rafmacalaba/datause-encoder-data huggingface dataset:
consensus that the benefits of financial deepening may decrease for large levels and large accelerations of financial depth, posing the possibility of “too much finance.” While much of the empirical evidence uses cross-country data at the national scale, several studies have also shown a strong relationship between finance and growth at the local and firm levels. They have also concerned themselves with issues of causality and homogeneity. See, for example, Jayaratne and Strahan (1996), Guiso, Sapienza, and Zingales (2004), and Kendall (2012). A key paper in this vein is Rajan and Zingales (1998), which, using firm-level data, finds evidence that industrial sectors that require greater levels of external finance grow faster in countries with more developed financial markets. This documents one mechanism through which financial development can positively affect growth: a reduction in the cost of external finance to firms.<sup>3</sup> Similarly, Demirguc-Kunt and Maksimovic (1998) show that firms in countries with developed financial institutions and efficient legal systems obtain more external financing than firms in countries with less-developed institutions. Beck, Demirguc-Kunt and Maksimovic (2005) assess the impact of financial, legal, and corruption problems on firms’ growth rates. It finds that smaller firms are more constrained by these problems but also benefit the most from financial and institutional development. Finally, Love (2001) shows that financial development diminishes financing constraints by reducing information asymmetries and contracting imperfections.

Turning now to the crisis literature, it must be said that it is at least as rich and diverse as the growth literature. Here, we only review the main messages from studies that have focused on the connection between financial deepening and economic crisis. The first finding is that rapid acceleration of bank credit, sovereign debt, and equity prices are robust predictors of the occurrence and intensity of financial crisis. See, for example, Kaminsky and Reinhart (1999) and Jordá, Schularick, and Taylor (2011). The second, related finding is that this rapid acceleration in volumes and prices of financial assets is part of a boom-bust cycle, with the financial bust producing the negative effects on the real economy. Dell’Arrica et al. (2015), for instance, finds that one out of three credit booms is followed by a crisis, and that the longer is a boom, the most likely it ends in a crisis. See, in addition, Higgings and Osler (1997), Allen and Gale (1999, 2000),

> 3 Khan (2001) develops a theory of financial development that is based on the costs associated with the provision of finance, consistent with the evidence of Rajan and Zingales (1998).

5
Table 5: Initial wage differentials, individual level data, 1996

|Dep. Var.: Log(Income)|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Black (=1)|-1.002***|-0.509***|-0.553***|-0.590***|
||[0.004]|[0.003]|[0.004]|[0.004]|
|Mining (=1)|0.489***|0.374***|0.426***|0.417***|
||[0.012]|[0.010]|[0.010]|[0.011]|
|Black*Mining|-0.247***|-0.082***|-0.093***|-0.114***|
||[0.013]|[0.011]|[0.011]|[0.011]|
|Observations|378,125|357,240|357,240|357,240|
|R-squared|0.188|0.49|0.504|0.529|
|Individual controls|-|Yes|Yes|Yes|
|Province FE|-|-|Yes|-|
|MunicipalityFE|-|-|-|Yes|

Notes: The estimation method is OLS. Data from the 1996 population census 10% sample. Sample of males between 15 and 65 years old, employed in firm/company (not self-employed or family employed). Individual controls include age, age squared, indicators for highest achieved education, and occupation indicators. Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.
- 55 -

also the budget projections for 1988-1991, are broadly<sup>consistent with a</sup> general notion of stabilization. Similarly, the official<sup>projections for</sup> growth and inflation between 1988-1991 suggest that, if the<sup>fiscal targets are</sup> adhered to, the basic stabilization goal will be met and probably overshot.

In the case of Ghana, the historical data show that both the<sup>fiscal</sup> balance and the actual growth rate would have been broadly adequate to<sup>achieve</sup> stabilization. The future projections parallel those of<sup>Kenya insofar as they</sup> appear to present the Government with substantial room of<sup>maneuver for its</sup> stabilization goals. Therefore, even with moderate slippages<sup>on all fronts,</sup> the Ghanaian economy appears to be on the track of achieving its<sup>desirable</sup> goal of net repayments of its debt to the banking sector. 18/ The obvious suggestions for future research in this area are, first, an extension of the conceptual framework to incorporate<sup>some basic behavioral</sup> relationships and, second, a serious effort to improve on the consistency between stock and flow statistics, especially in the area<sup>of debt and budget</sup> financing. It is also apparent that a deeper analysis of the issues<sup>addressed</sup> in this paper would require a more thorough examination of fiscal and Central Bank statistics, particularly the balance sheet of the latter.<sup>This suggests</sup>

- 18/ This is a somewhat stretched statement. Interest payment<sup>projections</sup> for both Kenya and Ghana were not available on a domestic-foreign debt breakdown and, consequently, the implied debt reduction cannot, strictly speaking, be attributed to the domestic or the<sup>foreign</sup> component of the debt. This statement, therefore, reflects<sup>more the</sup> intended policy of the Government than a conclusion based<sup>on data.</sup>
- Marriage Law. 2001. The Marriage Law of the People’s Republic of China, Section 3, Article 21. Available online at http://www.nyconsulate.prchina.org/eng/lsqz/laws/t42222.htm.

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27
# _Demand_

Demand for natural gas is expected to grow at about 10 percent a year in the next decade (figure 4). Key drivers are demand for electricity generation, environmental standards that require fuel oil-run industrial facilities in critical zones to convert to natural gas, and the build-out and operation of distribution systems throughout the country. The Gulf region will continue to absorb a large, but decreasing, share of gas consumption based on projected increases in crude extraction and refining/petrochemicals processing activities in Pemex installations that require large quantities of natural gas. These figures for demand growth imply a significant increase in gas penetration in the energy matrix. Between 1998 and 2007 the share of natural gas in energy consumption is expected to increase from about 18 percent to 58 percent for thermal power generation, 50 percent to 70 percent for industrial use, and, most remarkably, from 7 percent to 25 percent for distribution systems serving residential, commercial, and municipal users (figures 5 and 6, tables A3 and A4).

**Figure 4. Growth in National Natural Gas Consumption, 1991-2007** Thousands of cubic meters a day

<!-- Start of picture text --> 300,000<br>250,000<br>200,000<br>150,000 ____<br>100,000<br>50,000<br>L ... .. .. .<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>- L_ m~C<br>Electric Industrial Residential and Transport Oil<br>commercial<br><!-- End of picture text --> _Production_ Domestic production increased 33 percent, from 3.6 billion cubic feet per day in 1994 to almost 4.8 billion cubic feet per day in 1999. In the offshore zone, production decreased in 1999 and 2000 from 1.353 billion cubic feet per day to 1.120 billion cubic feet per day. But production is expected to increase to 1.418 billion cubic feet per day during 2001. In the south zone production is expected to decrease from 2.69 billion cubic feet per day to 1.966 billion cubic feet per day during 2000, with a further reduction to 1.933 billion 22
13 pressures of special-interest politics." Helpman (1989, p.<sup>21</sup> 3), while showing that the new theories have had several<sup>empirical successes in</sup> explaining trade phenomena, similarly concludes<sup>that "free trade remains a</sup> good rule of thumb". (See also Corden, 1990;<sup>Helpman and Krugman, 1989;</sup> Krugman, 1990 and Srinivasan, 1989).

# 3. Financial Sector Reforms

Financial sector reforms aim to improve the efficiency<sup>of intermediation</sup> and the use of resources, including the allocation<sup>of investment. The reforms</sup> attempt to reduce distortions in credit allocations,<sup>typically in part through</sup> the removal of controls _oh_ interest rates, with<sup>the intention of allowing them</sup> to become market determined. In addition, appropriate<sup>information and</sup> prudential supervision systems need to be put in place.

Initial conditions play a central role in the design and implementation of financial reforms. If a large proportion of<sup>the assets of financial</sup> institutions are held at below market rates or are<sup>non-performing, then</sup> financial reform will create difficulties for existing<sup>institutions. In</sup> particular if deposit and lending rates are deregulated<sup>simultaneously and</sup> free entry into the financial system is allowed,<sup>existing banks will be forced</sup> to pay market intetest rates. They will suffer substantial<sup>losses,</sup> jeopardizing the banking system's solvency and<sup>macroecc;;omic stability. It</sup> may be necessary in such cases to allow for a transition<sup>phase, in which</sup> lending rates are deregulated first, with deposit rates<sup>following only</sup> gradually (World Bank, 1989). In other cases, most<sup>banks may be insolvent</sup> before the financial reforms are implemented. The banks<sup>then have to be</sup> recapitalized, thereby creating a potential fiscal<sup>problem. In countries</sup> where domestic financial systems are practically non-existent,<sup>the creation of</sup>
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35
taxes are sometimes ad valorem or sometimes the maximum of the ad valorem or specific tax rate. The results are reported in table 3.2.

**Input-output table.** The core input-output table for this section is the 1995 table produced by Goskomstat. (This was updated for the applications in Rutherford and Tarr (2008; 2010.)  The official table contained only 22 sectors, and importantly has little service sector disaggregation. Consequently, Russian input-output expert S. P. Baranov disaggregated this table into a 35 sector input output table. Baranov used unpublished data available to Goskomstat based on the surveys that were used to construct the 1995 table. The principal elements of this disaggregation were: a split of the oil and gas sector into oil, gas and oil processing; a split of the transport sector into railroad, maritime, air, pipeline, truck and other transportation services; the breakup of communication into post services and telecommunications; and disaggregation of the data in several business services sectors regarding market and non-market activities. The documentation by Baranov is available on the website www.worldbank.org/trade/russia-wto.

# **3.3. Results**

In our general WTO scenario, we assume that: (i) barriers against foreign direct investment are reduced as indicated in table 3.2; (ii) seven sectors subject to anti-dumping actions in export markets receive slightly improved market access. This is implemented as an exogenous increase in their export price as shown in table 3.2; and (iii) the tariff rates of all sectors are reduced by fifty percent.<sup>30</sup> We first discuss (and present in table 3.3) our estimates of the impact of Russian WTO accession on aggregate variables such as welfare and the real exchange rate, aggregate exports, the return to capital, skilled labor and unskilled labor, and the percentage change in tariff revenue. In order to obtain as assessment of the adjustment costs, we estimate the percentage of labor and mobile capital that must change industries. The gains come

> 30 Actual tariff reductions were part of the accession negotiations and were not known when this study was done. The paper was finalized in January 2012, one month after Russia was offered membership in the WTO. The WTO reported that the Russian average tariff will decline to 7.8 percent, which is a tariff cut slightly less than we had assumed.

48
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23
Table 3: Impacts on Total Cash Savings, Debt, and Savings Net Debt

||(1)|(2)|(3)<br>Savings|(4)|(5)|(6)<br>Deb|(7)<br>t|(8)|
|---|---|---|---|---|---|---|---|---|
||Savings<br>at NVRB<br>(Admin)|Savings<br>at NVRB<br>(Survey)|Other<br>Formal<br>Savings|Informal<br>Savings|Total<br>Savings|Took Any<br>New Debt|Total<br>Debt|Savings<br>Net Debt|
|Treatment _×_ During|303.1<sup>_∗∗∗_</sup>|288.2<sup>_∗∗∗_</sup>|-47.7|-20.7|215.1|0.076<sup>_∗∗∗_</sup>|283.3|-82.9|
||(43.7)|(42.0)|(118.1)|(22.3)|(139.4)|(0.029)|(283.3)|(346.5)|
|Treatment _×_ After|91.3<sup>_∗_</sup>|25.0|127.5|2.57|145.0|0.046|195.1|1.38|
||(52.8)|(49.3)|(142.6)|(45.6)|(167.7)|(0.031)|(269.1)|(354.6)|
|Control Mean (During)|164.6|214.2|979.9|172.9|1377.4|0.19|3080.8|-1687.8|
|Control Mean (After)|199.5|256.7|921.1|236.2|1424.1|0.15|2636.7|-1244.9|
|N|1528|1499|1518|1527|1488|1528|1519|1481|
|Baseline Control?|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|

Notes: All regressions include strata fixed effects, time period fixed effects, and the baseline outcome of the dependent variable whenever possible. Robust standard errors clustered at the individual level in parentheses. Administrative NVRB savings is the balance on the relevant survey day, with negative balances coded to zero. All variables in Ghanain Cedis top coded at the 99th percentile. In 2013 GHS 2.15 _≈_ USD 1. * p _≤_ 0 _._ 10, ** p _≤_ 0 _._ 05, *** p _≤_ 0 _._ 10.
accounts for 0.2% of the sample (Online Appendix Table 2). Some of the largest outliers include Iraq during the Gulf War, Rwanda during the 1994 genocide, and Lebanon during the civil war in the 1970s-1980s; different data sets disagree dramatically as to growth rates during these periods. We also drop Liberia as it has many extreme growth outliers, and Myanmar due to irregularities in its PWT9 growth data (e.g., a three-fold increase in GDP per capita in 1970).

A major caveat is that our results are only as good as our growth data. These are not only noisy but also possibly even manipulated. For example, Martinez (2018) uses the brightness of nighttime lights as a proxy for GDP and finds that autocracies systematically overstate GDP growth. We address this data quality issue in Online Appendix 3 by calculating leader effects using two other growth data sources and find that the set of significant leaders often changes.

**Estimates of variance components in the data.** Table 2 shows estimates of the variance components { } used to construct shrinkage factors and for the LS leader estimates. μμ ii εε They are calculated using PWT9 growth data and Equations 9-11 above. In the pooled sample of σσ , σσ , σσ ψψ γγ all leaders, our estimate of the underlying standard deviation of leader quality is = 1.33%, μμ which is similar to Jones and Olken’s (2005) all-leaders estimate of = 1.5%. The standard σσ� μμ deviation of the country effect is = 0.65%, and the standard deviation of the iid error is much σσ� larger, around =4.6%. σσ�ii rr σσ� **Table 2: Estimates of Variance Components in PWT9 Growth Dataponents in PWT9 Growth Dataonents in PWT9 Growth Data**

**Table 2: Estimates of Variance Components in PWT9 Growth Dataponents in PWT9 Growth Dataonents in PWT9 Growth Data**

|**SD(leader)**<br>**1**|**SD(CE)**<br>**. Pooled**|**SD(iid)* **|**SD(leader)**<br>**2. Auto**|**SD(CE)**<br>**cracies/Tr**|**SD(iid)***<br>**ansition**|**SD(leader)**<br>**3. Establis**|**SD(CE)**<br>**hed Dem**|**SD(iid)***<br>**ocracies**|
|---|---|---|---|---|---|---|---|---|
|1.33%|0.65%|4.58%|1.35%|0.72%|4.99%|1.04%|0.42%|2.62%|
|(0.13%)<br>|(0.13%)<br>|(0.21%)<br>|(0.15%)<br>|(0.15%)<br>|(0.23%)<br>|(0.18%)<br>|(0.12%)<br>|(0.27%)<br>|

~~Notes: Estimates of variance components using the method described in the text (Equations 9-11) on PWT9~~ data (excluding outliers). Established Democracies are defined as countries with an average polity score>7.5 (Autocracies/Transition Countries otherwise) *SD(iid) is the across-sample SD of the iid error, though in the estimation of leader effects, we allow this to vary across countries. Standard deviations of estimates based on a country-level block bootstrap are shown in parentheses.

21
Table A.7: Eect of a direct SLM training intervention on other farmers' knowledge and adoption, basic specication

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Other Farmers' Knowledge and Adoption, unweighted|||||
|Knowledge score|0.341<br>[0.200]|-0.003<br>[0.012]|10,955|0.049|
|Number of techniques known by name|1.654<br>[1.538]|0.013<br>[0.119]|10,955|0.012|
|Number of techniques adopted|0.845<br>[0.891]|-0.020<br>[0.071]|10,955|0.049|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: Regressions include the following variables: a constant, treatment variables, a male dummy, an endline dummy, and district indicators.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat eect; SLM=sustainable land management.

50