rafmacalaba/datause-encoder-data huggingface dataset:
technology choices in period _q,_ there is a lag between the firm’s investment decision and plant commissioning. The technology is installed and becomes fully functional in period _q_ +1 _._ Firms’ production decisions in period _q_ are thus made based on production technology installed in period _q −_ 1.

The quantity of input _j_ = _l, e, m_ in period _q, x_<sup>_j_</sup> _i,q_<sup>_,_andtheindexofinput</sup> efficiency of capital vintage, _γi,q_<sup>_j_</sup> _−_ 1<sup>, determine the input service to the production</sup> function, _x_ �<sup>_j_</sup> _i,q_<sup>:</sup> Similarly, the relationship between the price of the input in period _q, wi,q_<sup>_j_</sup> and the price of service _w_ � _i,q_<sup>_j_isgivenby</sup> Equations (2) and (3) imply that the lower is the value of the index of the input efficiency of capital vintage, _γi,q_<sup>_j_</sup> _−_ 1<sup>,themoreservicescapitalvintage</sup><sup>_xk_</sup> _i,q_ can produce from a given input _j_ = _l, e, m,_ and the lower is the price of that service. As the vintage of capital stock cannot produce more services with respect to itself, we assume that the value of the index of capital efficiency of capital stock is equal to one for all capital vintages, The input efficiency of capital vintage _q_ that firms choose to produce a flow of services from energy, labour, and materials is determined endogenously based on input prices and the exogenous technological change<sup>8</sup> where _<u>w</u>_ is the average price of input _j_ across countries and all time periods,<sup>9</sup> work from manufacturing heating system, labor efficiency of capital stock will depend on the degree of thermal system’s automation, energy efficiency of capital stock will depend upon the thermal efficiency of the boiler, whereas materials’ efficiency of capital stock will depend upon the quality of thermal insulation.

> 8As we explained earlier our model focuses on medium-term investment decisions, where firms choose between different types of capital from an existing portfolio of available technologies. Exogenous technological change here captures the fact that available technologies improve gradually over time.

> 9We have chosen the OECD average input price across countries and all time periods to

9
Nonetheless, the combination of these three outcomes— female homicides, female overnight hospitalizations for assault, and mandatory reports by health workers of physical and sexual violence against women—covers a range of types of violence, from the most severe (homicide) to less severe, and serves as an important complement to survey data.

_Contextualizing violence outcomes_ . The outcome variable on hospitalizations for assault is only associated with overnight stays. Aizer (2010) refers to hospitalizations for assault, but does not indicate if the variable also includes emergency room visits, which would not be included in the hospitalization outcome variable if they were of short duration. One-fifth of Californians were uninsured in 1995 (Bennefield 1996); the rate is likely higher among the low-skilled workers in Aizer’s sample. These victims would be more likely to seek emergency room care because of their more prevalent lack of insurance coverage. Brazil, on the other hand, offers universal health insurance; so, similar injuries could be treated at local clinics, suggesting that Aizer’s measure of violence is most similar to the report outcomes in this study.

# **Gender Wage Ratio**

The analysis relies on administrative individual-level microdata from Brazil’s Ministry of Labor and Employment on the formal sector labor force in 2011–16 to measure the gender wage ratio in each Brazilian municipality.<sup>8</sup> In addition to the raw wage ratio, following Aizer (2010), a predicted wage ratio was also constructed for the analysis that isolates the variation caused by shifts in labor demand that affect labor market opportunities among women relative to men. This technique helps predict the average wage for each sex in a municipality based on the distribution of workers across sectors in the municipality at the start of the sampling process, as well as changes in the industry average wage among workers outside the municipality. This measure is thus not affected by the movements of women into and out of employment and across sectors that may be associated with violence. This measure is referred to as the leave-out measure because it is constructed by leaving out some municipal-specific information.

The raw data of the Annual Social Information Report (Relação Anual de Informações Sociais, RAIS) include one observation for each employment contract that was active at any point during the year under investigation. As in the case of the data that measure violence against women, the analysis restricts attention to individuals ages 15– 49. It also focuses on workers who hold, at most, two jobs during the year and have job

> 8 The source of the data is Annual Social Information Report (Relação Anual de Informações Sociais, RAIS) (dashboard), Ministry of the Economy, Brasília, http://www.rais.gov.br/sitio/index.jsf.

11
adopting new technologies, we would expect _A_ to be relatively lower in low-income countries (e.g., Comin and Hobijn, 2010).

The second part of the lemma establishes an additional testable prediction regarding the relationship between income per-capita and the labor income share. We directly test this prediction, and establish no strong relationship between the labor income share and income per capita. Even a more generous interpretation of the model, that restricts attention to labor in occupations that are relatively more substitutable with ICT, is not supported by the data.

To evaluate this prediction, we use data from the World Bank’s International Income Distribution Database (I2D2). This database covers a standardized set of demographic, education, labor market, household socioeconomic and income/consumption variables for over 150 countries drawn from more than 1000 nationally representative household surveys, with the earliest data starting in 1960. We use this data source to measure the total annual wage bill by occupation and then classify occupations as performing “routine” and “non-routine” tasks following Acemoglu and Autor (2011). That is, we classify (1) managerial, professional and technical occupations as “non-routine cognitive”; (2) sales, clerical and administrative support occupations as “routine cognitive”; (3) production, craft, repair, and operative occupations as “routine manual”; and (4) service occupations as “non-routine manual”. This then allows us to calculate the routine (groups 2 and 3) and non-routine (groups 1 and 4) wage bill as a percent of the total wage bill within each country. Finally, we obtain routine and non-routine income shares by multiplying the resulting wage bill shares with the aggregate labor income share taken from the PWT.

Figure 5 illustrates that, if anything, both routine and non-routine labor income shares (and hence the aggregate labor income share) are slightly positively correlated with income per-capita, 1. rejecting part two of Lemma Finally, note that the above model assumes a single final good and an aggregate production technology. It can also be interpreted as a production technology for a particular industry. Thus, this model implies a positive correlation between ICT abundance and income per-capita at the 4. industry level—a prediction we reject based on empirical evidence provided Section 19
basic results, but has a larger e¤ect on our analysis of treatment heterogeneity. As discussed below, we therefore focus most of our analysis involving heterogeneity of treatment response on the ...rms in quadruplets which have baseline pro...ts of 1500 cedis per month or less. Since randomization occurred within quadruplets, balance on baseline characteristics is achieved for this subsample also.

Table 1 shows that overall the two treatment groups look similar to the control group in terms of pre-treatment characteristics. The exceptions are October/November 2008 pro...ts and January 2009 sales, which show signi...cant di¤erences across treatment groups in the trimmed sample, and di¤erences in magnitude, if not statistical signi...cance, in the full sample. Recall the matched randomization used the wave 2 pro...ts. However, the correlation between wave 1 and wave 2 pro...ts is only 0.19, compared to a correlation of 0.58 between wave 2 and wave 3 pro...ts, and of 0.72 for the control group between waves 5 and 6 (which is the same seasonality as between waves 1 and 2). This di¤erence in baseline pro...ts is due to pure chance, and is in a variable which the data suggests involves considerable noise and perhaps learning on the part of survey respondents as well. Imbalance on this baseline pro...t measure is thus unlikely to imply imbalance on follow-up pro...ts, particularly given the pre-treatment balance on wave 2 pro...ts (Bruhn and McKenzie, 2009). Nevertheless, we will show our results are largely robust to the use of ...rm ...xed e¤ects which account for any baseline imbalances, the main di¤erence being in the cash-treatment for males, which we discuss in detail later.

As seen in Table 1, the mean owner in our sample is 36 years old, has almost 9 years of schooling, and has been running the ...rm for 7 years. The mean number of digits recalled in a Digitspan recall test is 5.1, which is almost one digit lower than the 5.9 average among Sri Lankan microenterprise owners (de Mel et al, 2008). The majority of ...rms are run out of the home, with 83 percent of women and 69 percent of men operating a business from their dwelling. Most ...rms are informal, with only 14 percent registered for taxes, and only 10 percent have ever had a loan from a bank or micro...nance institution. Half of the ...rm owners use a susu collector, with this more common among women (58 percent) than men (34 percent). A susu collector is an informal mobile banker, who typically collects a savings deposit daily from individuals and returns them at the end of the month after subtracting one day’s deposit as a fee. That is, saving is at negative interest rates in exchange for safekeeping. Besley (1995, p. 2150) states that “a frequently heard rationale for the existence of this institution is that there are di¢ culties for those who have a stock of liquid assets in resisting the claims of their friends and relatives 19
19

# II. Explainina the Growth in Government Expenditures

Explanations by economists for why governments grow have a long tradition, dating at least as far back as the late-nineteenth century writings of Adolph Wagner. More recently, the evolution of positive theories of government, often under the banner of the "Public Choice School", has made the subject a rich area for intellectual and political debate. Surveys of this literature include Mueller (1987), who offers an especially up-to-date treatment.20 What the surveys reveal is that the literature on government expansion has had an almost exclusive focus on developed economies. Accounting for the growth of government in developing countries, therefore, involves less a review of previous studies than it does suggesting how concepts employed in industrialized settings can be applied to developing nations. These concepts or basic approaches to understanding government growth can be labelled: (a) "cost-accounting"; (b) demand-side arguments and (c) supply-side factors. One additional set of explanations, also operating on the demand-side, (d) "developmen **t** heorizing", is also considered below.

A. "Cost-Accounting" The "cost-accounting" approach is employed by Saunders and Klau (1985) in their analysis of spending growth in OECD economies. These authors admit that they are not directly contributing to the debate over a positive theory of government, but rather, are simply trying to decompose the growth in OECD fiscal expenditures in a meaningful way.21 Specifically, they look at government programs including, education, health and social security, and
25

## d(DYY)/d(RR) - d(DYY)/d(RFSTS).d(RFSTS)/d(RR) - 0.08.

Therefore, these regression **s** uggest that a rise in the real interest rate of one percentage point will translate, through an increase in the intermediation of savings of about 0.7 percentage points, into an increase of somewhat under one tenth of a percent in the growth rate. This is about 40 percent of the increase suggested by the coefficients of the equations between growth and the real interest rate estimated previously. Much of the previous relationship may therefore.be due to reverse causality (from growth to interest rates), but the causal effect from real rates to growth is still considerable, and indicates an important role for financial sector policy in the growth process. In Table ld, the growth difference between the high and low growth countries was 3 percentage points and the average interest rate difference was 6 percentage points; the latter might therefore have accounted for about 0.5 percentage points of the growth rate difference. Over 21 years this is not negligible.

# 4.2.4 Financial Depth and Growth

Another indication of the influence of the financial sector on growth could be provided by using the ratio of broad money to GDP (M3GDP) and its determinants as explanatory variables.30 As noted above, the average of M3GDP over a period reflects the initial conditions as well as policies during the period, and thus could relate more to average productivity than to incremental productivity and growth during the period. M3GDP does appear to be related to growth rates, but it does not seem to weaken the association between interest rates and growth: DYY - c + .180 RR + .034 M3GDP - .021 GROUP RsqAdj - .486 (12) (5.22) (2.13) (-3.91) Real interest rates are a significant eyplanator of M3GDP, but lose significance once inflation is included in an equation: M3GDP - c. + .251 RR + .0002 GNPPE -.141 INF +.099 GROUP (13) (1.13) (5.92) (-3.36) (3.31) RsqAdj - .416 Equation (13) confirms the conclusion of Hanson and Neal that inflation has had a stronger influence on the level of monetisation than has the interest rate regime. At the same time, it seems that interest rates relate to growth independently of their effect on financial depth, and it was shown above that including inflation did not influence their impact on the financialization of savings flows. There is room for further research in these areas.

30 M3GDP is computed as the average of M3 at the start and at the end of the year divided by the GDP flow during the year.
22

**Figure 9A**

<!-- Start of picture text --> Net Secondary Enrollment vs Education Expenditure<br>KOR<br>UKR KNALT UCZE POL EST BRB<br>BH S URYBHR BGR CHLGEOROMARMT T OTJKAZEKAZBRAMKDGHR VSVKUYIRNAR GMD ABLRHU NJ ORJAMFJIT ON DMAL VA MYSLCA KWT<br>MAC LBN OMN MUS WSMBOL T UN<br>PERVNM MNGDZAPAN BLZ ZAF<br>COL MEX<br>T UR PHL CPV BW A SAU<br>ID N<br>SLV BGD GRDPRY CRI<br>ZWE<br>DOM SYR<br>NI C NAM<br>GHA YEMMAR SWZ<br>GT M PNGL AOGMBT GOKEN VUT<br>KHM BENERI LSO<br>DJIMRT<br>GIN MD GET H<br>BFAT CDMOZBDI<br>NERT ZA<br>300 400 500 600 700 800<br>Orthogonalized Public Expditure on Education<br>Daat Source: World Bank WDI<br>100<br>80<br>60<br>40<br>20<br>Net Secondary School Enrollment<br>0<br><!-- End of picture text --> Source: Herrera and Pang (2006) In terms of policy implications, it is vital to differentiate between the technically efficient level and the optimal or desired spending level. Even if a country is identified as an “efficient” benchmark country, it may very well still need to expand its public spending levels to achieve a target level of educational or health attainment indicators. Such is the case of countries with low spending levels and low attainment indicators, close to the origin of the efficient frontier. The important thing is that countries expand their scale of operation along the efficient frontier.

This measure of technical efficiency, unfortunately, has nothing to say about the optimal spending level. It simply states benchmarks of costs per unit of output, focusing on doing things right (at the lowest possible technical cost) but an important consideration is if it is doing the right things. This benchmarking method necessarily has to give way to analysis with more economical content Hence the discussion has to move to estimation of social benefits and costs, as well as causal inference, topics covered in the next sections.

The above discussion illustrates the point that policies that simply provide more resources will not necessarily improve education outcomes; it is imperative to examine other links of the chain. Teachers are key inputs in the production function and account for most of the cost of education. In both developed and developing nations teachers’ unions have
areas. It was only after the Asian financial crisis of the late 1990s that both institutions started focusing more systematically on financial sector issues, notably through the Financial Sector Assessment Program (FSAP), and only in the last decade or so that the IMF expanded its mandate to cover explicitly macrofinancial issues. Still, the two institutions’ documents contain the most comprehensive record of financial sector reforms available for these countries.

- Not all country documents of the two institutions are in the public domain. This is particularly a constraint for IMF documents, as the Fund had a much more restrictive dissemination policy than the World Bank, especially in the 1980s and early 1990s. In one case (Benin) where IMF reports are not available, the paper relies on World Bank documents.

# **Main findings**

**_Finding No. 1. Financial sector reforms were part of the strategy of exit from fragility in all cases, but not always among the top priorities: their timing relative to other reforms varied significantly from country to country._**

**Based on the evidence from the sample of country cases, successful exit strategies always included some financial sector interventions** . Whether they involved liquidating, restructuring, or recapitalizing failing banks (and dealing with the fiscal implications or recapitalization), cleaning up balance sheets, establishing a proper legal and regulatory framework, strengthening financial sector oversight, or a combination of these elements, some financial sector reforms were part of the policy strategy associated with exit from fragility in all seven countries.

**However, the financial sector was not always a separate focal point of early IFI operations** . In some cases ( _Armenia, Dominican Republic_ ), a financial sector reform agenda was a distinct component of the World Bank and IMF programs around the time of exit from fragility. In other cases ( _Cambodia, Rwanda_ ), these early IFI operations did not include a separate financial sector component: instead, individual financial sector interventions appeared as elements of other parts of the strategy (see also _Finding No. 2_ ). In those cases, these early operations focused primarily on other economic policy priorities, such as addressing acute macroeconomic instability, establishing control over the public finances, or financing humanitarian needs. Financial sector reforms appeared as a separate agenda in later World Bank and IMF country strategies and programs—a year or more after exit from fragility.

**This may reflect a different order of priorities across countries** . It may, of course, be simply a matter of presentation. But it seems plausible to deduce that in cases where there was no separate

10
Table 2: Pre-treatment outcome trends

|sample|||girls|||||boys|||
|---|---|---|---|---|---|---|---|---|---|---|
|outcome|test_|takers|t|est_scores||test_|takers|t|est_scores||
|units/subject|level|log|English|English|Math||level|log|English|English|Math|
||||& Math|||||& Math|||
||QM|2|@®|@|@®||@®|@|@®|©|«9|
|timetotreatment*1(region=3,4)|7.2<br>|0.29<br>|0.01<br>|-0.01<br>|0.003<br>||14.3<br>|0.20<br>|0.04<br>|0.03<br>|0.03<br>|
||(6.3)<br>|(0.24)<br>|(0.09)<br>|(0.10)<br>|<br>(0.05)<br>|<br>|(15.7)<br>|(0.17)<br>|(0.04)<br>|(0.05)<br>|(0.01)<br>|
||[36]|[37]|[84]|[.80)|<br>[97]|<br>[37]|[37]|69)|~—s.73|—Ss.5|
|timetotreatment*1(region=5,6)|41<br>|0.11<br>|0.03<br>|<br>0.04<br>|<br>=-0.03*<br>|<br>|3.9<br>|0.06<br>|-0.04<br>|-0.07<br>|=-0.02<br>|
||(2.5)<br>|(0.08)<br>|(0.02)<br>|(0.04)<br>|<br>(0.01)<br>|<br>|(11.6)<br>|(0.13)<br>|(0.02)<br>|(0.03)<br>|<br>~—_(0.02)<br>|
||(32)|[77]|[.50)|49)|<br>~—s«.07])|<br>|[ou|Ess]|L.59)|iL7]|sg]|
|N|37|37|1,380|1,380|<br>1,380|<br>37|37|3,738|<br>3,738|3,738|
|R-squared|0.29|0.41|0.40|0.30|0.33|0.25|0.25|0.37|0.27|0.30|
|meanoutcome|36.5|3.4|-0.27|-0.28|-0.19||96.4|4.5|-0.17|-0.22|-0.09|
|<br>p-valueonregion-specifictrends<br>|[97]<br>|[.52]<br>|[.55]<br>|[.49]<br>|<br>[.98]<br>|<br>[49]<br>|[.57]<br>|[.31]<br>|[.94]<br>|[.78]<br>|
|<br>p-valueonRegion5-6v.Region3-4||[.85]|<br>—[.91]|[.35]|[.68]|[.82]|[.96]|[87]|[.97]|[.96]|[.60]|

Outcomes for Grade 12 exam shown. All regressions use pre-treatment years only, with time rescaled within region relative to first year of girls scholarship program. All regressions include linear trend in this rescaled time and calendar year fixed effects. Sample for level and log of test takers is district panel, and includes district fixed effects. Sample for test scores is students, and includes age, age squared, and school fixed effects. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Significance levels are based on these p-values: * significant at 10%; ** significant at 5%; *** significant at 1%. p-value on region-specific trends refers to joint hypothesis test for significance of interactions between time trend and region dummies. p-value on Region 5-6 v. Region 3-4 refers to test of equality between coefficients on time trend-region interactions.