rafmacalaba/datause-encoder-data huggingface dataset:
as a result, the share of nonfood aid in total aid rises by about 11.3 percentage points (from 62.5 percent initially to 73.8 percent). The results are, of course, very intuitive: because the effect on public investment is larger (with an increase of about 7.9 percentage points of GDP in the long run), the effects on the growth rates of output and disposable income per capita, as well as on private investment, are magnified. As a result, poverty drops by a much larger amount than before—between 28 and 29 percentage points according to the survey data and with a partial elasticity of -1.8, and by 9 percentage points with an elasticity of -0.5.

Finally, instead of a permanent shock, one could ask if a “big push”, taking the form of a sizable, but temporary, increase in (nonfood) foreign aid, would not lead to similar outcomes. The results of this experiment are summarized in Table 7. They correspond to a temporary, 10 percentage point increase in the aid-GDP ratio over 5 years (from 2003 to 2008), allocated entirely to nonfood aid, which therefore increases by 17.3 percentage points in proportion of total aid. The results are, again, very intuitive: given the significant supply-side effects that the public capital stock exerts, a temporary but large increase in the flow of aid-financed public investment has a sizable effect on output and income growth. These effects persist for several years after the shock is reversed, because the supply-side effects of public investment are driven by stocks (which depreciate only slowly over time), not by flows. Although the growth rates of both real GDP and disposable income per capita turn negative after 2011, the reduction in the poverty rate, by 2015, is significant and similar in size to the results shown in Table 6.

Of course, an increase in aid of the magnitude displayed in Tables 5, 6 and 7 may be a source of concern for the recipient country, because it may create severe absorption problems. Our econometric results found no evidence that the relationship between aid and public investment has an inverted-U shape; but this cannot be construed as definitive evidence against the existence of absorption constraints in Ethiopia, as noted earlier. Such constraints can arise from limited administrative capacity, weaknesses in public expenditure and financial management, or 41
<!-- Start of picture text --> 1<br>75<br>ra<br>° ° ee: ee ee<br>.25<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>75<br>= ——_. ZA»<br>s —— —s" ————<br>.25<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Year<br>—e— Vardarski —e— lIsto¢en —®*— Jugozapaden *— Jugoistocen<br>—e— Pelagoniski —*— Poloski *— Severoistocen —*— Skopski<br><!-- End of picture text -->
The preference-granting country typically suffers a decline in welfare, because the main impact of increased in SSA exports is not on consumer welfare but on tariff revenue as imports from tariff-paying sources are displaced. The US and the EU would lose small amounts from such trade diversion. Losses in Japan from liberalization of access for all products are larger, around $5 billion, but have little impact on national welfare (around 0.1 percent).<sup>1 9</sup> In all cases, world welfare is lowered by the granting of preferential access - largely because of the loss in tariff revenue. While this may suggest that preferential access is not a particularly efficient mechanism to assist SSA, there are two important considerations. First, the welfare gains accrue to the poorest countries, whereas the welfare losses are concentrated in the rich countries, and are small in relative terms: even full liberalization in the QUAD would lead to a less than 0.01 percent decline in the QUAD's welfare. Secondly, it is not certain that alternative means of assistance, e.g. through direct transfers, would materialize and that their impact on SSA would be preferable to that of improved market access.

To put the gains in SSA in perspective, we find that if total factor productivity in SSA could be increased by 1.5 per cent, gains in welfare would be comparable to the gains from completely unrestricted access to the QUAD market. This, of course, raises the question of how such increases in productivity might be accomplished.

**'9** Since econometric evidence on the Armington elasticities of substitution is weak and results could be sensitive to their values (Gehlhar, 1994), we conducted a systematic sensitivity analysis (Arndt, 1996) with respect to the Armington parameters in GTAP - _O-D_ and _CM_ e **_CD_** specifies the elasticity of substitution between aggregate imports of commodity i and domestically produced i. We assume that **_CYD_** has a uniform distribution in the interval [x/2,2x], where x is its value in the GTAP database. _aM_ governs the

substitutability between imports of commodity i from different regions. We assume that _aM =_<sup>**_2_**</sup> **_0D_** as in GTAP (Zeitsch, _et aL,_ 1991). The systematic sensitivity analysis suggests that the welfare results for all regions other than Sub-Saharan Africa are not very sensitive to the Armington elasticities. However, some results for SSA could vary substantially. Using Chebyshev's inequality and the standard deviation from the systematic sensitivity analysis, we can be 75% sure that the welfare change (%) for SSA lies in the interval [0,3].

19
# **How Effective Are Active Labor Market Policies in Developing Countries?**

# **A Critical Review of Recent Evidence**<sup>**#**</sup>

David McKenzie, _World Bank_ _JEL classification codes:_ O15; J08; J68 _Keywords:_ Active Labor Market Policy; Jobs; Wage Subsidies; Vocational Training; Job Search.

> # I thank Asli Demirguc-Kunt for encouraging me to give a policy talk on this topic, and the authors of the different papers summarized for helpful clarifications and comments.
Although the Preston curve shows a close relationship between income and health in the crosssectional data, longitudinal data suggest that this relationship may not hold within individual countries over time. Figure 3 draws on data presented by Deaton (2006) on the evolution of the cross-country distribution of national incomes and health status between 1960 and 2004. Each curve represents the standard deviation of a variable relative to its value in 1960. The figure shows that per capita incomes have steadily diverged, in keeping with the well-established evidence that incomes in poor countries have not grown fast enough to catch up with incomes in richer countries (Commission on Growth and Development 2008; Pritchett 1997). By contrast, country-level health indicators have converged—until 1990 for life expectancy and through 2004 for the infant mortality rate.<sup>3</sup> Thus Figure 3 suggests that, over time, changes in income seem to be unrelated, or even negatively related, to changes in health status: incomes have continued to diverge, while health status has converged. That is, health status has improved in poor countries at a faster rate than in rich countries (albeit from a lower base), despite the fact that incomes have grown more slowly in poor countries than in rich.

In view of the difficulties and limitations of cross-country comparisons, we summarize the evolution of incomes and health status in two countries—China and India—since 1960. (This exercise follows Deaton 2006; Drèze and Sen 2002). Figure 4 suggests that both of these countries have improved their health status and per capita incomes over the last 40 years but that their experiences have differed.

**Figure 4. Income Growth and Infant Mortality Rate Reductions in China and India, 1960–2000**

<!-- Start of picture text --> China India<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>1 96 5 1975 1985 1995 2005 1965 1975 1985 1995 2005<br>-20%<br>-20%<br><!-- End of picture text --> _Source_ : World Bank data, as used by Deaton 2006; see his figure 8. _Note:_ Each line shows the annualized proportional change for a variable over the preceding five years. IMR is infant mortality rate.

3. The reversal of the converging trend in life expectancy in the last 15 years is likely due to the collapse of the former Soviet Union, which exhibits high adult mortality, and to the explosion of HIV/AIDS in Sub-Saharan Africa in the 1990s. HIV/AIDS, while it has implications for children and potentially for their incomes later in life— through its impact on schooling—has a more pronounced impact on adult life expectancy than on infant and child mortality.

10
Table 10. Continued

||||||Share|of OECD Im|ports Facin|g Nontariff M|easures||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1992 OEC<br>($mil|D Imports<br>lion)|All N<br>Meas|ontariff<br>ures|Price<br>Mea|Raising<br>sures|Quot<br>Prohi|as and<br>bitions||VERs|Non-A<br>Lice|utomatic<br>nsing|M|Other<br>easures|
|Exporter|Total|Non-Fuel|Total|Non-Fuel|Total|Non-Fuel|Total|Non-Fuel|Total|Non-Fuel|Total|Non-Fuel|Total|Non-Fuel|
|Sierra Leone|354|354|0.1|0.1|0.1|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Somalia|19|19|9.0|9.0|4.1|4.1|5.0|_5.0_|0.0|0.0|0.0|0.0|0.0|0.0|
|South Africa|11,132|9,490|13.0|11.5|9.4|10.6|3.9|1.3|0.0|0.0|0.0|0.0|0.2|0.2|
|Sudan|149|149|12.4|12.4|12.3|12.3|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Tanzania|266|266|3.8|4.1|3.1|3.4|0.7|0.8|0.0|0.0|0.0|0.0|0.0|0.0|
|Togo|121|118|0.5|0.5|0.2|0.2|0.2|0.2|0.0|0.0|0.0|0.0|0.0|0.0|
|Uganda|162|162|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Zaire|1,277|1,126|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Zambia|630|630|0.7|0.7|0.3|0.3|0.4|0.4|0.0|0.0|0.0|0.0|0.0|0.0|
|Zimbabwe|875|875|20.3|20.5|18.7|18.9|2.3|2.3|0.0|0.0|0.0|0.0|0.0|0.0|
|SSA|44,791|25,137|13.1|10.8|4.9|7.5|8.0|3.0|0.3|0.5|0.0|0.0|0.1|0.1|
|Memo Item|||||||||||||||
|SSA exci S.Africa|33,659|15,647|13.1|10.4|3.5|6.0|9.3|3.8|0.4|0.7|0.0|0.0|0.0|0.0|
|Developing Countries|705,634|540,783|16.6|16.6|3.3|3.6|4.8|2.0|9.6|12.5|0.0|0.0|0.1|0.1|
|Developed Countries|1,986,779|1,900,481|10.2|9.7|2.6|2.4|2.9|2.3|5.0|5.2|0.0|0.0|0.3|0.3|

Note: For a listing of ali measures that were classified as nontariff barriers see the notes to Table 3. The classification used for the different types of nontariff barriers is as follows:

- (i). Price raising measures include tariff quotas, increased duties, safeguard duties, retaliatory duties, customs surcharges, variable levies and flexible import fees, import price controls including reference and minimum import prices, and 'voluntary' export price restraints.

- (ii) Quotas and prohibitions include non-automatic and discretionary licensing requirements, bilateral and global quotas, seasonal quotas, and state monopoly of imports.

- (iii). 'Voluntary' export restraints include orderly marketing arrangements, all restrictions relating to the Multifiber Arrangement, other textile export restraint agreements and quotas.

- (iv). Nonautomatic licensing includes import permit requirements, imports restricted to select purchasers, permit dependant on the purchase of local goods, and other discretionary licensing schemes.

- (v). Other entry formalities include prohibitions for noncommercial purposes, miscellaneous regulations including import certification requirements, and local content import requirements.

# Source: Trade statistics from United Nations COMTRADE records. Nontariff barrier informiation from the UNCTAD-Wortd Bank SMART Data Base.

**45-**
We also estimate a growth incidence curve for the 43 available countries to determine whether predicted welfare changes are pro-poor or not. One important advantage of the LASSOPMM is that, unlike traditional growth incidence curves developed by Ravallion and Chen (2003), we do not need to rely on the rank preservation assumption. On the contrary, for each household surveyed in the first round, we can estimate the annualized income growth at individual level, using the predicted income the second round. Afterwards, we construct centiles (deciles) at world (regional) level using the baseline income and we compute the average of the individual income growth for each group. Pooling all countries together, we observe in Figure 7 that annualized income growth is positive until the 68<sup>th</sup> percentile and becomes negative afterwards, suggesting that predicted income growth is pro-poor. Observing differences by region highlights some heterogeneity in these predictions. For example, income growth in the SAR region is always positive until the 8<sup>th</sup> decile, while in ECA the lower half of the distribution is predicted to grow, while the top half is not.

# **6. Conclusion**

This is the first paper, to the best of our knowledge, that uses supervised machine learning and multiple imputation techniques to estimate welfare dynamics in the absence of panel datasets. The LASSO-PMM approach proposed in this paper estimates household-level welfare using a set of deterministic and time-invariant harmonized variables as controls and employs Predictive Mean Matching (PMM) with a household’s nearest neighbor to provide estimates of poverty rates, transitions, and income changes. Compared to previous poverty prediction methods in the literature, LASSO-PMM makes fewer assumptions and estimates both poverty transitions and income changes. This research presents, validates, and applies the method using harmonized data 34
_56_ _Social Capital, Household Welfare and Poverty in Indonesia_

**Table 15: Household Welfare and Social Capital: Distinguishing Types of Associations**

||**Gover**<br>**Nati**<br>**Gro**|**nment/**<br>**onal**<br>**ups**|**Religiou**|**s Groups**|**Produ**<br>**Gr**|**ction**<br>**oupsl**|**Social**|**Groups**|
|---|---|---|---|---|---|---|---|---|
|Number of Memberships|0.0084|(0.73)|-0.0223|(0.79)|0.0428|(1.94)|0.0623|(2.39)|
|Heterogeneity Index|0.0005|(0.41)|0.0016|(1.32)|0.0028|(1.78)|0.0014|(1.13)|
|Meeting Attendance|0.0122|(1.58)|-0.0008|(0.61)|0.0060|(1.41)|0.0017|(0.92)|
|Index of Participation in<br>Decision Making|0.0023|(3.30)|0.0007|(0.94)|0.0009|(1.14)|0.0013|(1.38)|
|Cash Contribution Score|0.0278|(1.87)|0.0136|(1.79)|-0.0008|(0.11)|-0.0193|(2.20)|
|Work Contribution Score|0.0040|(1.34)|-0.0030|(0.97)|-0.0035|(1.38)|-0.0095|(1.85)|
|Community Orientation|-0.0006|(0.41)|0.0007|(1.09)|-0.0012|(2.19)|-0.0005|(0.88)|

Note: 1. Dependent variable = In (household expenditure per capita).

2. Entries are coefficients and robust t-statistics from OLS regression with social capital variables broken down by type of association (i.e. 7 x 4 regressors) and the usual set of control variables.

Our earlier results pointed repeatedly at the importance of an association's internal heterogeneity. It turns out that this is most important for production groups. Such groups rely perhaps more than others on sharing information and knowledge and a more heterogeneous membership base may lead to a wider pool of knowledge to be shared. In contrast, the index of active participation in decision making is found to be most significant for governmentlnational groups. Since memberships in most of these groups is mandatory, it suggests that benefits accrue most to those who take an active role. This is confinned by the positive coefficient of cash contribution.

The negative coefficients of the cash and work contribution variables are a puzzling result at first sight. However, the detailed annex tables reveal that the poorest households participate more actively in social groups, including making larger contributions in cash and in kind. This clearly brings out the importance which the poor attach to these organizations. Presumably, the rich have less need to associate in groups for education or health's sake since they can afford to buy these services. Likewise, the rich have less need than the poor to get together for mutual support in making affordable ceremonies or house construction (frequent objectives of social associations).
Table 10: Current salary

|||Log (curr|ent salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.358<sup>_∗∗∗_</sup>|-0.129<sup>_∗∗∗_</sup>|-0.086<sup>_∗∗∗_</sup>|-0.118<sup>_∗∗∗_</sup>|
||(0.050)|(0.036)|(0.030)|(0.032)|
|Social networks*tenure|0.011|0.011|0.004|0.005|
||(0.007)|(0.007)|(0.005)|(0.005)|
|Tenure in years|0.032<sup>_∗∗∗_</sup>|0.070<sup>_∗∗∗_</sup>|0.077<sup>_∗∗∗_</sup>|0.067<sup>_∗∗∗_</sup>|
||(0.006)|(0.017)|(0.016)|(0.023)|
|Abilities||X|X|X|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.222|0.493|0.520|0.377|
|N|3396|3390|3390|3390|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

Table 11: Productivity: Promotion

||||Dummy|for having|been prom|oted|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.043<sup>_∗∗_</sup>|-0.023|-0.017|-0.025|-0.001|-0.026|-0.003|-0.015|
||(0.019)|(0.024)|(0.024)|(0.022)|(0.032)|(0.033)|(0.031)|(0.031)|
|Social networks*tenure|||||-0.009|0.002|-0.001|0.000|
||||||(0.007)|(0.007)|(0.007)|(0.007)|
|Tenure in years|0.016<sup>_∗∗∗_</sup>|0.016<sup>_∗∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|0.014<sup>_∗∗∗_</sup>|0.021<sup>_∗∗∗_</sup>|0.001|0.003|-0.001|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.006)|(0.018)|(0.018)|(0.021)|
|Abilities||X|X|X||X|X|X|
|Abilities*tenure||||||X|X|X|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.077|0.088|0.091|0.110|0.081|0.122|0.137|0.167|
|N|3396|3390|3390|3390|3396|3390|3390|3390|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

32
efficiency index is measured in terms of fixed effects in the regression of import charges, its fall by 1.3 percent just means the fall in import charges by the same percentage. The estimated results regression (1) implies that the same shock will bring about the fall in import charge by 2 percent. These comparable results from the two difference approaches reinforce the plausibility of our estimates.

# _Specifications and Data of the Trade Cost Regression: The Japanese Data_

The same theoretical formulation as above can be applied to estimate the impacts of the port infrastructure improvement to trade costs by using the Japanese data. However, the constraint of the data in Japan to only the aggregated country level without the commodity and modal subdivision requires to the imposition of the various controls in regression. The estimation period covers from 1996 to 2006. The adopted specification for the regression is as follows in equation (2): where: _TCikt_ : the amount of the transport costs imported by Japan, estimated by imports in CIF value subtracted by imports in FOB value.

_distit_ : bilateral distance between country _i_ and Japan. The same method as the U.S. data is applied to adjust the distance in 1998 and 2003. The distance estimated for 1998 is applied to the observations from 1996 to 2002, and that for 2003 is applied to those thereafter.

_Wgtit_ : the weight of the imports of Japan from country _i_ , including both the shipments via vessels and air, at the year _t_ .

_Valueit_ : the import customs value in FOB value of Japan from country _i_ at the year _t_ .

_Airvalueit_ : the import customs value in FOB value of Japan via air shipping from country _i_ at the year _t_ . This divided by _Value_ makes the ratio of air shipment in value to be used to control the air shipments.

25
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