rafmacalaba/datause-encoder-data huggingface dataset:
Levine 2001). Competition has clearly increased with the Herfindahl index (a measure of concentration) for advances and assets dropping by over 28% and about 20% respectively between 1991-1992 and 2000-2001 (Koeva 2003). Within a decade of its formation, a private bank, the ICICI Bank has become the second largest bank in India.

While the problem of NPLs does exist for some public sector banks (also a “new” private sector bank almost collapsed in 2003 when it was bailed out by merging with another bank), compared to most Asian countries the Indian banking system has done better in managing this problem. The “healthy” status of the Indian banking system is in part due to its high standards in selecting borrowers (in fact, many firms complained about the stringent standards and lack of sufficient funding), though there is some concern about “ever-greening” of loans to avoid being categorized as NPLs. Table 3-E provides comparative statistics on this issue. In terms of profitability, Indian banks have also performed well compared to the banking sector in other Asian economies, as the returns to bank assets and equity in Table 3-F convey.

We close this section by emphasizing three facts about the Indian society and economy. First, a large and diverse country, India has had recent success in its overall economic development. Second, despite strong investor protection purportedly provided by the law, actual protection is weak in India owing to the inefficiency of legal institutions and corruption. Third, despite the development and growth of India’s financial system (both banking sector and markets), its size is small relative to the economy while its roles of resource allocation and provider of external financing are expected to become much more important in the near future.

# **III. Aggregate Evidence on India’s Corporate Sectors: Organization, Financing and Growth**

Since Independence India has sought to follow a “mixed economy” model with co-existing state and non-state sectors. In reality, the next four decades saw the widening and strengthening of the state sector with simultaneous crippling of the non-state sectors through severe controls, 16
**Table 1 Variables definition and sources**

|Variable|Definition|Sources|
|---|---|---|
|Survivali, t|A dummy variable indicating the firm is currently open and have never<br>temporarily closed since COVID-19 declared, value is missing if firm<br>permanently closed during COVID-19.<br>|COVID-19 Follow-<br>up ES|
|Survival Pi, t|A dummy variable indicating the firm is currently open and have never<br>temporarily closed since COVID-19 declared, value is 0 if firm<br>permanently closed during COVID-19.|COVID-19 Follow-<br>up ES|
|Sales growthi, t|Growth of sales in monthly compared to one year ago, percentage/100.<br>Winsorized at 1% level.|COVID-19 Follow-<br>up ES|
|Output demand stablei, t|A dummy variable indicating the firm have not experienced decreased<br>demand for production or service compared to 1 year ago.|COVID-19 Follow-<br>up ES|
|Input supply stablei, t|A dummy variable indicating the firm have not experienced decreased<br>supply of inputs compared to 1 year ago.|COVID-19 Follow-<br>up ES|
|Liquidity stablei, t|A dummy variable indicating the firm have not experienced decreased<br>liquidity or cash flow availability since COVID-19 began.<br>A dummy variable indicating the firm introduced new production or|COVID-19 Follow-<br>up ES<br>|
|New productsi, t|<br>service in responses to COVID-19, mostly interviewed in second-round<br>surveys.|COVID-19 Follow-<br>up ES|
|2<sup>nd</sup>Roundi, t|A dummy variable equaling to 0 for the first-round survey observations<br>and 1 for the second-round.|COVID-19 Follow-<br>up ES|
|Non-smalli, t-1|A dummy variable indicating medium and large firm which permanent<br>employees is above or equals to 20.|Standard WBES|
|Log Agei, t-1|Log form of firm age by 2020.|Standard WBES|
|Foreigni, t-1|A dummy variable indicating firm’s share of ownership by private<br>foreign is above 0|Standard WBES|
|SOEsi, t-1|A dummy variable indicating firm’s share of ownership by government<br>or state is above 0.|Standard WBES|
|Subsidiaryi, t-1|A dummy variable indicating the firm is part of a large firm.|Standard WBES|
|Resource scorei, t-1|The sum of Non-small, Foreign, SOE and Subsidiary|Standard WBES|
|Log Experiencei, t-1|Log form of manager's years of experience by 2020.|Standard WBES|
|Log LPi, t-1|Log form of LP, LP equals to sales/ (Lperm+ 0.5* Ltemp). Lperm<br>(Ltemp) is the number of permanent (temporary) employees.|Standard WBES|
|Log GDP perc, t-1|Log form of GDP per capita in 2019.|WDI|
|Policy stringencyc, t|A composite measure based on nine response indicators including<br>school closures, workplace closures, and travel bans, rescaled to a<br>value from 0 to 1 (1= strictest). At one day before the first day when<br>the survey had been implemented in the country.|Oxford COVID-19<br>Government<br>Response Tracker|

25
7

# **_<u>Fig. 4: Evolution of interest rate spread for individual loans and deposits (RUR, FX)</u>_**

<!-- Start of picture text --> 16.0<br>14.0<br>RuR Loans<br>12.0<br>10.0 FX Loans<br>8.0<br>6.0 RuR Deposits<br>4.0<br>2.0 FX Deposits<br>0.0<br>Mar-03 Jun-03 Sep-03 Dec-03 Mar-04 Jun-04 Sep-04 Dec-04 Mar-05 Jun-05 Sep-05 Dec-05 Mar-06<br>Average Interest Rate on Deposits in Rubles Average Interest Rate on Deposits in Foreign Currency<br>Average Interest Rate on Loans in Rubles Average Interest Rate on Loans in Foreign Currency<br><!-- End of picture text --> _Source:CBR._

# **III. MARKET STRUCTURE MATTERS**

As shown in table 1 below, the Russian banking system still seems both highly concentrated at the top and very fragmented in the middle and bottom, as well as mainly centered in the wealthier Moscow–St. Petersburg corridor. The structure of the banking industry offers great potential for further consolidation, in spite of the enormous land mass of the Russian Federation. This structure is a legacy of both the socialist past—with its large publicly-owned banks—while the fragmentation in the medium and small bank sizes reflects the lax entry policies followed in the early 1990s by the CBR in granting banking licenses, as well as CBR’s reluctance to force exits or consolidations by using stricter regulatory standards and higher capital requirements.<sup>10</sup> Yet, the number of deposit-taking banks decreased from 1,239 in 2001 to 1,045 by January 1, 2006. This was partly due to mergers and acquisition activities, which have recently picked up and are likely to accelerate.

In spite of the large number of institutions, only a small fraction of Russian banks can be considered true financial intermediaries with stable franchises _._ Many of the country’s banks are tiny, with over 80 percent of Russian banks operating with capital of less than US$10 million<sup>11</sup> . Many smaller banks are so-called “pocket banks,” serving a single company or its controlling financial–industrial group.<sup>12</sup> The five largest banks accounted for 45.1 percent of total bank assets as of early 2005, whereas the top 200 banking institutions represented jointly 89 percent of total assets. Although the majority of banks

> 10 Moody’s Investors Service, “Russia”, Banking System Outlook, October, 2005.

> 11 Standard & Poor’s, “The Russian Federation”, Bank Industry Risk Analysis, June 28, 2005. 12 According to the OECD, a number of these “dwarf” banks are used by their owners for “tax optimization” or money laundering. See OECD (2004), p. 5.
Table B1: Choice of replacement control villages

|||Tigre|||Wealth||
|---|---|---|---|---|---|---|
|Sample restricted|Goluj|Tesseney|Shambko|Goluj|Tesseney|Shambko|
|to sub-zone:|||||||
|village 4|-0.17|||-2.45**|||
||(0.15)|||(0.78)|||
|village 5|-0.17|||-2.23**|||
||(0.15)|||(0.78)|||
|village 7|-0.17|||-1.71*|||
||(0.15)|||(0.78)|||
|village 52||0.38||-0.59|||
|||(0.20)||(0.41)|||
|village 93|||-|||0.25|
|||||||(0.13)|
|village 95|||-|||-0.68***|
|||||||(0.13)|
|Constant|0.24|0.62**|-|2.22**|0.38|0.09|
||(0.15)|(0.20)|-|(0.78)|(0.41)|(0.13)|
|Observations|73|88|90|72|87|90|

Note: one observation per household. This Table presents the coefficients _β_ 1 estimated from LS regression _Yi_ = _β_ 0 + _β_ 1 _Xi_ + _✏i_ , with standard errors in parentheses. In models (1)-(3), _Yi_ is an indicator variable =1 if household _i_ belongs to the Tigre tribe, and =0 otherwise. In Columns (4)-(6) _Yi_ is an asset index for household _i_ . Samples restricted to the sub-zones where listed villages are located, shown in each header. Notice that no Tigre households were surveyed in sub-zone Shambko. Observations clustered at village level. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

13
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The conclusion of this breakdown is that at federal level only about 10% of the budget is subject to system internal ex-ante budget controls. The remaining 90%, including the majority of salary and pension payments, is handled outside the system. The system can thus not credibly affect aggregate expenditure control or expenditure targets. As the payment and reporting is only integrated for those payments transacted through FABS, the integrity of reporting can only be guaranteed for those 10%.

At the provincial level FMIS coverage is significantly larger ranging from 44% in Punjab to over 65% for other provinces. Payroll and pension expenditures constitute a large share of the budget and are well captured by the system. This supports greater overall expenditure control at this level.

Agencies whose payroll and pensions are processed outside the system should be brought in. This is true mainly for the federal level where the percentages of the payroll and pensions processed thru the system are very low. The low percentages for payroll and pensions at the federal level are due to the fact that defense does not use the FMIS. The pension figures for Sindh and Khyber Pakhtunkhwa are low because not all pension payments in Sindh and Khyber Pakhtunkhwa are done through the FMIS and still follow the legacy PPO process.

# Analysis of the Pakistan Expenditure Transaction Profile

Assessing the transaction profile in more detail reveals that most non-salary transactions processed by the system are of low value and don’t make up a large share of the budget. At the federal level, non-HR related transactions smaller than PKR 10,000 make up 40% of the budget and the percentage of transactions below PKR 20,000 is about 50% for the center and all provinces. However, all of these transactions together only make up between 0.4% and 1.5% of non-HR related disbursement through the system. Conversely, high value transactions (>PKR 500,000) are few across all systems but make up a large share of the budget as detailed in table 5. The entire transaction profiles for non-HR related expenditure processed through FABS for the federal government and for each of the provinces are given at Annex I.

_Table 5 Analysis of Transactions Profile in Pakistan_

||**Share of High**<br>**Value**<br>**Transactions**<br>**Processed**<br>**Through FABS**|**Share of the Total**<br>**Non‐HR Related**<br>**Disbursement**|**Total Number**<br>**of**<br>**Transactions**|**No of High Value**<br>**Transactions > PKR**<br>**500,000**|
|---|---|---|---|---|
|_Federal Government_|_3.65%_|_87.31%_|_625,596_|_22,823_|
|_Punjab_|_3.85%_|_73.8%_|_861,031_|_33,197_|

15
# **Supplementary Country Notes**

**Albania:** Ratio reported as 8.5 and 12.4 percent in 1990 and 1991 respectively. Figures reported in the table are based on 1992 estimnates as of December 1, 1992.

Armenia: Includes payments to war veterans and military pensions

**Australia:** Change reflects revised GDP figures.

Austria: Change reflects revised GDP figures.

Belgium: OECD (1993) excludes invalidity/early retirement/redundancy benefits. Spending in these categories is taken from Eurostat 1992a,b and added to the OECD total based on the same source.

Belize: World Bank report #7178-BEL reports spending on public employees' pensions of 6 million Belize dollars which are added to the reported 606 million reported by the Belize Social Security Board Annual Report for 1989. Changes also reflect revisions in GDP since publication.

**Benin:** The 1986 value estimated by adding the spending in the main scheme to projected spending on public employee scheme assuming the ratio of the former to the latter remained constant between 1983 and 1986.

**Brazil:** Includes old age, length of service, disability, survivors, destitution, work bonus and civil servants pensions.

**Bulgaria:** Includes I /10th of one percent of GDP in social assistance spending.

**Burkina Faso:** 1986 total estimated by adding the spending of the main scheme to projected spending on public employees' scheme assuming ratio of former to the latter remained constant between 1983 and 1986.

**Chad:** GDP taken from internal World Bank estimates not from IMF IFS Statistical Yearbook.

Chile: Spending on both old and new systems included although most spending is on the public scheme which is being phased out as the new scheme matures.

China: Figures do not include a relatively small amount of means-tested spending in the Wu Bao programn.

Colombia: Spending on public employees' scheme from Ministerio de Trabajo 1993 "La Prevision Social para los Empleados del Sector Publico: Analisis del Censo de Prevision Social .. Referente a Empleados Publicos". The estimated 25 billion pesos were added to the pension spending of the main scheme and "additional" pensions of the old age, anticipatory, special anticipatory and survivors' branches.

**France:** Includes mandatory pay-as-you-go, occupational plans. Invalidity/early retirement/redundancy are taken from Eurostat 1992a,b and added to OECD figure.

Germany: Includes public employees and general schemes from ILO, means-tested spending and redundancy benefits from Eurostat 1992a. Means-tested spending is assumed to remain constant in real terms between 1988 and 1989. Greece: Invalidity spending from Eurostat 1992b added to the OECD figure.

**Guatemala:** Includes burial grants and survivorship from the invalidity programs as well as InvalidezNVejezlMuerte

benefits. Assumes spending on scheme for public employees remained constant in real terms from 1980 to 1986.

**Guinea:** GDP data from World Bank intemal estimates.

10
cratic distortion prole is calibrated to keep the capital-output ratio xed across the stationary allocations. The idea is to evaluate the dynamics of investment without the interference of distortions in the rate of return to capital<sup>15</sup> . We dispense from xed production costs in the benchmark reforms, setting _fc_ = 0 _._ The reform consists of a once and for all withdrawal of each distortion. The resulting parameter values of the distortions are reproduced in table 2 Table 2: Calibration of Distortions in Benchmark Experiments

|Parameter Value<br>Target|
|---|
|Productivity-elasticity of distortions _υ_<br>0.35<br>_TFP_(_undistorted_)<br>_TFP_(_idiosyncratic−distortions_) <sup>= 1</sup><sup>_._2</sup>|
|Prot tax _τ _<sup>_π_</sup><br>0.74<br>_T F P_ (_undistorted_)<br>_T F P_(_profit−taxes_) <sup>= 1</sup><sup>_._2</sup>|
|Scale parameter _ZI_<br>5.47<br>_K_<br>_Y_ <sup>= 2</sup><sup>_._36</sup>|
|Fixed cost _fc_<br>0|

Parameter values apply to model economies with one type of distortion at a time. Values are set so that model's long run growth in _TFP_ from achieving the undistorted steady state allocation matches the 20% detrended _TFP_ growth observed in the data for an average acceleration.

# 5.2.2 Calibration of the Case Study

For the case study, we pursue a richer calibration that enables us to pin down parameter values for the combination of distortions at the onset of China's acceleration and their rate of reversal throughout the transition. As a reminder, we are modeling China's communist regime as a combination of taxes to the prots gross of innovation expenses and xed production costs, which mimic the barriers to entry and the egalitarian forces that characterize these regimes, and idiosyncratic distortions.

The idiosyncratic distortion prole is parameterized by the productivity-elasticity _υt_ and the scaling parameter _ZI,t_ . We calibrate the productivity-elasticity estimating the regression coecient between the logarithm of _TFPR_ and the logarithm of _TFPQ_ between 1998 and 2005. The data stems from the Annual Surveys of Industrial Production for the years 1998 through 2005. These surveys are conducted by the National Bureau of Statistics covering the universe of industrial rms (both

> 15Recall that _ZI_ determines the average value of the idiosyncratic distortion in the economy, which exerts a direct eect on the marginal return to capital.

24
that investing in better governance and monitoring of service providers may be an important component of improving state capacity for service delivery in low-income countries (Besley and Persson, 2009; Muralidharan, Niehaus and Sukhtankar, 2015).

Finally, recent research has pointed to ‘misallocation’ of capital and labor in low-income countries as an important contributor to lower total factor productivity (TFP) in these settings (Hsieh and Klenow, 2009), and has also documented that a plausible reason for this misallocation is that ‘management quality’ is poorer in low-income countries, and that publicsector firms are managed especially poorly (Bloom and Van Reenen, 2010). Our results provide a striking example of weak management and misallocation in publicly-produced primary education in India (a sector that accounts for over 3% of GDP in spending). In particular, our estimates suggest that reallocating a portion of the $5 Billion/year increase in education spending budgeted for hiring more teachers towards measures focused on reducing teacher absence (for instance, by hiring more supervisory staff) may be a much more cost effective way of increasing effective teacher-student contact time. Thus, misallocation is likely to be a first-order issue in this setting, and reallocating education spending towards better governance may substantially increase TFP in publicly-produced education.<sup>7</sup> The rest of this paper is organized as follows: Section 2 discusses our empirical methods and analytical framework. Section 3 reports summary statistics on school inputs and teacher absence. Section 4 presents the cross-sectional and panel regression results. Section 5 discusses the fiscal costs of weak governance and compares the returns to investing in better monitoring with that from hiring more teachers. Section 6 discusses policy implications, and Section 7 concludes.

# **2 Data and Analytic Framework**

The nationally-representative sample used for the 2003 surveys, which our current study uses as a base, covered both urban and rural areas across the 19 most populous states of India, except Delhi. This represented over 95 percent of the country’s population. The 2010 sample covered only _rural_ India. The sampling strategy in 2010 aimed to maintain representativeness of the current landscape of schools in rural India and to maximize the size of the panel. We met these twin objectives (representativeness and panel) by retaining the villages in the original sample to the extent possible, while re-sampling schools from the full universe of schools in these villages in 2010, and by conducting the panel analysis at the

> 7Such misallocation in education spending is also seen in other low-income countries. An even more striking example is provided by de Ree et al. (2015) who experimentally study the intensive-margin impacts of an Indonesian policy reform that doubled teacher pay across the board (at a similar cost of $5 billion/year) and find that the teacher pay increase had no impact on student learning.

5
# **3.2 Income Inequality**

Part of the reason why the poverty indicators of Mexico are worse than in other countries with similar per-capita incomes is because of income inequality. Mexico has an extremely unequal income distribution. Moreover, Mexico’s income inequality has not changed much during the last decade and is stubbornly high. In 2002 **,** the Gini coefficient for Mexico was above 0.51, slightly higher than the coefficient for Mexico’s rural areas of 0.48 (Figure 3.5). Moreover, the Gini coefficient reveals that rural income inequality has increased by 7 percentage points during 1992-2002. It is worth noting that international research shows that the more unequal income is distributed the less effective is economic growth in reducing poverty (Lustig et al 2001). Income poverty changes are driven by the interactions between growth and income inequality. In Mexico, even with steady growth, poverty reduction tends to be slow, as a consequence of the country’s high-income inequality (World Bank 2004).

Changes in inequality are typically very slow, except during periods of radical social and institutional change. Where inequality has fallen it has usually happened in association with major expansion and equalization in educational attainment, as in Korea and Malaysia in the 1970s and 1980s. Mexico’s expansion in education (reduction in education inequalities) may have been too recent to have a significant effect on the composition of skills, and occurred during a period in which the overall returns to high levels of skills were rising and returns to basic skills were falling.

In Mexico, inequality has tended to be counter cyclical with the 1994-95 crisis slightly equalizing, the 1996-2000 recovery disequalizing, and the 2000-02 period of stagnation equalizing for both income and expenditures, see Figure 4.5 from World Bank 2004.

15
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32

# Table 4: Types of Female-Headed Households (FHH) in Urban Areas

(percent)

## Share of Total, All Urban Areas, 1984

|Female-Headed Households|18.2|
|---|---|
|- without husbands<br>|17.9|
|- withminors(under 18)|8.2|
|- with minor children|7.5|
|- with adult children|4.3|
|- with minor children, no other adult|3.4|

|||Regional Distr|ibution (1986)|||
|---|---|---|---|---|---|
||Northeast|North/Center|Southeast|South|Metropolitan<br>Areas|
|FHH as share of<br>total population|21|18|19|17|20|
|Percent of FHH<br>without adult male|61|61|63|64|65|
|Percent of FHH<br>with children|52|55|37|41|40|
|Percent of FHH<br>with children<br>without adult male|33|33|26|24|25|
||**Di**|**stribution**by M|arital Status (19|84)||
||||||With Minors<br>|
|Marital Status|Al**FHE**||With Minors||No Adults|
|Single|37.7||30.0||35.9|
|Married<br>|2.6||2.7||1.8|
|Widow<br>|25.9<br> <br>||28.2||19.1|
|Divorced or Separated|<br>33.8||39.1||43.2|

Source: author's tabulations