rafmacalaba/datause-encoder-data huggingface dataset:
their own portfolios. Typically, these were bonds of roads backd by ste government credit or bonds that were partculary well known and long established. Many British investors limited their purchases to a few large eastern companies for which infonnation problems regarding western companies were least severe.?

Other Europeans (mainly the French, Swiss, and Germans) invested more widely but conducted le business with European-based intermediaris like Barngs, relyg instead on American investment houses. Rather than paing them up with mstitutonal partners m the home country as the British did, these Europeans dispatched to America a representative to examine the prospective investment in order to ascertain e value of the securities.26 The immigrnt community fonned another conduit for information. Specialized publicains such as _The Amerk alrd Jown_ and _Poor's Mwwaf tae Rairoads of the United Sates_ also provided information for investors. From the 1860s, Brtish investors organized _thmselves_ as the Council of Foreign Bondholders and te English Associatin of Amidcan Bond and Shacholders to collect information on arrears and negotate th debtors. Protective or ons were established also in France, Germany, and Holland27 Though most American **railway** bonds were sold privately, a sewndary market **in** these securities had sprung up by the 1850s. Increasingly investors turned **their** attention from bonds to common shares. Stocks were not marketed by the usual issuing agencies; they 2 Adler (1970, p.66-67).

' AdIer (1970, p-<sup>47</sup> ).

**2'** On the operations of the Council of Foeign Bondholders, soe Eichgrc and Portes (1989). Wiins (1989) motes that tie newspaper files of the Coumcil include 25 volumes devoted to American **raiways**

15
# **Table 3. Service EXPY and growth: 3 year panel, fixed effect and System GMM, 1990-2007**

(dependent variable – GDP per capita growth)

||FE<br>|||||System G<br>|MM<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|log lag initial GDP/cap|-0.03***|-0.03***|-0.04***|-0.04***|-0.04***|0.002|0.004|0.008|0.01|0.007|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|
|**log lag initial service EXPY**|**0.05*****|**0.04*****|**0.04*** **|**0.03*** **|**0.03*****|**0.06*****|**0.05*** **|**0.07*****|**0.07****|**0.08****|
||**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.02)**|**(0.03)**|**(0.04)**|
|log human capital||0.05***|0.03|0.01|0.01||-0.005|-0.01|-0.02|-0.02|
|||(0.02)|(0.02)|(0.02)|(0.02)||(0.07)|(0.06)|(0.05)|(0.04)|
|log financial development|||0.02***|0.01*|0.01*|||-0.004|-0.008|-0.01|
||||(0.008)|(0.008)|(0.008)|||(0.02)|(0.03)|(0.03)|
|Trade (% of GDP)||||0.04***|0.04***||||-0.002|-0.004|
|||||(0.01)|(0.01)||||(0.01)|(0.01)|
|Rule of Law|||||-0.006|||||0.01|
||||||(0.007)|||||(0.03)|
|Constant|-0.23***|-0.15*|-0.18*|-0.22**|-0.22*|-0.58***|-0.49***|-0.71***|-0.75**|* -0.78***|
||(0.08)|(0.09)|(0.10)|(0.10)|(0.10)|(0.16)|(0.16)|(0.20)|(0.25)|(0.21)|
|R-squared|0.08|0.09|0.14|0.20|0.20||||||
|observations|428|421|375|370|370|428|421|375|370|370|
|Countries|108|106|95|95|95|108|106|95|95|95|
|Rho|0.80|0.71|0.81|0.86|0.87||||||
|Hansen J-statistic|||||||||||
|(p) value||||||0.24|0.32|0.49|0.38|0.23|
|Second-order serial|||||||||||
|correlation (p-value)||||||0.002|0.002|0.001|0.002|0.002|
|Note: All equations include<br>Number of Instruments|period dum|mies. Fi|xed effe|cts inclu|de dum|mies for c<br>7|ountries<br>10|. GMM<br>10|is the<br>10|Blundell-<br>12|

<mark>Note: All equations include period dummies. Fixed effects include dummies for countries. GMM is the Blundell-</mark> Bond System-GMM estimator using lagged growth rates and levels as instruments. The GMM estimation also uses log population and log area as additional instruments. * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. Robust t-statistics are in parentheses.

As noted earlier a key constraint of the fixed effects regressions is that growth and export sophistication may be endogenous.<sup>15</sup> However, the results in the second panel of Table 3 show that dynamic _Service EXPY_ matter for growth even when we control for endogeneity using a version of the Blundell-Bond system GMM estimator.

Table 4 presents the results of fixed effect and System GMM with robustness checks. First, we control for the size of the domestic service sector in the economy. Results are shown in columns (1-2) of Table 4. As evident, even after controlling for the size of the domestic service sector, _Service EXPY_ continues to have a significant and positive association with economic growth. Second, we control for the sophistication of goods export (in column 3-4 of Table 4). Again, our

> 15 This is a key concern as witnessed in standard OLS regressions. The system GMM addresses this issue.

19
welfare of the most vulnerable during a macroeconomic shock<sup>1</sup> . Saavedra and Chong (1999) use a panel between 1991 and 1994 to study the patterns of the formal versus the informal sector in the country.

# 2. **DATA AND METHODOLOGY**

We use three national-level household surveys for the years 1994, 1997 and 2000 and construct panels for the corresponding periods: (i) 1994-1997; (ii) 1997-2000; and (iii) 19942000. As a way of testing for robustness in our findings, we also look at three different increasingly restrictive panel sub-samples in each case. First, we focus on households that have the very same head of household between the periods considered (World Bank, 1999). Since the household head is typically the breadwinner in a developing country family, a change in the head can change the pattern of consumption radically. Second, we also focus on all the households that did not change size during the corresponding period of study. This, under the premise that changes in household size will affect the consumption pattern of the household (Lanjouw and Ravallion, 1995). Third, we focus on those households that both have the same head of household and the same household size during the corresponding period of the panel employed. This last comes as close as possible to a controlled experiment. In particular, the reduced form empirically estimated follows: where the term in the left hand side is the rate of change in consumption per-capita between the period period _x and the period (x + t)_ for a specific household _h._<sup>_2_</sup> Also, _S_ represents the variables that refer to characteristics of the household at the beginning of the measurement period. They include, the years of education of the head of the household, the age and gender of the head, mother tongue of the head, the number of members in the household, and whether the household belongs to a rural or urban area. _P_ includes our set of variables of interest, that is, access to basic services, in particular, (i) water, (ii) sewerage, (iii) electricity, and (iv) telephones, again at the beginning of the measurement period. While this is simple approach is relatively standard (Bisogno and Chong, 2001; Glewwe and Hall, 2000), the key advantage is

> 1 The 1986 panel it is not representative at the national level, and only so for Lima, the capital.

> 2 _Ch(x)_ is the consumption level at the initial period _x_ , and _Ch(x+ t)_ is the consumption level at the end of the period, _(x + t)._

3
**<u>Table 4: Estimates of program effects on agricultural and nonagricultural wages</u>**

||**Total**|**Agricultural**|**Non-agric.**|
|---|---|---|---|
|||<br>**Panel A: All workers**||
|MNREGS * post|0.103***|0.111**|0.112|
||(0.027)|(0.041)|(0.089)|
|R-squared|0.786|0.782|0.811|
|Observations|8,488|5,663|3,212|
||**Panel B**|**: Allowing for heterogeneity by**|**gender**|
|MNREGS * post * male|0.093***|0.099**|0.105|
||(0.027)|(0.048)|(0.091)|
|MNREGS * post * female|0.127***|0.117**|0.185*|
||(0.035)|(0.050)|(0.105)|
|R-squared|0.786|0.778|0.813|
|Observations|8,488|5,771|3,212|

_Notes:_ Household and year fixed effects as well as controls included throughout but not reported. Standard errors, clustered at household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1 18
countries where regional accounts are unavailable or of low quality, yet remote sensing data are available. This substantially expands the empirical basis for the inequality-development analysis since the traditional sample bias that had limited the analysis to more developed economies with official regional accounts is now removed.

# **7 Conclusion**

As policy makers move forward in their efforts to improve living conditions and economic growth across regions of a country, acknowledging and understating that the mechanisms through which regional economic growth can be achieved differ from those that lead to national growth. Researchers and policy analyses that seek to such policies must consider that assumptions of traditional neoclassical growth models pose serious constraints for the analysis of regional economies. For regions, interregional factor endowments, and differences in levels of factor productivity, may account for the advantage or constraints faced by an area compared to the rest of the country, or the world.

This paper provides a motivation as to why different methods are needed to understand the challenges of regions. It then follows to review some of the methods available to understand and assess the challenges regions face. While this review is not comprehensive, it provides the reader a set of tools that can be used to better understand the works of regional economies. These tools can be used to better assess needs of regions and carefully determine the possible impact of policies. This paper suggests several avenues to improve our understanding of regional economies. First, a better understanding of the economic structure of local economies is key for policy design. The layout out the interindustry and inter-regional industrial structure and trade relationships play a critical role in determining the effects of various policies and program. Second, a complete description of regional economic structure begins with an inventory of employment, compensation (wages and salaries), and output by industry. Third, this will allow understanding how specialized or diversified the regional economy, what are its comparative and absolute advantages, help build behavioral models for assessing impacts of policies, and increase the understanding of interregional linkages. Regional accounting systems appear as a fourth tool that can contribute to the accurate identification of interactions that take place both within the region and those that cross the regions borders – in the form of imports and exports. Finally, the spatial dimension of regional analysis can leverage the use of geospatial information and bring additional information to 46
34 calculate the amount due is as a fraction (one-twelfth or one quarter) of the previous year's tax liability. Although such a method of calculation is not as accurate as one based on current records, it is easy to apply in a country where reliable records are often nonexistent, and it is preferable to a system based exclusively on gross turnover. As accounting and auditing standards improve, a more accurate system of estimation can be used, accompanied by fines for underpayment.

Self- vs. official assessment.

Given administrative constraints, one goal of tax reform in developing countries should be to place more responsibility on the taxpayer (or tax withholder). A move from a system of official assessment of all taxpayers by tax authorities to a system of selfassessment is one way to further this goal. Under a self-assessment system, taxpayers are legally required to obtain and file a tax return, and official assessments are issued only if a taxpayer fails to file a return, if an audit concludes that tax was underpaid or a refund wrongly given, or if the taxpayer does not keep books and records that are adequate for calculating the amount of tax due.

Such a move to self-assessment can address several administrative problems simultaneously. First, it can reduce the number of cases that tax officials must address each year, leaving more time to study each case more carefully.<sup>30</sup> If strong penalties are imposed when wrongdoing is found in a few cases, the example should have a deterrent effect on other taxpayers. In addition, the move toward self-assessment reduces 30 For example, Pakistan recently implemented a simple self-assessment procedure for individual tax payers with income less than Rs. 100,000. This allows tax authorities to concentrate their auditing efforts on a smaller number of taxpayers (approximately 30,000) who account for 90 percent of total income tax receipts. World Bank (1989), pp 42-43.
The overall patterns in Table 10 suggest that some forces of selection could explain a small part of the conditional African premiums, but that the selection story is far from the whole story. The conditional African premiums in labor productivity, its growth, and sales growth remain intact and significant. High-informality countries also limit the influence of finance, but amplify the influence of party monopoly and telecom.

# V. **Explaining Africa’s Disadvantage**

We now examine why Africa falls behind the better comparison group—by reporting 100* βX (XAfrica – Xbetter)/Δy, the percentage of outcome differences attributable to the independent variables _X_ . Here _y_ is the outcome variable, and “better” stands for the better comparison group. βX refers to the coefficient for a generic variable _X._ The coefficients are drawn from our baseline specification in Table 4. _XAfrica_ and _Xbetter_ refer to the mean for Africa and for the better comparison group, and Δy refers to the outcome difference between Africa and the better comparison group. For simplicity, we only report the statistically significant and quantitatively important effects (i.e., accounting for more than 4% of the outcome difference in absolute value), and we only report those variables that are of policy concern. The results are in Table 12.

The key factors explaining Africa’s disadvantage are infrastructure, access to finance, and party monopoly. Telecom, our proxy for infrastructure, is consistently the number one factor in explaining the African disadvantage: accounting for 114% of the 66 log points disadvantage in TFP, 142% of the 85 log points disadvantage in labor productivity, 172% of the 12 percentage point disadvantage in labor productivity growth, 535% of the 3.4 percentage point 32
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# **IV. Bank Regulation and Supervision Around the World** : **What the Data Mean**

# IV.A. How reforms affect banking systems: Overview

How have reforms to bank regulations and supervisory practices affected national banking systems? In countries that changed their regulatory policies, have these reforms reduced banking system fragility and boosted banking system development? Have these policy changes enhanced the efficiency of intermediation and moderated corruption in the lending process? Answers to these questions will help some countries adjust their reforms and help other countries avoid mistakes and select more appropriate reform strategies.

Given the available data, we conduct some illustrative simulations to assess the potentially impact of bank-regulatory reforms over the last decade on national banking systems. In the first step, we estimate the relationships between bank regulations and banking-system fragility, development, efficiency, and corruption in lending. These estimates are based on Survey I and analyses in BCL (2006). In the second step, we use the estimate coefficient from the first step to compute the impact of regulatory reforms between Survey I and Survey III on banking-system fragility, development, efficiency, and corruption. We make these computations for each country and describe this simulation strategy in greater detail below.<sup>5</sup>

# IV.B Baseline regressions

Table 1 presents estimates of the relationships between various bank regulations and bankingsystem fragility, development, efficiency, and corruption. Since BCL (2006) explain these estimation processes in great detail, we provide a very brief synopsis of that description.<sup>6</sup>

> 5 One difference between the estimates reported in this paper and our earlier work is that here we now use indexes based on the summation of the individual questions, rather than computing the principal component of the individual questions underlying the indexes.  We do this because it makes it much more transparent to see how changes in an individual question changed the index, and hence the estimated probability of a systemic banking crisis.  Also, ideally, we would examine how changes in regulatory reforms affect banking-system fragility, development, efficiency, and corruption.  This would involve first computing changes in bank regulations for each country, which we documented above in Section III.  Second, we would need to compute changes in banking system fragility, development, efficiency, and corruption from 1999 (Survey I) through 2007 (Survey III).  Unfortunately, these data are not yet available. Thus, an examination of how changes in banking regulation affect changes in banking-system characteristics will have to wait until these data are constructed.

> 6 Due to poor response quality in Survey III on question 8.3.1, we made a small adjustment to the Private Monitoring Index for conducting the baseline regressions based on Survey I.  We do not include 8.3.1 in the private

9
Table A.17: Placebo regressions

|**Dependent variable:**|**Household Per **|**Capita Sav**|**ing in 2008**|
|---|---|---|---|
||Saving in fnancial<br>institutions<br>(1)|Plus cash<br>saving<br>(2)|Plus interest<br>earned<br>(3)|
|**Panel A: Without CST FE**||||
|1. Firstborn girl * Avg HH exp_cst_|0.01<br>(0.04)|0.02<br>(0.04)|0.02<br>(0.04)|
|2. Firstborn girl * Avg PC HH exp_cst_|0.12<br>(0.30)|0.19<br>(0.26)|0.22<br>(0.28)|
|3. Firstborn girl * Avg Father’s edu_cst_|25.84<br>(132.63)|61.62<br>(126.53)|68.60<br>(128.64)|
|4. Firstborn girl * Avg Mother’s edu_cst_|125.83<br>(127.23)|149.72<br>(146.78)|194.27<br>(154.22)|
|5. Firstborn girl * Avg Land owned_cst_|-182.09<br>(172.06)|-123.50<br>(187.10)|-84.53<br>(199.28)|
|6. Firstborn girl * Avg PC Land owned_cst_|-708.98<br>(1,421.66)|-107.29<br>(1,462.39)|213.90<br>(1,560.24)|
|N|2,840|2,840|2,840|
|**Panel B: With CST FE**||||
|1. Firstborn girl * Avg HH exp_cst_|0.03<br>(0.06)|0.04<br>(0.05)|0.04<br>(0.05)|
|2. Firstborn girl * Avg PC HH exp_cst_|0.14<br>(0.43)|0.23<br>(0.38)|0.27<br>(0.40)|
|3. Firstborn girl * Avg Father’s edu_cst_|-43.21<br>(218.49)|-15.35<br>(205.90)|-12.31<br>(208.81)|
|4. Firstborn girl * Avg Mother’s edu_cst_|95.09<br>(220.09)|112.22<br>(235.23)|157.64<br>(241.96)|
|5. Firstborn girl * Avg Land owned_cst_|45.84<br>(206.91)|118.04<br>(187.99)|146.31<br>(197.46)|
|6. Firstborn girl * Avg PC Land owned_cst_|445.35<br>(1,694.71)|1,176.72<br>(1,347.82)|1,407.86<br>(1,427.59)|
|N|2,840|2,840|2,840|

NOTES: Each cell comes from a separate regression. Panels A and B correspond to specifications (1) and (2), respectively, except that the Expected dowry term is replaced with the state-caste-year average of another variable, such as HH expenditure. *** 1%, ** 5%, * 10%.

60
that a microcredit program was more effective than a conditional cash transfer program in increasing consumption, because the credit was used for productive purposes. More evidence needs to be accumulated on the relative merits of different kinds of programs, both from a theoretical and empirical perspective.

In summary, many of the evaluation studies of microcredit programs document high rates of return to such programs, but the returns can be compromised by targeting the loans to narrowly targeted specific groups. In implementation, the relative benefits of targeting loans at women or at poor borrowers, in terms of redistribution, need to be weighed against the benefits of allocating funds to the highest return activities.

We also reviewed two evaluation studies of land titling programs that were implemented in Peru and Argentina. Such programs are an innovative approach to increasing the wealth of poor households and are observed to change an array of household behaviors, including the labor supply of household members. The land titling programs we reviewed increased female labor supply, increased the tendency for women to work outside the home, and decreased fertility.

In recent years, conditional cash transfer (CCT) programs have spread throughout the developing world as a new strategy for reducing poverty and encouraging investment in children‟s schooling. Many of the CCT programs currently in operation give the transfers to mothers, under the assumption that mothers are more apt to spend the transfers on children, are less likely to spend them on alcohol and that providing transfers to women improves their relative bargaining position within the household. The Mexican _Oportunidades_ program led to major changes in the share of household income coming from mothers. The program was found to substantially increase school-going and decrease work of children and adolescents. The program does not appear to have had substantial effects on mothers‟ labor force participation, at least in rural areas where impacts on mothers‟ work have been examined. An added bonus is that many beneficiary women have reported other kinds of benefits, such as higher rates of participation in household decision-making. For a small fraction of households, though, wives have reported a higher rate of aggressive behavior, suggesting that care needs to be taken in implementing CCT programs to take into account their potential for increasing domestic violence.

Most studies of CCT programs focus on their effects on schooling and health. Thus far, the effects of CCT programs on women‟s labor force participation have not yet been systematically 60
36

||1960-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|
|---|---|---|---|---|---|---|---|---|---|
|Olive|0|0|0|0|0|0|0|0|0|
|Onion|na|na|na|na|na|55|81|144|284|
|Orange|25|25|26|33|38|13|3|1|1|
|Pear|na|na|na|na|na|35|24|64|157|
|Peas|0|0|0|0|0|0|0|0|0|
|Pepper|na|na|na|na|na|175|245|146|197|
|Potato|80|79|35|74|45|17|17|13|28|
|Spinach|na|na|na|na|na|89|138|237|134|
|Strawberry|na|na|na|na|na|11|25|26|17|
|Tomato|16|25|28|26|23|19|8|9|6|
|Wine|18|4|4|4|10|11|10|4|2|

Source: Authors’ calculations based on data in Anderson and Valenzuela (2008).