rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>9. Determinants of Decentralization - Third Front Construction as IV</u>**

||IV Probit<br> <sup>d</sup>|model<br>|IV Probit<br> <sup>d</sup>|model<br>|2SLS<br> <sup>d</sup>|model<br>|
|---|---|---|---|---|---|---|
||(1)2<sup>n</sup> stage|(2)1<sup>st</sup> stage|(3)2<sup>n</sup> stage|(4)1<sup>st</sup> stage|(5)2<sup>n</sup> stage|(6)1<sup>st</sup> stage|
||_Decentralized_|_Distance_|_Decentralized_|_Distance_|_Decentralized_|_Distance_|
|Distance(t-1)|0.0143**<br>(0.0059)||0.0431**<br>(0.0189)||0.0318*<br>(0.0175)||
|Firm asset(t-1)|-0.0045**|0.1864***|-0.0044***|0.0028|-0.0031***|0.0028|
||(0.0017)|(0.0216)|(0.0016)|(0.0135)|(0.0010)|(0.0135)|
|ROS(t-1)|-0.0060**|0.3160***|-0.0059**|0.0504***|-0.0050**|0.0504***|
||(0.0024)|(0.0377)|(0.0024)|(0.0144)|(0.0021)|(0.0144)|
|Firm importance(t-1)|0.0204*|-2.6195***|-0.0013|-0.1162***|0.0019|-0.1162***|
||(0.0122)|(0.1458)|(0.0047)|(0.0404)|(0.0030)|(0.0404)|
|Fully state-owned(t-1)|-0.0079***|0.2003***|-0.0087*|-0.0057|-0.0050***|-0.0057|
||(0.0030)|(0.0719)|(0.0045)|(0.0238)|(0.0018)|(0.0238)|
|GDP per capita(t-1)|0.0090|0.5752|0.0553**|-1.2842**|0.0432|-1.2842**|
||(0.0400)|(0.5234)|(0.0276)|(0.5223)|(0.0289)|(0.5223)|
|State sector share(t-1)|0.0848|-0.7283|0.0850|-1.6849|0.0579|-1.6849|
||(0.05748)|(0.9390)|(0.0696)|(1.4856)|(0.0645)|(1.4856)|
|Unemployment rate(t-1)|-0.0023|0.0310|-0.0166*|0.4420*|-0.0094|0.4420*|
||(0.0036)|(0.0734)|(0.0094)|(0.2284)|(0.0100)|(0.2284)|
|Third front firm||0.4684***||0.1913**||0.1913**|
|||(0.1089)||(0.0869)||(0.0869)|
|Oversight government dummies|||YES|YES|YES|YES|
|Year dummies|YES|YES|YES|YES|YES|YES|
|Province dummies|YES|YES|||||
|Observations|56,700|56,700|41,684|41,684|56,705|56,705|
|First-stage F statistic||18.48||4.85||4.85|
|Anderson-Rubin test||||||0.0937|
|P-value|||||||

In this table, we present the results of distance on decentralization using _Third Front Construction (TFC) Dummy_ as the instrument. _TFC_ equals to one if firm was established during 1964-1966 or 1969-1971 in the “Third Frond Construction Area.” Marginal effects evaluated at the mean and standard errors are listed. The dependent variable _Decentralized_ in the 2<sup>nd</sup> stage takes on the value of 1 if a firm is decentralized in year t, and 0 otherwise. Columns 1-4 present the 2<sup>nd</sup> and 1<sup>st</sup> stage results from IV Probit model, which is estimated using conditional MLE (i.e., the first stage result is estimated jointly with the parameters of the Probit equation when implementing conditional MLE). Columns 1-2 control for province dummies. Columns 3-4 control for a full set of oversight government dummies. Columns 5-6 present results from a standard 2SLS model. Standard errors are clustered at the level of the same oversight government.

*, ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

40
# **Financial Development in Asia: Beyond Aggregate Indicators**

Tatiana Didier Sergio L. Schmukler<sup>*</sup> January 2014

**Abstract** This paper documents the major trends in financial development in Asia since the early 1990s and the spillovers to firms. It compares Asia with advanced and emerging countries and uses both aggregate and disaggregate indicators. Financial systems in Asia remain less developed than in advanced countries but more developed than in Eastern Europe and Latin America. Bond and stock markets play a larger role and institutional investors have gained importance. Nonetheless, capital-raising activity has not expanded. A few large companies capture most of the issuances. Many secondary markets remain illiquid. The public sector captures a significant share of bond markets. The largest advancements in Asia occurred in China and India. But still in these countries, few large companies use capital markets to expand and grow, becoming much larger than nonuser firms. In sum, Asia’s financial systems remain less developed than aggregate measures suggest, with few spillovers to many firms.

**JEL classification codes:** G00, G20, G21, G23

**Keywords:** Banking, capital markets, corporate financing

* The authors work for the World Bank in, respectively, the Office of the Chief Economist for the Latin America and the Caribbean Region and the Research Department. This paper draws on the background work presented in de la Torre et al. (2011) and Didier and Schmukler (2014). The authors are particularly grateful to Juan Jose Cortina and also to Francisco Ceballos and Lucas Núñez for outstanding research assistance. We received very helpful comments from Edward Chen, Peter Drysdale, Wendy Dobson, Francis Hutchinson, Luis Serven, Shekhar Shah, and participants at the PAFTAD36 conference “Financial Development and Cooperation in Asia and the Pacific” (Hong Kong SAR, China). Generous research support came from the World Bank’s Knowledge for Change Program and the Latin America and the Caribbean Region’s Chief Economist Office. The views expressed here are those of the authors and do not necessarily represent those of the World Bank. Email addresses: <u>tdidier@worldbank.org, sschmukler@worldbank.org.</u>
Table 8: School Closures and Parental Engagement

|||Outcome: School Clos|ures|
|---|---|---|---|
||First Experiment|Second Experiment|Second Experiment, IV|
|API Original|0.031<br>[0.549]|-0.031<br>[0.396]|-0.031<br>[0.410]|
|API Plus||-0.083<br>[0.030]||
|Overall Parental Engagement|||-0.217<br>[0.021]|
|Observations|80|224|1045|
|Clusters|.|.|224|
|F-Stat (Excl. Instruments)|||13.833|

_Notes_ : This table reports the estimates for the reduced-form effects of the API modalities during the two experiments (columns 1 and 2) on the probability of school closures, as well as the instrumental variable estimates of the impact of parental engagement on school closures. In the third column, the randomized API _Plus_ modality during the second experiment is used as an instrumental variable, while the randomized API _Original_ modality is included as a control variable. The dependent variable is an indicator variable for whether the school is closed in the fall of 2014 (column 1) or in the fall of 2018 (columns 2 and 3). The variable “Overall Parental Engagement” is the same variable used in the last column of Table 3. All _p_ -values account for clustering at the school level. _p_ -values reported in brackets account for clustering at the school level.

the contextual information on the role of the parental association in deciding school closures discussed previously in this section, we posit that parents are the main channel through which the _Plus_ modality of the API program affects school closures. The differential impacts of the two program modalities on both parental investment and school closures shown in the first two columns of Table 8 are consistent with this exclusion restriction. We find that an increase of half a standard deviation in the overall parental engagement index is causally associated with a reduction of 11 percentage points in the probability that their children experience a school closure. This effect is both statistically and quantitatively significant.

We complement these findings with qualitative evidence on the role of parents in ensuring continuity in schooling activities (see Appendix A.3). As reported by the community instructors, parents may have more at stake in keeping the schools open as they invest in durable goods for the local school:

- _“[Parents] help manage the school and contribute by improving the fencing, painting the walls, fixing the toilets, as well as buying school materials.”_

- _“[Parents] serve the needs of the school with construction works and they provide food to the local instructor.”_

30
# Figure 6

|Hungary: O|perating As<br>- _IAS_|set and In<br>Dati-|come Ratios||
|---|---|---|---|---|
||All<br>Banks<br>(7)|Large<br>Banks<br>(4)|Small & Med.<br>Banks<br>**(2)**|Savings<br>Banks<br>(1)|
|_Operating Asset Ratios_<br>(% of average total assets)|||||
|Interest Margin|4.0|4.8|6.5|2.7|
|Non-interest Margin|3.8|2.9|1.8|5.2|
|Gross Income Margin|7.8|7.7|8.3|7.9|
|Operating Costs|3.8|3.6|1.7|4.2|
|Provisions|0.7|0.9|0.5|0.5|
|Total Costs|4.5|4.5|2.2|4.7|
|Pre-tax ROA|3.3|3.2|6.1|3.2|
|_Operating Income_**_Ratios_**<br>(% of gross inoome)|||||
|Non-interest Income|48.4|38.1|21.4|65.4|
|Total Cost|58.3|59.2|'|59.4|
|Pre-tax Profit|41.7|40.8|73.6|40.6|
|_Operating Equity_**_Ratios_**<br>(% of average equity)|||||
|Pre-tax Nominal ROE|68.0|65.9|34.2|82.6|
|Pre-tax Rea ROE|30.2|28.6|4.0|41.6|
||||Sources:<br>B|<br>ank Staff Estimates|

13a
The remainder of the paper is organized as follows. Section 2 describes the data employed, and section 3 documents the evolution of China’s bilateral trade relationships. Section 4 develops and implements an econometric framework for examining the relative importance of supply and demand shocks associated with China’s trade dynamics on exports of B&R economies. Section 5 provides a descriptive analysis on the current degree of exposure of each B&R economy to supply and demand trade shocks associated with China’s trade. Section 6 reviews empirical evidence on the impacts of trade shocks on labor markets in other countries, and discusses policy options to deal with the adjustment costs imposed by these trade shocks. Section 7 concludes the paper.

# **2. Data**

The analysis in this paper makes use of product-level bilateral trade data from BACI spanning the period 1995-2015. BACI is the world trade database developed by CEPII, building on original data provided by the COMTRADE database of the United Nations Statistical Division. BACI is constructed using an original procedure that reconciles the declarations of the exporter and the importer. This harmonization procedure makes it possible to extend considerably the number of countries for which trade data are available. BACI provides bilateral values and quantities of exports at the HS 6-digit product disaggregation for more than 200 countries since 1995.

CEPII developed original statistical procedures to reconcile data reported by almost 150 countries to the United Nations Statistics Division. First, as import values are reported CIF (cost, insurance and freight) while exports are reported FOB (free on board), CIF costs are estimated and removed from import values to compute FOB import values. Second, the reliability of country reporting is assessed based on the reporting distances among partners. These reporting qualities are used as weights in the reconciliation of each bilateral trade flow twice reported. Due to the use of this double information on each flow, BACI ends up covering a large set of countries not reporting at a given level of the product classification. The dataset gives information about the value (in thousands of US dollars) and the quantity (in tons) of trade.

The BACI database was supplemented with the CEPII _gravity_ data set, which contains multiple economic and sociodemographic information for all world pairs of countries from 1948 to 2015.

6
of school who are in school at that level or the level above; (ii) the “net enrollment rate” (NER), measuring the fraction of children in the theoretical age range for a given level of school who are in school at that level; and (iii) the “gross enrollment rate” (GER), measuring the number of children of any age who are enrolled in a given level, as a fraction of the number of children in that age range.

To maximize country coverage, the following procedure populates the enrollment rate series used to calculate expected years of school:

- Available data for all four enrollment rates for all four levels of school were retrieved, combining information from the three external data platforms maintained by the UNESCO Institute of Statistics.  For pre‐primary, TNERs are not reported as there is no level below pre‐primary, and ANER is available only for the age corresponding to one year before the official start of primary school.

- Gaps in each enrollment rate for each level and country were filled by taking the most recently available data, going backwards up to 10 years for each country‐year observation.

- Within each level, the preferred enrollment rate available in the filled‐in data as of 2017 was chosen, in the following order of priority:  TNER, ANER, NER, and GER.  The filled‐in series for this enrollment rate was then used for all years for this level of school.  Note that in some cases differences in availability of enrollment measures means that different enrollment rates are used for different levels of school.  However, with a level for a given country, same type of enrollment rate is used.

- Data on repetition rates for primary, lower‐secondary, and upper‐secondary school were retrieved, and filled in with up to 10 years of lags in the same way as for enrollment rates. These are needed to adjust enrollment rates obtained in the previous step for repetition. Adjusting for repetition is important because failing to do so would count students repeating a grade as gaining an additional year of school, and in some school systems at some levels repetition rates are as high as 25 percent.  Data on repeaters is available for most countries included in the HCI;  However for a few countries where data on repetition is not reported by UNESCO, out of necessity repetition rates are assumed to be zero.

- Finally, in a few cases where only GERs were available and repetition‐adjusted GERs exceeded 100 percent.  These are topcoded at 100 percent.

26
lower wage in the negotiation.

Since it is assumed that the wage is renegotiated every time a productivity shock arrives, wages for employed workers of type _y_ and current productivity _x_<sup>45</sup> are determined by workers and firms using Nash bargaining, given the exogenous worker bargaining parameter _β_ , as follows: The corresponding standard sharing rule using the free entry condition ( _VF_ = 0) is: The formal-sector wage equation for an incumbent worker of type _y_ and current productivity _x_ is: Notice that _w_<sup>_s≥wF_(</sup><sup>_x, y_)sincetheseverancetaxnowworsensthefirm’sbargaining</sup> _F_<sup>(</sup><sup>_x, y_)</sup> position and reduces the benefits the firm gets if it does not accept the bargain with the old hire, thereby reducing its bargaining strength in the negotiation and increasing the negotiated wage.

# **3.3.2 Informal Sector**

I assume there is bargaining over wages in the informal sector.<sup>46</sup> The wage for a worker type _y_ solves: The F.O.C (assuming interior solution), gives us the following standard sharing rule: The wage equation for the informal sector is: In this case, neither the productivity nor the continuation value term needs to be adjusted since there is no severance tax or flows from informal to formal. However, high benefits in the form of

> 45 This is the wage for incumbent workers who have been affected by a shock, so _RF U_ ( _y_ ) _≤ x ≤∞_ .

> 46I conceive the informal sector as a ‘disadvantaged’ countercyclical sector in which low-skilled workers are negotiating wages with small firms, rather than as unregulated pro-cyclical self-employment. In Colombia, lack of compliance with social security is also a small firm phenomenon: approximately 87.2 percent of those informal under the SP definition are in small firms ( _≤_ 5 employees).

16
|**TABL**<br>**CO**|**E 1: SUMM**<br>**MPARATIV**<br>Depend|**ARY of COEF**<br>**E SECTOR A**<br>ent variable: M|**FICIENTS – M**<br>**NALYSIS with**<br>anufacturingE|**ULTIPLE DUM**<br>**in SOUTH AFRIC**<br>xports(log)|**MIES**<br>**A**|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)<br>Mlil|
||Base case|Base case<br>with<br>controls|Base case<br>with<br>additional<br>controls|Multiple year<br>and interaction<br>dummies|utpe year,<br>sector and<br>interaction<br>dummies<br>(fixed effects)|
|**auto * >95 (effect**|0.526|**0.756**<br>|**0.768**<br>|||
|**MIDP)**|[0.406]|[0.299]******|[0.304]******|||
|Auto|0.440<br>[0.341]|**1.253**<br>[0.235]*******|**1.280**<br>[0.231]*******|**1.319**<br>[0.346]*******||
|>95|0.485<br>[0.374]|**-0.484**<br>[0.278]*|**-0.880**<br>[0.431]**|||
|Economic density||**0.032**<br>[0.007]*******|**0.035**<br>[0.009]*******|**0.022**<br>[0.009]******|**0.007**<br>[0.003]*******|
|Terms of trade||**-0.086**<br>[0.044]******|**-0.105**<br>[0.059]******|**-0.136**<br>[0.075]*****|**-0.029**<br>[0.018]*|
|Human capital<br>intensity||**0.043**<br>[0.003]*******|**0.045**<br>[0.003]*******|**0.047**<br>[0.003]*******|**-0.007**<br>[0.003]*******|
|Infrastructure|||0.0005<br> [0.001]|||
|Inflation|||0.061<br>|||
||||[0.073]|||
|GDP per capita|||0.062|||
|growth|||[0.098]|||
|Real interest rate|||0.075<br> [0.047]|||
|Auto 94||||-0.007|-0.218|
|||||[0.498]|[0.184]|
|Auto 95||||0.126<br>[0.470]|-0.072<br>[1.666]|
|Auto 96||||0.131|-0.200|
|||||[0.471]|[0.170]|
|Auto 97||||0.333|0.041|
|||||[0.464]|[0.175]|
|Auto 98||||0.474|0.064|
|||||[0.415]|[0.204]|
|Auto 99||||**0.986**|**0.396**|
|||||[0.463]******|[0.158]**|
|Auto 00||||**0.947**<br>[0.476]******|**0.368**<br>[0.161]**|
|Auto 01||||**1.514**<br>[0.511]*******|**0.563**<br>[0.178]***|
|Auto 02||||**1.570**|**0.658**|
|||||[0.556]*******|[0.181]*******|
|Auto 03||||0883|**0864**|
|||||.<br>[0.588]|**.**<br>[0.166]*******|
|Auto 04||||0.837|**0.836**|
|||||[0.604]|[0.182]*******|
|Year Dummies|-|_-_|_-_|Yes|Yes|
|CountryDummies|-|_-_|_-_|_-_|Yes|
|Constant|**12.769**|**16.645**|**17.237**|**23.008**|**15.277**|
||[0.341]*******|[4.276]*******|[5.216]*******|[7.789]*******|[1.720]*******|
|Observations|120|108|108|108|108|
|R-squared|**0.06**|**0.54 **|**0.55**|**0.59**|**-**|

Robust std errors in brackets * significant at 10%, ** significant at 5%, *** significant at 1% 17
- 54 -

A third general problem relates to the proper measurement of the external debt. As Anand and van Wijenbergen (1988) demonstrate, crossexchange rate variations may have serious consequences for the proper valuation of debt denominated in various currencies, and they thus recommend the use of a weighted debt-output and debt-exports ratio, along the lines suggested by Cohen (1986). 17/ Absence of a detailed breakdown of external debt statistics by currency necessitated the use of the assumption that the debt statistics of Kenya and Ghana have properly and adequately incorporated valuation changes resulting from dollar fluctuations vis-a-vis other currencies.

A particular problem for Kenya was the use of fiscal year data in its budgetary accounts. The transformation of national income accounts data on a fiscal year basis through a simple arithmetic average may have introduced a margin of error of unknown magnitude. This particular problem, as well as the more general one of official data reporting in a timely and consistent fashion, suggest that for both countries it is preferable to focus on average statistics rather than on year-to-year (point) estimates during the historical period.

All of these qualifications notwithstanding, the results for the two countries were interesting, if only because of their similar implications. In the case of Kenya it appears that deficit developments between 1980-1987, and 17/ See also Turkey: External Debt, Fiscal Policy and Sustainable Growth, volume I: The Main Report, September 9, 1988 Report No.7162-TU, The World Bank.
lack of regulations to cover issues once the grid reaches the area served by the mini-grid may prove to be a bottleneck in spread of mini grids.

Table 6: Regulatory framework for electricity access in the Philippines and comparators, 2015<sup>11</sup>

||**Colombia **|**Peru**|**Philippines**|**Vietnam**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Energy Access Regulation**|67%|57%|74%|7%|56%|
|_Regulation of New Connections_|68%|88%|57%|14%|65%|
|_Regulation of solar home systems_|100%|50%|100%|0%|66%|
|_Regulation of mini-grids_|33%|33%|67%|NAV|44%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform

# 3.3 Efficiency and financial viability

The low level of operational and commercial efficiency of the power sector pre EPIRA was an important contributing factor to the acceptance of a need to change the way in which the sector would operate. To provide a detailed assessment of operational and commercial efficiency it was decided to focus on two companies. MERALCO (a distribution utility) and BENECO (an electricity co-operative) as representative of performance of different types of ownership structure – MERALCO being privately owned, and BENECO a non-profit cooperative. The scores for these two utilities are not directly comparable, but MERALCO can be compared to the utilities from Peru, Colombia and Vietnam.

Distributors are a regulated common carrier business operating on franchises and subject to regulation be the ERC. Distributors vary widely in size/technical/financial capacity and operating standard (e.g. voltage levels and metering). MERALCO is the Philippines’ largest distribution utility serving over a quarter of the population and the regions that accounts for 50 percent of the country’s GDP. Performance Based Regulation, which MERALCO is subject to, calculates a Performance Index Factor which includes service interruption, system loss and voltage level criteria. Consumers are to be compensated if guaranteed service levels are not met.

BENECO is organized and registered as a non-stock non-profit service-oriented entity providing services in the City of Baguio and Benguet province under a franchise of 50 years granted by the NEC. As a cooperative, BENECO is owned and operated by the consumers it serves which also elects the board of directors.

Electric cooperatives (EC) are generally smaller in terms of the market they serve. The ECs distribution infrastructure was financed almost completely by public money through the NEA. Most of the loans were subsequently mandated by EPIRA to be absorbed by PSALM. EPIRA also

> 11 For details on the electricity access index, see the annex.

33