rafmacalaba/datause-encoder-data huggingface dataset:
-9 -

availability of qualified faculty. But the poor provinces are deprived of more qualified faculty. Based on relative faculty ratio (RFR) the richest quintile of provinces--Beijing, Shanghai, Zhejiang, Liaoning clearly have an oversupply of professor and assistant professor, as the value of RFR far exceeds one (1.60 and 1.20); conversely, the poorest quintile of provinces--Guizhou, Xinjiang, Anhui, Henan, Guangxi and Sichuan --have an undersupply of such more qualified faculty (Table 12, Annex 1).

# **Outcomes and Efficiency**

This section focuses on outcomes that are internal to the educational system-specifically, cohort survival rates and transition rates, and outcomes that are external to the system, such as private and social rates of return to educational investments. Both sets of outcomes are critical, since together they help determine the global efficiency of the educational system. Yet, few efforts are made to assess the impact of public policy on these outcomes in China. The dearth of such assessments is due in part to the scarcity of appropriate data and in part to the fact that these outcomes were not important to policymakers under the command economy.

(a) Internal **efficiency.** Conventionally, an education system is internally efficient if it generates the maximum output at minimum cost. Outputs are measured by such indicators as transition rates and cohort survival rates or the proportion of students who start grade 1 and complete each subsequent cycle, students' cognitive and technical skills, scientific knowledge, improved and the like. Ideally, a combination of individual survey and time-series data are used for this analysis. However, in the context of the present study, readily compiled provincial-level time-series data are used to provide results that would be helpfuil for policy interventions for improving system's internal efficiency which also improves quality.

At the aggregate level, educational outcomes vary directly with income. The transition rates from primary to secondary and secondary to higher level of education are higher in higher income provinces us compared with those in lower income provinces. Other eclectic statistical analysis strengthen the conclusion that public expenditures are important determinants of transition from one level to another. A regression is done in which transition rates are explained on the basis of per capita income, per student educational expenditure and agricultural wages. Agricultural wages are used to measure the opportunity cost of children's' time and to the extend possible the expected value of education. The results clearly suggest that both income and educational spending tend to increase transition rates (Table 4). But more importantly, the results indicate that keeping budgetary allocation to primary education at a high enough level will compensate for the negative effect of low income by making the likelihood of transition to secondary and higher levels of schooling more equal throughout the country.
Bank of India (SIDBI) and state financial institutions (SFCs). The average annual credit flow to the SSI during the late 1990s hovered between US$ 2.4 and 3.6 billion.

It is important to point out that, while formal financing (equity and public/private debt through markets as well as through other means involving formal contracting) remains scarce and costly for firms in India’s SME sector (and in other emerging economies), informal and alternative financing sources can provide an effective (partial) substitute for formal channels and support the growth of these firms. In Section V below, we provide firm-level evidence on formal and informal financing channels based on our own surveys of firms. To summarize our findings in this section, with aggregate data (at industry-level) we first show that financing through internal and alternative channels provides the most important funding source for Indian firms. We also find that the growth of the non-state sectors, in particular, the SSI sector which is part of the SME sector, has been the most impressive among all firms in India.

# **IV. Evidence on Listed Firms**

In this section, we focus on publicly listed and traded companies and examine their financing and investment decisions. We also relate these firm characteristics to legal protection of investors in India, and examine whether these relations are different from firms studied in previous papers (LLS, 1999; LLSV, 1997a, 2000b, 2002). Before doing so, we first look at different types of listed firms and corporate governance mechanisms in listed firms.

# **IV.1 Types of Listed Firms and Corporate Governance**

Although the majority of listed firms belong to non-state sectors, an important component of the listed Sector is partially privatized and listed state-owned enterprises, usually known as public sector undertakings (PSUs). Privatization of PSUs has been a key element of India’s continuing economic reforms that began in 1991. The repeatedly declared goal of the privatization process has 21
time and across regions. Combining multiple data sources—including nation-wide private enterprise surveys, industrial firm surveys, the listed firm database, the Chinese patent database, and other related databases—we first study the evolution of the BE: how the provision of infrastructure has changed, and how firms’ dependence on formal market-supporting institutions (i.e., courts, contract, and external finance) has evolved over time. We also examine the set of institutions and government activities that are regarded as disruptive. These include inefficient regulations, barriers to entry, and expropriation in the form of taxation and extralegal fees. Second, we establish stylized facts about Chinese firms and entrepreneurs, which have likely changed alongside the business climate. We examine what changes have occurred over time and space for _firm attributes_ such as firm size, capital intensity, export, innovation, and diversification, as well as _entrepreneur attributes_ including their education level, family background, working experience, and participation in political and social organizations (or political connection). Finally, we discuss the business strategies adopted by Chinese entrepreneurs in an environment of imperfect property rights protections.

In our analysis, we highlight how non-elite firms (small and medium firms, non-state firms, non-politically-connected firms) fare relative to elite firms (large firms, state-owned enterprises, and private firms with political connections) in order to understand the nature of the Chinese economy, including its inclusiveness. Moreover, we examine how economically lagging regions compare with more advanced areas in China in terms of their BEs as well as their entrepreneur and firm characteristics. Regional disparity has been a key challenge for China. Indeed, while the ratio between GDP per capita of the richest region to that in the poorest region is 18 (Galor 2005), the corresponding ratio between the richest and the poorest prefecture in China is 27.<sup>6</sup> Such regional disparities within China have lasted for centuries (Pomeranz 2000). This study attempts to shed light on whether these disparities can be partly explained by the quality differential in BEs.

We obtain many findings. First, contracting institutions between private parties have become more formal, as demonstrated by more formal mechanisms for corporate governance and stronger reliance on formal external finance. Thus, Chinese private firms adapted quickly

> 6 A typical prefecture has four million residents, comparable to Croatia and Singapore.

4
Table <u>8. FDI inflows and local structural transformation: demand side channels</u>

||(1)<br>Panel<br>|(2)<br>A: employment<br>|(3)<br>growth<br>|(4)<br>Pa<br>|(5)<br>nel B: GDP gro<br>|(6)<br>wth<br>|
|---|---|---|---|---|---|---|
|Dependent var|∆lnagri_emp|∆lnindu_emp|∆lnserv_emp|∆lnagri_gdp|∆lnindu_gdp|∆lnserv_gdp|
|FDI_shock|-11.29***|10.93***|4.018*|-10.55***|3.063|3.732**|
||(2.283)|(2.793)|(2.183)|(2.012)|(1.931)|(1.776)|
|L.FDI_empshare|-0.708**|0.624**|-0.0650|-0.390*|0.122|0.152|
||(0.316)|(0.266)|(0.264)|(0.235)|(0.192)|(0.154)|
|Observations|637|637|637|591|591|531|
|R-squared|0.718|0.699|0.723|0.927|0.867|0.922|
|Prov-Year FE|Y|Y|Y|Y|Y|Y|
|Lagged city|||||||
|characteristics|Y|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on employment and GDP growth in the three broad sectors. Lagged city characteristics include population, average age, share of Han ethnicity, log GDP per capita and average years of schooling. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

Results in Table 9 confirm that manufacturing FDI inflows raise labor productivity in agriculture and services. A one percentage point increase in FDI shock contributes to 0.1 percent higher labor productivity growth in agriculture and 0.25 percent higher labor productivity growth in services. The positive effect of FDI on labor productivity in agriculture and services does not necessarily mean improved efficiency. Increased agricultural labor productivity is likely driven by a decline in excess labor, while higher labor productivity in services reflects the emergence of modern, high value-added services in FDI-receiving cities. Surprisingly, the effect of manufacturing FDI inflows on labor productivity growth in industry is insignificant. This could be due to a range of factors: given abundant labor, foreign firms entering China may choose to adopt technologies that use labor intensively, with limited improvement in overall labor productivity; the industry sector lumps together mining, manufacturing, utilities and construction, potential measurement errors could also contaminate the results. In Panel B, the results show that a one percentage point increase in FDI shock results in a 10 percent higher growth in GDP per capita. Higher income growth spurred by FDI inflows will further drive local structural transformation away from agriculture towards industry and services.

Table <u>9. FDI inflows and local structural transformation: supply side channels</u>

||(1)<br>Panel A|(2)<br>: Labor produ|(3)<br>ctivity|(4)<br>Panel B: Income|
|---|---|---|---|---|
|Dependent var|∆agri_lp|∆indu_lp|∆serv_lp|∆lnGDP per capita|
|FDI_shock|0.106***|-0.114|0.246***|10.43***|
||(0.0385)|(0.104)|(0.0931)|(1.645)|
|L.FDI_empshare|0.0203***|-0.0107|0.0374*|0.211|
||(0.00731)|(0.0178)|(0.0202)|(0.158)|
|Observations|590|590|531|620|
|R-squared|0.799|0.676|0.693|0.789|
|Prov-Year FE|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|

24
5 local constituencies. To the degree that this is true, reservations may reduce efforts on behalf of local constituencies.

Past efforts to examine legislator effort have relied on measures of legislator output as proxies for unobserved effort. These measures are generally unable to distinguish between beneficiaries of the output, and do not easily capture the degree to which output is due to effort rather than other factors. Our analysis of data from legislator usage of constituency development funds avoids these disadvantages and makes a methodological improvement over existing research on legislator behavior.

The next section of the paper describes the specific CDF program in India and explains why disbursements under the program should accurately represent legislator effort on behalf of their local constituency. Section 3 lists the conditions under which legislators have weak incentives to exert such effort, yielding tests for the influence of political parties. We then perform these tests using available data on spending under the CDF program. Section 4 describes the data and specifications we use to examine cross-constituency variation in spending from 1999 onwards, and sections 5 and 6 present the main results and a discussion of robustness. Section 7 concludes by describing the implications of the analysis for the spread of CDF schemes and directions for future research.

# **2. Measuring constituency service:  The MPLADS Program**

Since legislator effort is not directly observable, scholars use various proxies to identify the effort of legislators to direct benefits to their districts. None of these measures comes as close as the MPLADS data to meeting the two conditions of the ideal proxy: that the measure be uniquely attributed to the legislator’s effort, and that it be associated with benefits that flow uniquely to the legislator’s constituents.
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1990; Griffith et al. 2004; Spithoven et al.2011). Firms using both sources of innovation have better innovation performance than those using only one source of innovation (Cassiman and Veugelers 2006). Conditional on a significant level of internal R&D, a firm can achieve higher productivity gains by combining external and internal R&D; however, there are decreasing returns to scale at high levels of internal and external R&D (Lokshin et al. 2008).

However, a number of empirical studies report absence of a complementary effect or even a negative (supplementary) effect of using both sources of innovation. For instance, several studies document that internal and external innovation can substitute for each other, especially when resources are limited (Fikkert 1993; Basant and Fikkert 1996; Blonigen and Taylor 2000). A possible explanation is that, given a limited budget, an increase in either of one of the two options tends to reduce spending on the other. If the effect on innovation investment on productivity is not linear or requires a certain amount of investment (threshold) to produce a positive effect on the final outcome, splitting resources between two types of innovation would actually have a lower effect than consolidating resources on just one.

While it is difficult to generalize, evidence of complementary effects seems to be more common in studies on developed countries, and evidence of substitution effects seems to be mostly from studies on developing countries. For instance, complementary effects were documented in studies conducted on firms in Germany, Italy, the United Kingdom, Belgium, France, and Denmark, while those that document supplementary effects were conducted on firms in Andean-group countries, India, and China.<sup>6</sup> In addition, other studies find that the complementary or substitution effects could be changed depending on the ratio of the sources of innovation used, the internal innovation status of the firm, and the external environment. Specifically, in environments in which learning is less demanding, a firm's in-house R&D has little impact on absorptive capacity. In

> 6 . The empirical evidence on supplementary effects includes Mytelka (1987) on Andean-group countries, Fikkert (1993) on Indian manufacturing firms, and Basant and Fikkert (1996) on Indian firm-level panel data. Also, a recent study by Hou and Mohnen (2013) found the complementary effect on Chinese manufacturing for only medium-size firms, but found a supplementary effect for firms overall. There is more evidence of complementary and supplementary effects beyond this literature review.

10
# **1. Introduction**

The debate regarding the appropriate regulation of bank capital and provisions has come to the fore given the recent discussions regarding the proposed revisions to the Basel Capital Accord, known as Basel II. This proposal, developed by the 13 member countries of the Basel Committee for Banking Supervision (BCBS) is to be finalized this year for implementation in member countries by 2006. An underlying discussion hinges on whether regulatory capital should be related to risk.<sup>1</sup> We take this as a given and focus on the subsequent issue; if required capital is to be risk-related, how should this be achieved?

Basel II proposes two basic approaches; (i) the use of external credit ratings agencies and a table that maps those ratings into capital requirements (this is known as the standardized approach) and (ii) the use of banks’ internal ratings, that banks then map into default probabilities, and a particular formula laid down by the BCBS that determines capital requirements as a function of the default probability and other parameters (this is known as the internal rating based or IRB approach).<sup>2</sup> This paper focuses on two main issues. First we investigate whether the IRB approach’s formula is appropriately calibrated for the context of emerging economies. To do this we must first estimate credit risk. In a set of companion country papers<sup>3</sup> this is

> 1 See the many papers on the BIS website on the Accord www.bis.org.

> 2 See Basel Committee for Banking Supervision (2003).

> 3 See Balzarotti et al. (2003) for credit risk measurement in Argentina, Cronenberger et al. (2003) in Brazil, and Marquez et al. (2003) in Mexico. See also Foglia (2003) for a rich series of applications of credit risk measurement to regulation and supervision in G10 countries. All papers can be accessed on the World Bank website: www.econ.worldbank.org/programs/credit_reporting/topic/22476/

1
– Table A.24 Treatment effects on IGAs (endogenous stratification)

||Pr|edicted outco|me|T-|test p-valu|es|
|---|---|---|---|---|---|---|
||(1)<br>Low|(2)<br>Medium|(3)<br>High|(4)<br>(1)-(2)|(5)<br>(1)-(3)|(6)<br>(2)-(3)|
|**Women’s IGAs**|||||||
|Has an IGA|-0.002<br>|0.007<br>|0.022<br>|0.671|0.393|0.596|
||(0.015)<br>|(0.015)<br>|(0.024)<br>||||
|Had an IGA before COVID|-0.004|0.002|0.028|0.776|0.315|0.423|
||(0.014)|(0.015)|(0.028)||||
|Business proft (in Dinars)|-1.096<br>|-3.044<br>|3.714<br>|0.682|0.457|0.267|
||(3.705)|(2.982)|(5.301)||||
|Business employees|0.010|0.003|0.000|0.698|0.774|0.939|
||(0.011)|(0.014)|(0.032)||||
|Total income (in Dinars)|1.323<br>|0.609<br>|17.044<br>|0.951|0.309|0.320|
||(7.124)|(9.202)|(13.723)||||
|Has unused skills|0.054**|0.067**|0.117***|0.731|0.129|0.244|
||(0.026)|(0.028)|(0.032)||||
|**IGAs of other household members**|||||||
|Household head has an IGA|0.005|0.070**|0.021|0.124|0.729|0.274|
||(0.030)|(0.030)|(0.034)||||
|Number of other HH members with an IGA|0.007<br>|0.036<br>|0.073*<br>|0.447|0.149|0.443|
||(0.025)|(0.029)|(0.039)||||
|Household head income (in Dinars)|16.444|18.503|-1.115|0.921|0.457|0.430|
||(13.641)|(15.679)|(19.270)||||
|Other HH members income (in Dinars)|-2.864|3.580|18.764|0.694|0.292|0.477|
||(10.820)|(12.314)|(17.453)||||
|**Agriculture**|||||||
|Household has an agricultural IGA|0.021|0.028**|0.030|0.682|0.744|0.946|
||(0.013)|(0.012)|(0.024)||||
|Used chemicals|0.032*|0.021**|0.014|0.588|0.549|0.800|
||(0.019)|(0.010)|(0.024)||||
|Quantity produced|4.735*|6.618**|7.720**|0.629|0.510|0.814|
||(2.627)|(2.883)|(3.690)||||
|Value of the production|17.123**|10.902*|12.879|0.499|0.759|0.881|
||(7.087)|(5.858)|(11.890)||||
|**Livestock**|||||||
|HH has a livestock IGA|0.039**|0.056**|0.058**|0.597|0.594|0.958|
||(0.020)|(0.024)|(0.029)||||
|Total stock (in Dinars)|36.177|47.126*|62.596|0.785|0.643|0.785|
||(28.728)|(28.107)|(49.232)||||
|Bought since January 2019 (in Dinars)|29.460***|25.474***|30.283***|0.737|0.953|0.691|
||(9.701)|(6.848)|(9.983)||||

Notes: N = 1,824. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

69
**Figure 1: Emissions by sector, 2005**

<!-- Start of picture text --> Industrial<br>Energy Agriculture processes Other<br>South & SE Asia Pacific<br>US and Canada<br>E. Europe & Central Asia<br>Western Europe<br>Latin America & Caribbean<br>Middle East & North Africa<br>Sub-Saharan Africa<br>Australia & New Zealand<br>0% 20% 40% 60% 80% 100%<br>Share of total<br><!-- End of picture text --> Source: WRI (2011) and authors‘ calculations.

**Figure 2: Emissions by type of gas, 2005**

<!-- Start of picture text --> CO2 CH4 N2O Other<br>South & SE Asia Pacific<br>US and Canada<br>Western Europe<br>E. Europe & Central Asia<br>Middle East & North Africa<br>Latin America & Caribbean<br>Sub-Saharan Africa<br>Australia & New Zealand<br>0% 20% 40% 60% 80% 100%<br>Share of total<br><!-- End of picture text --> Source: WRI (2011) and authors‘ calculations.

58
Second, we control for all ...rm-level characteristics by including a parent ...rm ...xed e¤ect and limiting the comparison to establishments owned by the same parent ...rm. Multinationals with only one foreign subsidiary are hence dropped from the sample. In doing so, we establish the role of production linkages by comparing the performance of subsidiaries that belong to the same multinational but with di¤erent degrees of input-output linkages. The e¤ect of ...nancial linkages cannot be estimated in this speci...cation because our measure of ...nancial linkages does not vary within each parent ...rm.

## [Table 11 about here]

Table 11 reports the results. We continue to ...nd vertical production linkages to exert a signi...cant e¤ect on MNC subsidiaries’performance during the crisis. A one-percentage-point increase in the input-output relationship is associated with 1.28 percentage points higher growth rates, exceeding the estimated e¤ect in Table 3. Again, the production linkages do not appear to systematically a¤ect establishment performance in non-crisis years.

# 7 Conclusion

We investigate in this paper the role of foreign ownership in establishment performance during the recent Global Financial Crisis. Using a worldwide establishment panel dataset, we examine whether foreign ownership led to di¤erent resilience to the crisis between establishments with similar economic attributes and how the role varies from non-crisis periods. Moreover, we examine how, conditional on foreign ownership, production and ...nancial linkages explain the di¤erential responses of MNC subsidiaries.

We ...nd that MNC subsidiaries responded on average better to the recent crisis than local controls with similar economic characteristics. The advantage of foreign ownership was clearly pronounced during the crisis, while relatively muted in non-crisis years. Production and ...nancial linkages played critical roles in explaining the di¤erential responses of MNC subsidiaries. Establishments sharing stronger vertical production linkages with foreign parent ...rms exhibited more resilient performance during the crisis, especially in host countries with a greater incidence of the crisis. Horizontally linked establishments, in contrast, performed no better than the control establishments. The same pattern is not observed in non-crisis years. Vertical MNC subsidiaries did not perform di¤erently than their local matches in the noncrisis period, while horizontal subsidiaries fared slightly better. Financial linkages similarly played an important role during the crisis. MNC subsidiaries operating in industries with greater intra-...rm ...nancial linkages exhibited a greater advantage over local counterfactuals. Similar to production linkages, the role of ...nancial linkages was signi...cant only during the crisis period, especially in host countries with signi...cantly worsened credit conditions.

22