rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 15: The number of foreign residents by age and gender (Brazilian) (A) End of 1992**

|Age|Brazil|Composition<br>ratio|Male|Composition<br>ratio|Female|Composition<br>ratio|
|---|---|---|---|---|---|---|
|Total|147,803|100.0%|87,679|59.3%|60,124|40.7%|
|0-4year|5,082|3.4%|2,541|2.9%|2,541|4.2%|
|5-14year|7,244|4.9%|3,682|4.2%|3,562|5.9%|
|15-19year|12,997|8.8%|7,550|8.6%|5,447|9.1%|
|20-64year|122,269|82.7%|73,760|84.1%|48,509|80.7%|
|over 65years of age|211|0.1%|146|0.2%|65|0.1%|

|**(B) End of 2002**|
|---|

|Age|Brazil|Composition<br>ratio|Male|Composition<br>ratio|Female|Composition<br>ratio|
|---|---|---|---|---|---|---|
|Total|268,332|100.0%|147,322|54.9%|121,010|45.1%|
|0-4year|17,264|6.4%|8,852|6.0%|8,412|7.0%|
|5-14year|23,610|8.8%|12,035|8.2%|11,575|9.6%|
|15-19year|16,106|6.0%|8,397|5.7%|7,709|6.4%|
|20-64year|209,702|78.2%|117,214|79.6%|92,488|76.4%|
|over 65years of age|1,650|0.60%|824|0.6%|826|0.7%|

Source: "Foreign residents statistics" from Japanese Ministry of Justice.

37
# **III. Selecting sectors**

## **Selecting a country with a per capita income 100-300 percent above Nigeria’s**

32. Table 7 shows a list of countries that have a per-capita GDP of 100 to 300 percent of that of Nigeria. Removing slowly-growing countries, i.e., countries growing at less than 6 percent per year, leaves the following countries: Indonesia, China, Vietnam and India.

**Table 7:  GDP per capita, PPP (constant 2005 international $) in 2009**

||**GDP per capita**|**Percent of**<br>**Nigeria**|
|---|---|---|
|Nigeria|2001|100%|
|Vietnam|2682|134%|
|India|2970|148%|
|Philippines|3216|161%|
|Indonesia|3813|191%|
|Morocco|4081|204%|
|Paraguay|4107|205%|
|Egypt|5151|257%|
|China|6200|310%|
|Tunisia|7512|375%|

33. Using the criterion of factor endowment, among these countries Indonesia would stand out as the country with the greatest similarity with Nigeria because it is a natural resource-rich country and a former member of OPEC, but also specializes in labor-intensive production. Indonesia has effectively used both its natural resources as well its abundant labor supply to develop industries that correspond to its latent comparative advantage. As discussed in a blog by Justin Lin (March 2011), a resource-rich, labor-abundant country can use both resource-rich and labor-abundant countries as comparators.<sup>8</sup> 34. While not a resource-rich country, Vietnam’s high growth rate makes it an appropriate comparator, especially in view of its labor–intensive economy. Its strong growth performance and consequent rise in labor costs could also quickly erode Vietnam’s cost advantage in certain labor-intensive industries.

35. The other country that lends itself as a comparator is China. China has a per capita income that is about 300 percent higher than that of Nigeria and is not a natural resource rich

> 8The similarities been Nigeria and Indonesia had earlier been recognized in a World Bank publication which reviewed the economic performance of the two countries over the period 1960-85.

17
tax inspector in the year prior to program implementation were more likely to formalize. This result, which is only significant for group 3, may suggest that the program was perceived as a way to limit tax harassment.

These results show that the program was more effective on some sub-populations like male business owners, those operating outside Dantokpa market, with at least secondary education and those which look more like formal businesses before the intervention. Targeting these subpopulations could therefore improve program effectiveness.

# **6.4 How do the formalized firms compare to the already formal and to those who would formalize anyway?**

Table A7 compares the baseline characteristics of the firms formalizing through our various interventions to those who were already formal at baseline, and to the few control group firms that formalized. As expected from the theoretical discussion in section 5, the program brought in smaller firms, and firms that looked less like firm already formal at baseline. Firms in the control group that formalized have characteristics that are closer to firms that were already formal at baseline. Differences between newly formalized firms in the control group and in other treatment groups are all going in the expected direction. For example, firms that formalized in the control group had significantly higher level of baseline sales than firms that formalized in the treatment groups (CFAF 90,000, or USD150 against CFAF 54,000 to 62,000, or USD 90 to 104). Most other statistical tests comparing formal firms in control and treatment groups are not statistically significant but this is not surprising given the small number of firms that formalized in the control group (27).

# **6.5 Cost effectiveness for the impact on formalization**

Data on program costs during the two years of program implementation are presented in Table 5. Total program costs were high and the program as it was implemented for the 2,399 firms in a treatment group costed around CFAF 370 million (USD 620,000). Out this total, CFAF 50 million (USD 84,000) were used to made the _entreprenant_ status available at the one-stop shop for business registration for any firm who wants to come along and do it, and CFAF 320 million (USD 537,000) to pay for the additional interventions to encourage take-up (in-person visits, business trainings, etc.). This corresponds to a total cost per business included in the program 21
d. The quality of public health care provision The quality of health care provision was found to be poor in all regions, except developed countries. Only about 35 percent of surveyed businessmen in MNA and CEE thought that quality of public health care provision is efficient. The numbers for CIS and LAC are the lowest (14 and 13 percent respectively).

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>* *<br>10<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or (3) for question 22.d**

_Question 23: Frequency of power outages?_ Question 23 asked about another aspect of service provision - uninterrupted supply of electrical power over time.

23. How frequent are power outages?

<!-- Start of picture text --> Power outags happen oncenaweekor m often<br>outages?<br>(1) once in more 40<br>than 3 months O<br>(2) once a month 0i 35<br>(3) once in two weeks O 3<br>(4) once a week 0 30<br>(5) once a day °i 25<br>(6) no power for<br>long period El 20<br>15<br>As expected developed 10<br>countries have the lowest 5<br>percentage share of<br>Wodd LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text --> As expected developed countries have the lowest percentage share of businessmen who experience power outages at least once a week (about 1 percent of respondents). Sub-Saharan Africa has the highest share of businessmen having problems with power outages (almost 45 percent).

41
Before we commence on model estimation, we need to determine the stationarity nature of the variables of interest. In view of that, we begin our analysis by performing unit root test for each variable using Augmented Dickey-Fuller (ADF) and PhillipsPerron (PP) unit root tests. While we are doing the ADF test we pick the lag length on the augmentation term based on whether the exclusion of lagged term causes serial correlation in the test equation’s error term. As for the PP test, on the other hand, we use a truncation lag of one given the fact that the frequency of our data is annual.

**Table 3: Results of Dickey-Fuller test**

|**Variable**|**ADF**<br>**( Levels)**|**ADF**<br>**( Differences)**|**PP**<br>**( Levels)**|**PP**<br>**(Differences)**|**Critical value**<br>**(5%)**|
|---|---|---|---|---|---|
|**PUBINV/TOTEXP**|-2.91|-6.8<sup>**</sup>|-2.91|-9.77<sup>**</sup>|-3.61|
|**TAXREV**|-1.79|-4.64<sup>**</sup>|-1.79|-4.64<sup>**</sup>|-3.74|
|**ODA/GDP**|-2.91|-7.26<sup>**</sup>|-2.89|-8.75<sup>**</sup>|-3.58|
|**LAB**|-2.91|-6.8<sup>**</sup>|-2.91|-9.77<sup>**</sup>|-3.61|
|**EXP/GDP**|-1.79|-4.64<sup>**</sup>|-1.79|-4.64<sup>**</sup>|-3.74|

Note: ADF is the augmented Dickey Fuller test and PP is Phillips-Perron test. The null hypothesis is that the series is non-stationary. The asterisks (**) represent a rejection of the null hypothesis at 5% level. PubInv represents public investment as a share of total government expenditure, TaxRev is the ratio of tax revenue to GDP, PubInv_T is PUBINV/(100-PUBINV/TOTEXP_T is (PUBINV/TOTEXP)/(100-PUBINV/TOTEXP), TAXREV_T is TAXREV/(100-TAXREV), ODA is the ratio of official development assistance to GDP, Labor is total labor force and Export is the ratio of total exports in GDP.

Table 3 presents the estimated ADF statistics. All variables appear to have at least one unit root, since the nonstationarity test is not rejected for these variables in levels. To check for a second unit root we carried out a further unit root tests on first differences of the variables. When the variables are differenced once, all of the ADF statistics are significant at one percent level. Now, we can proceed with the analysis under the assumption that no variable contains more than one unit root and the first difference of each variable is stationary. Table 2 includes columns that describe the value for the test 25
Section 3 then tries to draw a link between the heightened integration and the accelerated growth and poverty reduction. Individual cases, cross-country statistical analysis, micro evidence from firms, and opinion surveys in developing countries all suggest that opening up to trade and direct investment has been a good strategy for such countries as China, India, Mexico, Uganda and Vietnam _._ My conclusions for policy are very much in the spirit of the comments from Presidents Fox and Museveni. Developing countries have a lot of “homework” to do in order to develop in general and to make effective use of integration as part of their development strategy. Rich countries could do a lot more with foreign aid to help with that homework. And, as Museveni indicates, access to rich country markets is important. There remains a lot of protection in OECD markets against the goods and people of the developing world, and globalization would work much better for the poor if developing countries and their people had freer access to those rich country markets.

# **_1. Growing integration between north and south_**

Global economic integration has been going on for a long time. In that sense globalization is nothing new. What is new in this most recent wave of globalization is the way in which developing countries are integrating with rich countries. As in previous waves of integration, this change is driven partly by technological advances in transport and communications, and partly by deliberate policy changes.

4
poor treated and control villages. We confirm that these patterns in the experimental sample are consistent with nationally representative data by comparing village-level unit values for the range of commonly consumed food items measured in national household expenditure surveys across the phased 6-year rollout of the program.<sup>7</sup> The size of the aggregate income transfer at the village level, determined by program saturation, is an important factor in determining the extent to which local prices shift. The transfers amounted to an aggregate increase in village income of about 15 percent. (Median saturation in the study sample is 65 percent, but in some villages as many as 95 percent of the households received the transfer.) This increase in village income did not affect the prices of storable and easily traded food goods but raised the local prices of protein-rich perishable foods, such as eggs and fresh fish, by 6 to 8 percent. The rise in relative prices decreases nonbeneficiaries’ real income and leads them to substitute away from protein-rich foods thus creating significant negative effects on the nutritional status of children in nonbeneficiary households.

We show that young nonbeneficiary children experienced a 0.4 standard deviation decrease in height-for-age z-scores (relative to a control mean of -1.1 standard deviations), which led to an 11-percentage point increase in stunting (relative to a control mean of 32 percent). Children in beneficiary households, which were compensated for the price change by the increase in household income, show a gain in nutritional status. The price increases for non-tradable goods and the reductions in child growth both increase in the proportion of households eligible for the transfers. This finding is consistent with our interpretation of the stunting increase as an unintended spillover effect of the program through local food markets.

Perhaps surprising at first glance, these impacts on child growth translate to estimated elasticities very much in line with the broader literature and consistent with changes in overall consumption patterns. Our results are also consistent with a nascent literature that highlights the particularly important role of animal-sourced proteins in avoiding child stunting (Heady, Hirvonen, and Hoddinott 2018). Using estimates from the nutrition literature of the elasticity of child height to eggs (Puentes et al. 2016; Iannotti et al. 2017), we show that the observed reduction

> 7 Unit values are defined as the reported expenditure on a particular commodity or commodity group divided by the reported quantity consumed. While unit values combine price effects and quality effects, many of the key goods we examine, including regular commercially-milled rice and eggs, are fairly homogeneous with relatively little scope for quality differentiation.

4
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Although power underpricing costs Cameroon about $266 million each year, or 1.2 percent of the country’s GDP, Cameroon’s power utility is actually doing better than the average for other resource-rich countries, where underpricing of power is commonplace (figure 30). It is estimated that the average historical cost of producing electricity is $0.17 per kilowatt-hour in Cameroon, while the average effective tariff, as of 2009, was $0.10.

Average tariffs charged by CDE, the water utility, stood at $0.70 per

**Figure 30. Underpricing of power and water in Cameroon**

Financial burden of underpricing, as percentage of GDP <!-- Start of picture text --> 2.0<br>1.7<br>1.5 1.2<br>1.0<br>0.5<br>0.1 0.1<br>0.0<br>Power Water<br>Cameroon LIC-Nonfragile<br>Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GDP = gross domestic product; LIC = low-income country.

cubic meter in 2009, far less than the estimated average cost-recovery tariff of $0.97 per cubic meter. The macroeconomic burden of undercharging for water services is 0.1 percent of GDP, significantly lower than the burden imposed by undercharging for power, but still slightly higher than that in other resourcerich countries.

**Figure 31. Household consumption of electricity in Cameroon is highly differentiated by income**

Prevalence of connection to power grid among population, by income quintile <!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>Power<br>% population<br><!-- End of picture text --> Source: Banerjee and others 2009. Note: Q1 – first budget quintile, Q2 – second budget quintile, etc.

Because of inequitable access to power and water services in Cameroon, subsidized tariffs are regressive. The majority of households that have electricity belong to the top quintiles of the income distribution—indeed; household connections to the electricity grid are nonexistent for poorer households (figure 31). This inequitable distribution virtually guarantees that any price subsidy for power will be regressive, as is common in Africa for both power and water (figure 32).

How expensive would utility bills become if cost-reflective tariffs were applied? With a costrecovery tariff of $0.17 per kilowatt-hour and a monthly subsistence consumption of 50 kilowatthours, the associated utility bill would come to $8.50 per month. Based on the distribution of household budgets in Cameroon, monthly utility bills at these levels would be affordable to everyone (figure 33). A bill for subsistence consumption of 10 cubic meters of water would come to $10 per month. The combined bill for electricity and water at these levels would be affordable to close to 80 percent of the population. A more limited level of subsistence consumption of 25 kilowatt-hours per month for power and 4 cubic meters per month for water—enough to meet the most basic needs—would cost $4 per month each and would be affordable to 100 percent of the population.

43
All estimated coefficients are highly statistically significant revealing that market size is positively correlated with quality. Applying the weighted or great-circle distance does not change substantively the result. We can conclude that there seems to be evidence that the market size differences affect the quality composition of trade via the competition effect described above.

**Table 8:** **_Quality and Market Size Effect_**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Wei. Dist.|0.136***|0.136***|||
||(0.020)|(0.020)|||
|Geo. Dist.|||0.112***|0.112***|
||||(0.017)|(0.017)|
|GDP Ratio|0.137***||0.142***||
||(0.011)||(0.010)||
|Pop Ratio||0.118***||0.122***|
|||(0.009)||(0.009)|
|Observations|857747|857747|857747|857747|
|R-squared|0.540|0.540|0.540|0.540|

Notes: All regressions are run with importing and exporting country fixed effects as well as product fixed effects, but for which the coefficients are not reported; G.C. Dist. and Wei. Dist. stand for the log of great-circle distance and the log of weighted distance, respectively; GDP Ratio and Pop Ratio denote the ratio (in logs) of GDP and of population between the importing and exporting country; ***,**,* denotes significance at the 1 %, 5 %, and 10 % level, respectively; robust standard errors, which are in parentheses, are corrected for clustering by country pair.

**Table 9:** **_Trade Costs and Market Size Effect by Country_**

||(AT)|(BE)|(DE)|(DK)|(ES)|(FI)|(FR)|(GB)|
|---|---|---|---|---|---|---|---|---|
|Wei. Dist.|0.176**|0.036|0.004|0.173***|0.127|0.118**|0.078**|0.145**|
||(0.081)|(0.029)|(0.055)|(0.048)|(0.098)|(0.054)|(0.035)|(0.050)|
|GDP Ratio|0.079**|0.153***|0.127***|0.069***|0.074***|0.007|0.129***|0.092***|
||(0.027)|(0.029)|(0.021)|(0.023)|(0.020)|(0.025)|(0.013)|(0.025)|
|Observations|46631|78659|109120|49025|64943|26917|89671|82048|
|R-squared|0.622|0.620|0.646|0.610|0.593|0.628|0.610|0.603|
||(GR)|(IE)|(IT)|(LU)|(NL)|(PT)|(SE)||
|Wei. Dist.|0.211|0.253*|0.210**|0.024|0.017|-0.136|-0.017||
||(0.163)|(0.135)|(0.093)|(0.112)|(0.035)|(0.103)|(0.100)||
|GDP Ratio|-0.070|-0.032|0.073**|0.034|0.124***|0.087**|0.062||
||(0.060)|(0.053)|(0.030)|(0.082)|(0.026)|(0.035)|(0.037)||
|Observations|17026|25897|81961|20453|85855|29827|49714||
|R-squared|0.607|0.622|0.616|0.680|0.619|0.598|0.612||

Notes: All regressions are run with importing and exporting country fixed effects as well as product fixed effects, for which the coefficients are not reported; Wei. Dist. and GDP Ratio stand for the log of weighted distance and the ratio (in logs) of GDP between the importing and exporting country, respectively; ***,**,* denotes significance at the 1 %, 5 %, and 10 % level, respectively; robust standard errors, which are in parentheses, are corrected for clustering by country pair.

In Table 9 we present the regression results for all 15 EU countries individually. One observes that the distance and GDP coefficients vary from country to country. In most cases, both 31
contracts if the different contexts have unobserved characteristics that influence its choice. Quoting Armendariz de Aghion and Morduch (2005), “The best evidence would come from well-designed, deliberate experiments in which loan contracts are varied but everything else is kept the same.” This is precisely the goal of the paper. We use a randomized control trial conducted by the Green Bank of Caraga in the Philippines to evaluate the relative impact of group versus individual liability on client repayment and its overall profitability. Half of Green Bank’s existing grouplending centers in Leyte, an island in central Philippines, were randomly converted to individual liability in three waves between August 2004 and May 2005. Using this conversion methodology, we are able to assess whether group liability overcomes information asymmetries and ultimately impact on access to credit.

The separation of adverse selection from moral hazard is one of the most difficult empirical challenges when studying information asymmetries in credit markets.<sup>2</sup> In this paper, we isolate the effects from moral hazard by “surprising” existing group liability members, already peer-screened under group liability. We therefore address the question of whether the shift to individual liability exacerbates moral hazard by focusing on existing clients.

Whereas the literature has focused almost exclusively on the superiority of group liability in ensuring repayment, we find no change in repayment for those centers converted to individual liability, and higher growth both due to fewer dropouts and more new clients. In addition, we find that new entrants to the program had _looser_ social ties to the prior members, suggesting that the screening imposed by group liability was limiting the growth of the existing programs. We find direct evidence that individual liability leads to less monitoring of each other’s loan performance (although as noted, this lowered monitoring does not lead to higher default). Lastly, we find that

> 2 See Karlan and Zinman (2006) for a similar separation of adverse selection from moral hazard in credit markets.

4
In the following section the main predictions of our model are tested: First, we compare the quality of goods produced and sold domestically versus exported goods (Result 2). Second, market size effects are analyzed (Result 3). Third the influence of trade costs on the quality level of export flows is studied (Result 4). Fourth, we verify the claim that quality and the composition of trade are related (Result 5).

# **3.2 Domestic versus Foreign Sales**

Testing our first hypotheses requires detailed data on domestically produced and sold products. Not only is the value of a product needed, but also its quantity. For European countries, these data are not available to our knowledge. However, in the USA the U.S. Census Bureau in partnership with the Bureau of Transportation Statistics of the U.S. Department of Transportation established for the year 2002 the Commodity Flow Survey (CFS) which contains detailed data on shipments by domestic establishments in manufacturing, wholesale, mining, and selected other industries. The data cover domestic shipments, available up to the four digit level of the Standard Classification of Transported Goods (SCTG), as well as exports, available up to the two digit level of SCTG. The data are recorded in values (millions US dollars) and quantities (thousands of tons).

In a first step, we compare the unit value of domestic and foreign shipments at the highest common level, the two digit level. Table 2 presents the comparison of both flows for all manufactured product groups within the SCTG classification. The last column reports the difference in percentage between the unit values of domestic and foreign shipments. The results indicate that the average unit value (U.V.) of domestically transported goods is substantially lower than the average unit value of exported goods. The only exception is the product group 36 SCTG (Motorized and other vehicles, including parts) which has the approximately the same unit value for internal shipments and exports.

It has to be noted that these shipment data do not satisfy completely our needs. Products, which are shipped domestically, are not only those that are manufactured within the US, but also abroad. This problem becomes especially worrisome for product groups in which the US is a net importer. Comparing domestic and foreign shipments therefore has to be taken with 22