rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 9 - Real Effect of the Political Lending**

The table examines whether political lending has real impacts on the economic activity of firms. The unit of observation is at the firm-year level. Columns 1, 3, 5 and 7 report the estimates for the full sample while C2, 4, 6 and 8 present the results for small firms (firms with less than 50 employees). The dependent variables are in logs and include number of employees, total assets, total liabilities and revenue. _Chairman_ equals to one if the firm is headquartered in the state that has a senator serving as committee chairman. Please refer to Appendix A for variable definitions. All regressions include _firm_ and _year_ fixed-effects. Standard errors are reported in parentheses and are clustered at the state level. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.

||Emplo|yment|As|sets|Liabi|lities|Rev|enue|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||All|Small|All|Small|All|Small|All|Small|
|Chairman|0.021***|0.025*|0.013*|0.027**|0.010|0.024*|0.008|0.008|
||(0.007)|(0.015)|(0.008)|(0.010)|(0.012)|(0.014)|(0.011)|(0.010)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-Square|0.037|0.041|0.073|0.068|0.055|0.043|0.038|0.040|
|N|13,004|6,635|11,488|6,006|11,408|5,974|21,022|10,726|

43
Table 51

|Dependent variable: Fisc|al adjustme|nt when ne|eded based|on Lavign|e(2011)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|-0.003|-0.004*|-0.006*|-0.008*|-0.007|0.005|-0.004|-0.006|
|structural as % GDP|(0.002)|(0.002)|(0.004)|(0.004)|(0.004)|(0.010)|(0.008)|(0.004)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.042||-0.085|-0.058|-0.083|-0.104|-0.090|
|||(0.136)||(0.172)|(0.154)|(0.172)|(0.166)|(0.174)|
|Inflation (t-1)||-0.307||-0.434|-0.442|-0.431|-0.432|-0.432|
|||(0.216)||(0.272)|(0.278)|(0.273)|(0.273)|(0.273)|
|U.S. Interest rate||-0.003||-0.006|-0.006|-0.007|-0.006|-0.006|
|||(0.007)||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|**Political factors**|||||||||
|Gini|||0.000|-0.000|-0.000|0.002|-0.001|-0.000|
||||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Rule of law|||-0.003|0.010|0.010|0.009|0.016|0.010|
||||(0.015)|(0.025)|(0.025)|(0.024)|(0.025)|(0.025)|
|Margin of Majority|||-0.057|-0.054|-0.058|-0.073|-0.050|-0.032|
||||(0.046)|(0.053)|(0.055)|(0.068)|(0.053)|(0.053)|
|Election's year|||-0.016|-0.021**|-0.021**|-0.022**|-0.021**|-0.021**|
||||(0.011)|(0.010)|(0.010)|(0.010)|(0.010)|(0.010)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.003<br>(0.007)||||
|Gini * Deviation||||||-0.000<br>(0.000)|||
|Rule of law * Deviation|||||||-0.001<br>(0.002)||
|Majority * Deviation||||||||-0.004<br>(0.008)|
|Constant|0.053***|0.092**|0.127|0.178**|0.178**|0.133*|0.151*|0.168**|
||(0.016)|(0.041)|(0.082)|(0.087)|(0.088)|(0.070)|(0.090)|(0.081)|
|Observations|648|645|464|463|463|463|463|463|
|R-squared|0.004|0.018|0.013|0.043|0.044|0.046|0.044|0.043|
|Number of ifscode|84|83|62|61|61|61|61|61|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors c<br>*** p<0.01, ** p<0.05, *|lustered by<br>p<0.1|country in|parenthese|s|||||

- 80 -
the country’s financial system, they add liquidity. A deeper financial system makes projects less risky by reducing the number of states in which liquidity is lacking. Entrepreneurs that enter the financially dependent sector thus exert a positive externality on the other entrepreneurs.

We find plausible the positive feedback from the size of the financial system to its quality. Levine and Schmukler (2003) document that when some firms in emerging markets begin raising external finance abroad rather than at home, trading liquidity of the remaining domestic firms actually decreases, providing evidence that financial depth is positively related to market size. Furthermore, this type of effect is implicit is most studies of financial development quoted above. These studies typically use measures of financial system size, such as ratios of private credit to GDP or stock market capitalization to GDP, to proxy for financial system quality.

Opening to trade will affect demand for external finance, and thus financial depth, in the trading countries. In particular, when a wealthy country starts trading with a poor one, it will naturally increase production of the financially dependent good, and its financial system will deepen. In the poor country, on the other hand, the financially dependent sector will shrink, leading to a deterioration in the size of the country’s financial system, as well as its quality.

The bottom line is that when a poor country no longer needs to produce the financially dependent good, demand for external finance will decrease as a result of trade, and the domestic financial system will suffer. This could induce losses from trade to the poor country, as could be expected given that the financially dependent industry exhibits external effects, and thus economy-wide increasing returns to scale (see Helpman and Krugman, 1985, ch. 3). Furthermore, the deterioration of the domestic financial system could be harmful to the poor country for reasons beyond gains from trade. Indeed, there is a great deal of empirical evidence that financial development is conducive to increasing growth and lowering volatility (Levine, 2003, Kose, Prasad and Terrones, 2003).

In illustrating the effect of trade on the financial system, we make a series of special assumptions. First, in modeling the market for external finance and the positive effect of financial system size on its quality, we abstract from the informational and enforcement frictions that are often invoked in this context. One can clearly adopt this approach as well, and think of the quality of the financial system in terms of how well it can overcome these distortions and achieve the efficient level of lending. A positive link between the size of the financial markets and their ability to resolve such frictions has been modeled, for example, 3
when firms signed PCs which contained all the "good" features--managerial bonds, profit orientation, higher wage elasticity, and lower markup ratios-one estimate suggests that their productivity growth rate would increase by 10 per cent.

Our findings cast strong doubts on perforimance contracts as a way of reforming or regulating SOEs. The Chinese government tried to implement PCs quite seriously, as demonstrated by the use of firm-level wage elasticity, large managerial bonds, bidding procedures, measures considerably more radical than those observed in other countries we have studied (Shirley and Xu, 1997). In fact, the central government once hailed the contract system as the official mode for SOE reforms on a national scale. Yet we still observed that the overwhelming majority of the PCs have little or negative growth rate effects, and that the observed frequency for contracts with "good" provisions was exceedingly low. These results, coupled with our earlier findings, suggest that the political econoimy of incentive contracts in government settings merits further study. Political determinants may preclude the design of incentive contracts for government actors that procluce the sort of productivity gains they have achieved in private firms.

# **Appendix A. the Data Set**

The data set we use is _A Survey of Chinese State Enterprises: 1980-1989._ It covers 769 SOBs in 21 cities of four provinces of China (Shanxi, Jilin, Jiangsu, and Sichuan). Chosen on the basis of a stratified random sampling, the 769 firms were all in manufacturing. The size of the SOEs shows large variations: while the median firm had 930 employees, the 10th percentile firm had 304, and the 90th percentile had 3175.

The data set has two parts. Part one is a quantitative table filled by the accountants of an enterprise. It includes 321 variables covering details about products, costs, wages and labor utilization, investment, financing, fixed assets, profit distribution, taxes, prices, and material inputs. Part two is a questionnaire answered by the manager of the enterprise. The manager answers questions about performance contracts signed with the 27
**Chart 17**

<!-- Start of picture text --> Percentage of total debt linked to inflation<br>12.0<br>Dom. Debt Weighted Average Unweighted Average<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>Source: JP Morgan EM Debt and Fiscal Indicators (2009) and BIS Financial Stability and<br>local currency bond markets (2008)<br>(%)<br><!-- End of picture text -->

**During the period 2000-2009, only four countries in the sample issued domestic debt linked to FX:**

**Turkey, Brazil, Chile and Colombia.** Turkey brought down the share of FX linked debt from 35% in 2001 to 6% in 2009 and Brazil stopped issuing dollar linked instruments in 2002.  For Brazil, Chile and Colombia the share of this type of debt in 2009 was negligible compared with 29%, 22% and 8% respectively at the beginning of the decade. This certainly was an important achievement in improving the composition of the debt portfolio in these countries. Most EMs in our sample however did not recur to this type of instruments during the period of analysis.

**In sum, EMs arrived at the global financial crisis with government debt portfolios that were more resilient to shifts in the economic cycle and market sentiment.** The increase in the share of domestic debt reduced the exposure to exchange rate shocks and the vulnerability to sudden stops in capital flows. The lengthening of maturities in local currency instruments opened new alternatives for debt managers that were no longer confronted with choosing between foreign currency and interest rate risk. Possibly the most important achievement in this area was the diversification of funding sources. Governments have significantly reduced their dependence on bank financing. The evolution of the financial system, pension and insurance reforms and the growth of mutual funds industry, as well the increasing presence of foreign investors, have changed the investor map opening new demand for long-term fixed-rate securities. It is acknowledged that development of the financial sector cannot be achieved quickly and is a result of concerted and deliberate policy actions over a period of years. At the same time, decisions to borrow more in local currency at longer maturities usually requires an acceptance of higher interest costs in the short-run, in order to reduce risk. These realities underscore the strength of policy-making in most EMs over the last decade.

14
interest rate (inclusive of the external risk premium), IF<sup>W</sup> , adjusted for expected depreciation: This equation implies that if domestic and foreign borrowing are perfect substitutes (that is, θ<sup>B</sup> FL<sup>→∞),thenthecentralbank’srefinancingrate</sup> cannot deviate from the premium-inclusive, and expectations-adjusted, world interest rate: As indicated earlier, banks’ net income is distributed in proportion shp<sup>E</sup> B to profit earners. Commercial banks’ net worth therefore evolves over time according to where the first term on the right-hand side represents retained earnings by commercial banks and the second term represents capital losses (gains) associated with nominal exchange rate depreciations (appreciations).

Lending by commercial banks to formal sector households, DLF , is assumed exogenous. This is consistent with the recent evidence suggesting that in Turkey households–and to some extent small businesses–appear to be significantly affected by supply-side constraints on the credit market (see World Bank (2003)). By contrast, lending to firms is taken to be demand determined, as shown in equation (78). At the same time, banks have access to an infinitely elastic supply of loans by the central bank at the prevailing official interest rate. They therefore borrow whatever residual liquidity they need, given their domestic deposit base and foreign borrowing. DLB is thus determined residually from the balance sheet constraint (79), that is, using (80): 37
<mark>Before the technology was available, most households delivered remittances by hand or informally through friends or bus drivers. This process was expensive, fraught with delays, and involved substantial losses due to theft. Now, all households need to do is send an SMS. Not only are the actual monetary costs of the transfers lower, but the safety and certainty of the process has meant substantial reductions in the cost of sending and receiving money. Four years after its launch, M-Pesa had been adopted by nearly 70 percent of Kenya’s adult population and three-quarters of Kenyan households have at least one user. The fast adoption of M-PESA would not have been possible without the creation of a dense network of agents, small business outlets that convert cash to e-money and vice versa for customers; as well as the rapid expansion of mobile phone networks in Kenya (Jack and Suri 2014).</mark> <mark>Effective logistics is critical for producers, retailers, as well as consumers for collection, aggregation, and delivery. Various field studies show that in many places digital technologies are quietly transforming how rural logistics function (World Bank 2011). The resulting improvement in logistics can be seen through lower transaction costs, improved profits, and less wastage. By giving people the ability to replace distance with “space-shrinking technology,” digital tools enable market agents to better coordinate product supply and demand, strengthen existing trade networks, facilitate the assembly of products to reach a critical mass, and enable products to be delivered cost-effectively to new markets. Despite these positive impacts, complementary factors such as limited access to transportation infrastructure and insufficient financial services can still limit the gains of supply-chain efficiency.</mark>

# **6. Conclusion**

<mark>As mobile phones and, to a lesser degree, the internet have reached rural areas in developing countries, a number of studies have shown encouraging impacts on overall returns received by rural producers. But there is also evidence that these innovations often fail to scale-up and achieve wider acceptance. Access to information and the ability to communicate are only two of many constraints farmers face. Others include market fragmentation (even though market consolidation will, over time, enhance growth prospects). Another reason could be the lack of financially sustainable business models that will attract private sector investments in providing innovative solutions for small scale agriculture. There is clearly high potential for the internet and related technologies to improve rural economies, but several lessons need to be kept in mind.</mark> 21
minimum wages. While union coverage is statistically significant, he concludes that “the bulk of the variation in earnings dispersion is generated by skill dispersion” (page C11).<sup>26</sup> The impact of the skill distribution across countries is shown dramatically in Figure 2.4, which is derived from a comparison of the dispersion of wages and the dispersion of prose literacy scores (each measured as the ratio of the 90<sup>th</sup> to the 10<sup>th</sup> percentile). The tight pattern around the regression line reflects a simple correlation of 0.85 (which is not affected by including the other institutional factors).

# **Figure 2.4: Inequality of Educational Quality and Earnings**

<!-- Start of picture text --> Earnings inequality4.5<br><!-- End of picture text --> <!-- Start of picture text --> USA<br>CAN<br>4.0<br>3.5<br>IRE UK<br>3.0<br>AUS<br>SWI<br>NET<br>2.5<br>FIN<br>GER<br>BEL<br>DEN<br>SWE<br>2.0 NOR<br>1.5<br>1.3 1.5 1.7 1.9<br>Test score inequality<br><!-- End of picture text --> Note: Measure of inequality is the ratio of ninth decile to first decile in both cases; test performance refers to prose literacy in the International Adult Literacy Survey.

Source: Nickell (2004).

26 De Gregorio and Lee (2002) find a (somewhat weaker) positive association between inequality in years of schooling and income inequality.

18
WMS countries (Brazil, Canada, India, Sweden, UK and US).<sup>6</sup> They find a strong positive correlation for these countries: moving from the bottom to the top quartile of management is associated with a large increase in student learning outcomes, equivalent to approximately 0.4 standard deviations.

# **2.2 A new approach using existing public datasets**

We now describe our approach, illustrating with the examples of PISA and Prova Brasil.<sup>7</sup> .

**Construction.** In 2012, alongside its famous student proficiency tests, PISA ran school principal surveys across 65 countries which included a wide-range of questions on both operations and people management.<sup>8</sup> As a first step, we classified each of the PISA questions that could fall under one of the WMS topics, identifying 53 PISA questions that fit into 14 of the WMS topics.<sup>9</sup> As a second step, we manually assigned scores for each of these PISA questions following the spirit of the scoring grid of the WMS and the US Census Management and Organizational Practices Survey (MOPS). As a final step, we built the overall management index, and the operations and people management sub-indices, following Anderson [2008]. This methodology weights the impact of the included variables by the sum of their row in the inverse variance-covariance matrix, thereby assigning greater weight to questions that carry more “new information”.<sup>10</sup>

PISA data is excellent for cross-country analysis, but it precludes in-depth analyses within countries as the sample of schools per country is typically small and does not include the necessary identifiers. Many countries, however, conduct their own national detailed surveys with school principals, teachers, and students in addition to administering standardized tests across grades. Latin America is particularly prolific: for example, Brazil’s Prova Brasil, Colombia’s SABER, Chile’s SIMCE, and Peru’s ECE are all available to researchers. These questionnaires provide rich information about

> 6We replicate the primary figure from Bloom et al. [2015a] in Figure B.3 in Appendix B. It plots the school-level student learning outcomes by each quartile of school management score.

> 7We provide details to enable replication in Appendix C

> 8Our main focus is on the 2012 data because that survey wave contains a richer set of questions, particularly relating to people management. See Appendix C for a mapping of the 2015 PISA data.

> 9The set of WMS topics that had matching PISA questions is detailed in Appendix C.

> 10We also built the indices using alternative methods (straightforward standardization, factor analysis, and factor analysis with Bartlett correction) which yielded similar results.

7
The ratio _ΔpF/ΔcE_ is negative and less than 1 (in absolute value). Notice however, that the second term in the denominator is smaller the lower are ( _γE,γT,ε_ ), and therefore farmers absorb a higher 0 share of the tax. Moreover, starting at an equilibrium price (say pF ), this ratio is lower the lower nT and nE. In words, there is a negative relationship between the share of the tax absorbed by farmers and the level of concentration in the exporters’ and traders’s segment.

From (A.8) the effect on intermediate price is which is also negative. In order to get a grasp of magnitude of the effect of the tax on exporters’ and traders’ margins (valued at the equilibrium), assume _A=7,473_ (this is the constant that fits the quantity _X=140,000_ with _pF=351_ and _ε=0.5_ ). We can use information from Table 4 ( _nT=39_ , _γT = 0.008_ and _γE_ ≈ _0.011_ , which is the average value of _γE_ ‘s) and Table 2 (given that this appendix builds on the assumption of symmetric exporters, assume _nE=20_ ).

If _ΔcE=100_ , then _DpT=-87_ and _DpF=-79_ . Exporters’ margin change in _ΔME=-ΔcE-ΔpT = -100+87=-13_ ; traders’ margin changes in _ΔMT=ΔpT-ΔpF=-87+79=-8_ ; and farmers’ margin changes in _ΔMF=DpF=-79._ Now assume that _nE=39_ and _nT=20_ (that is, traders are more concentrated than exporters) and let other parameters be the same. If _ΔcE=100_ , then _DpT=-93_ and _DpF=-78_ . Exporters’ margin change in _ΔME=-ΔcE-ΔpT = -100+93=-7_ ; traders’ margin changes in _ΔMT=ΔpT-ΔpF=-93+78=-15_ ; and farmers’ margin changes in _ΔMF=DpF=-78._ In both cases, farmers absorb approximately 80 percent of the tax. But the distribution of the remaining 20 percent is absorbed by exporters or traders depending on their level of concentration, with the more concentrated link assuming the largest share.

# **A.1.2. Special case: Unit farmer’s supply elasticity** **_ε=1_ .**

If the price-elasticity of farmers supply is _ε=1_ , the problem develops straightforwardly. Equations (A.8) and (A.10) turn out to be Even simpler, if _cE = 0_ and _cT = 0_ , 35
# **Figure 14.  Gross Operating Margin and Net Income over Time**

<!-- Start of picture text --> GOM Net Income to Assets<br>0.5 0.15<br>0.4<br>0.3 0.1<br>0.2<br>0.05<br>0.1<br>0<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>2001 2003 2005 2007<br>Net Income to Assets (Mean)<br>GOM(Mean) GOM (Median)<br>Net Income to Assets (Median)<br><!-- End of picture text --> Source: EGX data, staff calculations.

# **7. Regression Analysis of ICA Data**

Here we study how our five savings-related measures are determined by firm characteristics and access to finance using basic regression analysis. Because all the control variables are only available for the actual years of the survey, we limit our regressions to these years (i.e. 2003, 2005 and 2007). To control for macroeconomic differences across years we include time dummy variables for 2005 and 2007.

An important caveat of this analysis is that we will not be able to establish causality between our savings-related measures of interest and the factors we are using as controls, such as size, industry, and perceptions of business environment. This caveat especially applies to our investigation of the relationship between financial product usage and investment and operating income. It is plausible that firms that have more difficulties accessing external finance will not be able to make investment into productive assets. But the reverse is also possible – firms that are not planning to make investment into productive assets might be the firms with growth potential and hence unable to obtain external finance. Thus, we treat all results as suggestive of correlations between these factors and not as causal factors.

# **7.1 Determinants of Financial Indicators and Government Ownership**

Before discussing our main variables of interest, we present a brief summary of regression results for determinants of financial variables and measures of uncertainty. Table 2 presents regressions with determinants of financial variables. We find several commonly observed 21