rafmacalaba/datause-encoder-data huggingface dataset:
|(7)<br>(8)<br>Control<br>Stability|0.233<br>0.247<br>(0.082)<br>(0.075)|0.040<br>0.066<br>(0.103)<br>(0.097)|0.005<br>0.004<br>0.026<br>0.032<br>0.033<br>0.033<br>726<br>726<br>lumn shows the OLS estimates<br> variable (ANCOVA), camp and<br> (Belloni, Chernozhukov, and<br>ived a small weekly cash<br> Columns 2-8. ‘PHQ’ is the<br>nt reductions in depression<br>uch that higher scores<br> ‘sharpened q-values,’ adjusted<br> this table. The row labeled<br>he row labeled “Shrp. q-val|
|---|---|---|---|
|ual Components of PS Index<br>(5)<br>(6)<br>Social<br>Self Worth|0.172<br>0.137<br>(0.081)<br>(0.082)|0.084<br>-0.090<br>(0.098)<br>(0.090)|0.012<br>0.029<br>0.292<br>0.002<br>0.103<br>0.013<br>726<br>726<br>psychosocial outcomes. Each co<br>eline measure of the dependent <br>ble-selection LASSO procedure <br>o the control group (which rece<br>ighted index of the outcomes of <br> such that higher values represe<br>utcomes are likewise presented s<br> refects p-values, referred to as <br>erson (2008) for all outcomes in<br>e Work and Cash arms, while t|
|Individ<br>(1)<br>(2)<br>(3)<br>(4)<br>**PS Index**<br>PHQ<br>Stress<br>Life Sat.|Work<br>0.211<br>0.237<br>0.237<br>0.293<br>(0.041)<br>(0.075)<br>(0.086)<br>(0.073)|Cash<br>0.056<br>0.011<br>0.056<br>0.229<br>(0.050)<br>(0.088)<br>(0.097)<br>(0.084)|Shrp. q-val Work<br>0.004<br>0.005<br>0.001<br>Test Work=Cash<br>0.000<br>0.006<br>0.018<br>0.326<br>Shrp. q-val Work=Cash<br>0.019<br>0.031<br>0.103<br>Observations<br>726<br>726<br>726<br>726<br>**Notes:** This table reports the treatment efect of the employment and cash arms on <br>of Equation (1) including dummies for each treatment arm and controlling for the bas<br>enumerator fxed efects, and sociodemographic controls as determined through a dou<br>Hansen, 2014). Each row presents the coefcients of the specifed treatment relative t<br>payment).<br>All outcomes are standardized. The PS index (Column 1) is an inverse covariance-we<br>standardized score of the PHQ-9 depression screening tool, but with the sign fipped, <br>severity. Similarly, higher values for ‘Stress’ represent _reduced_ stress. All remaining o<br>represent improved well-being.<br>Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” <br>for multiple hypothesis testing using the false discovery rate (FDR) procedure of And<br>“Test Work=Cash” refects the unadjusted p-value for the test of equality between th<br>Work=Cash” refects the adjusted p-value for this test of equality.|

35
- Levine, R. (1997). Financial development and economic growth: views and agenda. _Journal of economic literature 35_ (2), 688–726.

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43
# **4. Outreach programs**

We grouped outreach interventions in three types that may affect students differently. The first group consists of low‐intensity interventions that address information barriers faced by high school students. These interventions of short duration mainly deliver general information on financial aid, college costs and returns to higher education or college application. A second group of interventions is designed to complement information with personalized assistance and aims to guide students during the steps of the enrollment procedures. These interventions are more often spread over a longer period, provided by tutors who engage in a personalized exchange with participants and often include proactive strategies to ensure that participants engage in the program. Recently though, some low‐cost nudging interventions have been designed to provide guidance to students through automated procedures. The third group of outreach programs offer academic tutoring during upper secondary education, in addition to information and counseling. Lasting several years, these interventions include extensive after‐ school activities and aim to increase students’ academic readiness for higher education.

We found 28 studies which provide causal effects of the effect of outreach interventions on access to higher education for disadvantaged students but only 4 which provide estimates on graduation rates (Table 1). The lack of evidence on graduation may be consistent with the aim of outreach interventions, which primarily aim to facilitate access to higher education. Nevertheless, it is crucial to know whether disadvantaged students who entered higher education after participating in an outreach program were able to eventually graduate and this should clearly be addressed more often in the future. Finally, outreach interventions are usually evaluated through experimental designs and have been tested in six different countries. However, we also note that the diversity of educational contexts is only found for interventions providing additional information only. The large evidence on the interventions classified as “information & support” comes exclusively from the United States and Canada, and testing such interventions in other contexts <mark>would also be necessary in the future.</mark> 11
# **Figure 3: Difference in Log Hourly Wage. Base Period 2008:1.**

<!-- Start of picture text --> Year - Quarter<br>All Primary or Less<br>Junior High School High School<br>College<br>10<br>0<br>-10<br>-20<br>% Difference Log Hourly Wage<br>-30<br>2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2015. Earnings refer to full-time workers (at least 30 weekly hours of work) with a valid wage. It includes all workers (men and women): salaried, self-employment and owners. Earnings below and above the 1 and 99 percentile (within gender and education group) are dropped. Lines are smoothed by a moving average with a a 0.4 weight to the current observation and 0.3 to the previous and post observation.

In this paper, we analyze whether there is evidence of a degraded tertiary education effect, a skills-obsolescence effect, or both. We do this by examining a series of results and assess their consistency with the presence—or absence—of either effect. We posit that if the decline in returns to the college-educated is the result of the deterioration of the quality of new entrants (or a mismatch in demand and supply that affects the younger workers in recent cohorts in particular), this should be reflected in a skewing of the wage distribution towards lower-wage earners among those with tertiary. In order to verify this, we apply the classic Datt-Ravallion decomposition method that disaggregates changes in the proportion of people below the poverty line into growth and redistribution effects (Datt and Ravallion, 1992). We use the average wage of the college-educated in the base year ( _in lieu_ of the poverty line). The skewing towards the lower end of the wage distribution exists but it is very small. This result would not be consistent with a degraded tertiary effect or demand-supply mismatch of skills for the younger workers in particular.

2
synergy in the production activities of these groups, even at the firm level..." (ABH 2000: 219; see also Okamoto and Shojolm 2000: 68; and Doner 1991). The result was a market that was highly fragmented even by developing country standards. Thus, in 2002 there were roughly 20 major assembly operations, none of which had significant scale economies. This problem has been especially significant in the passenger car market where, in the mid 1990s, there were 17 brands for a market of under 50,000 units. The largest assembler of passenger vehicles, Toyota, produced fewer than 9,000 vehicles in 1996. The picture has been better in the dominant commercial vehicle segment, where there are fewer producers and models. Yet even here, the three dominant assemblers have operated at under 100,000 vehicles per year. Under these conditions, component producers have a difficult time achieving scale economies, much less developing the skills necessary for technological and managerial improvements.

Second, the lack of scale economies and the weak brand synergy within groups, when combined with high levels of protection, have provided little incentives for Japanese parts producers to develop close ties with joint venture partners, to upgrade local technological capacities, and/or to develop local input suppliers. As of early 2003, the component industry itself was largely an assembly operation relying on imported, semi-finished products.<sup>10</sup> _Protectionism and Politics_ Market fragmentation and the attendant weaknesses in components production are themselves a result of explicit policy choices. Indonesian automotive policy has been characterized first and foremost by high levels of protection originally designed to establish full local manufacturing capacity by the mid-1980s. This policy has evolved in a number of stages (Doner 1991; Wonacott 1995; ABH 2000). In 1971, the government prohibited CBU imports to

> 10 ABH 2002: 219; "Post Crisis Condition..." 2002. These problems of fragmentation are especially striking in light of the relative success and stability of the Indonesian motorcycle industry, where a limited number of assemblers produce a limited number of models (five). Under these conditions, local parts producers, as well as assemblers, have maintained market share and grown in efficiency (Thee 1997; ABH 2000: 218).

57
Table 3.2 : Income-Related Parameters for Food Consumption in Indonesia

|^xer.dic_re<br>GrouP|±Rice|<br>Corn|Wheat|Cassava|Potatoes|Fish|Meat|Eggs<br>5 Poultry|Dairy|**Vegetables**<br>Products|**Legumes**|Fruit|<br>Other|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Java||||TOT|ALEXPNDrI|TUREELA|STICITIES|<br>OF DEMA|ND FOR FO|OD,ZNDONZSZA|,1978|||
|Lower 40%|3.022|-0.622|<br>0.061|<br>0.238|0.539|1.317|<br>3.948|<br>1.143<br>|0.076<br>|0.953<br>|2.613<br>|1.901<br>|<br>1.243<br> <br>|
|Middle 30%|0.914|-0.425|-0.027|<br>0.790|1.238|1.825|<br>2.162|<br>2.871|0.783<br>|0.990<br> <br>|1.991<br>|3.708<br>|<br>0.911<br> <br>|
|Upper 30%|0.034|0.203|<br>0.943|-0.074|1.673|0.979|<br>2.534|<br>2.544|2.203|<br>0.559|0.653|2.617|<br>0.696|
||||PR|OPORT5ONS|OF THE FO|OD BUDGE|T ALLOCA|TED<sup>"CDIF</sup>|<sup>FERENT F</sup>|<sup>OOD GROCPS, r</sup>|<sup>NDONESIA,</sup>|<sup>1978. (%</sup>|<sup>0</sup>|
|Lower 4C%|36.:8|6.41|0.82|2.80|0.77|5.38|0.86<br>|0.59<br>|0.'4<br>|8.04<br>|2.75<br>|1.67<br>|32.99<br>|
|Midd:e 3C3|36.i6|2.68|0.48|1.65|0.69|6.75|2.04|0.96<br>|0.49<br>|7.05<br>|3.14<br>|2.42<br>|34.78<br>|
|*Joper 303|28.<.|1.15|0.47|0.96|0.74|,.24|4.94|1.77|1.66|6.62|3.68|3.71|39.04|
||||PER CA|PITA DAIL|Y CONSUMPT<br>(in|TON OF F<br>grams, ex|OODS<sup>FO</sup><br>cept fo|<sup>R HOUSEHOL</sup><br>reggs<br>whi|<sup>DS REPORT</sup><br>ch are in|<sup>INGCONSUMPTI</sup><br>units)|<sup>ON, INDONE</sup>|<sup>SIA. 197</sup>|<sup>8</sup>|
|,ower 40%|310.7|261.6|110.1|21S.2|157.7,|34.2|32.2|0.16|24.6|143.4<br>|41.2<br>|88.1<br>|181.0<br>|
|Middle 301|346.'|194.6|73.1|170.2|117.4<br>|41.4|30.2|0.15<br>|18.2<br>|142.0<br>|43.3<br>|92.8<br>|211.5<br>|
|Upper30%|369.3|15;.2|49.8|139.5|76.5|54.3|33.1|0.20|20.2|158.8|62.:|113.0|295.5|
|||||PRICE<br>(in|S OF ?OOD<br>rUpLah pe|S FOR %1O<br>r:'c_ocram|USEHOLD<br> <br>except<br> <br>|S<sup>REPORTWNG</sup><br>for<br>eggs<br>|<sup>CONSUM.P</sup><br>_w*.ic_<br>&r<br>|<sup>`OON.IONESZA</sup><br>e_in_rup.an -<br>|<sup>.</sup><br>1978<br>er.mni)<br>|||
|,ower 40%|139|63|115|27|59|324|902<br>|34|738<br>|91<br>|166<br>|96<br>|335<br>|
|Middle 30%|141|68|121|27|67|349|936<br> <br>|38<br>|733<br>|105<br>|176<br>|107<br>|375<br>|
|Upper 30%|149|74|131|31|86|419|994<br>|42|737|125|194|129|651|
Using this decomposition, we sketch exporters’ behavior during this period and can determine whether they react as predicted in Melitz (2003) and Bernard, Redding and Schott (2009). In order to identify each of the components, we classify firms, products and markets according to three different statuses: new, existing and exit. A _new_ firm is a firm that exports in _t,_ but not in _t-1_ . An _existing_ firm is a firm that exports in _t_ and in _t-1_ . Finally, a firm that _exits_ is a firm that exports in _t-1_ , but not in _t_ . The same definition applies to firm-product relationships and firm-destination relationships. For example, a new firm-product relationship refers to a product exported by firm _i_ in _t_ , but not in _t-1_ . Figure 4 reports the number of firms by year according to their status.

Figure 4: Exporters by status (2003-2009) <!-- Start of picture text --> Exporters status<br>Firms Status<br>existing<br>new<br>exit<br>2003 2004 2005 2006 2007 2008 2009<br>4,000<br>2,000<br>Number of firms 0<br>-2,000<br><!-- End of picture text --> We observe that the number of newly-exporting firms surged in the year when DR-CAFTA took effect. As for exiting firms, about a thousand firms stop exporting in any given year during the 2002-2009 period, except in 2007 and 2008. From 2006 to 2007, the number of exits dropped significantly to 766, before reaching their high (i.e. 1595) in 2008. Two events may explain this result. First, the large number of entries during the DR-CAFTA year may have boosted the number of exits in 2008. Short survival among new exporters has been well documented in the literature (See _inter alia_ Besedes and Prusa, 2006, 2007; Eaton et al., 2007 and Iacovone et al., 2010) and recently modeled in Albornoz et al. (2009). In their framework, firms discover their export profitability only once they become exporters. Experienced exporters entering a new market are therefore better informed than new exporters about their skills and their chances to succeed. As a consequence new exporters are more likely to exit than experienced ones. It could be that drawn by the more attractive conditions created by the agreement, many exporters entered the market in 2007, only to realize their true export performance, which forced them to exit in mass the next year. But in addition to the inherent low survival of exporters, the global economic crisis that started at the end of 2007 could have amplified this phenomenon, increasing even more the likelihood of existing of both experienced and new exporters.

It is important to mention that the weight of the extensive margin in total exports varies across the U.S. and the CAFTA area (see Appendix 4). In the case of the exports to the U.S., the extensive margin accounts for a small share of the total trade to this country, around 5% to 13% of total exports. It has been characterized by periods of growth and decline. From 2005 to 2007 its value tripled but contracted the 9
**Table 5: Determinants of Startup Size: regression results**<sup>**8**</sup>

||_Model 1_|_Model II_|_Model III_|
|---|---|---|---|
|**Intercept**|0.25<br>(0.66)|0.06<br>(0.66)|0.24<br>(0.63)|
|**Log(agest)**|0.31*<br>(0.18)|0.32*<br>(0.18)|0.23<br>(0.17)|
|**Secondary**||0.05<br>(0.12)|0.06<br>(0.12)|
|**University**||0.56***<br>(0.15)|0.43***<br>(0.15)|
|**Tech.Ed.**||0.26**<br>(0.13)|0.25**<br>(0.13)|
|**Informal Loan**|||-0.11<br>(0.18)|
|**Bank Loan**|||0.56***<br>(0.14)|
|**Title**|||0.54***<br>(0.11)|
|**Asian network**|1.33***<br>(0.12)|1.25***<br>(0.12)|0.99***<br>(0.13)|
|**European network**|1.03***<br>(0.19)|0.99***<br>(0.18)|0.89***<br>(0.18)|
|**Food**|0.15|0.10|0.001|
||(0.16)|(0.15)|(0.15)|
|**Wood**|-0.01|-0.01|0.006|
||(0.14)|(0.14)|(0.13)|
|**Metal**|-0.11|-0.15|-0.15|
||(0.15)|(0.15)|(0.14)|
|**Kenya**|-0.22|-0.17|-0.23|
||(0.17)|(0.17)|(0.17)|
|**Zambia**|0.24<br>(0.17)|0.22<br>(0.16)|0.15<br>(0.16)|
|**Tanzania**|0.23|0.31*|0.30*|
||(0.17)|(0.17)|(0.16)|
|**Adj. Rsq**|0.22|0.25|0.31|
|**N**|472|472|472|

*** Significant at 99% confidence level; ** Significant at 95% confidence level; *Significant at 90%;

> 8 In the regressions, size at start is defined by log of the number of employees at start up. The explanatory variables are defined as follows: _Lagest_ :  is log of the entrepreneur’s age when he/she started the firm _Secdary_ : dummy, =1 if entrepreneur has secondary school education _Univ_ : dummy, =1 if entrepreneur has university degree _Teched_ : dummy, =1 if entrepreneur has vocational/technical degree

_Inf. Loan_ : dummy, =1 if entrepreneur obtained loans from friends and family, or supplier credit, for startup _Bank Loan_ : =1 if firm obtained formal loan for startup _Title_ : =1 if entrepreneur has ownership rights on business property _Asian Network_ : dummy variable, =1 if entrepreneur is Asian _European Network_ : dummy variable, =1 if entrepreneur is European The models also include dummy variables controlling for sector and country differences.

17
Studies also indicate that small banks are the preferred organizational structure if a lender wishes to extend credit based on soft information. Because the loan officer is the repository of soft information, an organization must enable her to act on this information and reward her for doing so.<sup>5</sup> Agency problems make this more difficult in large banks, and thus the preferred organizational structure is a small, closely-held bank with few managerial layers (Berger and Udell, 2002; Berger, Miller, Petersen, Rajan, and Stein, 2005). Empirical evidence indicates that large banks are less willing to lend to informationally challenged borrowers that lack adequate financial records and that, after controlling for the endogeneity of bank-firm matching, large banks lend at a greater distance, interact more impersonally with borrowers, have shorter and less exclusive relationships, and are less effective in alleviating credit constraints (Berger, Miller, Petersen, Rajan, and Stein, 2005).

There are some indications that large banks are increasing their lending to small businesses. The distance between small firms and their lenders is increasing, bankborrower communication is becoming more impersonal, and distant firms are no longer required to be the highest quality credits, suggesting they have better access to credit (Petersen and Rajan, 2002). While large banks devote a lower share of assets to small business loans, recent evidence suggests that their lending to small firms is roughly in proportion to their presence in a local market (Berger, Rosen, and Udell, 2007). Thus, local presence is more relevant for understanding large banks’ small business lending than is the total size of the organization. Coupled with previous results on the depth of bank-borrower relationships, this also suggests that large banks are using alternative methods to reach small borrowers that do not rely on soft information such as credit scoring methods (Akhavein, Frame, and White, 2005; Berger, Frame, and Miller, 2005).

Although this is the first paper we know of on competition between microfinance institutions and other providers of financial services, there is a small literature on competition between microfinance institutions themselves. McIntosh, de Janvry, and Sadoulet (2005) (MDS), for example, study the effects of entry by new providers of

> 5 In the microfinance context, theoretical approaches focus nearly entirely on models without information acquisition by loan officers, even though in practice much information is acquired and, as a matter of theory, information revelation may lead to superior outcomes (Armendáriz and Morduch, 2005).

6
INFRASTRUCTURE AND GROWTH IN AFRICA

# **Table 3. Changes in the growth rate due to growth determinants across regions, 2001–5 versus 1991–5**

## **Using IK1 index of aggregate infrastructure stocks**

_Explaining change in the growth rate of real GDP per capita across regions_

||Growth ch|ange|Transitional|Structural|Stabilization|External|Infrastr|ucture developm|ent|
|---|---|---|---|---|---|---|---|---|---|
|Variable|Actual|Predicted|convergence|policies|policies|factors|Total|Stock|Quality|
|**3.1 Using [IK1] index of**|**infrastructure s**|**tocks:**|_Main telephone lines and_<br>_total road network (in k_|_mobile phones (per 1_<br>_m. per sq. km. of arab_|_,000 workers), electricity_<br>_le land)_|_-generating capacity (in_|_megawatts per 1,000 w_|_orkers), length of_||
|Western Europe|-0.28|**-0.19**|-0.53|0.56|0.07|-0.86|**0.57**|0.51|0.06|
|EAP7|-2.96|**-0.36**|-1.21|0.37|0.18|-0.91|**1.21**|1.17|0.05|
|North Africa|3.13|**0.59**|-0.19|0.42|0.21|-0.76|**0.92**|0.64|0.28|
|West Africa|3.61|**1.19**|0.00|1.12|-0.10|-0.71|**0.88**|0.90|-0.02|
|East Africa|2.28|**0.47**|0.24|0.47|-0.09|-1.10|**0.94**|0.80|0.14|
|Southern Africa|2.21|**1.23**|0.17|0.55|0.27|-0.77|**1.01**|0.85|0.16|
|Central Africa|3.68|**2.07**|0.20|0.86|0.13|-0.20|**1.08**|0.96|0.13|
|AFRICA|3.88|**1.10**|0.12|0.68|0.08|-0.77|**0.99**|0.89|0.10|
|**3.2 Using [IK2] index of**|**infrastructure s**|**tocks:**|_Main telephone lines (pe_<br>_network (in km. per sq._|_r 1,000 workers), ele_<br>_km. of arable land)_|_ctricity-generating capacit_|_y (in megawatts per 1,0_|_00 workers), length of_|_total road_||
|Western Europe|-0.28|**0.33**|-0.33|0.58|0.07|-0.12|**0.12**|0.07|0.05|
|EAP7|-2.96|**-0.03**|-0.75|0.33|0.20|-0.17|**0.37**|0.33|0.04|
|North Africa|3.13|**0.81**|-0.12|0.37|0.18|-0.02|**0.40**|0.14|0.26|
|West Africa|3.61|**0.86**|0.00|0.85|-0.16|0.03|**0.14**|0.16|-0.02|
|East Africa|2.28|**0.18**|0.15|0.39|-0.23|-0.36|**0.23**|0.10|0.13|
|Southern Africa|2.21|**1.07**|0.11|0.45|0.26|-0.03|**0.29**|0.14|0.15|
|Central Africa|3.68|**1.81**|0.12|0.84|0.10|0.54|**0.21**|0.09|0.12|
|AFRICA|3.88|**0.87**|0.07|0.57|0.04|-0.03|**0.22**|0.13|0.09|

_The figures presented in table 3 represent the predicted increase in the growth rate of output per capita in 2001–5 relative to 1991–5 attributed to changes in the growth fundamentals. In calculating the contribution of infrastructure to predicted growth, we use the [IK1] index of aggregate infrastructure stocks and the regression [IK1] in table 2. Source: Author's calculations._ 46