rafmacalaba/datause-encoder-data huggingface dataset:
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# **Domestic Public Equity**

Public equity issues in Chile have spanned across companies of various sizes; but most issues by smaller firms date from the early 1990s. As Figure 11 shows, about 7 percent of listed firms in Chile are medium, small or micro in terms of sales volume. This is in sharp contrast with the breakdown of bond issuers described above. In addition, the share of medium, small and micro listed firms is higher than in other middle income countries (but smaller than in industrial countries such as Australia and New Zealand). However, there has not been any issue by a medium, small or micro firm since 1998 and most of the small issues date from before 1995 (see Figure 12). Also, equity amounts raised have become significantly smaller than bond issues (despite the lower cost).

**Figure 11:  Breakdown of Listed Firms by Size, Chile and Other Countries (2004)**

<!-- Start of picture text --> Listed firms by size Listed firms by size (quintiles)<br>(as per Chile's definititions) Mega Large Medium Small Micro<br>Mega Large Medium Small Micro 100%<br>100% 90%<br>80% 80%<br>70%<br>60% 60%<br>50%<br>40% 40%<br>20% 30%<br>20%<br>0% 10%<br>0%<br>Source: Bloomberg<br>Figure 12: Evolution of Equity and Bond Issues in Chile<br>Debt and equity issues in Chile (US$ million)<br>800 Micro<br>700 Small 3,000 Local equity<br>600 Medium Intl equity<br>500 Large 2,500 Local debt<br>400 Mega 2,000 Intl debt<br>300<br>1,500<br>200<br>100 1,000<br>0 500<br>-<br>IPO and capital increases<br>by firms of various sizes<br>Source: SVS<br>ArgentinaAustraliaBrazil ChileColombiaKoreaMexico SpainThailand ArgentinaAustralia Brazil ChileColombiaKoreaMexico SpainThailand<br>New Zealand New Zealand<br>1988<br>1990<br>1992<br>1994<br>1996 1998 2000 2002 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->
contracts that require at least 30 hours of work per week and that are active as of December 31. The salary period in Brazil is typically monthly. So, the individual’s real hourly wage on each job is calculated by dividing real monthly earnings, measured in 2017 reais, by hours worked per month. The hours worked per month are calculated as the reported weekly hours, multiplied by 4.3, the number of weeks in an average month.

With hourly wages in hand, the average wage is constructed for both sexes separately for each municipality-year. This predicted average wage for sex _g_ in municipality _m_ in year _y,_ is as follows: <u>wg</u> g (1) gg<sup>= ∑JJ</sup> jj jj<sup>× ww</sup> −ggjjgg<sup>,</sup> where gmj is the share out of all jobs held by workers of sex γγ _g_ in municipality _m_ that are in industry _j._ (Industry is defined here using the first two digits of Brazil’s National γγ Classification of Economic Activities.)<sup>9</sup> Industry shares are computed in the baseline year 2011 and do not vary by year. The term _w-mjy_ is the average wage in year _y_ and industry _j_ across all jobs in the same state, but not in municipality _m_ . The ratio of female to male wages is the variable of interest, the leave-out wage ratio. For descriptive purposes, the raw wage ratio is also calculated, as the ratio of the average female wage to the average male wage among workers in the same municipality. Specifically, letting g and g ff gg be the average wage of female and male workers in municipality and year , the raw ww ww wage ratio is simply g � g . mm yy Brazil’s population-weighted average real gender wage ratio across all ff<sup>gg</sup> ww ww municipalities over all years of the 2011–16 panel is 0.86, indicating that women are still not being paid equally to men. The comparable statistic from the United States is around 0.80. Brazil’s corresponding population-weighted average of the leave-out wage ratio is 1.02, precisely at parity. The fact that the leave-out measure is at parity is perhaps surprising. However, it is consistent with the data of Aizer (2010), which also provide an average leave-out wage ratio of 0.97 across California counties. That the leave-out wage ratio is above the raw gender ratio reflects the fact that Brazilian wages rose more quickly in those industries in which women were relatively more highly concentrated in 2011, but into which men disproportionately moved in subsequent years. Figure 2 illustrates the similarities in the dispersion of the raw and leave-out wage ratios for the analytical sample,

> 9 See CNAE (National Classification of Economic Activities) (dashboard), Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística), Rio de Janeiro, https://www.ibge.gov.br/en/statistics/technical-documents/statistical-lists-and-classifications/17245national-classification-of-economic-activities.html?edicao=17248&t=sobre.

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large increase in investment by treated firms in the post-treatment period (consistent with the later results in Table 3 below). This effect is apparent in the year in which the marginal incentive for the firm changes, consistent with the ambitions of the AIA policy, and continues in the five years post-treatment. Our event study plots are robust to alternative similar definitions of firms’ treatment status based on their historic investment.<sup>30</sup> For example, Figure A1 uses 2-year average investment data for the years 2006/07 while Figure A2 uses 3-year averages for the years 2005/06/07.

As a further check on the parallel trends assumption, we similarly examine the pretreatment trends for the wages of data analytics workers (Figure A3), exploiting that these are measured on an annual basis. In the periods before the AIA changes we do not observe a differential trend in wages of data analytics workers between the treated and control firms (although the worker-level estimation is somewhat more noisy than the preceding firm-level plots). Around two years after treatment we observe a decline in the wages at treated firms, mirroring later results in Table 6. These results are consistent with the existing literature which finds both that technology adoption is linked with subsequent labor demand (Bresnahan et al., 2002) but that this organizational change occurs overtime (Juh ́asz et al., 2020).

# **5 Results**

## **5.1 The effects of AIA on cloud and capital investment, 2007-13**

This section examines econometrically the treatment effect of changes in the AIA allowance on firm investment in IT capital and cloud adoption. The AIA was designed and implemented by policy makers with the objective to increase physical capital investment, including the stock of digital technologies through investments in traditional tangible IT capital. We find evidence that the policy was successful in this regard. The results in Table 3 find that firms

> 30The absence of pre-trends is also apparent if we estimate these regressions over the period to 2019.

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# TABLE OF CONTENTS

||Page|
|---|---|
|EXECUTIVE SUMMARY .....................................|I|
|1.<br>INTRODUCTION<br>.....................................|1|
|2.<br>THE PANAMA CASE .....................................|3|
|_Background ....................................._|_3_|
|_The Subsidy Study ..._|_3._|
|3.<br>ASSESSING THE NEED FOR A WATER SUBSIDY .....................................|5|
|_Existing Subsidy System ....................................._|_5_|
|_The Incidence of Poverty ....................................._|_6_|
|_The Case for Use of Service Subsidies ...................................._|_7_|
|_The Case for Connection Subsidies ....................................._|_8_|
|_Summary ...................................._|_10_|
|4.<br>DETERMINING ELIGIBILITY FOR THE SUBSIDY ....................................|.11|
|_Zonal Eligibility ....................................._|_11_|
|_Individual Eligibility ...................................._|_13_|
|_Summary ...................................._|_15_|
|5.<br>DETERMINING THE SIZEOFTHE SUBSIDY ....................................|16|
|_Affordability of Charges ...................................._|_16_|
|_Administrative Costs ...................................._|_18_|
|_Incentive Effects ....................................._|_l9_|
|_Summary ...................................._|_20_|
|6.<br>IMPLEMENTING<br>THE SUBSIDY SYSTEM ....................................<br>.|22|
|_Legal and Regulatory Framework ...................................._|_22_|
|_Administrative procedures ...................................._|_23_|
|_S/ummary ...................................._|_27_|
|7.<br>CONCLUSIONS<br>..|28|
of child care to retain female workers. Most of these initiatives are small in scale and patchy, compared to the vast needs for child care. SEWA and Mobile Crèche have long highlighted the needs of especially the poorest informal women in large urban centers – women who work on construction sites, street vendors, domestic workers, and others who work in taxing manual jobs, for low wages. As yet however, there is not a similar lobby for the needs of educated women who may want to work, but cannot.

Micro studies shine more granular light on women’s child rearing and domestic responsibilities and their influence on urban women’s labor market decisions. Qualitative research based on interviews with professional women, confirms accounts of women placing their reproductive and caregiving roles above their jobs and careers, where domestic help is hard to find and the employer does not provide child care facilities or flexible work options (Agarwal et al. 2012; Buddhapriya 2009; Valk and Srinivasan 2011). Sudarshan and Bhattacharya (2009), based on a household survey in urban Delhi in 2006 found that 60 percent of respondents had quit work after childbirth, and around 57 percent of working respondents felt that women should stop working when their children are young. Among women who withdrew from the formal labor market, the most common reason was that household work had become too burdensome. Both working and nonworking women reported neglect of children and conflict over domestic chores as the two factors that most constrained women’s employment.

Yet many women, not just the poorest, have to walk a fine balance between market work and child care, and while a growing feminist discourse is drawing attention to this, empirical studies are limited. A study in urban areas of Uttar Pradesh and Gujarat, found that women from certain communities would forego wage work, saying it was mostly due to family responsibilities and social norms (IAMR and ILO 2013). On average, working women spent 4–6 hours a day either attending to household duties or taking care of the children or elderly. Some women with young children preferred to work from home, when there was no one from the family to help with child care. Based on interviews with female engineers, an article in _Quartz_ cites the “maternal wall” as the main barrier for Indian women to advance in their careers (Madhok 2015), while in a 2015 poll of G20 countries, female respondents from India stated that balancing work with home was the main issue they dealt with in their working lives, followed by few opportunities for flexible work (Thomson Reuters Foundation 2015).

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|Study|Period|Countries|Findings|
|---|---|---|---|
|**Studies that m**<br>|**easure impact**|**of regulation on sec**|**torperformance**<br>|
|Guasch, 2004|1985‐2000|17 Latin<br>American<br>nations<br>(1,000 contracts)|Regulatory framework affects occurrence of renegotiation of infrastructure contracts in two ways. Presence<br>of a regulatory agency decreases the probability of contract renegotiation. Adoption of price cap regulation,<br>by allocating more risk to the contractor, raises the probability of contract renegotiation.|
|Andres et al.,<br>2008|1985‐2000|17 Latin<br>American<br>nations<br>(1,000 contracts)|A regulatory framework enhances the performance impact of privatization, particularly when there is an<br>autonomous regulatory agency. However, specific characteristics of the regulatory framework affect<br>different dimensions of performance differently; sometimes positively, and sometimes negatively.|
|Vagliasindi,<br>2013|1989‐2009|22 developing<br>countries|Econometric results show that a regulatory agency is associated with higher electricity access, higher labor<br>productivity, lower tariffs and lower carbon intensity in larger middle income countries. However, these<br>results are largelyreversed in the case of smaller and lower income countries.|
|Gassner and<br>Pushak,2014|2000s|70 developing<br>countries|The UK model of incentive‐base regulation (price cap) has only been partially successful in low and middle<br>income countries due to the major differences in the institutional andpolitical context.|

2
Table 5 Government Investment Expenditure by Functional Categories in Viet Nam (Million dongs, 1982 price)

|||1985|1991|Share in Total<br>Expenditure - 1991|
|---|---|---|---|---|
|1. Ec<br>|onomic services<br>|19356.0<br>|18056.9|83.0|
|i)|Indusury|7752.1|9397.1|43.2|
|ii)|Agriculture|4608.5|2973.5|13.7|
|iii)|Forestry|786.1|432.1|2.0|
|iv)|Transport|4454.6|3636.4|16.7|
|v)|Communication|140.7|834.4|3.8|
|vi)<br>s|Ocher economic<br>ervices|1614.0|783.4|3.6|
|2. So<br>Servic|cial and Community<br>es|5483.4|3705.5|17.0|
|i)|Housing|2067.0|1084.7|5.0|
|ii)|Education|747.3|763.8|3.5|
|iii)|Culture|494.9|434.9|2.0|
|iv)|Public health|766.9|615.2|2.8|
|v)|Other social and<br>community services|1047.3|806.9|3.7|
|Total||24839.4|21762.4|100.0|

The basic inadequacy of local fiscal resources has been exacerbated by stabilization policies since 1989, which have in particular severely compressed investment expenditures.' In. 1991, capital expenditures in constant prices was actually lower than the corresponding figures for 1985 even in absolute terms (Table 5). This compression appears to have been especially marked with respect to expenditures on agriculture and irrigation, and to some extent roads. Although the share of governrment expenditure in GDP increased from less than 20 per cent in 1985 to 23 per cent in 1992, the share of expenditures on agriculture, irrigation and roads - the three most identifiable and important items from the viewpoint of poverty alleviation - has actually declined.<sup>9</sup>

- 8 See, Viet Nam - Transition to the Market, The World Bank, 1993.

- 9 Although Table 4 shows important expenditures on "social services," the bulk of this item - about 10 per cent of total expenditures - consists of pensions to war veterans.

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reliance on natural resources and the years under socialism explain variation in the degree to which former communists were able to retain power and the degree of political openness at the beginning of the transition process. Further, the component of the initial political structure explained by natural resource reliance and years under socialism can explain the large variation in the building of market-compatible institutions we have observed over the last decade across transition economies. Our findings are robust to (i) controlling for other country traits that might explain cross-country variation in institution building, and (ii) the use of different measures of institution building and initial political structure. Finally, we relate the exogenous component of institutional development explained by natural resource dominance and socialist entrenchment to GDP per capita growth (averaged over the period 1992 to 2002), and find a strong and significant positive relationship. This finding is robust to controlling for a large number of macroeconomic, initial and other country characteristics.

Our findings confirm the importance of institutional development for economic growth. They are thus complementary to studies by AJR (2001, 2002) who use the colonization experience as exogenous event to assess the relationship between institutional and economic development. Similarly, we use the shock that the transition countries experienced in the early 1990s as starting point for our analysis. Our findings show that the quality of institutions is endogenous to the political environment and the structure of the economy. In many transition economies, political entrenchment of the socialist elites and rent-seeking opportunities offered by an abundance of natural resources have negatively impacted the creation of new marketcompatible institutions, with negative repercussions for economic development. Thus, using the shock experience of transition economies as a way to deal with endogeneity problems, we offer new evidence on the relationship between institutions and economic development.

35
countries. Not surprisingly, betweenness shares for political individuals and political parties are relatively high, although lower in the case of Russia. Relative to Spain where networks are more located around political parties and individuals, SOEs, in particular, have a more salient role in all the transition economies. In the following section, in looking at the association between scale and performance and firm level attributes, we explicitly incorporate these network features.

# **5 Correlates of being connected**

We now look at whether being connected and the manner and location of any connection is associated with specific firm level indicators, including sales, output and return on assets. Our measures of being connected may not be exogenous and unobserved factors affecting firm outcomes could also be explanatory factors for having political connections. With the data that we have available, as well as the lack of a significant temporal dimension, instrumentation is problematic. As such, the estimates we report should be viewed as correlates, rather than indicating causality.

We estimate the following: for each firm _i_ , in country _c_ , at year _y_ , where several outcome measures are related to a dummy variable for whether a firm is connected, as indicated by the FN+PN method. We also account for whether the firm is an SOE, using information reported directly in _Orbis_ . The reason for including SOEs is that due to their ownership and governance they will, almost by definition, be connected to politicians; features that have, of course, been well documented. The outcome measures include Return on Assets, Leverage, as well as the logarithm of Sales, Output and Wages. We also include fixed effects for firm size, sector, country and year. The definition of firm size (Small/Medium/Large/Very Large) is taken from _Orbis_ (see Appendix Table B1).

The results from estimating Equation (1) are reported in Table 10 using a pooled country specification. Note that Russia is excluded due to the severe limitations of coverage in _Orbis_ . The same equation has also been estimated separately for each country (see Appendix Table A1). For Log Sales, Log Output and Log Wages, it can be seen that being connected is positively associated across the board and, in most instances, highly significant. For SOEs, the coefficients on sales and wages are positive, large and highly significant but negative and 17
# **Table 3A Macro determinants of the shares of informal employees, informal self-employed, and  unemployed (incl. discouraged)  in the extended labor force. Eastern and Southern Europe, 2004-2009**

_(seemingly unrelated regressions)_

||(%|of labor force|Dependent<br>extended to<br>|variables<br>include disco<br>|uraged wor<br>|kers)<br>|
|---|---|---|---|---|---|---|
||Informal<br>|employees<br>(robust s<br>|Inform<br>emp<br>Estimated c<br>tandard error<br>|al self-<br>loyed<br>oefficients<br>s adjusted for<br>|Unemp<br>disc<br>clustering<br>|loyed and<br>ouraged<br>on countries)<br>|
||[1a]|[2a]|[1b]|[2b]|[1c]|[2c]|
|Employment Protection|3.129***|2.092***|3.647***|1.837*|-0.985|0.271|
|Legislation strictness, v. 2<br>Rule of Law|(0.804)<br>-13.181***|(0.813)|(1.271)<br>-10.178**|(0.917)|(1.269)<br>-3.185|(1.228)|
||(2.563)||(4.308)||(2.252)||
|Government Effectiveness<br>||-5.339**||-0.056||-3.946|
|(lagged 1 year)||(2.549)||(1.521)||(3.256)|
|Investment Freedom||-0.181***||-0.336***||0.046|
|||(0.069)||(0.055)||(0.074)|
|Trade Freedom||-0.074*||0.046||-0.143**|
|||(0.039)||(0.043)||(0.057)|
|Tax Wedge on low wage|0.196*||0.128||-0.126||
|earners, % (lagged 1 year)|(0.106)||(0.236)||(0.142)||
|Minimum Wage (% of average|0.429***|0.352***|0.356**|0.301***|0.447***|0.445***|
|wage in industry & services)<br>|(0.089)|(0.082)|(0.181)|(0.081)|(0.086)|(0.071)|
|log GDP per capita, PPP USD<br>l ld 1|6.643***|3.333**|7.751***|4.040***|-0.69|-0.653|
|(annua average, agge year)|(1.636)|(1.538)|(3.005)|(1.366)|(1.282)|(1.630)|
|Real GDP Growth, y-o-y,|0.034|0.129*|0.049|0.149***|-0.124|-0.107|
|quarterly data lagged 1 quarter|(0.063)|(0.069)|(0.074)|(0.057)|(0.113)|(0.112)|
|Union Density,|-0.194***|-0.252***|-0.252**|-0.411***|-0.073|-0.077|
|% of employees|(0.052)|(0.072)|(0.114)|(0.081)|(0.050)|(0.059)|
|Population with immigrant|0.178***|0.205***|-0.090|0.017|-0.100*|-0.122*|
|background, %|(0.050)|(0.066)|(0.107)|(0.090)|(0.058)|(0.065)|
|Root MSE|1.8761|2.0460|3.8393|2.9874|3.6423|3.6560|
|R-squared|0.8135|0.7867|0.6383|0.7895|0.5080|0.5234|
|# obs[# countries]|35|[14]|35|[14]|35|[14]|
|.|<br>Descriptive|<br>statistics on de|<br> pendent varia|<br>bles|||
|Mean|5|.14|9.|77|1|2.28|
|Std. Dev.|3|.80|5.|58||4.54|
|Min||1.2|3|.1||3.7|
|Max|1|8.7|2|6.2||23|

_Notes:_ Countries covered: CZ (2004, 2009); SK (2004, 2006, 2008); PL (2004, 2006, 2008); HU (2005, 2006, 2009); EE (2004, 2006, 2008); LV (2007, 2009); SI (2004, 2006, 2008); BG (2006, 2009); RO (2008); RU(2006, 2008); UA (2005, 2006, 2009); ES (2004, 2006, 2008); PT (2004, 2006, 2008); GR (2005, 2009). See Annex 1 for details on the explanatory variables. Constant terms are not reported.

*, **, *** -  coefficient is significantly different from zero at 10%, 5%, 1% level, respectively.

_Sources:_ Dependent variables: Calculations with ESS data (see Table 1 for details). Explanatory variables - see Annex 1 (Table A1). Model estimates - own calculations ( _Stata_  simultaneous estimates with clustered robust standard errors have been applied after fitting linear models by OLS).

40