rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Share of households who sent a work migrant, by region, location, and year**

||**2005/06**|**2009/10**|**2010/11**|**2011/12**|
|---|---|---|---|---|
|_All households_|_0.03_|_0.05_|_0.13_|_0.13_|
||(0.20)|(0.22)|(0.31)|(0.33)|
|_Regions_|||||
|Kampala|0.06|0.03|0.17|0.18|
|Central|0.04|0.06|0.14|0.17|
|Eastern|0.02|0.03|0.11|0.11|
|Northern|0.02|0.02|0.09|0.09|
|Western|0.04|0.07|0.16|0.16|
|_Rural/urban_|||||
|Rural|0.03|0.05|0.12|0.12|
|Urban|0.05|0.03|0.15|0.16|

_Source: Authors’ calculations with UNPS. Standard deviations reported in parentheses._ Table 3 relies on Equation (1) and Equation (2) to estimate the lagged correlates associated with a household’s decision to send a work migrant (while excluding the other potential drivers, �� � , as covariates).<sup>8</sup> The results in column (1) reveal that, on average, migrant-sending households are more likely to be headed by a woman<sup>9</sup> and by men who are more educated. In addition, having a larger relative supply of household labor, including adult women and men, is associated with a higher probability of sending out a work migrant in the next survey wave. Living in an urban area, meanwhile, is not correlated with sending out a work migrant. The OLS results also point to spatial variation in sending households: those found in poorer regions of Uganda (Eastern and Northern) are three to five percentage points less likely to send work migrants when compared with households in Kampala. The bulk of these variables fail to retain their significance after controlling for household fixed effects in column (2).

> 8 Lagged summary statistics for work migrant sending and non-sending households can be found in Appendix Table 1.

> 9 We distinguish here between _de facto_ female heads of household, who report being married, and _de jure_ female heads who report being single, divorced, or widowed (with male heads of household serving as the reference category).

8
aggravated because the absence of the test for one of the two partners implies that the entire couple cannot be used as an observation. If one makes the hypothesis that HIV positive individuals are more likely to refuse the test, it is possible to imagine a scenario where if men are more likely to refuse, this would reduce the number of discordant male couples in the sample and therefore increase the proportion of discordant female couples. Recent studies (Mishra and others 2006; Obare 2006 and Reniers and others 2006), however, suggest that refusal to be tested does not appear to be an important source of bias in estimating HIV prevalence.

Nevertheless, tables 8 and 9, constructed on the model of tables 2 to 4, take seriously the possibility of bias due to the coverage of the HIV test. Table 8 deals with the coverage of the HIV test and with cases where the test result is absent. It displays the proportion of couples in which both partners have a test, couples in which both do not have a test, couples in which the man has no test but the woman has one and couples where only the woman has no test. The coverage of the test is generally high, especially in Burkina Faso (92.1) and Cameroon (93.9) where there are test results for both partners in the union in more than 90 percent of the cases. The absence of the test result, however, is more likely among males, not so much in Cameroon, but more substantially in Burkina Faso, Kenya and Tanzania and especially in Ghana.

Table 9 looks at the refusal of the test by individuals<sup>9</sup> . This is the cause for the absence of a test which is the most likely to induce bias. Table 9 displays the proportion of couples in which both partners did not refuse to be tested, couples in which both refused, couples in which the man refused but the woman accepted and couples where only the woman refused. On the one hand, the results indicate that males are more likely to refuse the test, in particular in Burkina Faso and Ghana. On the other hand, the acceptance rate of the HIV test is relatively high: the fraction of concordant negative couples where both partners did not refuse is above 90 percent in Burkina Faso (95.0), Cameroon (94.4) and Ghana (90.5). It is somewhat lower in Kenya (81.6 percent), but in Kenya the percentage

> 9 Notice that the different reasons for the absence of the test are not reported in the Tanzania AIS 20032004, only whether or not the sampled individual has a test result is reported. This explains why no result can be reported for Tanzania in table 9.

14
<!-- Start of picture text --> a i \ — AGADEZ |<br>| |<br>Ay is<br>‘ge nee Base 7 i J<br>AW. ef \"s<br>sar ad ? inte me ee _— itor<br><!-- End of picture text -->

<!-- Start of picture text --> Current Irrigation (% Area)<br>271% 6 1 = 5% 6 > 5%<br>U Dams<br><!-- End of picture text --> <!-- Start of picture text --> . a Y Z 2”<br>. | 7<br>~<br><!-- End of picture text -->

~ Do Large scale IRR <= 6% Large scale IRR <= 12% BB Laree scale IRR <+ 20% BD Large scale IRR > 20% BD sma este rR c= 10% Small scale IRR <= 30% W small scale IRR <= 50% BD small scale me > 50%
development indicators and firm growth in what follows, relationships that vary with per capita income.

Our approach relies on a measure of firm growth from the World Bank Enterprise Surveys (WBES) database – the percentage increase in the number of workers over the two years prior to the survey. While labor growth is a topic of intense interest, there are also practical reasons for focusing on that measure in this analysis.<sup>4</sup> Other measures of firm growth (such as sales growth) are also available, but our measure of labor growth is available for a much larger set of firms and countries. Testing the hypothesis that financial structure affects firm growth differentially, depending on a country‘s level of economic development, requires as wide a sample of countries as possible, since the financial structure indicators are measured at the country level. Our most expansive regression models employ information from over 49,000 firms in 89 countries.<sup>5</sup> Relying on labor (rather than sales) growth has another side advantage: labor is likely to be measured with less error than sales for both accounting and tax reasons, and such measurement errors may differ systematically by the level of development.

To foreshadow our main result, we find that firms grow faster in countries with low levels of income per capita when the banking system is relatively well-developed. We find no such results for other measures of financial structure, including measures of stock market development. Moreover, we find no strong relationships between financial structure and firm labor growth for countries with high levels of per capita income, in line with previous findings in the literature (Levine, 2002; Demirguc-Kunt and Levine, 2001). Our results are robust to using the instrumental variables method to deal with the potential endogeneity of our financial structure variables.

We also apply the regression methodology pioneered by Rajan and Zingales (1998) (henceforth RZ) to test whether firms in industries that rely heavily on external financing have higher labor growth rates in countries with relatively well developed financial sectors. We find evidence consistent with that proposition, but only for firms in low-income countries with

> 4  For example, job growth is the topic of the upcoming World Development Report for 2013, and a key concern of the current U.S. administration.

> 5 Not only the firm-level survey information, but also a steady expansion in the number of countries that have financial structure data available enables us to undertake this analysis. For comparison, the original financial structure indicators presented in Levine (2002) were available for only 48 countries.

5
# **Figure 9. Manufacturing: ISI LatAm Countries and South-East Asia**

<!-- Start of picture text --> (a) As Share of GDP (b) As share of Exports<br>30 80<br>28 70<br>26<br>60<br>24<br>50<br>22<br>40<br>20<br>30<br>18<br>16 20<br>14 10<br>12 0<br>LatAm ISI South East Asia LatAm ISI South East Asia<br>1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text --> Note: Countries in South-East Asia includes the Republic of Korea, Malaysia and Thailand; countries in LatAm ISI includes Brazil, Colombia and Mexico. _Source: WDI._
25. Differences in the economic performance of East Asia and Latin America can also be illustrated using EXPY and PRODY indicators.<sup>9</sup> EXPY is a measure of the export sophistication of the product basket for each country. Technically speaking, it is the sum of the country's export share of each product weighted by the PRODY measurement for that product. The PRODY index is the sum of weighted per capita GDPs of all nations for each product, with the weight being the country's export share for the product divided by the aggregate of all nations' export share for the product.<sup>10</sup> 26. Figure 7 shows that China has been steadily upgrading their export basket during the past twenty years. The EXPY of Mexico, the highest in Latin America until 2010, slowly increased during the 1990s but stagnated in the past decade. Costa Rica, thanks to the establishment of the Intel semiconductor assembly and test plant in 1996 (Box 1), has leap-frogged all of Latin America to become the nation with the highest EXPY in the region. Colombia has also upgraded its export basket, achieving the same increase in EXPY as China and India but in less time. Argentina and Brazil‘s EXPY‘s, however, have stagnated during this period. On the bottom of the spectrum is Chile, whose EXPY has actually declined in the past decade, despite strong economic growth, owing to excessive reliance on copper and other commodities.

<!-- Start of picture text --> Figure 7: EXPY Trends for Latin America and China<br>20000<br>Costa Rica<br>18000<br>Mexico<br>16000<br>Argentina<br>14000 Brazil<br>Chile<br>12000<br>Colombia<br>10000<br>China<br>8000<br>Source: World Bank calculations based on Comtrade.<br>EXPY<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> 27. Also, in Mexico and Central America the value of medium and high tech exports as a percentage of total export value is considerably higher than their value added as a percentage of

> 9 The PRODY index is a weighted average of the per capita GDPs of countries exporting a given product, and thus represents the income level associated with that product. The index represents a weighted average of per-capita GDPs. EXPY is a weighted average of the PRODY for that country, where the weights are simply the value shares f the products in the country‘s total exports.

> 10 The intuition behind the _PRODY_ index is that it would show which products are more 'sophisticated,' i.e. which products tend to be exported by higher-income countries.  It is clear then that the _EXPY_ metric would be highly coordinated with high-income countries.  But there are outliers such as China and India, whose _EXPY_ measurement is much higher than their GDP per capita measurements should indicate.

10
- 7 -

Beijing, Tianjin, Guangdong and Liaoning spent almost 1.5 times more than the poorest provinces such as Guizhou, Guangxi, or Henan.

_Table 3 Drop out Rates by Area and by Gender, 1992_

|||_Rural_|||_Urban_||
|---|---|---|---|---|---|---|
|_Grade_|_Male_|_Female_|_Total_|_Male_|_Female_|_Total_|
|Primary<br>level|||||||
|1|37.8|31.9|33.8|21.7|42.1|31.0|
|2|21.1|22.7|22.2|8.7|10.5|9.5|
|3|17.4|21.4|20.1|43.5|15.8|31.0|
|4|13.4|13.5|13.4|17.4|21.1|19.0|
|_5_|9.4|8.6|8.8|8.7|5.3|7.1|
|6|1.0|1.9|1.6|0.0|5.3|5.3|
|Secondary<br>level|||||||
|7|29.5|33.4|31.8|23.5|22.5|23.1|
|8|55.1|53.7|54.3|47.1|65.0|53.7|
|9|15.4|12.9|13.9|29.4|12.5|23.1|

Source: SSB: Situation of Children, 1992.

**(c)** Access to **education** by **provinces.** The foregoing is substantiated by the differences in enrollment rates for primary, secondary and higher education by quintile of provinces in Table 2. The attendance rates at the primary level are, by and large, similar irrespective of poor or rich area, confirming the earlier observation from national data of universal entry at this level of education. But the enrollment-rate disparity between rich and poor provinces grows as students move on to secondary and higher levels. Such disparity is particularly acute among the 12-14 and 14 and older age groups-corresponding to post-primary education--than among the 7-11 age group. Urban-rural differentials are also pronounced, and this can be attributable to differences in income and quality. Primary education that corresponds to age range 7-11 is compulsory in China, but attendance is not universal in rural area particularly among the females. Urban children and those from high-income provinces are much more likely to attend secondary school (about 2.2 times) compared to children from rural area and low-income provinces. In the absence of compulsion in the subsequent age groups the gap in attendance rates widens steadily. Higher education is mostly received by households in richer provinces.
The first wave of the survey, conducted between August and December of 2007, included 658 HIV households and 341 comparison households. In the second wave, one year later, HIV households that could not be found and interviewed were not replaced, but comparison households were. The panel consists of 896 households interviewed in both waves: 616 HIV households and 280 comparison households. At the individual level, there are 616 identified HIV positive patients, 2579 individuals living in HIV households but not identified as HIV positive, and 1431 individuals belonging to comparison households.

In addition to the living standards and health questionnaire, we collect biomedical records for HIV positive individuals at the health facility, allowing an objective measure of the health and treatment status of patients. These biomedical records include the date of treatment initiation as well as the dates, weights and CD4 counts at the last visits (up to 7) by the patient at the health facility. There are 498 observations in this biomedical record dataset, out of the 616 potential patients identified in the living standards and health survey.

# **3. Is there an impact of the food crisis on the welfare of households in Mozambique?**

The first step of our analysis is to assess whether the food crisis had an impact on the welfare of households in Mozambique, and, in particular, on their nutritional status. Indeed, if the crisis lowers the income of households, then access to food could be restricted which will have a negative impact on adherence to treatment or health outcomes.

As mentioned in the introduction, a food crisis, namely, the drastic increase in the price of food products, affects different types of households differently. An urban household is mainly a net consumer of food and is therefore affected negatively by a price increase, whereas a rural household can be affected positively if he is a net seller of food. In the data, health facilities in which HIV patients are found for the survey are identified as rural or urban. Using this definition of urban or rural, we find that only 5% of the sample is rural, which is consistent with the fact that most HIV positive 7
# **Reference**

- (2013). New Zealand FOODfiles 2012 Manual. Palmerston North, New Zealand Institute for Plant & Food Research Limited.

- Angeles, I. T., W. J. Schultink, et al. (1993). "Decreased rate of stunting among anemic

- Indonesian preschool children through iron supplementation." Am J Clin Nutr **58** (3): 339-342.

- Arimond, M., C. Hawkes, et al. (2011). Agricultural Interventions and Nutrition: Lessons from the Past and New Evidence  FAO.

- Christiaensen, L. and H. Alderman (2004). "Child Malnutrition in Ethiopia: Can Maternal

- Knowledge Augment the Role of Income?" Economic Development and Cultural Change **52** (2): 287-312.

- Englberger, L., A. Lorens, et al. (2011). Approaches and Lessons Learned from Promoting Dietary Improvement in Pohnpei, Micronesia. FAO.

- FAO, WFP, et al. (2012). The state of food insecurity in the world. Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition, Food and Agriculture Organization of the United Nations. Rome. Italy.

- Ferro-Luzzi, A., N. Norgan, et al. (1975). " Food intake, its relationship to body weight and age, and its apparent nutritional adequacy in New Guinean children.  ." Am J Clin Nutr **<u>28</u>** : 1443-1453.

- Gibson, J. (1999). "Can women’s education aid economic development? The effect on child stunting in Papua New Guinea." Pacific Economic Bulletin **14** (2): 71-81.

- Gibson, J. and S. Rozelle (1998). "Results of the household survey component of the 1996 poverty assessment for Papua New Guinea. Report submitted to the World Bank, Port Moresby, Papua New Guinea. Institute of National Affairs, Port Moresby, Papua New Guinea.".

- Gibson, R. S., A. Heywood, et al. (1991). "Growth in children from the Wosera subdistrict, Papua New Guinea, in relation to energy and protein intakes and zinc status." Am J Clin <u>Nutr</u> **53** (3): 782-789.

- Lagania, W., D. Mokelab, et al. "Papua New Guinea: real progress towards MDG4 and real challenges.".

- Maraga, S., A. Sie, et al. (2010). "Population Hemoglobin Mean and Anemia Prevalence in Papua New Guinea: New Metrics for Defining Malaria Endemicity?

Nicolas Senn mail, ." PLoS ONE **2010** (5): 2.

- Marie T Ruel, Harold Alderman, et al. (2013). "Nutrition-sensitive interventions and

- programmes: how can they help to accelerate progress in improving maternal and child nutrition?" Lancet **382** (9891): 536-551.

- National Department of Health (2013). Consultancy National Nutrition Policy. .

- Omot, N. (2012). Food security in East Timor, Papua New Guinea and Pacific island countries and territories (ACIAR Technical Report 80). A. C. f. I. A. Research. Canberra, Australia.

- Oomen HAPC (1971). "Ecology of human nutrition in New Guinea. ." Ecol. Food Nutr. 1971, <u>1:1.</u> **<u>1</u>** (1).

- Rowland, M., T. Cole, et al. (1977). "A quantitative study into the role of infection in determining nutritional status in Gambian village children. ." British Journal of Nutrition **37** : 441-450.

14
Figure 1 shows the evolution of total net assets (TNAs) in all equity and bond funds by region. Panel A plots total assets for equity funds between January 1996 and December 2000 and between June 2001 and November 2010.<sup>18</sup> Panel B displays total assets for bond funds between July 2002 and November 2010. The figure shows not only the large increase in total assets over time, but also the sharp declines around crises, particularly around the Asian and Russian crises and the global financial crisis. A similar pattern is observed for bond funds. The figure also shows that, as a group, bond funds are much smaller than equity funds (100 versus 599 billion U.S. dollars in November 2010), even though the mean fund is of a similar size.

It is interesting to observe not only the variation in TNAs but also that of country weights, for which we focus on the period around the global financial crisis. Figures 2 and 3 show the weights for equity and bond funds, respectively, with global funds at the top and global emerging funds at the bottom. The figures illustrate the evolution of weights for some of the main regions of investment within emerging and developed countries. In particular, they show the weights in: (i) emerging economies (emerging Asia, emerging Europe, and Latin America), developed Europe, and North America for global funds and (ii) emerging Asia, emerging Europe, and Latin America for global emerging funds. The figures also mark some of the main events around the global crisis: the nationalization of Northern Rock, the collapses of Bear Stearns and Lehman Brothers, and the near collapse of AIG.

Figures 2 and 3 show several noteworthy features of the data. First, weights fluctuate substantially over time. Second, there are significant reallocations across regions, especially at times of stress. For example, the figures for equity funds show that, even though the epicenter of the crisis was in the U.S., managers started liquidating their exposure to emerging economies after the collapse of Bears Stearns while they increased their exposure to North America. This is consistent with a relatively smaller collapse in some asset prices in the U.S. than, for instance, in emerging Asia. Only in early 2009 managers started reversing that trend. Among global emerging funds, managers sold their positions in emerging Europe and Latin America and moved to emerging Asia. For example, between June 2008 and July 2009 the mutual fund exposure in Asia increased from 45% to 55%, while it decreased from 14% to 9% in emerging Europe (after having dropped to 7%) and from 24% to 21% in Latin America. Among bond funds, the large substitution took place between developed Europe and North America in

> 18 This time split is important due to the relevance of global equity funds. EPFR Global starts reporting information for global equity funds in June 2001. The introduction of this type of funds adds nearly 90,000 million U.S. dollars to the total assets of all equity funds.

9