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<mark>Duflo, E., Dupas, P., & Kremer, M. (2011). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in Kenya.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>101</mark>_ <mark>(5), 1739-74.</mark> <mark>Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., and Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions.</mark> _<mark>Child development</mark>_ <mark>,</mark> _<mark>82</mark>_ <mark>(1): 405-432.</mark> D'Zurilla, T. J., Maydeu-Olivares, A., & Kant, G. L. (1998). Age and gender differences in social problemsolving ability. _Personality and individual differences, 25_ (2), 241-252.

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<mark>Erol, R. Y., & Orth, U. (2011). Self-esteem development from age 14 to 30 years: a longitudinal study.</mark> _<mark>Journal of personality and social psychology, 101</mark>_ <mark>(3), 607.</mark> <mark>Exley, C. L., Niederle, M., & Vesterlund, L. (2020). Knowing when to ask: The cost of leaning in.</mark> _<mark>Journal of Political Economy</mark>_ <mark>,</mark> _<mark>128</mark>_ <mark>(3), 816-854.</mark> Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., & Beechum, N. O. (2012). Teaching Adolescents to Become Learners: The Role of Noncognitive Factors in Shaping School Performance--A Critical Literature Review. Consortium on Chicago School Research. 1313 East 60th Street, Chicago, IL 60637.

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We then analyze how the formation of a regional trading bloc alters these incentives. Intra-regional exporters may be, in terms of profit levels, better off than before since they are now the beneficiaries of the area's external tariffs, but at _the margin,_ their incentive to lobby against intermediate-good protection rises as duty-drawback and rebate schemes are endogenously eliminated on intra-regional exports. In equilibrium, this results in a lower level of external protection for those intermediate goods that are used heavily in sectors where intra-regional exports are large. Indirect effects of that type are at the very least consistent with the reduction in MFN tariffs that has accompanied the "New Regionalism" that has been observed by several authors.

We then try to see if the model's predictions are borne out in the case of the Common Market of the Southern Cone (Mercosur) which is an interesting case-study for at least three reasons. First, Mercosur members all had duty-drawback systems for exporters in place when they negotiated their Common External Tariff (CET) in 1994 which will no longer be allowed for intra-regional trade, once convergence to the CET is achieved in December 2000. Second, Mercosur has been identified as one of the recent regional blocs satisfying the "New Regionalism"'s characteristics that preferential tariff reductions are accompanied by general MFN tariff reductions. Third, recent work has shown that industry lobbying was an important determinant of Mercosur's **CET.** Our approach to testing the political-economy hypothesis explained above proceeds in two steps. First, we assume that the Mercosur's CET is endogenously determined through cooperative bargaining among its members, and there is evidence that this was indeed the case as will be discussed later. Then, using input-output tables to trace the use of imported intermediates in downstream industries, we test whether deviations from the optimal CET are correlated with the intensity of input use in downstream industries. The interest of the exercise is two-fold. First, it gives a statistical indication of the magnitude of the lobbying effects attributable to the elimination of duty-drawback schemes. Second, it provides an indirect test of the common-agency model of endogenous protection, whose empirical predictions have been the object of some controversy.

We find that during the negotiations for the Mercosur's CET, counter-lobbying against protection of intermediate goods increased following the elimation of dutydrawbacks for intra-regional exports. In the absence of this mechanism, we estimate that Mercosur's CET would have been on average 3.5 percentage points higher (25 percent higher).

.i.
A last, fundamental question to resolve is who benefits more from Chile’s social spending through inkind transfers of education and health services. Figure 10.A shows that the distribution of total social spending on education and health tends to fall with market income plus pensions —that is, the share of total benefits received is higher the poorer the households—. The first decile, composed by the poor, receives 13.6 percent of total spending, whereas the tenth decile, composed mostly by wealthy individuals, receives just above 5 percent. Moreover, half of total spending is distributed among the bottom 40 percent of Chile’s distribution, which is composed entirely by poor and vulnerable individuals.<sup>29</sup> That pattern holds when total spending is disaggregated by component, with the only exception being the social spending on tertiary education, which seems disproportionally distributed among the upper deciles.

**Figure 10: Distribution of total social spending on education and health, and concentration and incidence of social spending on tertiary education** _(Percentages by deciles of market income plus pensions)_

<!-- Start of picture text --> A. Distribution of total social spending on education and health (%)<br>16%<br>14% 13.6%<br>12.8%<br>11.9%<br>12% 11.2%<br>10.5%<br>9.9%<br>10% 9.0% 8.9%<br>8% 7.1%<br>6% 5.1%<br>4%<br>2%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of market income plus pensions<br>Pre-school Primary Secondary Tertiary Health<br>B. Concentration and incidence of social spending on tertiary education (%)<br>16% 14.8%<br>14%<br>12%<br>10%<br>8%<br>6.7%<br>6% 4.8%<br>4%<br>2%<br>0.4%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of market income plus pensions<br>Concentration Incidence<br>Distribution (%)<br>Concentration and incidence (%)<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016). _Notes_ : The sum of the areas measured in the panel A equals 100 percent.

29 The values of the ultra-poor ($1.25/day), extreme ($2.5/day), and moderate ($4/day) poverty lines lie, respectively, at the first, third, and eight percentiles of the income distribution. The value of the $10/day threshold dividing the vulnerable and the middle class lies at the 40<sup>th</sup> percentile, whereas the $50/day line dividing the middle-class and the wealthy lies at the 94<sup>th</sup> percentile.

20
# **Can Intense Exposure to Hand-Washing and Hygiene Information Campaigns Affect Children's SocioEmotional Skills? Evidence from Senegal**

_Christian Borja-Vega, Bertha Briceno, Vicente Garcia_ JEL Classification: D6, I14, I15, I18, I21 Keywords: Sanitation, Cognitive Development, Health Practice: WATER The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors, not from the institutions they are affiliated with. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent or WSP. Briceño’s work on this project between 2010 and 2014 was conducted as a Senior Monitoring and Evaluation Specialist at WSP. The authors are also grateful to the former WSP staff Eddy Perez, the global task team leader for the Scaling Up Rural Sanitation program. The impact evaluation surveys were led by Alexandra Orsola (CEGA, Berkeley) and Paul M. Wassenich(GDGM) provided initial estimations of survey data. The WSP-Senegal country technical assistance team was led by Seydou Koita; Momar Kane supported the project's monitoring and information system. Data collection was conducted by the _Centre de Recherché pour le Developpement Humain_ (CRDH) in Dakar, with oversight from Director Dr. Salif Ndiaye. Generous financial support was provided by the Bill & Melinda Gates Foundation. We also thank four peer reviewers of the paper. All opinions in this paper are those of the authors and do not necessarily represent the views of the Government of Senegal. For comments please email Vicente Garcia <u>(vag2120@columbia.edu); Bertha Briceno</u> (berthab@iadb.org); or Christian Borja-Vega <u>(cborjavega@worldbank.org). All errors or omissions are</u> ours.
26 Exchange controls and limitations on the repatriation of earnings reduce the incentives of both importers to import contraceptives and exporters to export contraceptives, thus reducing the size of the commercial market. Importers are allocated a limited amount of foreign exchange and will ci.ose to<sup>purchase the</sup> imported products that enjoy the highest mark up. Because other pharmaceutical products enjoy greater profit margins in some African countries, importers may not place a high premium on importing contraceptive products when foreign exchange is scarce and may lead to erratic and lover supplies of imported contraceptive products in Nigeria (Black, 1972, 1973).

When exporting products to different countries, producers prefer to realize their profits in hard currency. Multinationals produce and maintain research, marketing and managerial expertise at their home bases, vhose costs must be at least partially met by repatriated profits (unless profits generated from domestic sales totally defray these costs). Further, investment opportunities may nat be lucrative enough in these developing countries, so that repatriation restrictions are strong investment disincentives.

Advertising Constraints. Due to the sensitive and private nature of contraception, governments often forbid active advertising for family planning products. Further, advertising on ethical products is often restricted. In other countries, no brand name advertisement is permitted, making only general message promotion legal. This can lead to reduced demand due to lack of information and thus to reduced earnings opportunities for commercial sales. While the demand for contraceptive products depends to some extent on the income level and socioeconomic status of a population, an avareness and interest in family planning among couples of reproductive age also affects demand. To the extent that marketing and advertising can raise awareness and increase willingness to pay for contraception because of better information about the contraceptive

**l.fpW:antly KLaing Paper (tAoi*)**
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39
firm closures seems to point to an external shock. Otherwise, adjustment consists of changes in the employment level.

# **The Role of Employer Characteristics**

Job turnover tends to be higher in small private _finns_ located in more dynamic industries and regions. In the proceeding part of this section we will document this assertion.

# **_Firm_** _size_

Job turnover is substantially higher in small than in large firms. For illustration, in firms with up to 10 workers the job creation rate approaches 20 percent, the job destruction rate is almost 25 percent and close to 20 percent of jobs are reallocated from contracting to expanding firms (Table 7). In contrast, in the largest firms (with over 1,000 employees) the job creation rate is less than 1 percent, the job destruction rate is 10 percent and thus job turnover is much lower than in small firms.

**Table 7 Job Turnover by Firm Size, 1999-2000**

|Finnsize|Jobdestruction<br>Job creationrate<br>Jobtu<br>rate|N<br>rnoverrate|etemployment<br>growth|Excessjob<br>reallocationrate|
|---|---|---|---|---|
|Micro|19.3<br>23.1|42.5|-3.8|38.7|
|Small|9.3<br>19.7|28.9|-10.4|<br>18.6|
|Medium|5.3<br>16.0|21.3|-10.7|10.6|
|Large|7.1<br>9.6|16.7|-2.5|14.2|
|Extralarge|0.7<br>9.9|10.6|-9.2|1.3|

Note: Micro: I-10 employees Small: 11-50 employees Medium: 51-250 employees Large: 251-1,000 employees Extra large: 1,00I+ employees Classification **is** based on the employment level in the mitial year.

Source: Annual survey of wages and salaries, 1999 and 2000, Bank staff calculations.

Are then small firms the engine of employment growth? The answer is qualified yes. It is business start-ups and small firms which jointly create a bulk of new jobs. By 16
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23
**Table A6: Summary statistics after removing outliers**

|**2 DIGITNACE 1.1 **<br>**(MANUFACTURINGSUBSECTIONS) **|**NUMB**<br>**WOR**<br>|**ER OF**<br>**KERS**<br>|**COST OF**<br>**SOL**<br>|**GOODS**<br>**D**<br>|**OPERA**<br>**REVE**<br>|**TING**<br>**NUE**<br>|
|---|---|---|---|---|---|---|
||MEAN<br>|COUNT<br>|MEAN(IN<br>MILLIONS)<br>|COUNT<br>|MEAN(IN<br>MILLIONS)<br>|COUNT<br>|
|**Manufacture of food products, beverages**|95.55|72,694|2.46|99,988|2.75|99,988|
|**Manufacture of textiles and textile**<br>**products**|36.31|30,544|0.50|43,418|0.55|43,418|
|**Manufacture of leather and leather**<br>**products**|35.43|27,338|0.59|37,551|0.65|37,551|
|**Manufacture of pulp, paper and paper**<br>**products**|36.91|29,574|0.77|38,915|0.86|38,915|
|**Manufacture of coke, refined petroleum**|215.99|2,070|22.30|2,783|26.80|2,783|
|**Manufacture of chemicals, chemical**<br>**products**|80.12|22,553|2.35|29,078|2.75|29,078|
|**Manufacture of rubber and plastic**<br>**products**|44.33|27,992|1.02|34,272|1.14|34,272|
|**Manufacture of other non-metallic**<br>**minerals**|77.72|32,320|1.67|40,982|1.88|40,982|
|**Manufacture of basic metals and**<br>**fabrications**|62.19|54,406|1.75|67,876|1.94|67,876|
|**Manufacture of machinery and equipment**|59.12|61,269|1.20|81,237|1.35|81,237|
|**Manufacture of electrical and optical**<br>**equipment**|58.72|48,287|1.32|63,982|1.51|63,982|
|**Manufacture of transport equipment**|164.77|13,505|4.76|18,244|5.23|18,244|
|**Manufacturing n.e.c.**|35.72|23,028|0.75|30,611|0.85|30,611|

Source: RUSLANA and Author’s calculations.

Firm size is defined by the number of employees that the firm has. See table below for summary statistics.

**Table A7: Number of observations by size**

|**FIRM SIZE**<br>|**NO. OF**<br>**WORKERS**<br>|**FREQ. **<br>|**PERCENT**<br>|
|---|---|---|---|
|**Micro**|<15|169,762|39.25|
|**Small**|15-99|194,704|45.01|
|**Medium**|100-249|37,141|8.59|
|**Large**|>=250|30,925|7.15|

Source: RUSLANA and Author’s calculations.

23
# Table 16A. Benchmark factors' remuneration with respect to total value added according to NBS (2002a) (in percentage)

||Young<br>workers|Females<br>(1st)|Females<br>(2nd)|Females<br>(3rd)|Females<br>(4th)|Males<br>(1st)|Males<br>(2nd)|Males<br>(3rd)|Males<br>(4th)|<br>Subsistence|Capital|Land<br>All<br>females|All<br>males|**Total Value**<br>**Added**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**IRTS Goods and Services**|**0.0**|**0.0**|**0.3**|**0.0**|**0.0**|**0.2**|**1.9**|**0.2**|**0.3**|**0.4**|**11.6**|**0.4**|**2.7**|**15.1**|
|**CRTS Goods and Services**|**1.2**|**1.3**|**7.5**|**0.6**|**0.1**|**1.9**|**16.0**|**2.4**|**0.4**|**25.3**|**24.2**|**4.1**<br>**9.5**|**20.7**|**84.9**|
|**Business Services**|**0.00**|**0.02**|**0.16**|**0.01**|**0.03**|**0.07**|**1.20**|**0.18**|**0.26**|**0.0**|**3.9**|**0.0**<br>**0.2**|**1.7**|**5.9**|
|Telecommunication|0.000|0.003|0.025|0.003|0.002|0.014|0.232|0.020|0.014||0.43|0.03|0.28|**0.7**|
|Insurance||0.000|0.001||0.000|0.000|0.004|0.002|0.003||0.05|0.00|0.01|**0.1**|
|Banking||0.005|0.022||0.004|0.005|0.080|0.041|0.057||0.93|0.03|0.18|**1.1**|
|Professional business services||0.005|0.024||0.004|0.005|0.089|0.045|0.063||1.03|0.03|0.20|**1.3**|
|Air transport|0.000|0.001|0.009|0.001|0.001|0.005|0.086|0.007|0.005||0.16|0.01|0.10|**0.3**|
|Road transport|0.000|0.006|0.061|0.008|0.004|0.035|0.560|0.049|0.033||1.04|0.08|0.68|**1.8**|
|Railway transport|0.000|0.000|0.003|0.000|0.000|0.002|0.028|0.002|0.002||0.05|0.00|0.03|**0.1**|
|Water transport|0.000|0.001|0.014|0.002|0.001|0.008|0.126|0.011|0.007||0.23|0.02|0.15|**0.4**|
|**Dixit-Stiglitz Goods**|**0.01**|**0.01**|**0.13**|**0.01**|**0.00**|**0.14**|**0.72**|**0.07**|**0.01**|**0.4**|**7.6**|**0.2**|**0.9**|**9.2**|
|Processed food|0.001|0.003|0.028|0.002|0.001|0.031|0.157|0.016|0.002|0.33|1.44|0.03|0.21|**2.0**|
|Beverages & tobacco products|0.001|0.001|0.012|0.001|0.000|0.014|0.068|0.007|0.001|0.11|0.65|0.01|0.09|**0.9**|
|Textile & leather products|0.002|0.005|0.043|0.003|0.001|0.048|0.241|0.024|0.003||2.71|0.05|0.32|**3.1**|
|Wood paper printing|0.001|0.002|0.014|0.001|0.000|0.015|0.076|0.008|0.001||0.85|0.02|0.10|**1.0**|
|Manufacture of basic & industrial chemicals|0.000|0.000|0.003|0.000|0.000|0.003|0.018|0.002|0.000||0.20|0.00|0.02|**0.2**|
|Manufacture of fertilizers & pesticides|0.000|0.000|0.001|0.000|0.000|0.001|0.003|0.000|0.000||0.03|0.00|0.00|**0.0**|
|Petroleum refineries|0.000|0.000|0.003|0.000|0.000|0.003|0.014|0.001|0.000||0.16|0.00|0.02|**0.2**|
|Rubber plastic & other manufacturing|0.000|0.000|0.003|0.000|0.000|0.004|0.018|0.002|0.000||0.21|0.00|0.02|**0.2**|
|Glass & cement|0.000|0.001|0.006|0.000|0.000|0.006|0.033|0.003|0.000||0.37|0.01|0.04|**0.4**|
|Iron steel & metal products|0.000|0.001|0.008|0.000|0.000|0.009|0.043|0.004|0.001||0.49|0.01|0.06|**0.6**|
|Manufacture of equipment|0.000|0.001|0.009|0.001|0.000|0.010|0.051|0.005|0.001||0.57|0.01|0.07|**0.6**|

48