rafmacalaba/datause-encoder-data huggingface dataset:
services, Lh;t and that the utility function can be written as: where L(:) is an homogenous function of degree 1, HL is the number of types of labor and Pt<sup>Lh</sup> denotes the payment to a unit of Lh;t:<sup>8</sup> Similarly consumers can accumulate di¤erent types of capitals Kh;t and rent them out at Pt<sup>Kh</sup> . Proceeding exactly as before, the same equations will characterize the relationship between utility and the Solow residual, with the only di¤erence that the latter is de...ned now as:

# 2.3.2 Taxes

Our derivation of section 2.2 requires only reinterpretation to apply exactly to an environment with either distortionary and/or lump-sum taxes. The reason is that all prices in the budget constraint, equation (4), are from the point of view of the consumer. Thus, if there are taxes, the prices should all be interpreted as after-tax prices. Therefore our derivation implicitly allows for proportional taxes on capital and labor income as well as sales or value-added taxes levied on consumption and/or investment goods. The variable that we have been calling “pro...ts,”�, is really any transfer of income that the consumer takes as exogenous. Thus, it can be interpreted to include lump-sum taxes or rebates.

However, for the sake of exposition, we shall interpret all prices in equation (4) as being from the point of view of a ...rm, and thus before all taxes. To modify (4) to allow for taxes, we de...ne some notation. Let �<sup>K</sup> be the tax rate on capital income, �<sup>L</sup> be the tax rate on labor income, �<sup>C</sup> i<sup>betheadvaloremtaxonconsumptiongoodsoftypei,and�Ibethecorrespondingtaxon</sup> investment goods. We assume that the revenue so raised is distributed back to individuals using lump-sum transfers. (We consider government expenditures in the next sub-section.) Then it is apparent that we arrive at the following modi...ed version of equation (4): To make contact with the data, note that the national accounts de...ne nominal expenditure using prices as perceived from the demand side. Thus, equation (11) can be written exactly as before and still be consistent with standard national accounts data:

> 8 We assume that the nature of the utility function is such that positive quantities of all types of labors are supplied.

12
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44
KMS those without books have higher correlations between the two measures, while in the SLMS the Pearson correlations are very close to one another, and the Spearman correlations slightly larger with books.

We interpret the changes in reporting arising from the bookkeeping experiments as due to better measurement, rather than to the books themselves improving the profitability of the business. There are several reasons to believe this. First, the books affect expenses and goods used in home consumption most – the categories we think recall errors are likely largest for – and profits least. Second, the most significant results occur in the Kandy data, over one month, whereas we would expect any direct effect of bookkeeping on business profits to take some months to arise. Nevertheless, as a further check, we revisited the Kandy firms one year later, and collected sales and profits data for August and September 2006. This allows us to check whether annual sales or profits growth is higher for firms which received the book treatment. We find no statistically significant effect of books on annual profits or sales growth, with the point estimates suggesting, if anything, lower growth for those assigned books. This acts as further evidence that the books are not themselves increasing firm profitability.<sup>8</sup>

# **8. Deliberate misreporting**

Issues such as recall problems, fungibility with the household, and mismatching of purchases and sales can all be resolved to some degree through questionnaire design. However, a final cause for concern is the tendency of business owners to deliberately misreport revenue and expenses. As with corruption, it is difficult to directly ask firm owners whether they do this. We therefore followed a common approach in the corruption literature and asked firms to consider other enterprises similar in all respects to theirs, and asked “in your opinion, when providing information about expenses in surveys such as this, do enterprise owners tend to over-state expenses, under-state expenses, or state expenses correctly”. We asked similar questions for revenues.

> 8 This is not to say that business training, of which bookkeeping training may be an important part, may be successful in raising profits. Our analysis suggests that merely providing the books, without any business training, has no effect.

19
- Treatment C: Financial literacy training is provided to both the migrant worker and household member

- Group D: Control group with no financial literacy training provided

Out of the sample of 400 migrant workers, this random assignment resulted in 101 migrant households being assigned to treatment A, 97 to treatment B, 98 to treatment C, and 104 to control.

The motivation for these different assignments was that it was ex ante unclear who should be the focus of the training. Pre-departure training in a number of countries often focuses just on the migrants. Training migrants is convenient because they are already gathered in one place for job training, and offers the possibility of allowing them to better budget and save abroad, and hence send more remittances back. However, it may have limited impacts on the ability of remaining household members to manage the money they are receiving. In contrast, training remaining household members offers the possibility of teaching those receiving remittances how to better manage money, but requires more effort in getting family members to come to a training location and may have limited impact if the migrant controls how any money sent back is used. The question then arises whether there are complementarities from training both the migrant and her family member together. Our randomization allows us to test between these competing ideas for whom to train.

# **2.3 Baseline Survey**

The baseline surveys were conducted on a rolling basis from February to June 2010 to coincide with the training cycle. The baseline survey was directed at the family member of the migrant who would be responsible for receiving remittances and for household decision-making in their absence. In cases where the family member attended training, interviews were done at the training location prior to the commencement of training. For the control group, migranttraining only group, and cases in the other treatments where the family member was invited but did not show up for training, interviews were done at the dwelling of the household.

In cases where the primary remittance receiver was too old, sick, unable to leave his or her job, or otherwise reluctant to travel, in some cases another household member came to attend the training session – who was then the baseline respondent. The result is that there are some differences in baseline individual characteristics of the respondents by treatment status, with the respondents from the family training treatment groups being slightly younger and more likely to be male than in the migrant-only or the control group. Nevertheless, reported household level 7
infrastructure in terms of school enrollment). The BMA results concerning the importance and size of the effect of geological disaster risk were basically unchanged, while the mountainous terrain variable only achieved a posterior inclusion probability of 0.12. The posterior distribution of the parameter attached to the variable is, as expected, negative, but estimated with very low precision (the ratio of posterior mean to posterior standard deviation is ‐0.14).

We also explicitly assessed the issue of influential observations by estimating BMA parameters and inclusion probabilities based on subsamples. The results concerning the long‐run effects of geological disaster risk on secondary school enrollment rates are robust to the following changes in the dataset: • Trimming the observations corresponding to the disasters with the highest ratio of affected individuals per square kilometer (several percentiles of the distribution of affected people by area were tried as the cutting point, ranging from the 80th to the 95th, so as not to reduce the estimation sample dramatically)

- Trimming the observations corresponding to the poorest countries of the sample (thresholds based on observed income levels ranging up to the 30th percentile were tried)

- Trimming the observations corresponding to zero disasters, so as to ensure that the results are not driven exclusively by the differences between observations with zero versus positive disaster risk

- Excluding the 4 observations which are identified as outliers after inspection of the residuals of the specification which includes all ten potential variables.

# **4  Conclusions**

The effects of natural disaster risk on human capital accumulation have received little attention in the academic literature hitherto. We offer here a first fully‐fledged empirical study assessing the effects of natural disasters on secondary school enrollment rates. In order to avoid model‐ specific results, we use Bayesian model averaging techniques to assess the robustness and quantify the effects of natural disaster risk on human capital accumulation.

Our results give strong evidence of negative long‐run effects of geological natural disaster risk on secondary school enrollment rates. The effects are only present in the short and medium run to the extent that natural disasters cause instability and increase output volatility. The long‐run effects tend to be homogenous across countries and do not depend on income or the degree of human capital accumulation in the respective country. Furthermore, the political regime does not affect the disaster risk effect on human capital accumulation. The empirical results presented here are robust to numerous variations of the setting.
commodity trade using quotas and import licenses. As tariffs were decreasing, the use of quotas and import licenses rose to covering roughly half of the PRC’s imports.

The PRC government began in 1979 to grant special privileges to firms engaged in export processing (i.e., firms that process raw materials or assemble imported goods for export). Following reform, joint ventures and foreign-owned firms were in general allowed to import capital goods dutyfree. Thus, by the mid-1980s, the PRC had established dual trading systems: an open regime for foreign firms and domestic firms involved in export processing, and a more restrictive regime for all the other firms. However, the two regimes were harder to separate than it appeared—leakages and smuggling made it challenging to shield sectors of the PRC’s economy from global competition.

The PRC initially maintained an overvalued exchange rate prior to the reform, so it could subsidize imports of capital goods. In order to buttress the value of the renminbi, strict exchange controls, which included a foreign exchange surrender requirement, were necessary. During the reform period, the exchange rate policy was loosened, so much so that the International Monetary Fund estimated that the renminbi depreciated by about 70% vis-à-vis the US dollar between 1980 and 1995.

The PRC’s overall openness to FDI increased in tandem with its declines in barriers to trade and liberalization of its currency. Given the success of its four original SEZs in attracting foreign capital, the central government began opening other cities in 1984 to foreign firms. In 1986, the ―22 Regulations‖ made foreign invested enterprises eligible for lower business tax rates and loosened restrictions on profit remittances across the mainland. In addition, export-oriented and technologically advanced projects were given privileges. In the 1980s, FDI came primarily from Hong Kong, China; Macau, China; Singapore; and Taipei,China. After 1990, FDI increasingly flew in from Japan, the US, and other developed countries. FDI inflows fell in the mid-1990s, but resumed following the commencement of WTO accession talks. What explains the PRC’s FDI level? Analyzing cross-country panel data of FDI inflows, Fan et al. (2009) find that the main factors that attracted FDI to the PRC were the PRC’s high expected growth rate, low volatility of growth, and low ratio of dependent population.<sup>7</sup> What are the macroeconomic implications of the PRC’s growing openness before the WTO accession? Branstetter and Lardy (2008) find that net exports contributed modestly to GDP growth compared to capital investment or private consumption, and thus FDI did not drive aggregate

> 7Once controlling for the PRC’s good economic record, there is no evidence that the PRC is an exception.

17
**Table 8A: Bias in Expected Years of Schooling Conditional on Parental Schooling Rank**

||**BANGL**|**ADESH**|**IN**|**DIA**|
|---|---|---|---|---|
||**P25**|**P75**|**P25**|**P75**|
|**All Children Sample**|||||
|**Father's Schooling**|**18.46%**|**8.19%**|**17.53%**|**4.31%**|
|**Mother's Schooling**|**14.04%**|**8.13%**|**13.76%**|**20.19%**|
|**Average Schooling**|**15.22%**|**5.67%**|**17.83%**|**9.30%**|
|**Father-Sons**|**1.37%**|**-1.52%**|**4.67%**|**0.16%**|
|**Mother-Daughters**|**28.83%**|**16.17%**|**12.90%**|**23.88%**|
|**Table 8B: Bias in Expec**|**ted Normalized Scho**<br>**BANGL**|**oling Condition**<br>**ADESH**|**al on Parental S**<br>**IN**|**chooling Rank**<br>**DIA**|
||**P25**|**P75**|**P25**|**P75**|
|**All Children Sample**|||||
|**Father's Schooling**|**27.1%**|**17.9%**|**19.2%**|**6.2%**|
|**Mother's Schooling**|**23.3%**|**18.1%**|**15.5%**|**21.8%**|
|**Average Schooling**|**24.8%**|**16.4%**|**19.5%**|**11.1%**|
|**Father-Sons**|**13.7%**|**11.2%**|**7%**|**2.6%**|
|**Mother-Daughters**|**35.8%**|**24.4%**|**11.7%**|**22.8%**|

**Table 8B: Bias in Expected Normalized Schooling Conditional on Parental Schooling Rank**

NOTES: (1) P25 is the 25th percentile of parental education rank defined in terms of a given indicator such as father's schooling. P75 is similarly defined. (2) Normalized schooling is years of schooling divided by its standard deviation. (3) The numbers reported are the percentage bias as defined in Table 3.
<!-- Start of picture text --> wD<br>S<br>/d<br>(oo) \\<br>° 7<br>fop)<br>2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> <!-- Start of picture text --> year<br><!-- End of picture text --> <!-- Start of picture text --> In_laborPro2 —#— 1 In_laborPro2<br><!-- End of picture text -->
use the unemployment rate in the city in 2000 as the outcome of interest, instead of looking at the share of informal workers. All regressions are weighted by the inverse of the estimated variance of the coefficient. Again, the slopes of the regression lines in the figures are statistically different from zero (as reported in the note of the figures).

This procedure is valid if the effect of distance on labor market outcomes does not vary across states (except through its effect on enforcement), or if this variation is not correlated with the number of state inspectors. This assumption may not hold if, for example, those cities which are far from enforcement offices are also small, rural, and remotely located, and at the same time, those states with a large number of inspectors engage in active regional policies favoring small and remote cities. One defense against this argument is that decisions about regional policy and about the number of inspectors per state are probably done by different institutions, and even at different administrative levels (state vs. federal). Our belief in the validity of this procedure can be backed by empirical evidence.

Figures 2A and 2B display to two checks of the validity of our procedure (several more are presented below in the empirical section). For several reasons, discussed in detail in the paper, labor inspections only became effective in the 1990s. Hence, we do not expect the relationship between distance to the nearest enforcement office (measured in 2002) and city level variables measured in 1980, such as the share of informal workers, or GDP per capita, to vary systematically with the number of inspectors in the state (also measured in 2002). Figures 2A and 2B (similar to Figures 1B and 1C, with different dependent variables) document that this is indeed the case (we cannot reject that the slopes of the regression lines are equal to zero).

A formal empirical analysis presented below shows that a 10% increase in the level of enforcement in a city (measured by the annual number of labor inspections per firm in the city) leads to: a 0.6 percentage point (p.p.) increase in the share of the population in formal employment; a 0.6 p.p. increase in non-employment; a 1 p.p. decrease in informal employment; an 1.8% reduction in formal wages; a 2% increase in earnings of those who are self-employed (most of whom are informal); and a reduction in inequality (measured by Theil‟s index). There is little change in the employment and wages of those who are informal employees. These results show that even if labor market reform has a direct impact only in the formal sector, it will strongly affect workers outside of the formal sector because of linkages across markets.

3
14 For Model 1, the instrumental variables used in the first-stage regression are (i) a dummy variable equal to 1 if any household member spoke Greek or Italian in 1990 ( _Language90_ ), and (ii) the number of household children in the age group of [0,5] at the time of the return from the latest migration episode ( _HouseholdKids_ ).<sup>9</sup> In Model 2, we run two first-stage regressions to predict past migration experience in Greece and Italy and beyond separately. _HouseholdKids_ is used as an instrumental variable in both first-stage regressions for Model 2. While we use a dummy variable equal to 1 if any household member spoke Greek in 1990 ( _Greek90)_ while computing the predicted value of being a return migrant from Greece, a dummy variable equal to 1 if any household member spoke Italian in 1990 ( _Italian90)_ is used as an instrumental variable in the first stage regression of being a return migrant from Italy and beyond. Below, we present the theoretical arguments in favor of the validity of our instruments.

The knowledge of Greek or Italian by any household member in 1990 would indicate the affinity in culture and mentality in two main destination countries. It would also lower the costs assimilation at the time of migration, rendering both locations attractive. The familiarity with the language spoken in the host country has certainly been noted to be an important factor in determining the direction of migration in Albania (de Zwager et al., 2005). Consequently, mostly Orthodox-Christian Albanians from the southern and southeastern regions of the country, where a sizeable Greek minority has historically resided, emigrate to Greece, while Italy serves as the most preferred destination for Albanians from the central and coastal regions, where the Italian TV channels and way of life are most popular (de Zwager et al., 2005). The variation in _Language90_ is, therefore, expected to predict past migration, fulfilling in principle the requirement of instrumental relevance.

Moreover, the exposure to these languages was almost uniformly dependent on location and cultural background, mainly induced by exogenous factors such as the presence of Greek minorities or exposure to Italian TV broadcasts, and not necessarily a reflection of differences in education, skills or unobserved characteristics, such as ability or entrepreneurial drive, that might influence occupational outcomes. For this reason, we believe that _Language90_ does not influence the outcome of interest beyond its impact on past migration.<sup>10</sup>

> 9 To compute this variable for non-migrant households, we use 1998; the mid-point between 1990 and 2005, as the reference year.

> 10 The ALSMS05 reveals that over 90 percent of the heads of households with at least one member that knew Greek in 1990 are also ethnic Greek. This fact is another indication that knowledge of Greek in 1990 is unlikely to reflect