rafmacalaba/datause-encoder-data huggingface dataset:
Overall, Table 4 and figures 6-8 show that the initial starting point of firms and their growth rates are different depending on their industries’ technological size, production technology, and dependence on external finance. Firms grow more over their life-cycles in industries predicted to be large-firm-dominated based on US data and in capital-intensive industries and in industries more dependent on external finance.

# **_C. Financial Development and Firm Life-cycle_**

In this section, we examine variations in firm lifecycle across different levels of financial development of the states they are located in and across different industry characteristics. In these regressions, we don’t include state fixed effects but instead control for net State Domestic Product (SDP)/capita (in logs). In col.1 of Table 5, we investigate the role of financial development on firm life-cycle by interacting age dummies with FD, a dummy variable that takes the value 1 for financially developed states and 0 for financially under-developed states. The financial development dummy is by itself positive and significant suggesting that plants in more financially developed states are larger and so are plants in states with higher SDP/capita. Most of the interaction terms are insignificant. This can also be seen in Figure 9 where we plot the predictive margins of the interaction coefficients and find that financial development does not make a material difference to plant lifecycles in India. In col. 1 of Panel B, we take the ratios of the predicted margins in each age bin to that of the youngest firms (< 5), and find that plants in developed versus under-developed states are growing at very similar rates. In unreported tests we find similar results when we look at just the top 3 and bottom 3 states in terms of financial development. We also do not find evidence supporting the role of financial development for firm 23
Table E.2: Model Fit: Location-Specific Sample and Simulated Moments

|||Loc|ation 1|Loc|ation 2|Loc|ation 3|Loc|ation 4|
|---|---|---|---|---|---|---|---|---|---|
||Outcome|Sample|Simulated|Sample|Simulated|Sample|Simulated|Sample|Simulated|
|Panel (a): Child Care Choices||||||||||
|Percentage:||||||||||
|Married mothers, working|Relative|35.00|33.81|31.80|29.45|51.50|46.55|23.00|31.69|
|Married mothers, working|Non-relative|3.90|2.59|9.10|4.91|10.60|8.26|18.90|7.42|
|Married mothers, working|Center|51.90|53.42|57.60|53.49|34.80|33.28|52.70|51.97|
|Married mothers, not working|Relative|3.30|7.33|1.10|5.69|4.20|4.19|3.30|2.05|
|Married mothers, not working|Non-relative|3.30|0.47|2.20|0.69|2.80|0.67|2.20|0.83|
|Married mothers, not working|Center|5.00|4.79|7.70|5.65|4.20|4.28|8.70|7.26|
|Single mothers|Relative|28.90|21.74|28.00|17.62|19.10|31.48|13.00|32.46|
|Single mothers|Non-relative|2.20|0.42|8.00|1.07|4.30|2.52|4.30|8.59|
|Single mothers|Center|40.00|46.28|36.00|46.82|38.30|30.61|52.20|26.34|
|Panel (b): Child’s Cognitive Score||||||||||
|Non-parental care|Mean|12.70|12.63|12.93|12.85|12.74|12.73|13.10|13.09|
|Exclusive parental care|Mean|12.68|12.70|12.73|12.79|12.52|12.70|12.87|12.92|
|Panel (c): Mothers’ labor market||||||||||
|Labor Supply Choices (%)||||||||||
|Married mothers|FT|34.30|35.06|31.20|28.79|31.20|31.10|30.70|33.34|
|Married mothers|PT|21.90|13.94|10.80|14.14|16.70|13.10|13.90|12.93|
|Married mothers|No Work|43.80|51.00|58.00|57.07|52.20|55.81|55.40|53.73|
|Single mothers|FT|44.40|21.80|36.00|20.09|40.40|24.97|47.80|29.22|
|Single mothers|PT|11.10|26.27|32.00|32.39|6.40|20.28|8.70|28.25|
|Single mothers|No Work|44.40|51.93|32.00|47.52|53.20|54.75|43.40|42.52|
|Wage (gross $/hour)||||||||||
|All mothers, working|Mean|13.03|12.75|18.49|18.63|16.87|17.15|24.40|23.38|
|All mothers, working|SD|12.08|11.51|11.95|12.31|12.03|11.79|14.44|13.32|
|Married mothers, working|Mean|14.62|14.97|20.31|20.26|18.96|19.65|26.65|24.98|
|Married mothers, working|SD|13.38|12.41|12.63|12.55|12.74|12.20|14.46|13.42|
|Married mothers, working|Corr|48.80|57.55|65.80|61.05|64.60|64.18|30.70|55.88|
|Panel (d): Child care center supply||||||||||
|Price ($/hour)||||||||||
|All centers|Mean|2.82|2.87|4.32|4.25|4.53|4.95|5.80|5.45|
|All centers|SD|0.72|0.61|1.17|1.06|2.47|0.80|1.84|1.15|

Note: _Sample_ refers to moments computed from the estimation samples described in Table C.2. _Simulated_ refers to moments computed from the simulated sample. The sample is simulated at the model’s equilibrium corresponding to the parameter estimates reported in Table 2 and 4. Cognitive scores are divided by 10. All price and wage variables are in 2003 dollars per hour. ”SD” denotes standard deviation and ”Corr” denotes correlation with husband’s wage scaled 0–100. FT=full-time; PT=part-time. Location 1: Low Population – Low Income; Location 2: Low Population – High Income; Location 3: High Population – Low Income; Location 4: High Population – High Income.

74
-52 -

during the 1950s **and** 1960s, demand estimates generally were more accurate. A survey of 75 Bank loans to 37 countries In the 19506 and 19606 found that, on average, sales forecasts were only 2% more than actual sales, but that with certain loban there was a large discrepancy between the forecast and actual amounts. For projects approved In the 1950s and 19608, the standard deviation of actual energy sales from that forecast was 38%, which Is the same as was found In a similar study by OED.

On a Regional basis, sales for East Asia utilities were almost the same as forecast during the 1970s and for East Africa were only 6% below that forecast. LAC actually exceeded the forecast level of sales by 5%, but has since had considerable overestimates. Other regions showed significant overoptimism by as much as 23% In South Asia, 18% In EMENA, and 17% In West Africa.

# 2.7 Financial Performance of Power Prolects and the Sector

An Integral part of any Bank-financed project Is to ensure the financial health and stability of the Borrower. The eventual aim Is to make the utility financially Independent so that It Is able to fund a substantial portion of its Investment program from Internal sources and Is sufficiently credit-worthy to ralse the remaining funds from commerclal sources (both local and forelgn). The fundamental flnancial soundness of a utility can be measured by five key ratios - (I) rate of return on assets, (II) operating ratio, (Ill) self-financing ratio, (lv) debt service ratio and (v) days receivables. The debt/equity ratio Is also an Indicator of financial performance where the debt Is not owed to government. However, many utilities In developing countries borrow heavily from govemment, but when In flnancial straits this debt Is often waived, Interest payments rescheduled, or the debt converted to equity. Given the problems of definition of debt, revaluation and conversion of debt, and revaluation of assets, It was decided to focus'only on one capital structure ratio l.e., debt service coverage.

6/ "Ex-post Evaluation of Electricity Demand Forecasts," Energy Department Note No. 79. June 1975.
Competition is one obvious area for market-enabling policies aimed at enhancing supply outreach. Greater competition can result in efficiency gains that would move the system closer to the possibilities frontier. The salutary effects of greater competition are illustrated, for instance, by the recent vigorous expansion of sustainable and profitable micro- and consumer lending in emerging markets. It was spurred in many cases by the removal of unnecessary regulatory distortions to competition—including, for instance, allowing the use of scoring methods in credit risk management, calibrating entry capital requirements for microfinance institutions, abolishing of interest rate caps and providing regulatory incentives to the use of credit bureaus in screening debtors.<sup>51</sup> As competition thrived in the microcredit market, it stimulated innovation in loan products and the maximum use of available technologies, with consequent improvements in cost and risk management. The result has been a sustained broadening of access to micro credit, even where state variables have changed little.<sup>52</sup> Beyond targeting competition per se, market-enabling policies can also try produce a movement towards the possibilities frontier by addressing hindrances such as coordination failures, first mover disincentives, and obstacles to risk distribution and sharing. While not easy to define in general terms, given their variety, these government interventions tend to share a common feature in creating incentives for private lenders and investors to step in, without unduly

> 51 In its early stages of the development, microfinance tended to mitigate agency problems through group monitoring, i.e., lending to groups of borrowers rather than individuals with each member liable for the group loan. With the maturation of the microfinance industry, individual lending has become dominant.  As regards collateral, rather than taking traditional collateral, microfinance institutions often take security on crucial consumer durables such as TVs and refrigerators, since these have a higher personal value for borrowers and thus reduce ex-post moral hazard problems.   See Armendariz de Aghion and Morduch (2005) for an overview.

> 52 Christen (2000) discusses microfinance’s evolution from an NGO-based grant-funded activity to a vigorous and self-sustaining industry and associated “mission drift.”  Otero and Marulanda (2005) examine mayor trends and features in microfinance in Latin America, noting the simultaneous phenomenon of “upscaling” (the transformation of NGOs into regulated entities) and “downscaling” (the entering of well-established commercial banks into the microfinance business).  The current status of microfinance and the opportunities and challenges going forward are discussed in World Bank (2006a).  Microfinance case studies include Navajas, Schreiner, Meyer, Gonzalez-Vega, and Rodriguez-Meza (2002) for Bolivia; Benavente (2006) for Chile, and Zaman (2004) for Bangladesh.

44
# **Tables**

## **Table 1:  Summary Statistics**

|Variable|Obs|Mean|Std. Dev.|
|---|---|---|---|
|ln TFP Ackerberg et al.|22558|1.53|1.10|
|ln Output|22558|2.57|2.01|
|ln Energy|22558|-0.12|2.04|
|ln Capital|22558|2.52|1.77|
|ln Labor|22558|0.45|1.79|
|ln Material inputs|22558|2.62|1.90|
|ln Services inputs|22302|0.27|1.92|
|Services Index lagged|22558|0.18|0.10|
|Banking Index lagged|22558|0.06|0.07|
|Rajan Zingales Banking Index lagged|22558|0.71|0.74|
|Telecom Index lagged|22558|0.02|0.02|
|Insurance Index|22558|0.01|0.02|
|Transport Index lagged|22558|0.10|0.04|
|Foreign Dummy|22558|0.18|0.38|
|Tariff lagged|22558|36.47|17.17|
|Input Tariff lagged|22558|16.41|9.38|
|De-licensing lagged|22558|0.97|0.15|
|FDI reform lagged|22558|0.87|0.33|

**Table 2: Production function coefficients**

||OLS<br>|||Ackerberg et al.<br> <br>||
|---|---|---|---|---|---|
||Capital|Labor|Sum|Capital<br>Labor|Sum|
|Food processing and tobacco products|0.155|0.682|0.837|0.166<br>0.829|0.995|
|Textiles|0.345|0.604|0.949|0.357<br>0.543|0.900|
|Garments, leather goods and shoes|1.002|0.707|1.709|0.074<br>0.898|0.972|
|Wood products, paper products, printing and<br>publishing|0.116|0.864|0.980|0.302<br>0.780|1.081|
|Coke, fuel, petroleum and chemicals|0.216|0.616|0.832|0.295<br>0.811|1.106|
|Plastic and rubber products|0.326|0.660|0.986|0.261<br>0.778|1.039|
|Concrete, cement and glass|0.139|0.735|0.874|0.437<br>0.651|1.089|
|Iron and steel|0.211|0.611|0.822|0.257<br>0.677|0.934|
|Metal products, machinery and tools|0.056|0.832|0.888|0.145<br>0.831|0.975|
|Lifting, medical and industrial equipment|0.189|0.824|1.013|0.325<br>0.678|1.003|
|Motor vehicles and transport systems|0.218|0.870|1.088|0.312<br>0.745|1.058|

37
Table S8. Model With Interaction Between Inequality and Initial Income (Current and Lagged Inequality)

|Dependent Variable is:|l|n(yit)||
|---|---|---|---|
||(1)|(2)|(3)|
|Inequality Variable is:|Gini<br>Brueckner et al. (2015)|Net Gini<br>Solt (2015)|Market Gini<br>Solt (2015)|
|Inequalityit|46.52***<br>(11.31)|105.45***<br>(24.40)|61.33***<br>(12.32)|
|Inequalityit-1|50.10***<br>(16.63)|50.82**<br>(22.97)|23.49*<br>(12.54)|
|Inequalityit|-6.93***|-16.12***|-9.07***|
|*ln(yi1970)|(1.63)|(3.72)|(1.81)|
|Inequalityit-1|-7.12***|-7.67**|-3.25*|
|*ln(yi1970)|(1.66)|(3.35)|(1.83)|
|Kleibergen Paap F-Statistic|9|12|17|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|361|592|592|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The endogenous variables are _Inequalityit_ and _Inequalityit-1_ ; the instruments are _Residual Inequalityit_ and _Residual Inequalityit-1_ . _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequalityit_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.
5.26 Despite public resource constraints during the recession of the early 1980s, education's share of the public budget remained constant (except in 1984), and its share of GDP increased after 1984. Although education's share of the budget and GNP increased, real education expenditures in 1985 were three-fourths their 1980 levels. Declining real education spending after 1980 may have contributed to a lower primary enrollment rate. The growth of enrollments slowed at all levels after 1980, although total enrollments continued to increase.

# _MPsipies:_ eductn indcato

||197|5 198|0 198|1 198|2 198|3 198|4 1985|1986 1987 1988|
|---|---|---|---|---|---|---|---|---|
|Einance:|||||||||
|% of public budget|13.8|11.1|10.9|11.5|10.7|8.4|11.5|11.6|
|% of GNP||1.7|1.8|1.9|1.7|1.5|1.8|2.2 2.5 2.8|
|real expenditures (index)|87|100|||||75||
|Intrasectoral finance:|||||||||
|recurrent budget share(%)|82|96|||||93||
|<br>salary budget share (%)|87||||||62|59|
|<br>materials budget share(%)|0.3||||||0.9|0.6|
|<br>primary share of budget (%)|82|61|||||64||
|Qutput (enrollment rates--%):|||||||||
|gross primary|107|113|111|109|109|107|94|9)|
|<br>netprimary|94|94|93|92|88|88|90|89|
|<br>gross secondary|54|65|64|66|67|68|65|65|
|<br>gross tertiary|18|26|27|29|32|34|38||
|Ouality:|||||||||
|per student expenditures (ind<br>|ex)<br>||||||||
|primary|95|100|||||63|83|
|<br>secondary|44|100|||||41|66|
|<br>tertiary|<br>28|<br>100|||||45||
|<br>achievement test scores (inde|x)||||||||
|primary|85|||||||100|
|<br>secondary||||100||104|108||
|<br>primary repeaters(t)||2.4||2.1|2.3|2.3|1.8||
|<br>primary dropouts(t)||||2.9|2.8|2.7|2.0||
|<br>primary completion(%)||||67|67|65|63||
|liquity:<br>|||||||||

- % primary students female 51 49 49

5.27 Budget allocations to capital investment and materials expenditures increased slightly during the adjustment period. The recurrent share of the public education budget declined after 1980, but remained above its 1975 level. Perhaps as a result of increased 45
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# **Appendices**

# **Appendix A1. Proof of Theorem 2.1**

It is straightforward to check that each poverty measure in Equation 2.1 satisfies the four properties: OMN, SCD, FOC and SUD.

We now prove the necessary part, namely, if a poverty measure satisfies these four properties, then it takes the functional form in Equation 2.1. First, we define _S ∈_ N _\ {_ 1 _}_ basis vectors:

**e**<sup>_s_</sup> = ( _e_<sup>_s_</sup> 1<sup>_, . . . , es_</sup> _S_<sup>)</sup><sup>_∈_P</sup><sup>_∀s_=1</sup><sup>_, . . . , S_,suchthat</sup><sup>_es_</sup> _s_<sup>=1</sup><sup>_∀s_and</sup><sup>_et_</sup> _s_<sup>=0</sup><sup>_∀s_=</sup><sup>_t_.Foragiven</sup> _ck ∈_ **C** _−S_ , denote _P_ ( **e**<sup>_s_</sup> _, ck_ ) = _ωs_ for some _ωs ∈_ R+ for every _s_ = 1 _, . . . , S_ .

40
some negative consequences compared to when a single instrument is used. This issue of integrated policy framework has recently been discussed widely Das, Gopinath & Kalemli-Özcan (2022), and formally modeled by Basu, Boz, Gopinath, Roch & Unsal (2020). We discuss these issues further in section 5.

# **5 Regional Divergence and KC-EA Interaction**

So far, we have found mixed results on the relationship between country characteristics such as capital controls and U.S. monetary spillovers. Motivated by existing work on the divergence between responses of emerging markets and advanced economies to external shocks such as Kalemli-Özcan (2019), Hoek et al. (2021), Ahmed et al. (2021) and De Leo et al. (2022), we examine the relationship between KC and U.S. monetary policy shocks separately for EMDEs and AEs.

Table 3: U.S. Monetary Policy Transmission Across KC and Income Groups

||**Max**|**. R* Respo**|**nses**|**Ma**|**x. S Respon**|**ses**|
|---|---|---|---|---|---|---|
|**Group**|**NS**|**BRW**|**SS-FG**|**NS**|**BRW**|**SS-FG**|
||||_Panel A: F_|_ull Sample_|||
|All|1.37|1.07|0.49|0.23|0.06|0.09|
||[-0.47, 4.79]|[-0.70, 4.12]|[-0.03, 0.14]|[0.02, 0.45]|[-0.03, 0.19]|[0.01, 0.17]|
|Low KC|1.22|1.20|0.21|0.23|0.07|0.09|
||[0.13, 2.25]|[0.44, 2.31]|[0.00, 0.06]|[0.11, 0.36]|[0.00, 0.14]|[0.05, 0.14]|
|High KC|1.52|0.87|0.75|0.23|0.05|0.06|
||[-0.08, 4.12]|[-0.03, 3.14]|[0.00, 0.19]|[0.08, 0.39]|[0.00, 0.13]|[0.02, 0.12]|
||||_Panel B: AE _|_Sub-sample_|||
|AE All|0.88|1.03|0.17|0.25|0.08|0.10|
||[0.14, 1.70]|[0.47, 1.68]|[-0.04, 0.44]|[0.13, 0.37]|[0.01, 0.14]|[0.06, 0.15]|
|Low KC|1.15|1.06|0.18|0.25|0.08|0.09|
||[0.22, 1.70]|[0.55, 1.68]|[-0.04, 0.48]|[0.14, 0.37]|[0.01, 0.15]|[0.05, 0.15]|
|High KC|0.58<br>[-0.01, 1.69]|1.02<br>[0.36, 1.58]|0.15<br>[-0.04, 0.42]|0.24<br>[0.12, 0.37]|0.08<br>[0.00, 0.14]|0.10<br>[0.06, 0.15]|
||||_Panel C: EMD_|_E Sub-sample_|||
|EMDE All|3.04|1.17|0.94|0.20|0.04|0.05|
||[-0.09, 6.45]|[-0.34, 4.37]|[0.03, 2.56]|[0.05, 0.34]|[-0.01, 0.11]|[0.02, 0.10]|
|Low KC|4.31|1.37|1.55|0.14|0.03|0.05|
||[1.19, 9.64]|[-1.24, 6.23]|[0.18, 3.20]|[0.03, 0.28]|[-0.02, 0.11]|[0.02, 0.11]|
|High KC|1.36|1.01|0.52|0.23|0.04|0.06|
||[-0.33, 3.86]|[-0.01, 2.89]|[-0.07, 1.81]|[0.07, 0.37]|[0.00, 0.11]|[0.02, 0.09]|

Notes: (1) This table reports the median of maximum IRFs over 1-30 months for countries in each sub-group as indicated by the **Group** column; (2) Statistics in the brackets show the lower and upper bound for 68% confidence band; (3) “High (Low) KC” indicates countries with average KC above (below) median.

As indicated by Panel B and C of Table 3, we find that the negative relationship between impulse responses and capital controls becomes stronger and robust once we split the sample into AE 18
data is available, there has been a decline in the measured value of services output per employee. These countries also have not increased their overall labor productivity performance in the last decade. Noteworthy is the performance of the Baltic countries, where labor productivity in services outpaced the productivity increase in other sectors of the economy. Convergence with respect to high-income OECD countries in terms of productivity levels is still far from being achieved, however.

**Figure 2: Labor productivity**

<!-- Start of picture text --> Labor Productivity of Service Sector by Country Labor Productivity of All Sectors by Country<br>Group, 1991 vs. 2001 Group, 1991 vs. 2003<br>60000 70000<br>50000 60000<br>50000<br>40000<br>1991 40000 1991<br>30000 2001 30000 2003<br>20000 20000<br>10000 10000<br>0 0<br>OECD CEE SEE FSU1 FSU2 FSU3 FSU4 OECD CEE SEE FSU1 FSU2 FSU3 FSU4<br>1995 US$ 1995 US$<br><!-- End of picture text --> Note: For definition of country groups, see Figure 1.

Input-output tables for the year 2001, the latest available year for many ECA countries, provide information on differences in economic structure and the extent to which ECA countries have converged to comparators in the rest of world as regards both intermediate services use and final demand, as well as on the service intensity of exports. Table 1 reports information on the sectoral intensity of exports: the direct contribution of agriculture, mining, manufactures and services to total exports, expressed as a share of total exports of goods and services. Albania, Croatia and the Baltic States are the most services-intensive in exports. The first column in Table 2 reports the sum of the direct and indirect linkage effects generated by a unit of export revenue—the total activity generated by (going into) one unit of foreign exchange (exports). The average “multiplier” is 3.6, i.e., every US$ of exports generates $3.6 in economic activity. On average a little over one third of this total activity is services-related, ranging from a high of 52 percent (Albania) to a low of 27 percent (Czech Republic). Many transition countries are more services oriented than developing countries such as China or Malaysia.

4