rafmacalaba/datause-encoder-data huggingface dataset:
household per capita income will be our dependent variable. Our independent variables include: urbanity, household size, age composition, gender, ethnicity, education, and employment. There are a total of 16 regressors (excluding the constant). The regression model is shown in Table 7. We obtained an adjusted R-squared of 0 _._ 432, which denotes a typical level for this type of income (or consumption) regression model. The location effect is estimated at 0 _._ 027, which is not unusual for developed nations but rather small for developing countries where one might find location effects in the range of 0 _._ 05 to 0 _._ 25.

||lnyc<br>|ap<br>|
|---|---|---|
|metro<br>suburb|0.171<sup>_∗∗∗_</sup>|(8.21)|
|metro<br>none|-0.0542<sup>_∗_</sup>|(-1.68)|
|hh<br>~~s~~ize<br>1|0.733<sup>_∗∗∗_</sup>|(16.32)|
|hh<br>~~s~~ize<br>2|0.620<sup>_∗∗∗_</sup>|(16.00)|
|hh<br>~~s~~ize<br>3|0.344<sup>_∗∗∗_</sup>|(9.73)|
|share<br>~~c~~hild15|-0.160<sup>_∗∗_</sup>|(-2.20)|
|hh<br>~~h~~edu2|0.277<sup>_∗∗∗_</sup>|(6.47)|
|hh<br>~~e~~mployed|0.257<sup>_∗∗∗_</sup>|(14.24)|
|hd<br>~~f~~emale|-0.212<sup>_∗∗∗_</sup>|(-10.80)|
|hd<br>~~l~~ogage|0.898<sup>_∗∗∗_</sup>|(30.46)|
|hd<br>~~l~~edu|-0.331<sup>_∗∗∗_</sup>|(-6.40)|
|hd<br>~~h~~edu1|0.175<sup>_∗∗∗_</sup>|(7.06)|
|hd<br>~~h~~edu2|0.450<sup>_∗∗∗_</sup>|(9.81)|
|hd<br>~~e~~mployed|0.484<sup>_∗∗∗_</sup>|(15.25)|
|hd<br>~~h~~isp|-0.159<sup>_∗∗∗_</sup>|(-4.36)|
|hd<br>~~b~~lack|-0.248<sup>_∗∗∗_</sup>|(-7.78)|
|~~c~~ons|5.201<sup>_∗∗∗_</sup>|(39.18)|
|_N_|6330||
|adj. _R_<sup>2</sup>|0.432||

_t_ statistics in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 7: Regression model with log household income per capita as dependent variable (sample from 2010 IPUMS USA) Figure 3 shows the estimates of the probability density function associated with the area error _ua_ and the household error _εah_ , respectively. The “red” line denotes the normal mixture density estimate, “blue” line denotes the normal density function that best fits the data. As we are dealing with empirical data, not simulated data, we do not know the distributions from which the errors are drawn, and hence are not able to include the “true” density function as a benchmark. The estimates suggest that the household errors are arguably drawn from a distribution that deviates visibly from a normal distribution. We do not see a similar deviation from the normal density for the area error. The smallness of the variance of the area error relative to the total error however, suggests that the signal to noise ratio is low making it difficult to identify the underlying density function. The small variance also means that the area error makes only a minor contribution to the total error and so its density function is less important. It is clearly more important that our estimate of the density associated with the 22
experiences, work experience, and character development). Each student was also administered the student questionnaire. The resulting sample included 411 principals, 3808 teachers, and 9768 students.

35. A series of analyses of these data have already been reported (Raudenbush & Bhumirat, in press; Raudenbush, Bhumirat & Kamali, in press). However, a serious but unavoidable weakness in the survey design has prevented the integrated analysis pursued in the present paper. Specifically, it has been impossible to link each teachers' experience directly with the student ratings of instructional quality and with student measures of achievement. Our sub-sample was constructed to allow this linkage.

# The Sub-samplc

36. The survey procedures utilized by the Thai government allow the researcher to link school characteristics with individual teachers in each school and to link school characteristics with individual kids in that school. However, it has not been possible to link schools, teachers, and kids in a single analysis. Hence, the only teacher data available for predicting student outcomes have been aggregated level of the teacher variables. Characteristic flaws with in this type of analysis are well known (c.f., Aitkin & Longford, 1986). The central flaw in the present case is that, to the extent teachers within a school vary on the independent variable, the use of the mean teacher value on that variable to predict student outcomes produces uninterpretable results.

14
- 4 -

200 banks from 37 countries analyzed in DDT). This is not just a simple increase in sample size: the sample of rated banks was not a representative sample, since rated banks tend to be larger, more internationally active, and more likely to adhere to international accounting standards. From a policy point of view, we would like to investigate the effect on all types of banks operating in different country circumstances, rather than a select subgroup. In this study, the richer sample allows us to explore whether the relationship between BCPs and bank soundness varies across different types of banks.

All in all, we do not find support for the hypothesis that better compliance with BCPs results in sounder banks as measured by Z-scores. This result holds after controlling for the macroeconomic environment, institutional quality, and bank characteristics. We also fail to find a significant relationship when we consider different samples, such a sample of rated banks only, a sample including only commercial banks, and samples including only the largest financial institutions. In an additional test, we calculate aggregate Z-scores at the country level to try to capture the stability of the system as a while rather than that of individual banks, but also this measure of soundness is not significantly related to overall BCP compliance.

When we explore the relationship between soundness and compliance with specific groups of principles, which refer to separate areas of prudential supervision and regulation, we continue to find no evidence that good compliance is related to improved soundness. If anything, we find that stronger compliance with principles related to the power of supervisors to license banks and regulate market structure are associated with riskier banks.

While these results cast doubts on whether international efforts to improve financial regulation and supervision should continue to place a strong emphasis on BCPs, a number of _caveats_ are in order. First, insignificant results may simply indicate that accounting-based measures, such as Z-scores, do not adequately capture bank soundness, especially for small banks and in low income countries, where accounting standards tend to be poor. The lack of significant results may also be attributable to low quality of the assessments of BCP compliance, especially in low income countries where implementation may be difficult to judge accurately,
There is also substantial variation in restrictiveness across lower-income economies. Of the 45 low- and lower-middle-income economies covered by CTC, 38% impose moderate or heavy restrictions across most transactions measured by CTC, but another 38% have unrestricted foreign exchange regimes for FDI (and the remaining 24% have only administrative requirements across most transactions). This benchmarking suggests that easing the process of converting and transferring currency may be a viable regulatory reform consideration for economies at all levels of development.

Figure 3: Higher-income economies have fewer restrictions on converting and transferring currency <!-- Start of picture text --> 50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>0 20 40 60 80 100<br>CTC average indicator score per economy<br>Note: the Pearson correlation is 0.450, which is highly<br>significant. Source: WDI for GDP data.<br>Figure 5: Average CTC indicator scores, by economy<br>income classifications<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>GDP per capita, 2011 (USD)<br>Average economy CTC score<br><!-- End of picture text --> Figure 4: Larger economies have fewer restrictions on converting and transferring currency <!-- Start of picture text --> 16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>0 20 40 60 80 100<br>CTC average indicator score per economy<br>Note: the Pearson correlation is 0.176, which is moderately<br>significant (at the 10% level). Source: WDI for GDP data.<br>Figure 6: Percentage of economies with moderate or Percentage of economies with moderate or<br>heavy restrictions on average, by income classification<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>GDP, 2011 (billion USD)<br>Percentage of countries<br><!-- End of picture text --> Figure 6: Percentage of economies with moderate or Percentage of economies with moderate or heavy restrictions on average, by income classification Note: income groups are based on standard World Bank operational classification by <u>gross national income per capita from 2012.</u> 7
0:25 = 0:00025 after having unprotected vaginal sex, and 5=1000 � 0:25 = 0:00125 after having unprotected anal sex. For simplicity, we assume that the probability of infection from protected sex is 0.

An initially HIV negative woman who is perfectly insured will be infected with probability 1 � (1 � tvpclient)<sup>106y</sup> (1 � tapclient)<sup>20y</sup> after y years, while the average woman in our sample will be infected with probability 1 � (1 � tvpclient)<sup>113y</sup> (1 � tapclient)<sup>22y</sup> . Table 10 summarizes the probability of infection after di¤erent time periods. As might be expected, the probability of infection is high even if women were perfectly able to smooth their consumption, since all of these women have a large number of unprotected sex acts. However, for women that use unprotected sex to cope with health risk, the probability of HIV infection is 0.4 percentage points higher after 1 year, 0.8 percentage points higher after 2 years, 1.6 points higher after 5 years, and 2.5 percentage points higher after 10 years. In percentage terms, these increases are on the order of 4.5-8%. Since the price premium to unprotected vaginal sex is about 38 Ksh (US $0.54) and the premium to unprotected anal sex is about 91 Ksh (US $1.30), this means that women accept, for instance, a 8.0% increase in the risk of HIV infection after 1 year for about 7 � $0:54+2 � $1:30 = $6:38. The relatively small compensation paid for taking these tremendous health risks strongly suggests that the provision of better smoothing mechanisms (such as savings accounts) could have substantial impacts on the health of these women, by allowing them to rely on mechanisms other than their own labor supply to smooth consumption.

# 7 Conclusion

Using panel data from a sample of 192 formal and informal sex workers in Busia, Kenya, this paper is the ...rst to quantitatively investigate the relationship between sex work and risk. We conducted our study in rural Western Kenya, an area in which transactional sex is prevalent: we estimate that 12.5% of the adult female population aged 15-49 supplies at least some transactional sex. For this reason, our results are generalizable to a larger population of women than is true of studies of sex work in more developed countries, and our ...ndings ...t into the larger literature on risk-coping and consumption smoothing in poor countries.

Like many people in poor countries, the sex workers in our sample are subject to consid25
once we include the fixed effects and no longer significant in the OLS estimation.<sup>12</sup> The first columns in the second panel shows that even after controlling for time effects, the average impact of FCS in China and India are positive in the OLS estimation. The effect on LAC is not statistically different, but remains positive and significant.

Columns 6 and 7 show the Poisson and Negative Binomial results, using the conditional mean estimators. The estimates on the effects of China and India are consistent with those in the OLS. The impact of China and India on the rest of the world’s FCS is positive and significant, and it is not different for LAC.

The bottom panel of the table presents the fixed-effects results that also control for natural resource abundance in the host countries. The coefficients on these latter variables are not different from zero. Only in the Poisson model the fuel abundance variable appears significant and has a negative sign, indicating that abundance of fuel resources is negative correlated with FCS. The estimates of the Chinese and Indian variables remain unchanged, however.

One concern about these results is related to the data, which might underestimate China’s FCS. Hong Kong has been a part of China since 1997 and therefore should be considered part of the Chinese economy. Moreover, some observers have argued that China’s and Hong Kong’s trade data should be combined to approximate the trade flows coming from China mainland due to transshipments of merchandise through Hong Kong (Fernald et al. 1998). Hong Kong has a significant contribution in the marketing and distribution of Chinese exports, thus making it difficult to differentiate the value added in each country. Similarly, multinational enterprises may be moving to Hong Kong to

> 12 We include the time effects and the fixed effects one at a time. The comparison of these results indicates the fixed effects are driving these results.

14
concordance of district definitions that is longitudinally consistent. This concordance reduces the number of unique districts to 368. This number of unique areas is then reduced slightly to 325 when we exclude districts that have less than one million in population in the 2001 census or fewer than 50 establishments sampled. The exclusions are minor in terms of economic activity, and the resulting panel accounts for over 90% of employment in the manufacturing and services sectors throughout the period of study.

The NSSO surveys the ownership type of each establishment. Establishments can be listed as male proprietary, female proprietary, other owned, cooperative, household partnership, multi-household partnership, private LLC, and unknown. We focus primarily on the establishments listed as either male proprietary or female proprietary. As tabulated below, these two groups constitute more than 95% of establishments in the unorganized sectors for manufacturing and services. These questions regarding the gender of business ownership are an outcome of the survey and not a factor in the stratification design. Our business ownership statistics include establishments of all ages. The gender distinction is only available starting in 1994, which defines the first period of our study.

Unfortunately, the ASI does not collect the gender of business owners for establishments in the organized manufacturing sector. This survey limitation prevents us from studying women’s business ownership across the full manufacturing sector, with our focus instead on the stricter definition of women’s business ownership in the unorganized sector. As described below, the unorganized sector accounts for over 99% of establishments, over 80% of employment, and about 20% of output in manufacturing. Thus, for the first two dimensions of establishments and employment, the potential differences between our calculations based on the unorganized sector only versus what we would have calculated across the whole distribution are very small. For output, persistence of the unorganized sector is not as meaningful given that it is a small minority share. We thus focus on employment and establishments in this paper.

# **3. Tabulations of Women’s Business Ownership by Sector**

Tables 1a-1b tabulate the organized and unorganized distributions for manufacturing. Table 1a considers employment levels in establishments, while Table 1b considers establishment counts. The tables follow a similar format, with Panel A providing shares, Panel B providing baseline 8
case (Costa Rica, a country in which the real minimum wage increased), the minimum wage fell relative to average wanufacturing wages.

**Table** 1: Minimum Waaes in Latin America in 1989 (1980-100)

||Real MW|MW/W||
|---|---|---|---|
|Argentina|77.1|87||
|Brazil|70.6|67||
|Chile|79.7|77||
|Colombia|110.7|93||
|Costa Rica|116.5|133|(*)|
|Ecuador|42.3|na||
|Mexico|50.7|74|(*)|
|Paraguay|140.6|na||
|Peru|26.;|72||
|Uruguay|78.6|79||
|Venezuela|68.6|_na_||

Source: **CEPAL C1989a]. NW a Ninfmn Wabg,** W **u Was*** fn iufacturing, **C) I986.** Some multilateral organizations have voiced their concern **on** the negative consequances such policies have on poverty. The International Labour Organization, for example, in its 1987 report said that "unfortunately, in a limited but increasing number of developing countries caution in the adjustment of minimum wages had led to substantial declines in their real value so that they have lost all relevance in 8
explanation often found in the earlier literature (e.g. Franz and Pfeiffer, 2006; Kaufman, 1984; Campbell and Kamlani, 1997; Bewley, 1998). The danger of the best employees leaving the firm has been subject to less scrutiny, although Campbell and Kamlani (1997) find strong support for the adverse selection model as applied to quits in their US survey.

A third prominent reason preventing nominal wage cuts in Europe comes from institutional restrictions, imposed either in the form of labour regulations or by collective agreements. The institutional reason was considered important by 74% of firms. Unions and collective bargaining have generally been found to be of relatively little importance in US studies, if they are examined at all. Campbell and Kamlani (1997) find the average effect of union coverage on preventing wage reductions to be of “minor” importance. In contrast, the analysis of German firms by Franz and Pfeiffer (2006) suggests a considerably larger influence of unions in preventing wage cuts, indicating a potentially significant role for this reason in explaining cross-country variation.

At the opposite end of the scale, concerns that the firm’s reputation as an employer could be harmed if wage cuts were applied, and this could translate into more difficulties in hiring good workers in the future was one of the least commonly mentioned rationales (60% of firms). This is consistent with evidence for the US discussed in Bewley (2004). The implicit contracts model proposed by Azariadis (1975) similarly received little support in our survey.

The remaining three reasons relating to future difficulty in recruitment, increased costs associated with employee turnover and employees making negative comparisons with outside wages were all rated as relevant by between 67% and 72% of firms. The importance of comparisons with wages paid in similar jobs by other firms is rated highly, in contrast with results for the US (Bewley, 2004) but consistent with previous evidence for Sweden (Agell and Lundborg, 2003). This is perhaps due to the widespread presence of unions in the majority of countries in our survey, a point to which we return later.

The most relevant explanations were supported by the vast majority of managers in all countries. As such, there is no country where explanations relating to morale and losing productive staff were supported by interviewees representing less than 70% of the labour force. There is, nevertheless, some dispersion for a few of the reasons examined. For example, the impact on firm reputation, hiring difficulty and the existence of implicit contracts appear to be slightly more relevant for firms 15
# WPS4182

## **The Welfare Effects of a Large Depreciation: The Case of Egypt, 2000-05**

Aart Kraay The World Bank

**Abstract** :   The Egyptian pound depreciated sharply between 2000 and 2005, declining by 26 percent in nominal trade-weighted terms.  This paper investigates the effect of the large depreciation on household welfare operating through exchange rate-induced changes in consumer prices.  I estimate exchange rate pass-through regressions using disaggregated monthly consumer price indices to isolate the impact of the exchange rate changes on consumer prices.  I then use household-level data from the 2000 and 2005 Egyptian household surveys to quantify the welfare effects of these consumer price changes at the household level.  The average welfare loss due to exchange rateinduced price increases was equivalent to 7.4 percent of initial expenditure.  Stronger estimated exchange rate pass-through for food items imply that this effect disproportionately impacted poorer households.

### World Bank Policy Research Working Paper 4182, April 2007

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

__________________________________________ 1818 H Street N.W, Washington, DC, 20433, akraay@worldbank.org, <u>http://econ.worldbank.org/staff/akraay. This paper was prepared as background for the 2007</u> Egypt Poverty Assessment. I would like to thank without implication Sherine El-Shawarby, Heba El-Laithy, Faika El-Refaie, Francisco Ferreira, Jed Friedman, Michael Lokshin, Martin Ravallion, Luis Serven, and conference participants at the Egyptian Center for Economic Studies for helpful discussions.