rafmacalaba/datause-encoder-data huggingface dataset:
- 21 -

Table 3. Sub-Saharan African Exports of Textiles and Clothing 1/

||_Val_<br>_(in _<br>_milli_|_ue_<br>_US$_<br>_on)_|_Growth_<br>_(annual_<br>_average)_|_EE_|_C15_|_Sh_<br>_United_|_are_<br> _States_|_Develo_|_ping2/_|
|---|---|---|---|---|---|---|---|---|---|
||_1990_|_1999_|_1990-99_|_1990_|_1999_|_1990_|_1999_|_1990_|_1999_|
|Textilefibres|1328|1413|0.7|36.8|24.8|0.0|0.7|49.8|67.3|
|Textileyarn,fabricsetc|228|206|-1.2|57.7|64.4|16.6|3.5|24.5|29.1|
|Clothingandaccessories|947|1780|7.3|70.3|54.1|6.1|20.6|19.8|23.6|
|<br>**Total**|**2503**|**3398**|**3.5**|**47.8**|**42.6**|**6.8**|**11.3**|**37.5**|**42.1**|

Source: World Bank 1/ Excludes countries in the South Africa Customs union 2/ Excludes countries in Africa Table 4. Top Sub-Saharan Exporters of Apparel to the United States, 1990 and 1999

||_1990_|||_199_|_9_||
|---|---|---|---|---|---|---|
|_Country_|_Value_<br>_(inUS$ _|_Share_<br>_million)_|_Country_|_Value_<br>_(inUS$million)_|_Share_|_Growth_<br>_(annualaverage)_|
|Mauritius|131.3|70.2|SouthAfrica1/|253.7|40.9|26.6|
|SouthAfrica1/|30.3|16.2|Mauritius|246.0|39.7|7.2|
|Zimbabwe|7.7|4.1|Madagascar|49.1|7.9|68.6|
|Mozambique|6.0|3.2|Kenya|42.1|6.8|33.0|
|Malawi|5.3|2.8|Zimbabwe|18.6|3.0|10.3|
|Kenya|3.2|1.7|Ghana|3.8|0.6|52.6|
|Tanzania|0.9|0.5|Tanzania|2.9|0.5|14.2|
|Coted'lvoire|0.5|0.3|Malawi|1.5|0.2|-13.2|
|Madagascar|0.4|0.2|Seychelles|1.0|0.2|n.a.|
|Burundi|0.2|0.1|Coted'lvoire|0.6|0.1|0.9|
|Comoros|0.2|0.1|Comoros|0.4|0.1|9.2|
|Mauritiania|0.2|0.1|Mali|0.1|0.0|-1.2|
|Mali|0.2|0.1|Nigeria|0.1|0.0|2.2|
|SierreLeone|0.1|0.1|Sierra Leone|0.1|0.0|-1.4|
|Total|187.2|100.0|Total|620.3|100.0|14.2|

Source: World Bank.

Note: n.a. indicates that data are not available.

1/ Includes all countries in the South Africa Customs Union.
||7b<br>W<br>in<br>g|hen applicable, explanation of any<br>terim analyses and stopping<br>uidelines||**N/A**|
|---|---|---|---|---|
|**Randomization:**|||||
|**Sequence**<br>**generation**|8a|Method used to generate the<br>random allocation sequence||**Methods – paragraph 8**|
||8b|Type of randomization; details of<br>any restriction (such as blocking<br>and block size)|Details of stratification or<br>matching if used|**Methods – paragraph 8**<br>**Figure 2**|
|**Allocation**<br>**concealment**<br>**mechanism**|9|Mechanism used to implement<br>the random allocation sequence<br>(such as sequentially numbered<br>containers), describing any steps<br>taken to conceal the sequence<br>until interventions were assigned|Specification that allocation was<br>based on clusters rather than<br>individuals and whether allocation<br>concealment (if any) was at the<br>cluster level, the individual<br>participant level or both|**Methods – paragraphs 5, 6, 7 & 8**<br>**Figure 2**|
|**Implementation**|10|Who generated the random<br>allocation sequence, who enrolled<br>participants, and who assigned<br>participants to interventions|Replace by 10a, 10b and 10c||
||10a||Who generated the random<br>allocation sequence, who enrolled<br>clusters, and who assigned clusters<br>to interventions|**Methods – paragraph 8**|
||10b||Mechanism by which individual<br>participants were included in<br>clusters for the purposes of the<br>trial (such as complete<br>enumeration, random sampling)|**Methods – paragraphs 6 & 7**|
||10c||From whom consent was sought<br>(representatives of the cluster, or<br>individual cluster members, or<br>both), and whether consent was<br>sought before or after<br>randomization|**Methods – paragraph 2**|
|**Blinding**|11a|If done, who was blinded after<br>assignment to interventions (for<br>example, participants, care<br>providers, those assessing<br>outcomes) and how||**Non-blinded trial**<br>**Discussion – Paragraph 8**|
||11b|If relevant, description of the<br>similarity of interventions||**N/A **|
|**Statistical**<br>**methods**|12a|Statistical methods used to<br>compare groups for primary and<br>secondary outcomes|How clustering was taken into<br>account|<br>**Methods – paragraphs 18, 19 &**<br>**20**|
||12b|Methods for additional analyses,||**Methods –paragraphs 20 & 21**|

51
1c: Risk of Death From Drought (Per Million – Log Scale) <!-- Start of picture text --> 1,000.00<br>100.00<br>10.00<br>1.00<br>0.10<br>0.01<br>1960 1970 1980 1990 2000<br><!-- End of picture text -->

- 1d: Risk of Being Affected by Drought (Per Million – Log Scale)

<!-- Start of picture text --> 100,000.00<br>10,000.00<br>1,000.00<br>100.00<br>10.00<br>1.00<br>1960 1970 1980 1990 2000<br><!-- End of picture text --> 38
26 Table 2.5: INDEX OF POLITICAL PLURALISM

|=<br>|E~~.*.*..*-**.*-|*.*|.'19|85.8|
|---|---|---|---|---|
|EIAL|||||
||LIC|_5.1_|_5.1_|_5.1_|
||SSA|5.2|5.0|5.4|
|OAL|||||
||LIC|5.7|5.7|6.0|
||SSA|6.0|_5.9_|6.2|
|NAL|||||
||LIC<br>|_5.1_<br>|_5.4_|5.3|
||SSA|5.4|_5.6_|5.6|

Notes: The index is a simple average of two indeces on political rights and civil liberties as reported in Gastil (1987) The political rights index measures the extent of a fully competitive electoral process. The civil liberties index measures the extent of freedom of expression of rational political opinion. In each scale a rating of (1) is the most free and (7) the least free.
to a university. The use of 2007 TLSS data allows me to include a set of controls for ethnicity and education level of his/er parents. Such control variables are not available in the 2003 data.

Next, to estimate whether students who lost on their education during the war were able to catch up between 2003 and 2007, I use pooled data from the 2003 and 2007 TLSS. To be consistent between the surveys, in the regression analysis of the pooled 2003 and 2007 data, I use the answers to the survey question on the highest level of schooling completed. In both surveys this question appears in Module 3, part B, question 5: “What is the highest diploma you have obtained? (do not include incomplete degrees)” with the following categories: none; primary (grades 1-4); basic (grades 1-8(9)); secondary general (grades 9-10(11)); secondary special; secondary technical; higher education; graduate school/aspirantura. Table 1 reports results of regressions using the 2007 TLSS data that control for an individual’s ethnicity and rural residence. All regressions include fixed effects at the raion level and are estimated with robust standard errors. The results from these regressions provide a longer-term perspective of the effect of the conflict on schooling as the 2007 data were collected about nine years after the end of the war in 1998. Two dependent variables are being used. The first dependent variable is equal to one if an individual completed at basic level of schooling or more and the second is equal to one if an individual completed secondary school or more (zero otherwise).

Looking at these two levels of completion separately allows us to understand at what level of education the conflict had the most impact in the long-run. The main coefficient of interest is the one estimated on the interaction between the war cohort dummy (born in 1976-1985) and living in an area more affected by the conflict.

The regression results (Table 1, Panel B: Col. 5) suggest that women from the war cohort and who lived in the affected regions are about 2.3 percentage points less likely (significant at 5% level) to complete at least nine years of mandatory schooling as compared to women of the same age who live in the lesser affected areas. The effect decreases to -3.1 percentage points (significant at 1% level) when I 17
# **Table 3: Water Shortage Incidents and Average Duration of Incidents**

||**Labor**|**Productivity**|**(logs)**|
|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|
||**(1)**|**(2)**|**(3)**|
|Average length of water shortage, 0 if no shortage experienced|-0.007**||-0.007**|
||(0.003)||(0.003)|
|Total number of water shortage incidents over the last month, 0 if no shortage||0.001|0.002|
|||(0.009)|(0.007)|
|Log of total number of employees in regular month|-0.561***|-0.552***|-0.561***|
||(0.111)|(0.110)|(0.111)|
|Largest owner has secondary education or higher: Y/N|0.263**|0.280**|0.262*|
||(0.134)|(0.138)|(0.135)|
|Manager experience (years)|0.016**|0.015**|0.016**|
||(0.007)|(0.007)|(0.007)|
|Largest owner has a job in formal sector or looked for one over last 2 years Y/N|0.032|0.017|0.033|
||(0.136)|(0.136)|(0.135)|
|Largest owner started another business in the last three years Y/N|-0.109|-0.120|-0.107|
||(0.078)|(0.081)|(0.079)|
|Firm uses electricity for activity Y/N|0.098|0.097|0.099|
||(0.081)|(0.080)|(0.081)|
|Firm uses machinery in its current operations Y/N|0.028|0.040|0.029|
||(0.090)|(0.091)|(0.090)|
|Firm uses vehicles in its current operations Y/N|0.450***|0.470***|0.450***|
||(0.093)|(0.100)|(0.093)|
|Manufacturing firm Y/N|0.149*|0.133|0.148*|
||(0.085)|(0.081)|(0.085)|
|Produces or sells under contract from another business or person Y/N|0.373***|0.382***|0.373***|
||(0.132)|(0.134)|(0.132)|
|Firm uses bank account to run business Y/N|0.192|0.186|0.193|
||(0.120)|(0.121)|(0.121)|
|Firm has a loan at this time Y/N|-0.000|-0.013|-0.001|
||(0.072)|(0.075)|(0.072)|
|Firm pays informal payment to remain unregistered Y/N|-0.232|-0.225|-0.231|
||(0.168)|(0.175)|(0.168)|
|Firm experienced crime last month Y/N|0.052|0.060|0.050|
||(0.236)|(0.247)|(0.237)|
|Firm pays for security Y/N|-0.074|-0.077|-0.073|
||(0.174)|(0.175)|(0.174)|
|Constant|6.136***|6.074***|6.129***|
||(0.197)|(0.195)|(0.203)|
|Location (within country) Fixed Effects|YES|YES|YES|
|Number of observations|710|710|710|
|Adjusted R2|0.342|0.334|0.341|

note: *** p<0.01, ** p<0.05, * p<0.1, Hubert-White robust standard errors clustered at the region-sector level in brackets 26
While the magnitude of the effect may seem large, it is not implausible. Many sectors experience zero and close to zero inflows, and if we consider only positive flows of US FDI, the median 2004 value is 21 and 11 million dollars in the full and the developing country sample, respectively. Thus, the estimated 155% percent increase would mean an additional annual inflow of 17 million dollars for the median sector-country observation in the developing country subsample.<sup>21</sup> A quick look at the amounts multinational corporations actually invest in developing countries reveals that FDI inflows of that magnitude are not uncommon. For example, in 2005 Wal-Mart planned to open 70 new units in Mexico with an expected investment of USD 736 million<sup>22</sup> and in 1995 Pepsi announced a USD 55 million investment in a snack-food company in South Africa,<sup>23</sup> Boeing McDonnell Douglas invested USD 31 million in the Czech Republic in 1998.<sup>24</sup> It is reasonable to expect that some time is needed before the full effect of targeting materializes. Therefore, Table 9 also includes specifications with the targeted variable lagged by one, two or three periods. Another, already mentioned, positive aspect of using lagged values is that they attenuate potential concerns about endogeneity. We find positive and significant effects of lagged targeting in all specifications estimated, both in the full sample and for developing countries. Lagging appears to make the results stronger.

To investigate the possibility that our results are driven by reverse causality–that is investment inflows determine subsequent targeting rather the other way around– in Table 10 we include a dummy variable taking the value of one for targeted sectors in the years _before_ targeting started and zero otherwise. In different specifications, we consider one year before the start of targeting (column 1 and 5), two years (2 and 6), three years (3 and 7) and four years (4 and 8).<sup>25</sup> A positive and significant coefficient on the dummy would indicate that sectors receiving higher FDI inflows were the ones subsequently chosen for targeting. In other words, it would indicate that the investment promotion agencies were picking successful sectors as their priority sectors and thus

> 21 The median value of 11 million dollars pertains to those country-sector combinations, included in the regression presented in column 5 in Table 9, which received a positive amount of US FDI in 2004. The figure of 155% is based on the estimate reported in the same column.

> 22 <u>http://walmartstores.com/GlobalWMStoresWeb/navigate.do?catg=379</u>

> 23 <u>http://query.nytimes.com/gst/fullpage.html?res=990CE0DF1430F931A15755C0A963958260</u>

> 24 http://www.factbook.net/countryreports/cz/cz_fdi_us.htm

> 25 Thus, for instance, if country c decided to target sector i in year 2000, the dummy will take on the value of one in 1999 (columns 1 and 5), in 1998 and 1999 (columns 2 and 6), etc., and zero in all other years.

20
# **TABLE 4: ARE ATTRITORS MORE MOBILE?**

Quarterly Pseudo-Panel Estimates from the ENEU Dependent Variable: Log of Real Household Income

||(1)<br>Full<br>Sample|(2)<br>Non-<br>Attritors|(3)<br>Attritors|(4)<br>Full<br>Sample|(5)<br>Non-<br>Attritors|(6)<br>Attritors|(7)<br>Non-<br>Attritors|(8)<br>Attritors|
|---|---|---|---|---|---|---|---|---|
|Quarterly lag of Log Income|0.991|0.987|0.977|0.857|0.788|0.789|0.821|0.754|
|T-statistic|157.39|132.22|111.44|43.68|46.84|47|36.51|29.66|
|Confidence interval|[.979,1.003]|[.972,1.001]|[.960,.994]|[ .819,.896]|[.755,.821]|[.756, .821]|[.777,.865]|[.704,.804]|
|Cohort Effects|No|No|No|Yes|Yes|Yes|Yes|Yes|
|Cohort effects restricted to<br>be equal for attritors and<br>non-attritors:|---|---|---|---|Yes|Yes|No|No|
|Cohort-quarter observations:|638|638|638|638|638|638|638|638|
|Adjusted R squared:|0.9749|0.9648|0.9512|0.9763|0.9617|0.9617|0.9673|0.956|

# Notes

Attritors are households that participated in all 5 quarters of the survey Non-attritors are households that did not participate in all 5 quarters of survey Columns (5)&(6) were run as one regression where only the slope coefficient was allowed to differ Columns (7) & (8) were run separately as two regressions
of the migrant's portfolio to be placed in assets in the home country. The authors test their model using Greek migration data and find per capita remittances to be positively related to per capita income and interest rates in the host country and negatively related to inflation rates in the home country. The home interest rate is significant with a negative sign but becomes insignificant when domestic inflation is introduced, suggesting that the home interest rate and domestic inflation are positively correlated.

To summarize, while the impact of income and demographic variables seem to be corroborated by most studies, the evidence on the impact of relative rates of return is more mixed. Swamy (1981) and Straubhaar (1986) found that interest rate differentials between the host and home countries have no effect on remittance flows, while Katseli and Glytsos (1986) find per capita remittances to be related to the foreign interest rate, although the coefficient is positive, implying that the wealth effect dominates the substitution effect.

The failure to find a significant influence of differential rates of return on remittances may be caused by a correlation of home country interest rates with other variables. For instance, high interest rates on foreign exchange-denominated deposits in the home country may simply reflect the risk of appropriation of these deposits.<sup>5</sup> Likewise, high interest rates on domestic currency deposits in the home country may simply reflect the high level and volatility of domestic inflation. In these cases, the migrant's perception of risk and uncertainty may more than offset the interest differential. Indeed, Katseli and Glytsos (1986) interpret the negative influence of home country 5 Many labor exporting countries introduce foreign exchange deposits in order to attract the remittances of workers abroad. In the cases where foreign exchange is primarily used to finance imports there is always some risk of force conversion, freeze, or appropriation, because the financial system becomes unable to meet large net withdrawals.
Given our focus, we restrict attention to children age 0-35 months born and residing in rural areas for our analysis of weight-for-age. For height-for-age on the other hand, we make use of the expanded sample of children 0-59 months as it allows us to assess the persistence of impact of rainfall shocks experienced in the early years over a longer period of time. Height (or length) and weight are standardized based on WHO (2006) to create height-for-age (HAZ) and weight-for-age (WAZ) z-scores.<sup>7</sup> After eliminating biologically implausible z-score values, we are left with 6,941 children pooled across DHS rounds for the analysis of weight-for-age and 11,491 for height-for-age. Tables 1 and 2 present summary z-scores for the sample children broken down by gender, age group and the three survey years. The averages can be compared with the international reference group which has an expected mean z-score of zero. Z-scores below -2 indicate severe growth retardation or “stunting” in the case of HAZ and acute malnutrition or underweight in the case of WAZ.

Average WAZ for children in our sample is -1.70 with an underweight prevalence of 49%. Underweight prevalence among boys is slightly higher than for girls and most serious for children older than 0-11 months of age. The latter fact is consistent with the weight-loss children typically experience once they have transitioned from breast milk to the family diet. Comparing across the three survey years, there appears to have been a steady improvement, with underweight prevalence decreasing from 41% in 2001 to 31% in 2011.

Summary statistics for height-for-age exhibit similar patterns but are somewhat worse relative to the reference population. Average HAZ is -2.23 with a corresponding stunting prevalence of around 58%. Stunting rates are fairly similar for boys and girls, but starkly higher for children 12-59 months-old compared to infants, which again corresponds to the point of full transition to the family diet.<sup>8</sup> Finally, between 2001 and 2011, the rate of stunting fell from 62% to 48%.

> 7 Another commonly used anthropometric indicator is weight-for-height, a measure of how “lean” a child is for her height. Weight-for-height, like weight-for-age, is considered a short-run indicator of nutritional status, especially useful when age cannot be determined precisely.  Results of our empirical analyses replacing weight-for-age by weight-for-height are very similar (available upon request).

> 8 The median duration of exclusive breastfeeding in Nepal is 4.2 months, whereas roughly 70% of children between the ages of 6 months and 9 months combine breast milk with complementary food. Global standards of infant and child feeding practices recommend that children who have fully transitioned to the family diet be fed a minimum of four food groups and be fed a minimum number of times per day. Only 25% of the children less than 23 months of age in our 2011 sample met that requirement.

7
the labor market conclude that insider power cannot be rejected as a determinant of the high wage settlements.

New entries kept the size of the labor force constant. Entering outsiders maintained some effective pressure on insiders, despite the fact that some unemployed outsiders sank into long-term unemployment. Another piece of evidence showing that labor was paid more than its opportunity cost in many state firms is the difference between average wages in the economy and in the private sector (table 1).

The Central Statistical Office, _Glowny Urzad Statystyczny_ (GUS) reports that employment _(zatrudnienie)_ fell from 12 million in 1989 to 9 million in 1993. Thanks to self-employment, labor income earners outside of private agriculture _(pracujacy)_ fell only from 12.5 million to 11 million. Productivity gains at such an employment cost indicate that some labor is paid above its opportunity cost. The economy has not become more productive, rather income is distributed differently: employed workers earn larger gross wages and pay more taxes. The others earn benefits.

Table 1: Average private sector wages relative to average wages in the economy, 1993-95 (percent)

|Relative Average Gross Wage in<br>Private Firms|1993a|1993<sup>b</sup>|**1 9 9 4**<sup>**b**</sup>|1994e|**1995c**|
|---|---|---|---|---|---|
|All sectors|87.8|94.4|92.6|92.5|94.5|
||82.4|88.0|83.3|84.3|86.0|
|Industrial workers||||||

Note: Figures one for average monthly gross wages.

a Covers all workers.

b Covers only the workers employed in reporting enterprises. c Same as b, but 9-month averages (Jan.-Sept.).

Sources: GUS Monthly statistical bulletin, various issues.

4
Table 2 - Median Hourly Wages by Gender and Sector of Employment

|Hourly Wages|19<br>|98<br>|20<br>|06<br>|
|---|---|---|---|---|
||Male|Female|Male|Female|
|Government|1.25621|1.355311|2.458791|2.716758|
|Public|1.602564|1.572115|2.884617|2.509615|
|Private|1.153846|0.75|1.923077|1.098901|
|Others|1.141167|0.769231|2.183087|1.970567|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

Because we are interested in exploring the hypothesis of complementarily of education and technology, we report on whether the individual owns a computer. Table 3 shows that personal computer (PC) ownership increases with educational attainment. Only 12 percent of workers report owning a computer, but the ownership rates are very different across education levels: less than 2 percent of workers with primary education owned a computer, 6 percent of those with preparatory level education, 10 percent with general secondary education, 17 percent with post-secondary education, 29 percent with university-4 education, and 53 percent with university-5 level education. The average rate of computer ownership, 12.4 percent, is similar to Colombia’s (13 percent), but significantly lower than in Chile (57 percent), Brazil (51 percent), and Mexico (32 percent) (World Bank, 2011).

Table 3 - PC Ownership

|Doesyour familyown a PC?(2006)|Yes|No|
|---|---|---|
|Illiterate|3.62%|96.38%|
|Read & Write|1.99%|98.01%|
|Primary|1.46%|98.54%|
|Preparatory|5.75%|94.25%|
|General Secondary|10.26%|89.74%|
|Voc. Sec. Agriculture(3-Yrs)|1.46%|98.54%|
|Voc. Sec. Industrial(3-Yrs)|7.02%|92.98%|
|Voc. Sec. Commerce & Other(3-Yrs)|11.98%|88.02%|
|Vocational Sec.(5-Yrs)|12.50%|87.50%|
|Post-secondary|17.32%|82.68%|
|University (4-Yrs)|29.06%|70.94%|
|University (5-Yrs)|53.28%|46.72%|
|Post Graduate|46.15%|53.85%|
|Total|12.44%|87.56%|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

- C. Formal vs. Informal Workers

In 2006 the percentage of formal labor (i.e., have a work contract and contribute to social insurance) increased to almost 36 percent compared with only 11 percent in 1998 (Table 4). This trend toward 7