rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A.37. The district fixed-effect RDD-DD regressions of households’ income with heteroscedasticity-consistent standard**

**<u>errors</u>**

||||D<br>|ependent variable<br>|s<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program * Post-program period|-0.0181|0.4116**|0.8039***|-0.3774***|0.5492***|0.8739***|0.9973***|
||(0.0260)|(0.1738)|(0.1290)|(0.0752)|(0.1210)|(0.1051)|(0.1231)|
|(Poverty rate – 50) * Post-|0.0052|-0.0756***|-0.0658***|0.0099|-0.0303**|-0.0566***|-0.0153|
|program period|(0.0032)|(0.0211)|(0.0170)|(0.0097)|(0.0140)|(0.0123)|(0.0155)|
|Program * (Poverty rate – 50) *|-0.0092***|0.0284|0.0511***|-0.0052|-0.0064|0.0444***|0.0215|
|Post-program period|(0.0034)|(0.0223)|(0.0176)|(0.0100)|(0.0148)|(0.0133)|(0.0162)|
|Age of household heads|0.0262***|0.0485***|0.0565***|0.1330***|-0.0451***|-0.0931***|-0.0598***|
||(0.0015)|(0.0094)|(0.0061)|(0.0056)|(0.0067)|(0.0070)|(0.0071)|
|Age squared of household heads|-0.0002***|-0.0008***|-0.0007***|-0.0013***|0.0006***|0.0012***|0.0007***|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head|0.0053|-0.0124|0.2031***|0.6791***|-0.4089***|-0.0837*|-0.3800***|
|(male=1, female=0)|(0.0111)|(0.0682)|(0.0491)|(0.0392)|(0.0461)|(0.0427)|(0.0510)|
|Ethnic minorities (yes=1, Kinh=0)|-0.6139***|-0.2264***|-1.7972***|0.8630***|-0.5437***|0.6164***|-0.7285***|
||(0.0121)|(0.0752)|(0.0639)|(0.0455)|(0.0453)|(0.0406)|(0.0516)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.6202***|3.0156***|1.6227***|3.7472***|4.8958***|1.9215***|3.0930***|
||(0.0390)|(0.2471)|(0.1687)|(0.1423)|(0.1733)|(0.1701)|(0.1796)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.364|0.164|0.107|0.148|0.228|0.149|0.091|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

87
may allow us to cleanly identify treatment effects for the majority of interested firms.<sup>9</sup> One can go further and restrict attention to a smaller number of firms from a specific industry, as Bloom et al. (2011) do for textile firms in India. Homogenous firms operating in the same industry are likely to face many of the same seasonal effects and industry-level shocks, which can then be differenced out of the data, making it easier to distinguish the impact of an intervention from all these other factors that change firm outcomes from one period to the next. Such an approach is also helpful for structural modeling, since the production function and the nature of potential market failures facing the industry can likely be better understood and modeled than is the case with a mix of firms from different industries.

Table 6 illustrates using the World Bank Enterprise Surveys the reduction in the coefficient of variation (C.V.) of sales that one obtains by focusing on different sets of more homogeneous firms. I take the set of surveys where there are at least 200 firms with 10 to 100 workers, and then examine the C.V. for all firms in this group, then for the firms in specific industries, for the firms with sales below a certain threshold, and for firms with 10 to 30 workers. We see that focusing on a specific industry often, but not always, reduces the C.V. For example, in Ghana, the C.V. reduces from 3.0 for all firms to 1.2 for garment firms and 2.5 for firms in the food sector. However, in South Africa, the C.V. actually increases from 1.9 for all firms to 2.0 for garment firms and 2.1 for food firms, with the industry-level samples only about 12 percent of the size of the full sample. Focusing on more homogeneous firms in terms of employee size likewise has a general tendency to reduce the C.V., but some cases with small increases. The strategy that is most successful in reducing the C.V. of sales is to concentrate on firms with sales below a certain range (here $100,000 annual sales). This reduces the C.V. below one in all country cases. However, note that if sales are not strongly autocorrelated, then the C.V. of future sales may be larger than that of baseline sales in this group.

Reducing the C.V. can make an enormous difference to the sample size required to achieve adequate power. For example, to detect the 20 percent increase in sales with ρ = 0.5 and a single baseline and follow-up, Table 3 shows a treatment group size of 3547 firms is required. Reducing the coefficient of variation from 3 to 2 reduces the required treatment group size to

> 9 One can also increase power further within this subgroup by stratified or matched pair randomization designs (see Bruhn and McKenzie, 2009).

14
<!-- Start of picture text --> Medium-term Credit Cycle in Advanced Countries<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> USA Norway<br> Netherlands Japan<br> Spain UK<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Average correlation: 0.43<br><!-- End of picture text -->

# **c) Did the amplitude and the length of the medium-term credit cycle change after break-points in MENA?**

In an empirical work, <mark>Drehmann et al. (2012) provide evidence that amplitude and duration of the financial crisis in Western economies and Japan are related to the financial and monetary regimes. They use 1980, as a proxy for the financial liberalization and the changes in the monetary regimes. Given the different financial structure between the countries in the MENA region and developed countries, we examine the relation between changes in the volatility of the medium-term component of the credit cycle and two other breakpoints that are related to the dramatic changes in oil prices.</mark> This figure shows the changes in the volatility of medium-term credit cycles before and after the breakpoints of 1985 (figure on the right) and 2001(figure on the left).

# **Figure III-7: Volatility of Medium-term Credit Cycle Before and After 2001 and**

# **1987**

<!-- Start of picture text --> Volatility of Medium-term Credit Cycle Before and After 2001 Volatility of Medium-term Credit Cycle Before and After 1987<br>0 1 0 1<br>Saudi Arabia Jordan Saudi Arabia Jordan<br>Iran Kuwait Iran Kuwait<br>Algeria Bahrain Algeria Bahrain<br>Eqypt Tunisia Eqypt Tunisia<br>Syria Lebanon Syria Lebanon<br>Oman West Bank and Gaza Oman West Bank and Gaza<br>15 20<br>15<br>10<br>10<br>5<br>5<br>0 0<br><!-- End of picture text --> 21
found to have a robust positive and significant coefficient only in the latter group of countries. This result provides strong support to Lacko’s (1996) findings we described earlier in the paper. Further, it is interesting to note that autocratic countries display a higher degree of persistence of the youth unemployment compared for instance to more democratic regimes. As expected, lagged corruption has a significant positive impact on educated job seekers particularly in totalitarian regimes. This result indicates that the absence of rule of law, institutional checks and balances and freedom of speech increase the incentive for all sorts of corruption practice which ultimately worsens the distortions in the labor market. Similarly to the tables above, various specification tests confirm the consistency of the model.

**Table3:  Sensitivity Analysis to alternative institutional contexts**

||(1)<br>Commercial Law|(2)<br>Common Law|(3)<br>Democracy|(4)<br>Dictatorship|
|---|---|---|---|---|
|Youth Unemployment(t‐1)|0.980<sup>***</sup>|0.864<sup>***</sup>|0.941<sup>***</sup>|0.878<sup>***</sup>|
||(20.81)|(16.55)|(32.56)|(17.51)|
|Corruption Index(t‐1)|9.298<sup>**</sup>|6.311|‐3.092|12.14**|
||(2.58)|(1.57)|(‐1.28)|(1.95)|
|Education|0.261*|0.202|0.0851|0.135|
||(1.80)|(1.00)|(0.65)|(0.66)|
|Political Opposition|0.0149*|‐0.0186|0.00302|‐0.0145|
||(1.94)|(‐1.22)|(0.20)|(‐0.66)|
|Log(FDI)|0.122|0.574*|0.0436|1.087*|
||(0.42)|(1.95)|(0.24)|(1.68)|
|Inflation|‐0.655<sup>**</sup>|0.0544|0.657*|‐0.0532|
||(‐2.45)|(0.24)|(1.74)|(‐0.23)|
|Worker’s Rights|1.220<sup>**</sup>|0.0882|0.539|‐1.546|
||(2.29)|(0.18)|(0.66)|(‐1.10)|
|Monetary Aggregate|‐0.0457|‐0.585|1.685|‐1.685|
||(‐0.02)|(‐0.31)|(1.17)|(‐0.59)|
|Constant|‐3.832|0.804|0.538|‐1.386|
||(‐1.18)|(0.40)|(0.33)|(‐0.45)|
|Observations|267|323|508|158|
|AR(0)|0.00|0.05|0.03|0.04|
|AR(1)|0.03|0.42|0.77|0.50|

Notes: Standard errors are in parentheses. All GMM regressions use robust standard errors and treat the lagged corruption measure as endogenous. In the case of two‐step GMM, the Windmeijer (2005) finite sample correction for standard errors is employed. *, ** and *** denote significance at the 10%‐, 5%‐ and 1%‐level, respectively. The values reported for AR(1) and AR(2) are the p‐values for first and second order autocorrelated disturbances in the first differences equations.

Similar to Altavilla and Caroleo (2006), we extend our empirical analysis of the dynamic relationship between corruption and youth unemployment by using the PVAR applied to a six variable system to examine the impact of various shocks on youth unemployment. The results from the impulse response functions in figure 3 suggests that the shock to the corruption practices (i.e. an increase in the unlawful practices) tends to have a Page | 17
East Asian issuers, including firms listed in stock exchanges and unlisted firms. To explore which investors purchased the issued securities, we analyze the portfolios of mutual, pension, and insurance funds using data at the security level. To examine how firms used the funds raised in domestic markets, we merge the transaction-level data with firm-level balance sheet and income statement data.

The evidence shows that the most significant part of the expansion in bond issuance activity between 1990 and 2016 occurred in domestic bond markets, especially after 2008, surpassing the activity in international markets. The annual amount raised in domestic (international) bonds increased by about 381 (58) percent between 1990-98 and 2008-16. As a result, the share of bond financing obtained domestically increased over time, becoming firms’ main source of bond financing since 2008. Between 1990-98 and 2008-16, the average share of domestic bonds relative to the total annual amount raised (domestic plus international bonds) increased from 21 to 69 percent. Most of the bonds raised since 2008 were denominated in domestic currencies. Moreover, this growth in domestic markets comprised the participation of more and smaller issuing firms. The annual number of issuing firms in domestic bond markets increased 125 percent and the size of the typical issuer of domestic bonds declined by 30 percent when comparing 1990-98 and 2008-16. Instead, the number of issuing firms and the size of the typical issuer in international markets barely changed between these two periods.

We then study whether the acceleration in domestic bond issuances in 2008-16 was more likely driven by an increased appetite for domestic bonds by investors (supply-side expansion) or by a higher demand of capital by firms (demand-side expansion). If a supply-side expansion by investors seeking to buy bonds was the main force behind the acceleration in domestic bond financing, we could observe a decline in bond issuance yields, higher leverage growth for new (smaller) issuers relative to recurrent (larger) issuers, and an accumulation of issuance proceeds in cash as firms took advantage of favorable liquidity conditions to raise more debt than the amount needed to finance existing operations. If, on the other hand, an increasing demand for capital by firms was the main factor behind the acceleration 3
# **On the Contrary—Deregulation and Globalization Have Helped Stabilize the Crisis**

The 1999 Gramm-Leach-Bliley act repealed part of the 1933 Glass-Steagal Act and thereby allowed commercial banks and investment banks to merge. But this merger capability has helped to stabilize the financial markets rather than contribute to it. In the 2008 financial crisis, deregulation allowed Bear Stearns to be acquired by J.P. Morgan,<sup>38</sup> Merrill Lynch to be acquired by Bank of America and allowed Goldman Sachs and Morgan Stanley to convert to bank holding companies and help shore up their positions.

Moreover, deregulation, consolidation and globalization of the banking system has permitted the banks to recapitalize to a far greater extent that in previous crises. That is, financial crises periodically occur throughout the world. There were 100 in the world in the past 30 years alone. The two most serious in the US were the Great Depression and the crisis of 1989. What has been common to past crises has been that banks have been unable to raise capital largely because potential investors are uncertain about how deep the problems are with the bank seeking to recapitalize. What has been different about the current US crisis is that banks have been able to recapitalize to some nontrivial extent. For example, in the S&L crisis, bank recapitalization averaged about $3 billion per year in 1989-1991.<sup>39</sup> In the year ending September 2008 banks raised $434 billion in new capital.<sup>40</sup> What explains this unprecedented ability to raise capital in a financial crisis? Calomiris and others argue that deregulation, consolidation and globalization contributed to substantial profits in the banks in the past 15 years and left banks in a stronger position at the start of the crisis so they could more credibly argue they would survive the crisis and thereby attract investors. Moreover, many analysts have noted that regional concentration of banks contributed to US financial crises in the past, as regional banks are vulnerable to regional shocks (Bernanke and Lown, 1991). With consolidation and deregulation of interstate branching, however, banks are less vulnerable to regional shocks. As a further benefit, globalization of banking has allowed banks to access credit from sovereign wealth funds and other international sources.<sup>41</sup>

> 38 The Bear Stearns-J.P Morgan merger was facilitated by a $30 billion government subsidized loan. Jaffe and Perlow (2008).

> 39 Federal Reserve data cited in Bernanke and Lown (1991, 239).

> 40 For details of which financial institutions raised capital and how much, see Calomris (2008, 106).

> 41 See Calomiris (2008, 47-55) for details.
The Uganda school feeding study took place in Internally Displaced Persons (IDP) camps in Northern Uganda (Adelman, Alderman, Gilligan & Konde-Lule, 2008; Adelman, Alderman, Gilligan & Lehrer, 2008; Alderman, Gilligan & Lehrer, 2008). Sampled households lived in the camps at the time of the baseline survey in 2005 but had resettled out of the camps (either returning home or moving to smaller camps closer to their original homes) by the time of the follow-up survey in 2007. The follow-up survey located and re-interviewed 81% of the baseline sample.

Nutritional effects of the Uganda school feeding program were consistent with the Burkina Faso study. Both on-site and rations programs reduced anemia prevalence among older girls (10-13). Preschool children whose siblings were exposed to the on-site feeding had significant increased in height-for-age and reduction in anemia prevalence, with no similar finding for preschoolers exposed to rations.

Recent discussions of school feeding programs globally (Bundy, et al., 2009) and locally in Lao PDR (World Food Programme, 2009b) have emphasized the need to think strategically about the role that school feeding programs should play in broader social and educational policies. The unusually high transport costs in rural Lao PDR make school feeding very expensive relative to other interventions and to school feeding programs in comparable countries. This threatens future sustainability of the program. While this evaluation has not been able to provide unqualified support for school feeding impact, it has pointed out some relevant implementation challenges that need attention. Results should be incorporated into ongoing planning efforts within country, and be added to the growing impact evaluation literature on school feeding worldwide.

27
32 Regression 3 controls for terms of trade shocks. The dollar-denominated indices of export and import prices are missing for many countries in International Financial Statistics. This not only reduces the sample size by about 25 percent, but does so by removing countries with lower than average values of checks and higher than average fiscal costs of crisis.33 Unsurprisingly, given the selection bias introduced by the variable, the coefficient values on checks and the interaction term decline in this regression. Among those countries that report terms of trade data, the regression indicates, as predicted, that adverse terms of trade shocks are positively, though not significantly, related to fiscal transfers.

|Table 2: Incomple<br>Vhite-adjusted sta|teinformation andthemagnitude ofbanking crises<br>ndard errors in parentheses|
|---|---|
|Dependent<br>variable: Fiscal<br>costsofcrises|(1)<br>(2)<br>Q)<br>Radio andTV<br>Newspaper<br>Availabilityof<br>station<br>independence<br>trade statistics<br>independence<br>{exportor<br>import prices|
|Information|1.8<br>-0.08<br>7.6|
|(seecolumn<br>heading)|(2.2)<br>(2.8)<br>(4.7)|
|No.ofyearsfrom|<br>|1.6<br>15<br>1.3<br><br><br>|
|<br>firstyearof crisisto <br>year ofnext<br>election|<br><br><br>| (1.6)<br>(1.6)<br>(1.7)|
|R|0.11<br>0.09<br>0.17|
|N|36<br>36<br>39|
|Note: Regressions als<br>significanceandsigns <br>isnotavailableforTa|o include a constant, laggedM,/GDP , and GDP/capita, not reported. The<br> of the lattertwo variables arecomparable to Table 1, however. Income per capita<br>iwanfromInternationalFinancialStatistics,IMF.Regression2thereforeexcludes|
|<br>Taiwan. Observations<br>betweenobservations|<br> are “clustered”, such that independence isassumed across countries but not<br>fromthesamecountry(Argentina,Indonesia,Malaysia,Turkey,andThailand).|

The role of incomplete information in the determination of fiscal transfers in the event of bank crisis is addressed in Table 2. The estimates reported use the specification from regression 2 in Table 1. The hypothesis is that where voters are incompletely informed, fiscal 33 Countries that do not report these data exhibit average checks equal to 2.1, and average fiscal costs of
**Figure 13. Currency structure of corporate loans**

<!-- Start of picture text --> FX loans Zloty loans<br>%<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text -->

**Figure 14. Currency structure of household loans**

<!-- Start of picture text --> FX loans Zloty loans<br>100 %<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text -->

**Figure 15. NBP reference rate and interbank interest rates**

<!-- Start of picture text --> NBP reference rate WIBOR 3M<br>% (p.a.) EURIBOR 3M LIBOR CHF 3M<br>12,0<br>10,0<br>8,0<br>6,0<br>4,0<br>2,0<br>0,0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: NBP, Bloomberg.

**Figure 16. Nominal exchange rate of zloty (monthly data)**

<!-- Start of picture text --> USD/zl EUR/zl CHF/zl<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Notes: monthly average exchange rate; January 2002 = 100; the growth means depreciation of the zloty. Source: NBP.

**Figure 18. Gross capital inflows (quarterly data)**

**Figure 17. Structure of loans to households**

<!-- Start of picture text --> Consumer loans Housing loans Other 25 billion USD % of GDP (right - hand scale) % 20<br>%<br>100<br>90 20<br>15<br>80<br>70 15<br>60 10<br>50 10<br>40 5<br>30 5<br>20<br>0<br>10 0<br>0 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 -5 -5<br>Source: NBP.<br><!-- End of picture text --> Notes: gross capital inflows are: total FDI in Poland, portfolio investments liabilities and other investment liabilities. Source: authors’ calculation.

32
# Ownership of banks.'6

Besides political openness, we examined an extensive array of additional supervisory and institutional factors that might mitigate the corrupting influence of official supervisory po,ver and the pernicious effects of having a restricted banking system. In terms of supervisory structure, we examined whether greater supervisory independence from the political system, greater supervisory independence from legal action by banks, greater overall supervisory independence, or having multiple supervisory agencies reduce the corrupting effects of strong and restrictive regulation and supervision. We find that neither greater supervisory independence nor the existence of multiple regulators ameliorates the positive association between corruption and greater denial of entry applications, more regulatory restrictions on bank activities, or greater official supervisory power. In terms of the institutional environment, we examine the openness of the media, both print and broadcast. Based on the data assembled in Djankov, McLiesh, Nenova, and Shleifer (2001), we test whether countries (a) where the government does not repress the media and (b) where the media is privately-owned suffer less corruption from regulation and supervision of banks. Although we confirm Djankov et. al.'s (2001) findings that corruption is positively correlated with government ownership of the media and government repression of the media, we do not find that an open, privately-owned media systematically ameliorates the corrupting influences of official supervisory power or restrictions on bank entry.

Next, we use instrumental variables to determine whether controlling for potential simultaneity bias alters the Table 3 results. To select instrumental variables for the regulation and supervisory variables, we use theory and recent empirical work that stresses legal systems and natural resource endowments. LLSV (1998) argue that Common law countries protect private property rights against the State to a greater degree than Civil law countries. In contrast, LLSV (1998) argue that the civil law is frequently used to empower the state against private property holders. Thus, according to what is known as the law and finance view, civil law countries will tend to have a hands-on, government-controlled approach to supervision and regulation, while Common law countries will tend to rely more heavily on private sector corporate control of banks. LLSV (1999) show that French civil law countries (as well as Socialist countries) have much lower levels of private 16 Supervisory independence does not have a different impact depending on the extent of political openness.

26