can render intervention unsustainable. The output growth slow down is large however, across the same comparison points: around three per cent. The same is true for the ratio of M0 to reserves, which is a measure of the central bank’s contingent liabilities. The ability to sustain high intervention is associated with a fall in the growth of reserve money: the number jumps to twenty percent in the pre-floating phase.

**Table 2: Descriptive Statistics**

||**Sample**|**Debt/asset**<br>**ratio**<br>**(natural logs)**|**GDP**<br>**growth**|**Stock**<br>**market**<br>**volatility**|**REER**<br>**change**|**Change in**<br>**M0/reserves**|**Change in**<br>**inflation**<br>**differential**|**Inflation**<br>**“acceleration”**<br>**relative to US**<br>**inflation**|
|---|---|---|---|---|---|---|---|---|
|(1)|Fixed|0.48|0.04|0.53|0.00|-0.01|-0.03|-0.04|
|(2)|Pre-float|0.68|0.01|0.49|-0.01|0.20|-0.02|-0.01|
|(3)|Post-float|0.60|-0.07|0.59|0.02|0.22|0.02|0.00|
|(4)|Float|0.40|0.05|0.67|0.00|0.26|0.02|0.05|
|||||**t-te**|**sts**||||
|(1)≠(2**)**|p-value<br>P>|t||0.0321|0.014|0.354|0.877|0.000|0.596|0.591|
|(1)≠(4)|p-value<br>P>|t||0.3157|0.805|0.001|0.744|0.000|0.0061|0.007|

# _Panel Specification and Analysis:_

For the entire panel, we model the probability of floating as being dependent on the control variables contained in _Xit_ , (listed in Table 1). Equation 10 specifies that there are country fixed effects (captured by _αi_ for the i<sup>th</sup> country).

17
# **1. Introduction**

In 2000, about one out of three adults, or 1.1 to 1.2 billion people worldwide, smoked (WHO 1999). By 2010 the number of smokers is expected to grow to around 1.3 billion (FAO 2003). The vast majority of the world’s smokers (82 percent) live in developing countries where, in sharp contrast to developed countries, the consumption of tobacco is on the rise. Tobacco control is increasingly seen as a public health priority, motivating campaigns such as the World Health Organization’s Tobacco Free Initiative as well as country-specific tobacco control campaigns. These efforts are justified not only on grounds related to well-established adverse effects of smoking on health, but more generally on economic grounds.<sup>1</sup> The gross health care cost of smoking for the developing countries is estimated to be as high as 1.1 percent of GDP (Jha and Chaloupka 2000). The economic costs associated with losses in productivity because of smoking could be at least as large and be disproportionately incurred by the poor (The World Bank 1999).

Estimates of the economic costs of smoking tend to reflect costs associated with increased premature death and health care costs related to smoking-induced chronic diseases (which can affect smokers as well as non-smokers exposed to smoke). In addition to these pathways, there is a third category of costs that has been considered: the earnings and wage reductions associated with smoking. The literature on the relationship between smoking and labor market performance is very limited and based on data from Western Europe and the US. Berger and Leigh (1989) examine the effect of smoking and being overweight on earnings using the 1973 Quality of Employment survey in the US. Their study finds no statistically significant association between smoking and earnings for male and female workers. Levine _et al_ . (1997) investigate the effect of smoking on wages using panel data from the US. They compare wages of continuous smokers with the wages of workers who quit smoking. Controlling for observable and time-invariant unobservable characteristics, smoking is estimated to lower a worker’s wage by about 4 – 8 percent. Drawing on data from Germany, Heineck and Schwarze (2003) examine

> 1 The WHO estimates that tobacco is the second major cause of death worldwide and the fourth most common risk factor for disease worldwide.

2
- **Jalota, Suhani and Lisa Ho** , “What Works For Her? How Work-from-Home Jobs Affect Female Labor Force Participation in Urban India,” _Working Paper_ , 2024.

- **Jayachandran, Seema** , “Social norms as a barrier to women’s employment in developing countries,” Technical Report, National Bureau of Economic Research 2020.

- , “Social Norms as a Barrier to Womenâs Employment in Developing Countries,” _IMF Economic Review_ , September 2021, _69_ (3), 576–595.

- **Klasen, Stephan and Janneke Pieters** , “What Explains the Stagnation of Female Labor Force Participation in Urban India?,” _The World Bank Economic Review_ , January 2015, _29_ (3), 449–478.

- **Kleven, Henrik** , “The Geography of Child Penalties and Gender Norms: A Pseudo-Event Study Approach,” June 2022.

- **, Camille Landais, and Gabriel Leite-Mariante** , “The Child Penalty Atlas,” August 2023.

- **, , and Jakob Egholt Soegaard** , “Does Biology Drive Child Penalties? Evidence from Biological and Adoptive Families,” _American Economic Review: Insights_ , June 2021, _3_ (2), 183–198.

- **, , Johanna Posch, Andreas Steinhauer, and Josef Zweimueller** , “Child Penalties across Countries: Evidence and Explanations,” _AEA Papers and Proceedings_ , May 2019, _109_ , 122–126.

- **, , , , and** , “Do Family Policies Reduce Gender Inequality? Evidence from 60 Years of Policy Experimentation,” _American Economic Journal: Economic Policy_ , May 2024, _16_ (2), 110–149.

- **Nandi, Arijit, Parul Agarwal, Anoushaka Chandrashekar, and Sam Harper** , “Access to affordable daycare and womenâs economic opportunities: evidence from a cluster-randomised intervention in India,” _Journal of Development Effectiveness_ , July 2020, _12_ (3), 219–239.

- **Petrongolo, Barbara and Maddalena Ronchi** , “Gender gaps and the structure of local labor markets,” _Labour Economics_ , 2020, _64_ , 101819. Publisher: Elsevier.

- **Psacharopoulos, George and Harry Anthony Patrinos** , “Returns to investment in

21
**Table 2 Key Determinants of Introduction of PPP in Transmission & Distribution**

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|**GOVERNANCE VARIAB**<br>**SECTORALGOVERNANCE V**<br>|**LES**<br>**ARIABLES**<br>||
|**Introduction of a regulatory agency**<br>_REG_<br>0.344***<br>(0.124)|0.354***<br>(0.131)|0.338***<br>(0.137)|
|**Sequencing of policy (introduction of private**<br>**sector first in distribution)**_SEQU_<br>0.552***<br>(0.150)|0.368***<br>(0.157)|0.338***<br>(0.136)|
|**ECONOMYWIDEGOVERNANCE **<br>|**VARIABLES**<br>||
|**Control of Corruption**_wgi_cc_<br>0.244**<br>(0.110)|0.238**<br>(0.125)|0.101<br>(0.132)|
|0.032***|0.030**|0.023*|
|**Degree of democracy****_Polity2_**<br>(0.012)|(0.013)|(0.014)|
|<br>**LONG RUN ENVIRONMENTALSUSTAI**|<br>**NABILITY FACT**|<br>**ORS**<br>***|
|**PPP Investment in Renewables**||0.944<br>(0145)|
|**MACROECONOMIC CON**|**TROL**<br>|.<br>|
|**GDP**<br>_ln_GDP_|0.033<br>(0.081)|0.059<br>(0.234)|
|**Population**<br>_ln_Pop_|0.245***<br>(0.092)|0.146<br>(0.097)|
|**REGIONALCONTRO**<br>|**LS**<br>||
|**LAC**<br>-0.188***<br>(0.189)|-0.523***<br> (0.213)<br>|-0.834***<br> (0.242)|
|**Constant**<br>-2.027***<br>(0.189)|-6.821***<br>(0.957)|-5.971<br>(1.012)|
|**N**<br>1185|1143|1143|
|**Log likelihood**<br>-287.62|-262.85|-241.84|
|**χ**<sup>**2**</sup><br>66.71***|109.41***|151.42***|
|**Pseudo R**<sup>**2**</sup><br>10.39|17.23|23.84|

_Note:_ *, **, *** indicate significance of the coefficient respectively at 10%, 5% and 1% confidence level.

In what follows the key results are highlighted, focusing on the specific hypotheses tested in each of the regressions.

Regression 1 confirms Hypothesis 1A with the introduction of regulatory agencies positively and highly significantly associated to the introduction of PPPs in T&D. Also Countries that attracted first PPPs in distribution and then in generation are also more likely to attract investment in distribution. Hypothesis 2A is however not confirmed with higher standard of economy wide governance making countries more likely to attract PPPs in T&D, but not significantly so. Sector governance dominates economy wide governance in attracting PPPs.

12
Table A4: Robustness check using the subsample with tenure being 3 years and less— Unobservable abilities

|||Math|(z-score)|||Language|(z-score)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.293<sup>_∗∗∗_</sup>|0.090|0.171|<sup>_∗_</sup><br>0.235<sup>_∗_</sup>|-0.463<sup>_∗∗∗_</sup>|0.060|0.061|-0.001|
||(0.103)|(0.105)|(0.09|2)<br>(0.122)|(0.079)|(0.076)|(0.066)|(0.071)|
|Schooing in years||0.115<sup>_∗∗∗_</sup>|0.113<sup>_∗_</sup>|<sup>_∗∗_</sup><br>0.133<sup>_∗∗∗_</sup>||0.134<sup>_∗∗∗_</sup>|0.116<sup>_∗∗∗_</sup>|0.146<sup>_∗∗∗_</sup>|
|||(0.014)|(0.01|5)<br>(0.027)||(0.014)|(0.017)|(0.021)|
|Age at hire||-0.006|0.00|2<br>-0.003||-0.011<sup>_∗_</sup>|-0.005|-0.002|
|||(0.011)|(0.01|2)<br>(0.013)||(0.007)|(0.009)|(0.007)|
|Father’s pri.school||-0.015|0.14|0<br>0.171<sup>_∗∗_</sup>||0.210<sup>_∗∗_</sup>|0.183<sup>_∗∗_</sup>|0.136|
|||(0.111)|(0.09|4)<br>(0.082)||(0.087)|(0.088)|(0.089)|
|Mother’s pri.school||-0.005|-0.08|9<br>-0.180||0.158|0.047|0.072|
|||(0.138)|(0.17|0)<br>(0.174)||(0.123)|(0.117)|(0.149)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.125|0.270|0.21|3<br>0.169|0.149|0.440|0.293|0.276|
|N|1745|1745|1745|1745|1745|1745|1745|1745|
|||Big 5 (z-|score)||||||
||(9)|(10)|(11)|(12)|||||
|Social networks|0.063<sup>_∗_</sup>|0.098|0.043|-0.032|||||
||(0.038)|(0.063)|(0.057)|(0.065)|||||
|Schooing in years||0.007|0.001|-0.008|||||
|||(0.009)|(0.008)|(0.007)|||||
|Age at hire||0.002|-0.005|-0.006|||||
|||(0.007)|(0.007)|(0.006)|||||
|Father’s pri.school||0.053|-0.056|-0.061|||||
|||(0.084)|(0.062)|(0.063)|||||
|Mother’s pri.school||-0.003|-0.003|-0.001|||||
|||(0.084)|(0.056)|(0.047)|||||
|Firm fxed efect|||X||||||
|Firm-occ fxed efect||||X|||||
|R-squared|0.122|0.128|0.028|0.029|||||
|N|1744|1744|1744|1744|||||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

41
is currently one standard deviation below steady-state income will require about 23 years to close the gap at a convergence rate of 2 percent per year (Gennaioli et al. 2014).

In this paper, we investigate whether labor productivity converged across Brazil’s states (“departments”) between 2002 and 2018. During the sample period, our findings show strong evidence of unconditional regional convergence in which states with lower initial levels of labor productivity experienced substantially faster labor productivity growth. Additionally, the convergence rate is faster for the period 2002-10 compared with the 2010-18 period. The convergence rate is particularly strong in agriculture, extractives, and manufacturing. These findings also hold when controlling state and industry fixed effects as well as states’ initial poverty rate, human capital, per capita tax collection, and infrastructure.

This paper is related to the literature on convergence, especially regional convergence. It is related to Gennaioli et al. (2014), who show that regional convergence is relatively faster than crosscountry convergence but generally slower than expected using a large sample of countries, including Brazil. We complement their findings by focusing on labor productivity (rather than per capita income) and using a matched employer-employee dataset. Unlike Azzoni and Castro (2023) who provide evidence of stronger convergence across states of Brazil only during periods of economic crisis (2008 and 2014) and show that periods of better aggregate growth are linked with increasing regional dispersion, our results generally show evidence of regional convergence between 2002 and 2018. Our results also contribute to country case studies on regional inequality that account for the sectoral dimension (e.g., Kinfemichael and Morshed 2019a; Iacovone et al. 2015).

The rest of this paper is organized as follows. Section 2 provides a brief survey of the evidence on the patterns and drivers of convergence from cross-country and country case studies. Section 3 presents the main data sources and summary statistics on labor productivity, poverty rate, and geographic concentration of different sectors. It then introduces the test used to detect if there is any form of regional convergence. Section 4 discusses the results, and section 5 concludes.

3
# **Data Appendix**

Data come from the World Economic Forum, Global Competitiveness Report, 2001-02 (GCR), IMD Lausanne, World Competitiveness Yearbook 2002 (WCY), and Kaufmann, Kraay and Zoido-Lobaton (2002) (KKZ). All survey data in GCR comes from the World Economic Forum’s Executive Opinion Survey. A total of 4022 firms were surveyed. “In order to provide the basis for a comparative assessment on a global basis, it is essential that we interview a sufficient number of senior business leaders in individual countries and that the sample in each country is not biased in favor of any particular business group. We have taken a number of steps to ensure this. First, we have asked each of our partner institutes, the organizations that administer the surveys in each country, to start with a comprehensive register of firms. From this, they were asked to choose a sample whose distribution across economic sectors was proportional to the distribution of the country’s labor force across sectors, excluding agriculture. They were then asked to choose firms randomly within these broad sectors (for example, by choosing firms at regular intervals from an alphabetic list), and to pursue face-to-face interviews, following up for clarifications where necessary. The employment distribution was taken from data in the 1998 _Yearbook of Labour Statistics_ of the International Labour Office. The respondents to the survey are typically a company’s CEO or a member of its senior management.” The WCY uses a 115 question survey sent to executives in top and middle management of firms in all 49 countries of the WCY. The sample size of each country is proportional to GDP, and firms "normally have an international dimension." The firms are selected to be a cross section of manufacturing, service, and primary industries. There were 3532 responses to the Survey.

KKZ (2002) updates the data on governance that were developed in Kaufmann, Kraay and Zoido-Lobaton (1999) “Governance Matters.” The database contain more than 300 governance indicators for 175 countries compiled from a variety of sources in 2000/2001. Six aggregate indicators are constructed corresponding to six basic governance concepts: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption.

The various raw data series were chosen because of their relevance to the four concepts of trade facilitation.

- Port efficiency for each country _J_ is the average of two indexed inputs (all GCR): o Port facilities and inland waterways are :(1=underdeveloped, 7=as developed as the world's best, GCR)

- Air transport is :(1=infrequent and inefficient, 7=as extensive and efficient as the world's best, GCR)

- Customs environment for each country _J_ is the average of two indexed inputs (all GCR):

- Hidden import barriers other than published tariffs and quotas

23
Farms run by women are _more_ productive (7 percent) than farms run by men in disperse rural areas (Table 7.3). The dummy variable included for male head of households is negative and significantly different from zero.

Access to infrastructural services is important for income generation in disperse rural areas of Argentina. Farmers with access to paved road and electricity are more productive and earn statistically significantly higher incomes than do farmers without access. Access to paved road or electricity increase income by 29 and 44 percent respectively.

The use of productivity enhancing production techniques such as fertilizers and irrigation are also important for increasing income in rural Argentina. Fertilizer use and access to irrigation are both significantly positive determinants of farm income, although there may be possible endogeneity at play. Farms that apply these productivity enhancing technologies such as irrigation and fertilizers experience 28 and 26 percent higher incomes than farms that do not use or have access to irrigation or fertilizers respectively.

# **8. Conclusion**

A comprehensive vision of rural development beyond sectoral approaches is forming. The rural world is not only an agricultural world—although it is more so in Argentina than for example in Mexico—and the rural economy is not only an agricultural economy; indeed, the combination of economic activities is the dominant characteristic of rural households and communities. Unfortunately, the governments in Argentina have still not embraced a more comprehensive view of the rural world, including changing the institutional set up.

The demographic changes and those in the characteristics of the labor force reflect a rural society in transformation––a view reinforced by the increasing extent of migration (see Verner 2006). When examining correlates of nonfarm employment in rural Argentina, findings suggest that key determinants of access to employment and productivity in nonfarm activities are education, skills, land access, location and gender. There is strong evidence that educated people have better prospects in both the farm and nonfarm sectors. This is emphasized when nonfarm activities are divided into low-return and high-return activities. Education is a particularly important determinant of employment in the betterpaid nonfarm activities. Furthermore, the labor income analysis shows a rather heterogeneous impact pattern of individual characteristics across the income distribution. That is, the magnitude of the effect of an income determinant is different depending on the worker being rich, poor or placed in the median of the income distribution. The agricultural producer income analyses reveal that education is also essential for producers to earning higher incomes.

It is key that governments assist in augmenting the human capital of the rural dwellers in more dispersed rural areas so they can improve their productivity. Secondary education has been repeatedly found to be strongly linked to participation in the rural economy, and also to enhance the income obtained from a variety of occupations and 29
Table A.2 (continued): Baseline statistics and balance test for endline respondents

||||Tes|t of rando|mization ba|lance (N=|947)||
|---|---|---|---|---|---|---|---|---|
||Sample|Assigne|d therapy|Assign|ed cash|Assign|ed both|F-Test|
|Baseline covariate|Mean|Coef.|_p_ value|Coef.|_p_ value|Coef.|_p_ value|_p_ value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Committed theft/robbery in past 2|0.54|0.04|0.59|0.02|0.49|-0.03|0.60|0.70|
|weeks|||||||||
|Number of nonviolent stealing|5.07|-0.54|0.50|-0.48|0.67|0.49|0.61|0.80|
|incidents|||||||||
|Number of felony stealing incidents|0.45|0.04|0.85|0.17|0.60|-0.17|0.66|0.85|
|Disputes and fghts in past 2 weeks|2.10|0.08|0.88|0.56|0.47|-0.71|0.25|0.56|
|(0-9)|||||||||
|Aggressive behaviors (mean of 19),|0.00|0.00|0.96|0.11|0.23|-0.17|0.20|0.27|
|z-score|||||||||
|Conscientiousness index (0-24)|15.37|-0.08|0.67|-0.28|0.34|0.07|0.86|0.01|
|Neuroticism index (0-21)|12.11|-0.09|0.67|0.15|0.65|0.18|0.73|0.36|
|Grit index (0-21)|13.75|-0.02|0.82|-0.20|0.62|0.14|0.70|0.80|
|Reward responsiveness index (0-24)|14.72|-0.19|0.40|0.06|0.90|-0.21|0.75|0.61|
|Locus of control index (0-24)|14.43|-0.11|0.71|-0.54|0.09|0.49|0.26|0.00|
|Impulsiveness index (0-21)|9.41|0.42|0.38|0.29|0.51|-0.93|0.14|0.41|
|Self esteem index (0-24)|13.47|-0.11|0.71|-0.11|0.62|0.16|0.69|0.89|
|Patience in game play index (0-6)|4.10|0.14|0.19|-0.06|0.77|-0.05|0.89|0.61|
|Time inconsistency in game play|3.28|-0.24|0.04|-0.03|0.78|0.11|0.50|0.01|
|index (0-6)|||||||||
|Risk aversion index (0–3)|1.57|-0.02|0.86|-0.03|0.74|0.10|0.33|0.70|
|Self-reported patience (mean of 7),|0.00|-0.05|0.75|-0.10|0.32|0.10|0.55|0.42|
|z-score|||||||||
|Declared Risk Appetite (mean of 6),|0.00|0.03|0.88|0.02|0.90|-0.15|0.43|0.86|
|z-score|||||||||
|Cognitive ability (z-score)|0.00|0.13|0.14|0.16|0.18|-0.31|0.02|0.09|
|Executive function (z-score)|-0.02|0.07|0.21|0.11|0.40|-0.27|0.11|0.25|
|R-Squared||0|.18|0.|11|0.|34||
|_p_ Value on F-statistics on all<br>covariates||0|.55|0.|77|0.|27||

_Notes:_ Column (1) reports the sample mean. A small number of missing values are imputed at the median. Columns (2)-(7) report the coefficients and _p_ values from ordinary least squares regressions of each baseline covariate on three indicators, one for assignment to each treatment arm, controlling for block fixed effects. Column (8) reports the p value from a joint test of statistical significance of all three treatment indicators.

v