rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 22: Manufacturing exports of China by technology level**

<!-- Start of picture text --> (a) Chinese Manufacturing as a Percentage of Total Exports (b) Chinese Total Exports<br>100% 1.6<br>90%<br>1.4<br>80%<br>1.2<br>70%<br>HT<br>60% HT 1<br>MT<br>50% MT 0.8<br>LT<br>40% LT 0.6 PPRB<br>30%<br>0.4<br>20%<br>10% 0.2<br>0% 0<br>Trillions<br>19... 19... 19... 19... 19... 19... 19... 20... 20... 20... 20... 20... 19... 19... 19... 19... 19... 19... 19... 20... 20... 20... 20... 20...<br><!-- End of picture text --> Note: Technology classification from Lall (2000). HT = High tech (electronics, scientific equipment, aircraft); MT = Medium tech (automobiles, processed chemicals and metals, and machinery); LT = Low tech (textiles, basic metal manufactures); PPRB = Primary products and resource based goods. Source: Author’s calculation and WITS Comtrade SITC Revision 2.

68. The risk is that, if LAC countries cannot achieve dynamic industrial diversification and upgrading, their economies could further regress and increase their dependency on natural resources, thus remaining trapped in middle-income status, causing unemployment and other social and political issues.

69. This structural transformation has become even more imperative because of the growing competition from China to traditional manufacturing products from the region, discussed above. As a result, the traditional export base of the economy is declining (Figures 3–5). Yet, diversification towards higher value-added exports is increasingly hampered by an excessive appreciation of the exchange rate—also called Dutch disease—resulting from large capital flows associated with the natural resources exports as well as low domestic savings rates (Figure 23).

70. While the resource boom may be maintained, the effect may diminish given the expected decline of resource intensity of China‘s future growth. China will continue to be the largest consumer of commodities in the developing world, importing more primary resources than the rest of the developing world through the 2020s (Garnaut 2012). But projections indicate that the share of industrial production in China‘s economy will decline from 43.8 percent in 2011 to 34.6 percent in 2030. With less industry, especially heavily polluting and dirty industry, China will be less reliant on primary commodities (World Bank and DRC 2012). LAC countries will still have opportunities to utilize the rents from commodity exports to facilitate structural upgrading, but the growth of these rents will decline.

31
determinants of FDI surges and stops in the developing world. The focus on this topic is warranted because during the past decade there has been a significant increase of FDI flows to developing countries but the rise has not proceeded in a smooth fashion, prompting concerns about sudden stops even in countries where FDI inflows dominate capital flows. Furthermore, whereas most global FDI waves have been associated with an increase in M&As, it is not clear to what extent GF investments have contributed to surges and stops in FDI in developing countries. It is important to answer this question because GF investments dominate FDI flows to the developing world, especially in resource-rich and low-income countries, and as shown in the paper, GF-led surge and stop episodes occur more frequently than M&A-led ones.

This paper contributes to the literature by constructing a database of episodes when foreign investors substantially increase or decrease FDI inflows to developing countries and distinguish between these episodes based on the dominance of the entry mode. We use this database to document the incidence of FDI surges and stops by mode of entry, region, and resource status. Using this database, we analyze the factors associated with GF-led and M&A-led surge and stop events and show that the two types of surges and stops have different incidence and determinants, and therefore must be studied separately. Our analysis shows that global liquidity is the only common predictor of different types of FDI surges, while a decline in global growth and an FDI surge in the preceding year are the only significant and consistent predictors of FDI stops. GF-led stops and surges are more likely in low income and resource-rich countries than elsewhere. Policies aimed at increasing financial openness are enablers of M&A-led surges, which are also more likely during periods of global growth and domestic economic and financial instability.

25
of a poverty equation where the dependent variable is binary (poor/non-poor). Explanatory variables are the same as in the welfare regression. It is clearly a judgment call whether the loss of information embodied in the binary regression (collapsing the entire distribution into two values) outweighs the risk of bias due to measurement error. However, to the extent that results from a binary model confirm levels-regression results, they can act as- a robustness test for the latter. In recent years, use of probit and logit models (mainly the former) have become common practice in poverty analysis (see e.g. Alderman and Ga7rcia, 1993; Lanjouw and Stem, 1991; World Bank, 1995d, 1996d; Appleton, 1996; Grootaert, 1997b).

In summary, the determinants of poverty will be estimated in this paper on the basis of three models:

# (i) OLS regression of welfare equation (1);

To account for differences in parameters between the poor and nonpoor, (the poor are not rich with less money) without losing information from level-regressions:

- (ii) Tobit estimation of the welfare level of the poor, based on equation (2); this is equivalent to modeling the poverty gap;

To solve the problem of non-random measurement error (especially mismeasurement as a function of level of expenditure):

- (iii) Probit estimation of a binary poverty equation.

13
|Kenya|0.19|19|0.38|39|0|1|
|---|---|---|---|---|---|---|
|Madagascar|0.19|19|0.22|12|0.17|31|
|Malawi|0.20|24|0.41|47|0|1|
|Namibia|0.21|27|0.41|47|0|1|
|Nigeria|0.15|11|0.30|23|0|1|
|Senegal|0.55|60|0.43|53|0.67|60|
|South Africa|0.41|43|0.33|30|0.5|40|
|Tanzania|0.47|46|0.44|56|0.5|40|
|Uganda|0.08|4|0.15|4|0|1|
|Zambia|0.23|29|0.46|59|0|1|
|Zimbabwe|0.14|9|0.28|20|0|1|
|_East Asia and the Pacific (EAP)_|||||||
|<br>Cambodia|0.20|24|0.39|42|0|1|
|China|0.29|31|0.57|72|0|1|
|Indonesia|0.58|67|0.41|47|0.75|63|
|Malaysia|0.15|11|0.30|23|0|1|
|Philippines|0.16|13|0.32|27|0|1|
|Singapore|0.05|1|0.11|1|0|1|
|Thailand|0.57|63|0.30|23|0.83|70|
|_Eastern Europe and Central Asia_|||||||
|<br>_(ECA)_|||||||
|Albania|0.17|16|0.35|31|0|1|
|Armenia|0.37|39|0.49|64|0.25|32|
|Azerbaijan|0.53|55|0.47|61|0.58|50|
|Belarus|0.50|51|0.41|47|0.58|50|
|Bosnia and Herzegovina|0.59|69|0.35|31|0.83|70|
|Bulgaria|0.34|35|0.17|5|0.5|40|
|Croatia|0.67|75|0.60|77|0.75|63|
|Czech Republic|0.30|32|0.27|19|0.33|38|
|Estonia|0.19|19|0.39|42|0|1|
|Georgia|0.37|39|0.50|66|0.25|32|
|Hungary|0.19|19|0.29|21|0.08|28|
|Kazakhstan|0.11|7|0.22|12|0|1|
|Kyrgyz Republic|0.50|51|0.41|47|0.58|50|
|<br>Latvia|0.66|73|0.49|64|0.83|70|
|Lithuania|0.47|46|0.36|37|0.58|50|
|Macedonia, FYR|0.48|48|0.46|59|0.5|40|
|Moldova|0.54|57|0.50|66|0.58|50|
|Poland|0.18|17|0.35|31|0|1|
|Romania|0.79|83|0.70|81|0.88|76|
|Russian Federation|0.16|13|0.24|16|0.08|28|
|Serbia and Montenegro|0.33|34|0.40|44|0.25|32|
|Slovak Republic|0.13|8|0.18|6|0.08|28|
|Slovenia|0.42|44|0.59|76|0.25|32|
|Turkey|0.42|44|0.35|31|0.5|40|
|Ukraine|0.67|75|0.76|83|0.58|50|
|Uzbekistan|0.54|57|0.50|66|0.58|50|

11
We collect endline data in a 25-minute phone survey 3-4 months after treatment.<sup>12</sup> The phone survey response rate is 96%, leaving an endline sample of 6,607 respondents. The response rate is balanced across treatment groups (Table D.5) and unrelated to most baseline covariates (Table D.6). We also conduct a short text message survey 2-3 days after treatment. Respondents receive mobile phone airtime payments for answering the text message and phone surveys.

# **2.4 Job Search and Employment in the Sample**

This section describes relevant patterns around labor market outcomes and job search in our sample. We report summary statistics for key baseline and endline variables for the 6,891 workseekers in Tables D.1 and D.2. Respondents are 99% Black African, 62% female, and on average 24 years old. And 17% have a university degree or diploma, 21% have some other post-secondary certificate, and 99% have completed secondary school. Malindi (2017) shows that young, black workseekers with relatively low levels of education face discrimination in this labor market, with wage dynamics consistent with information frictions and statistical discrimination.

Of the sample, 38% worked in the week before the baseline and 70% had ever worked, but only 9% had ever held a long-term job. Conditional on working, mean weekly earnings in the week before the baseline was 565 South African rand (94 USD in purchasing power parity terms), slightly below the minimum wage for a full-time worker in most sectors. Wage work was eight times more common than self-employment. Most work was relatively short-term, with median and mean tenures of 2 and 7 months respectively.

Of the sample, 97% searched for work in the week before the baseline. In that week, they spent on average 17 hours and 242 South African rand (40 USD PPP) searching. The relatively high search costs suggest that welfare gains for workseekers are possible from improved search targeting. Workseekers submitted on average 10 applications in the preceding month and received 1.2 offers, though the medians for both measures are zero. The job search and application process is somewhat formal: 38% of the candidates employed at endline reported that they submitted written applications for their current job and 47% reported that they had a formal interview.

# **2.5 Assessments**

We conduct six assessments with workseekers: communication, concept formation (similar to a Raven’s test), focus, grit, numeracy, and planning. Firms have demonstrated interest in the results of these assessments, though they obviously also use other information in hiring decisions. Client firms have paid Harambee to screen roughly 160,000 prospective workers using these assessments. Appendix A describes each assessment in detail, their psychometric properties, and how some Harambee client firms use them in hiring.

Each assessment session is led by two or three industrial psychologists, who manage a team of

> 12See Garlick et al. (2019) for an experimental validation of labor market data from phone surveys in this setting.

10
<!-- Start of picture text --> Figure 2<br><!-- End of picture text --> In Benin (whose attainment profile is reproduced in Figure 2) only 26 percent of the poor aged 15 to 19 have <!-- Start of picture text --> Benin 1993<br>0.8<br>.o 0.6 >- . |<br>>,, - 0.4 ~ > < (<br>0.2<br>0<br>1 2 3 4 5 6 7 8 9<br>Grade<br><!-- End of picture text -->

completed of grade 1 or higher, so 74 percent have either

| attended school (or more precisely, not completed even one ( year of schooling). Completion of grade 5 or higher is only J 7.9 percent and only 0.7 percent complete grade 9. Among the rich, 80 percent complete grade 1 or higher but drop-out 9 is such that only 54 percent complete grade 5. Even among the rich only 17 percent complete grade 9.

- Poorest * Middle A Richest

We define two "wealth gaps". First, the wealth gap in the completion of any given grade (which, graphically, is the vertical distance between economic groups). For example, the wealth gap in grade 1 completion is .54 in Benin (.74 for the rich versus .20 for the poor with no schooling), while the wealth gap at grade 5 is .46. Second, the wealth gap in median grade completed. Visually the median is where a horizontal line at .5 would cross the attainment profile hence the gap is, graphically, the horizontal distance between the two groups. The median grade completed of the poor in Benin is zero, while that of the middle group is 2, and that of the rich is 5 years. The wealth gap in attainment is 5 years.

There are three main patterns that emerge from these figures and they are discussed in turn.

- First, the role of "ever enrollment" versus drop-out in explaining education

# outcomes,

- second, differing wealth gaps and attainment patterns across countries,

18
inequality. Finally, a lower share of industry in GDP will lower the resource intensity of growth and mean less environmental degradation.

Table 4. China: Sources and aspects of growth (1993-2024) (in percent)

||On currentpolicies|With rebal|ancedpolicies|
|---|---|---|---|
|1993-2004|2004-14<br>2014-2024|2004-14|2014-2024|
|GDP growth 1/<br>9.0|8.0<br>8.0|8.0|8.0|
|Total employment growth<br>1.1|0.4<br>0.0|0.8|0.4|
|Urban employment growth<br>2.9|1.7<br>1.2|3.4|2.9|
|Labor productivity growth<br>7.8|7.6<br>8.0|7.2|7.6|
|From TFP growth<br>2.7|2.7<br>2.7|3.5|3.5|
|From increasing capital-labor ratio<br>5.1|4.9<br>5.3|3.7|4.1|
|Memorandum item (period average, in percent)||||
|Investment/GDP ratio<br>36.8|43.4<br>55.3|34.5|36.4|
|At end-period||||
|Share employment in agriculture<br>46.9|41.1<br>33.4|31.3|12.5|
|Ratio productivity non agriculture-<br>5.7<br>agriculture|7.3<br>8.2|4.7|2.3|

_Source: NBS, and authors' estimatates._

- 1/ Potential GDP growth (in 2005, actual GDP growth is assumed to differ from potential GDP growth, in 2006-2024, actual GDP growth is assumed to equal potential GDP growth).

Looking at international experience, the outcomes of the alternative scenario are quite achievable if policies are rebalanced. For example, the illustrative movement of the employment share of agriculture from 48 percent in 2004 to 15 percent in 2024 compares to a similar pace in South Korea, where it was reduced from 50 percent in 1973 to 10 percent in 2001. Malaysia decreased its agricultural employment from 37 percent in 1980 to 18.4 in 2001.

11
food between younger women with a first-born girl or boy (also by education level) (panel (a) and (b)). Among older women, those with a first-born girl appear to have a slightly higher consumption of milk, green vegetables, and fruits (panel (b)). This can be partly explained by selective mortality.

Table 14 further checks whether women are differentially likely to receive iron and folic acid (IFA) supplements during pregnancy. It can be seen that a first-born girl is not associated with having received more supplements during the first (columns (1)-(2)) or second (columns (3)-(4)) pregnancies. The results also indicate that consumption of food or iron supplementation does not increase after the birth of a girl to compensate for higher nutritional needs.

**Domestic violence.** Next, I test whether self-reported experience of physical, emotional or sexual violence perpetrated by the husband (collected in NFHS-III) differs between women by the sex of their first-born. Table 15 presents the results. Panel (a) considers the subsample of women who had their first birth less than a year before the time of the survey. As can be seen in columns (1)-(2), women with a first-born girl do not appear to be more likely to have suffered _any_ forms of physical violence. It is only when the variable for physical violence is disaggregated between severe and less severe that an effect is visible. Columns (4) in fact shows that among uneducated women, those with a first-born girl are 3.6 percentage points more likely to have been the victims of severe violence (including strangling or burning, or threatening with a gun or knife). This effect is in accord with Bloch and Rao (2002) who find that the number of male children reduces the probability of violence consistent with increased husband’s satisfaction with the marriage in a maledominated society that greatly values males. The effect on severe violence is specific to the period immediately after the birth of the child (11 months or less) seemingly as a form of immediate punishment inflicted on women (panel (b) and (c) indicate no effect on women aged 15–29 and 30– 49). It is also specific to uneducated women (who are generally more likely to experience spousal violence). No effect is found for the other forms of violence.

Domestic violence thus appears to be another channel (complementary to or potentially exacerbating fertility behavior) that contributes to explaining excess mortality of women with first-born girls around the time of the first-birth. As for extreme fertility behavior, this is also motivated by strong son preference. However, even though it is consistent with selective mortality (the weakest are more likely die), the fact that it is only specific to the time of birth does not reflect the differential age patterns of anemia status (higher among women with a first girl during their entire reproductive years or below age 30, as shown in figure 7). The anemia patterns are instead more consistent with the explanation based on son-preferring fertility behavior.

26
# Table 2. Regional debt

|||(Perc|Levels<br>ent of GD|P)||(Percentage po<br>Chan|ints of GDP)<br>ges|
|---|---|---|---|---|---|---|---|
||2010|2015|2018|2019|2020|2010-15|2015-20|
|East Asia and Pacific||||||||
|Government debt|34.3|40.9|51.4|54.0|63.2|6.6|22.3|
|Private debt|125.1|176.4|180.2|183.8|200.3|51.3|23.9|
|Total debt|159.3|217.3|231.5|237.8|263.5|58.0|46.2|
|East Asia and Pacific e|xcluding|China||||||
|Government debt|35.5|38.0|38.8|38.7|46.3|2.6|8.2|
|Private debt|58.0|72.8|75.8|75.6|81.6|14.7|8.8|
|Total debt|93.5|110.8|114.5|114.2|127.9|17.3|17.1|
|Europe and Central Asia||||||||
|Government debt|27.6|30.6|29.8|29.4|38.2|3.0|7.6|
|Private debt|70.1|88.0|85.1|82.5|90.4|17.8|2.4|
|Total debt|97.7|118.5|114.9|111.9|128.5|20.8|10.0|
|Latin America and the Cari|bbean|||||||
|Government debt|47.3|52.8|67.1|68.0|77.7|5.5|24.9|
|Private debt|46.0|58.5|62.0|63.3|69.3|12.5|10.8|
|Total debt|93.3|111.3|129.1|131.3|147.0|18.0|35.7|
|Middle East and North Afri|ca|||||||
|Government debt|25.5|36.4|41.3|45.8|52.8|10.9|16.3|
|Private debt|49.7|60.1|58.0|58.5|75.2|10.4|15.0|
|Total debt|75.2|96.6|99.3|104.3|127.9|21.4|31.4|
|South Asia||||||||
|Government debt|63.6|65.4|67.4|71.3|84.0|1.7|18.6|
|Private debt|89.9|83.0|75.8|76.9|82.7|-7.0|-0.3|
|Total debt|153.6|148.3|143.2|148.2|166.7|-5.2|18.4|
|Sub-Saharan Africa||||||||
|Government debt|28.1|40.0|50.2|53.3|61.6|11.9|21.6|
|Private debt|32.2|30.4|29.9|29.6|29.5|-1.9|-0.9|
|Total debt|60.3|70.3|80.1|82.9|91.1|10.0|20.8|

Source: Kose et al. (2021).

Note: Data are available for up to 153 EMDEs, consisting of 23, 24, 33, 18, 8, and 47 countries in East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, South Asia, and Sub-Saharan Africa, respectively.

33
tential changes in the quality of post-unemployment jobs, we cannot rely solely on the exogenous variation of PBD introduced by the change in UI law. Even if we observe, for example, that shortened PBD is associated with changes in the quality of post-unemployment jobs, we cannot rule out the effects of changes in underlying conditions in the labor market (perhaps as a consequence of the business cycle). Fortunately, in our case the legislative change introduced different rules for different groups of unemployed workers, and left the PBD of one group (workers employed less than a year and a half) unchanged. The legislative design allowed us to form one control group (members for whom the PBD has not changed) and four treatment groups (whose members were subject to different PBD before and after the law changed). We assume that the relative effect of changes in labor market conditions on the job-finding rate is the same or similar for all categories of workers.

In general, one treatment group suffices, but the natural presence of four treatment groups makes our conclusions more convincing. One could object, claiming that changes in underlying labor market conditions could affect a control and a single treatment group differently. But such an objection becomes untenable once there are multiple treatment groups with different changes in PBD.

So our empirical analysis rests on data about five groups of unemployed workers: one control group and four treatment groups. Within each group, some unemployed started to collect benefits before the law changed, and some after. Had the law not changed, all members of a group would be entitled to the same PBD. Because some of the recipients in a group registered after the law changed, their entitlement was much reduced in duration. Of course, the exception is the control group for whom the PBD stayed unchanged.

For the five groups we consider in our analysis ‘old’ and ‘new’ benefit entitlements are presented in Table 1. The first group has limited work experience (up to 18 months) and it is also the only group of which the potential benefit duration has not changed - it was kept at 3 months. For the second group, which has a work experience of 1.5-5 years, the maximum benefit duration has been reduced from 6 to 3 months. All the other groups are also confronted with a reduction of the maximum benefit duration. Implicitly, the formation of groups is also strongly correlated with age. The older workers are, the more work experience they have and the longer their potential benefit duration when they loose their job.

To avoid selectivity in the inflow into unemployment around the date of the 9
Figure 4 compares the performance of the PMT model estimated using the (full) annual and the quarterly samples. We see some variation in the undercoverage, leakage, and eligibility across the quarters, although this variation is not nearly as large as we see for different sample sizes in Figure 3.<sup>15</sup> This result alleviates some concern that a PMT pilot that is only conducted during a few months of a year may still give valid results provided it is not based on a very small sample.

Figure 4: PMT performance and survey quarter <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=5,429) Q2 (N=5,540) Q3 (N=5,056) Q4 (N=4,511) Full/annual sample<br><!-- End of picture text --> Note: HIES 2016 was conducted during January‐December 2016. The surveys were conducted roughly evenly across the survey months. The variables in the PMT model are the same for all the samples but PMT coefficients are estimated for each sample.

Although HIES interviews were conducted fairly uniformly across the survey quarters, we find some differences in the sample sizes across the quarters. Could the differences in PMT performance we observe across quarters be due to sample size in addition to seasonality? Figures A3‐A5 present results of the simulations in which we draw random samples of 4,500, 1,000, and 250 households, respectively, in each quarter.<sup>16</sup> We also present three scenarios for the annual sample: same sample size drawn from the full year, sum of all the quarterly samples, and the all 21,756 households interviewed in the survey. We see that the performance of the PMT models varies slightly more when we use constant (and smaller) samples compared to Figure 4 in which the full available sample was used across the quarters. The model performance varies significantly when we use very small samples of 250 households per quarter. The takeaway from Figures 4 and A3‐A5 is that variation in PMT performance across quarters may not be a concern if large samples are used.

_PMT performance at sub‐national levels_ 15 The differences in poverty rates across the quarters may simply reflect the seasonality of household consumption, which is why a national estimate of poverty should be based on a survey that covers all months.

16 The sample size of 4,500 was chosen to maximize the number of observations per quarter while keeping it constant across the quarters (Q4 surveyed only 4,511 households). The sample size of 1,000 was chosen since we previously found that this may be the sample size beyond which gains in the performance of the PMT model may be very small. The sample size of 250 was chosen to allow the full sample to be 1,000 households.

16