rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: OLS and GLS Estimates of TFP Equation Uncorrected for selectivity bias**

||**Depende**|**nt variable **|**Log TFP(ad**<br>OLS|**justed for **|**skills)**||||Random|effects (GLS|)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Adjustment for|||||||||||||
|market power|1.842||1.839|1.843|1.838|1.860|1.524||1.523|1.524|1.522|1.526|
||(50.28)**||(50.43)**|(50.26)**|(50.37)**|(50.07)**|(51.41)**||(51.44)**|(51.39)**|<br>(51.42)**|<br>(51.32)**|
|Foreign market|||||||||||||
|access|-0.120|-0.147|-0.122|-0.111|-0.114||-0.113|-0.143|-0.115|-0.103|-0.105||
||(10.05)**|(9.54)**|(10.40)**|(9.64)**|(9.97)**||(6.08)**|(5.89)**|(6.30)**|(5.71)**|(5.92)**||
|Domestic market|||||||||||||
|access|2.887|3.311|2.845|2.602|2.531||3.155|3.540|3.106|2.805|2.719||
||(7.21)**|(6.34)**|(7.15)**|(6.70)**|(6.59)**||(4.79)**|(4.13)**|(4.75)**|(4.39)**|(4.31)**||
|Own industry local|-0.166|-0.010|-0.148|-0.102|-0.074||-0.128|0.035|-0.110|-0.044|-0.016||
|local size|||||||||||||
|Constrained by<br>labor|(0.95)|(0.04)|(0.86)|(0.59)|(0.43)||(0.46)|(0.10)|(0.40)|(0.16)|(0.06)||
|regulation (%)|-0.033|-0.032|-0.034|||-0.005|-0.040|-0.045|-0.041|||-0.013|
||(2.84)**|(2.12)*|(2.97)**|||(0.48)|(2.14)*|(1.85)|(2.23)*|||(0.74)|
|Output lost to<br>power|||||||||||||
|outages (%)|-0.032|-0.043||||-0.076|-0.031|0.098||-0.044||-0.067|
||(0.94)|(1.28)||||(2.33)*|(0.61)|(1.47)||(0.87)||(1.35)|
|Business age|0.158|0.182|0.159|0.154|0.156|0.160|0.161|0.199|0.162|0.157|0.158|0.164|
||(10.13)**|(8.94)**|(10.22)**|(9.93)**|(10.03)**|(10.15)**|(6.91)**|(6.57)**|(6.96)**|(6.76)**|(6.82)**|(7.00)**|
|High FDI State|-0.478|-0.579|-0.500|-0.518|-0.551|-0.344|-0.474|-0.652|-0.495|-0.522|-0.554|-0.338|
||(9.61)**|(10.05)**|(11.39)**|(10.87)**|(13.60)**|(7.03)**|(6.22)**|(6.58)**|(7.25)**|(7.15)**|(8.79)**|(4.52)**|
|Low FDI State|-0.529|-0.581|-0.552|-0.502|-0.532|-0.412|-0.557|-0.702|-0.580|-0.526|-0.557|-0.434|
||(10.32)**|(9.82)**|(12.27)**|(9.95)**|(11.94)**|(8.72)**|(6.96)**|(6.75)**|(8.17)**|(6.68)**|(7.92)**|(5.97)**|
|Constant|-23.991|-18.441|-23.807|-22.368|-22.031|-5.328|-24.479|-20.431|-24.254|-22.504|-22.091|-3.724|
||(9.42)**|(5.54)**|(9.38)**|(9.01)**|(8.92)**|(27.21)**|(5.86)**|(3.76)**|(5.83)**|(5.52)**|(5.46)**|(20.51)**|
|Industry dummies?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Yer dummies?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3525|3525|3525|3525|3525|3531|3525|3525|3525|3525|3525|3525|
|Establishments|||||||1431|1431|1431|1431|1431|1431|
|R-squared|0.60|0.31|0.60|0.60|0.60|0.59|0.6|0.31|0.6|0.59|0.6|0.58|
|Rho|||||||0.88|0.88|0.88|0.88|0.88|0.89|

Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

44
The first group of hypotheses (type I) are concerned with measures of the internal efficiency of operations of the firm. As a result of private sector involvement and competition, once unbundling has taken place, efficiency measures are expected to improve.

_Connections per worker_ increased significantly with respect to private sector involvement, while unbundling was not included as a separate policy. This matches expectations as private ownership is expected to seek ways to reduce costs and reducing the labor relative to the number of connections would reduce costs. Provided that all observations that had private involvement had also been unbundled, the effect measured is the joint effect of unbundling and the private sector. However, if some privatized observations had been unbundled while others had not, then the specification is incorrect, and the effect of the privatization dummy is overestimated because it would include some effect due solely to unbundling (unless there is zero effect to unbundling alone).

_Energy sold per worker_ increased significantly with privatization in three studies, while being insignificant in a fourth. Two of these studies also tested for an effect due to unbundling, while one of these also tested for effects due to regulation and competition. Neither study found a separate effect for unbundling, but regulation increased efficiency. This supports the view that unbundling on its own makes no change in efficiency, but when combined with private sector involvement the labor force was reduced relative to energy sales.

_Distribution or T&D_ losses showed a significant decrease in four studies when there was private sector involvement. No other variable was significant in the other five studies. Two of these included all four policy components and two included only interaction terms. The reasons for the differences between the different studies are worth further investigation because this is known to be a large component of inefficiency in many of the countries covered by the QFD analysis referred to in section 2.2.

_Energy generation per worker_ is expected to improve with private sector involvement and competition, once unbundling has been undertaken. The role of regulation is less clear because it is unlikely to be focused on this aspect of firm behavior. Private sector involvement alone was insignificant in the three studies, but it was significant in increasing this indicator when regulation and privatization were combined.

_Plant load factor_ was significantly increased when there was unbundling, but showed no link to private sector involvement, regulation or competition when these were taken separately.<sup>8</sup> Unbundling and competition together were significantly linked to a higher plant factor. These results together support the argument that relying only on the dummy

> 8 It should be noted that the plant load factor is influenced by the generation technology available and the dispatch order. These should be included as conditioning variables in a cross-country context, otherwise the correlation with reform variables may be misleading.

37
financial service providers, given the state variables. Note that the S* curve assumes efficiency in supply, i.e. that service providers maximize profits _for a given price_ and are, thus, located on the highest feasible iso-profit curve. The potential supply, S*, differs from the actually observed supply curve, S, which denotes the share of population that currently existing financial service providers are actually willing to cater to for a given price. As illustrated in Figure 2, S is to the left of S*, implying that the former is a higher cost supply due to some form of inefficiency, including distortionary regulatory policies, an excessive number of banks for the size of the market (leading to a failure to achieve economies of scale), or lack of adequate market contestability.

We are now ready to analyze various key points in Figure 2. Point I, the intersection of potential demand D* and potential supply S* schedules, denotes the access possibilities frontier, i.e. the maximum outreach point for payments and savings services that can be reached in a country’s financial system, given the state variables. Point I is, thus, the constrained optimum, associated with a bankable population at point A on the horizontal axis. Hence, 1-A is the nonbankable population, that is, the share of population that cannot be provided with market-based payments and savings services, given the state variables.

The access possibilities frontier can be moved by shifts (or changes in curvature) in the potential supply and the potential demand curve as a result of changes in state variables. An outward shift of the potential demand curve due, for instance, to a higher level of per capita income increases access, as measured by the equilibrium share of bankable population. Similarly, a downward shift in the potential supply curve due to improvements in a country’s infrastructure or institutional environment will result in an expansion of the bankable population.

16
## **Figure 11: Marketability of Public Land**

- **2.7 Alternative Methods for Estimating Potentially Marketable Land in Partially Developed Land**

# **<u>Alternative I - Based on unused FSI:</u>**

Steps followed in this Alternative are described below:

- (a) First the footprints of existing buildings were identified from the satellite imageries and then digitized in CAD.

- (b) The number of floors for every building was noted from field surveys.

- (c) The footprint multiplied by number of floors gave the floor area of every building and then the total floor area of buildings on the plot. (FA)

- (d) This in turn gave the existing FSI in the form of ratio of floor area divided to the plot area (PA). (Existing FSI=FA/PA). However this is not directly comparable to the FSI prescribed in the regulations as certain areas like staircases, lifts, balconies etc. are exempted for FSI computation. For comparison therefore the regulatory FSIs were enhanced by 22% to account for areas otherwise not computed for FSI.  (e.g. FSI 1.8 prescribed in regulation was  considered equivalent to  2.2 for present study). The plots were then classified according to utilization of FSI. “Low consumption of FSI i.e. less than 45% of   FSI”, “medium consumption of FSI between 45% and 85% of FSI” and “high consumption over 85% of FSI”. Land related to unconsumed FSI was considered as marketable land.

- (e) However the value of such land cannot be realized unless the existing buildings are reorganized and reconstructed. The net value of land was therefore arrived at by deducting the cost of reorganizing and reconstructing the existing structures from the value of land related to unconsumed FSI. In cases of high consumption of FSI the net value came to be negative and was treated as zero (or non-marketable)<sup>16</sup> .

# **<u>Alternative 2 - Based on residual plot area</u>**

In this case the residual plot area is calculated by deducting the building footprint area(s) from the plot area. If the residual plot is more than 50% of the total plot area the residual is considered as potentially marketable land. This scenario is meant to represent the potential for releasing excess land were there is scope for relaxing what may be excessive plot coverage and setback regulations for certain neighborhoods and land uses. A recent study of Ahmedabad’s real estate market has shown that these regulations applying unrealistically and unnecessarily high standards uniformly throughout the city can drive excessive land consumption without necessarily achieving their goals

> 16 Demolition costs were assumed to be zero since demolition contractors in Ahmedabad can sell the materials recovered from demolition and therefore do the work for no charge. Reconstruction costs were estimated at Rs. 25,000 per square meter.

23
# **Table A.6. Differences in Poverty Rates for Households with and without Children for Different Equivalence Scales, RLMS 1994-2017**

**Panel A. Headcount poverty in households with children (percent)**

|**Child scale**<br>|||||**Adult s**|**cale par**|**ameter e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|0.4|0.8|1.2|1.7|2.8|5.0|7.5|10.9|15.9|23.1|
|**0.02**|0.3|0.7|1.1|1.5|2.5|4.8|6.6|9.9|15.1|21.5|
|**0.03**|0.2|0.6|1.0|1.4|2.2|4.1|6.1|9.4|13.9|19.7|
|**0.04**|0.2|0.6|1.0|1.4|2.1|3.6|5.5|8.7|13.0|18.6|
|**0.05**|0.2|0.6|1.0|1.4|2.0|3.1|5.2|8.1|11.9|17.3|
|**0.06**|0.2|0.4|0.9|1.2|1.8|3.0|5.0|7.5|10.9|16.6|
|**0.07**|0.2|0.3|0.8|1.1|1.6|2.7|4.6|6.8|10.2|15.5|
|**0.08**|0.2|0.3|0.8|1.1|1.6|2.6|4.3|6.2|9.8|14.1|
|**0.09**|0.2|0.3|0.8|1.1|1.6|2.4|4.1|6.1|9.4|13.0|
|**0.1**|0.2|0.3|0.8|1.0|1.4|2.2|3.7|5.6|8.7|12.5|

**Panel B. Headcount poverty in households without children (percent)**

|**Child scale**<br>||||**A**|**dult scal**|**e parame**|**ter e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.02**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.03**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.04**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.05**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.06**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.07**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.08**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.09**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.1**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|

**Panel C. Absolute difference in poverty headcount between households with and without children (percent)**

|**Child scale**||||**Ad**|**ult scale p**|**aramete**|**r e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|-2.5|-2.3|-2.3|-2.2|-1.4|0.3|2.1|4.4|7.3|11.8|
|**0.02**|-2.5|-2.4|-2.3|-2.4|-1.7|0.1|1.2|3.4|6.5|10.1|
|**0.03**|-2.6|-2.5|-2.5|-2.5|-1.9|-0.7|0.7|3.0|5.3|8.4|
|**0.04**|-2.6|-2.5|-2.5|-2.5|-2.1|-1.2|0.2|2.2|4.4|7.2|
|**0.05**|-2.6|-2.6|-2.5|-2.5|-2.2|-1.6|-0.1|1.6|3.3|5.9|
|**0.06**|-2.6|-2.7|-2.6|-2.6|-2.4|-1.7|-0.4|1.0|2.3|5.2|
|**0.07**|-2.6|-2.8|-2.6|-2.8|-2.6|-2.0|-0.8|0.3|1.5|4.1|
|**0.08**|-2.6|-2.8|-2.6|-2.8|-2.6|-2.2|-1.1|-0.3|1.1|2.7|
|**0.09**|-2.6|-2.8|-2.6|-2.8|-2.6|-2.4|-1.3|-0.4|0.8|1.7|
|**0.1**|-2.6|-2.8|-2.6|-2.9|-2.7|-2.5|-1.7|-0.9|0.0|1.1|

**Note:** Panel C provides the absolute differences in the poverty rate for the same combination of child and adult scale parameters shown in Panels A and B. Absolute poverty line is defined as a minimum regional subsistence level per person for each year. Both the poverty thresholds and household income are converted to constant 2011 rubles using regional CPI indices provided by the Rosstat.

41
2. _Tech/Sales_ (the Census category of “Technical, Sales and Administrative Support Occupations,” occupation codes 203-389). Dental hygienists (204) and computer programmers (229) are examples.

3. _Service_ (combination of the two smallest Census categories: “Service occupations,” occupation codes 403-469, and “Farming, Forestry and Fishing Occupations,” occupation codes 473-499). Childcare workers (406), firefighters (417), and waiters (435) are examples.

4. _Craft_ (the Census category of “Precision Production, Craft and Repair,” occupation codes 503-699). Automobile mechanics (505) and Drywall installers (573) are examples.

5. _Laborer_ (Census category “Operators, Fabricators, and Laborers,” occupation codes 703-889.). Printing-press operators (734) and truck drivers (804) are examples.

The only departure we make from the Census categories is to combine the last two categories into the ‘laborer’ occupation, since these two are quite small. In addition to sector and occupation, we group workers into two education groups, workers with no college education, workers with some college education.

# **2.2 The Switching-Cost Function**

We can now show how we implement the switching-cost function _Ct_ ( _i, k, j, l, s_ ) empirically. In principle, we could assume a different value for the moving cost for each value of ( _i, j, k, l, s_ ) and estimate each one, but this would create a vast number of parameters and make identification of those parameters impossible. Therefore, we need to parameterize the moving cost function somehow. We have attempted to build a specification that is rich but at the same time parsimonious, and have allowed for four types of effect on switching cost that might or might not turn out to be important in the data. First, we allow for the possibility that some industries or occupations are harder to get into than others. This leads us to estimate an ‘entry cost’ for each sector and for each occupation. Second, we allow for the possibility that the cost of switching both sector and occupation is different from the sum – of the cost of switching sector and the cost of switching occupations that there may be some non-linearity in the cost of joint switching. Finally, we allow for the possibility of a 11
Figure 10. Coverage of CCT programs (percent of total population) in Brazil, Ecuador, and Mexico <!-- Start of picture text --> % Population<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>BRA ECU MEX<br><!-- End of picture text --> Source: LAC SP Administrative Database, The World Bank. Note: The data only includes central government level programs.

To further study the impact of public transfers on inequality, we analyze the cases of Mexico and Ecuador, two countries for which household surveys allow us to systematically disentangle public transfers from private transfers. Both countries represent good case studies for two reasons: i) public transfers covered a large proportion of the population (especially at the bottom of the income distribution) with coverage at 26 percent in Mexico and 36 percent in Ecuador in 2012; and ii) these countries illustrate extreme situations in terms of the evolution of income inequality in the region, with the Gini coefficient in Ecuador falling throughout the 2003-2010 and 2010-2012 periods, while in Mexico it declined in the first period and increased in the second period. Figure 11 shows the coverage rate and the share of transfers in income by quintiles of the total household per capita income, while Figure 12 presents the decomposition of income changes by sources of income, including public transfers.

18
4 comparisons, individual country studies, industry-level analyses, and firm-level investigations point in the same direction: the functioning of financial systems is importantly linked to economic growth. Specifically, countries with larger banks and more active stock markets grow faster over subsequent decades even after controlling for many other factors underlying economic growth. Industries and firms that rely heavily on external financing grow disproportionately faster in countries with well-development banks and securities markets than in countries with poorlydeveloped financial systems. Moreover, ample country studies suggest that differences in financial development have, in some countries over extensive periods, critically influenced economic development. Yet, these results do not imply that finance is everywhere and always exogenous to economic growth. Economic activity and technological innovation undoubtedly affect the structure and quality of financial systems. Innovations in telecommunications and computing have undeniably affected the financial services industry. Moreover, 'third factors', such as a country's legal system and political institutions certainly drive both financial and economic development at critical junctures during the growth process. Nevertheless, the weight of evidence suggests that financial systems are a fundamental feature of the process of economic development and that economists will not enjoy a satisfactory understanding of the factors underlying economic growth until we understand more about the evolution and structure of financial systems.

As in any critique, I omit or treat cursorily important issues. Here I highlight two.<sup>4</sup> First, I do not discuss the relationship between international finance and growth. This paper narrows its ' Also, the theoretical review focuses on purely real economies and essentially ignores emerging work on finance and growth in monetary economies.
# **Old Pension System**<sup>**8**</sup> **: Revenues and Expenditures**

|Year|Ben|efits|Contri|butions|Def|icit|
|---|---|---|---|---|---|---|
||Bsmn.1991|%ofGDP|Bs.mn.1991|<br>%ofGDP|Bs.mn.1991|%ofGDP|
|**1993**|477|2.35|446|2.20|31|0.15|
|**1994**|531|2.50|484|2.28|47|0.22|
|**1995**|621|2.49|503|2.26|118|0.23|

Without changes to the system's framework that addressed the incentives of the stakeholders the reforms were doomed to fail. In particular, participants in the scheme had no sense of ownership of the resources being handled. This led to low levels of accountability in the management of these resources and in some cases to outright corruption as is being discovered now that the complementary funds are being liquidated.

# C. THE NEW PENSION SYSTEM

The previous unsuccessful reform experiences and the unsustainability of the old pension system led already the previous Government of 1989-1993 to design a pension reform that would radically change the system. The current Government completed the design of the new pension system when it passed Law 1732 in November 1996. The Law closed the old pension system and established a new one based on individual capitalization accounts. It also established a social program ("Bonosol") designed to provide old-age relief and financed by the shares of capitalized enterprises9.

> 8 Includes both FOPEBA and complementary funds (including university funds).

> **9** The "capitalization" program has involved the transfer of 50% of the capital and managerial control of a public company to a private partner, often a foreign investor, in exchange for an amount of investment in the company determined through bidding for that control. The program has been applied to the six largest public enterprises in Bolivia. The remaining 50% shares in the privatized and recapitalized companies are held in trust until their transfer to the privately managed pension

5
identifying the combination of words that a child uses to express ideas or feelings; for the gross-motor skills caregivers are asked how children interact with objects to conduct every day activities, including playing and leisure. These activities reflect the coordination and range of movements performed by children at this age for various activities. Finally personal-social skills for this age cohort are more complex in the sense that caregivers are asked how children interact with toys, people or social situations. Empathy of objects and people at this stage is critical to understand how the child is evolving socially and with his or her personal interactions.

The collection of ASQ scores was intended to measure behavioral trajectories and competence in relation to children’s age. Such scores are meaningful to analyze when comparing growth and development with other related socio-economic indicators of the household and the caregiving activities at critical ages. These indicators may also quantify and approximate the quality of caregiver-child interaction, and the lack of learning opportunities (stimulation) as outcomes.

# _Treatment Group Balance and Summary Statistics_

We first analyze indicators of the sample of children in the follow up cohort assuming equal expectation, as the treatment and control groups were equally likely to be selected at the baseline through the randomization process. Table 1 displays the descriptive statistics of the baseline and follow up cohorts, before and after the treatment following the same children in the baseline after the intervention. The baseline side of the table reveals there is no difference between the control and the treatment group in most of the variables accounted for. However, the variables TV, VCR, and Communication Skills produced coefficients that suggest that there is, in fact, a difference between the treatment and the control group. This difference is statistically significant at the 99 percent level for the first and second variables, and statistically significant at the 95 percent level for the third variable. In other words, the difference between the experiment group and the control group might be due to the difference in families’ assets that exists between the two groups prior to the experiment, even without observed differences in income levels between groups (Table 1). Because these statistics are based on longitudinal data, the statistical test estimators that measure the existence of differences between comparison groups are more efficient compared to using cross sectional data to estimate the statistical 13