rafmacalaba/datause-encoder-data huggingface dataset:
program would be subtracted from Norway’s statistics of ODA if results were either falling short or were not measurable (incidentally, this was the conclusion in the recent evaluation of said program).<sup>11</sup> Norway’s claim to be one of the most generous donors among high-income countries, with its 1% of GDP annual budget allocation to ODA, would be in jeopardy. The Norwegian aid administration would then obviously come under pressure to care much more about outcomes, and internalize the externality.

Once outcomes are internalized into the development business, market-based contracting becomes possible, just as emissions trading becomes possible with the creation of tradable permits and a constraint on their availability. Similar to emissions trading, which is expected to produce desired emission cuts at lower costs through spurring competition and innovation, market forces in the development business could be used to produce superior outcomes at lower cost. To open up the possibility for a market solution, let the ‘purchaser’ (the partner country in question) of the desired outcome open a tender for turn-key solutions (e.g. improve maternal health by x percent), and let implementing agencies or development institutions bid. The country chooses the best bid, and pays upon independent verification of results. Development agencies would have to develop credible profiles by showing their successes in similar programs in order to win bids. And while it may be counterintuitive at first – why should they bid for being contracted to finance development activities? – they would be compelled to bid, because, after all, it is their mandate to engage with recipient countries, and they want to give, even if only for the ‘warm glow’.

When development outcomes are internalized in the development business, risks will also be taken into account more explicitly. Risk-reward relationships are most explicitly modeled in contracts between oil and gas companies and governments in resource-rich countries. The companies invest large sums before knowing the extent to which hydrocarbons found underground can be monetized. Governments, on the other hand, want to make sure that the resources they suspect are within their jurisdiction are developed in the most efficient way possible. Contracts therefore specify the pace of exploration, and have detailed formulae by which the returns on the investment are shared between developers and host country. Sliding scale royalties, for example, allow investors a higher share of rewards early-on to repay their

> 11 <mark>Norad, 2013.</mark>

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Living Standards.” _American Economic Journal: Applied Economics_ , 4(1): 1-32.

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Hernandez, M. and S. Pudney. 2007. “Measurement Error in Models of Welfare Participation.” _Journal of Public Economics_ 91 (1): 327-41.

Leroy, J., H. Vermandere, L. Neufeld, and S. Bertozzi. 2008. “Improving Enrollment and Utilization of the Oportunidades Program in Mexico Could Increase Its Effectiveness.” _Journal of Nutrition_ 138 (3): 638-41.

Martinelli, C. and S.W. Parker. 2009. “Deception and Misreporting in a Social Program.” _Journal of the European Economic Association_ 7 (4): 886-908.

Moffitt, R. 1983. “An Economic Model of Welfare Stigma.” _American Economic Review_ 73 (5): 10235.

Parker, S.W., L. Rubalcava, and G. Teruel. 2008. “Evaluating Conditional Schooling and Health Programs.” In _Handbook of Development Economics,_ Vol. 4, eds. T.P. Schultz and J. Strauss, 3963-4031. Amsterdam: Elsevier.

Pudney, S., R. Hancock, and H. Sutherland. 2006. “Simulating the Reform of Means-tested Benefits with Endogenous Take-up and Claim Costs.” _Oxford Bulletin of Economics and Statistics_ 68 (2): 135-66.

Pudney, S., M. Hernandez, and R. Hancock. 2007. “The Welfare Cost of Means-Testing: Pensioner Participation in Income Support.” _Journal of Applied Econometrics_ 22 (3): 581-98.

Ravallion, M. 1996. “Issues in Measuring and Modeling Poverty.” _Economic Journal_ 106 (438): 132843.

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statement of the CGE model is in the appendix.) The illustrative database for the CGE application is presented in Section 2.3.

# _2.1 Alternative treatments of labor mobility in CGE models_

The CGE literature includes a large variety of treatments of the labor market, including labor mobility, which is the focus of this paper.<sup>5</sup> As a reference point, it may be helpful to remind the reader of the relatively standard and simple CGE treatment of labor that is found in Lofgren et al. (2002). In this model, for each labor type (one or more, often categorized on the basis of education or “skill”), (a) the demand side is represented by first-order conditions for producer profitmaximization in the form of a value-added function and an equation that, for each production sector, imposes equality between the wage and the marginal value product of labor; (b) the quantity of labor supplied for employment is exogenous; and (c) a labor market equilibrium condition imposes equality between quantities demanded and supplied, an outcome that is realized via adjustments in an economy-wide wage variable. Labor is measured in physical units (e.g., number of full-time workers employed during one year). Base-year wages, which vary across activities, are computed on the basis of data on labor incomes (from the SAM) and employment quantities. For any labor type, this market-clearing mechanism does not change base-year relative wages by sector; it merely imposes a uniform scaling of the wages across all activities. Each labor type is perfectly mobile across the activities in which it is employed; a worker that moves from one sector to another earns the same wage and is as productive as those who already work in the receiving sector.

The CGE literature includes alternative approaches that deviate from this simple formulation in various ways, including the treatment of labor supply (which alternatively may be endogenous, as a point on a wage curve or driven by utility-maximizing household decisions), and sectoral wage differences (which vanish if in every employing sector, one unit of each labor type is defined as the quantity that earns a wage of one). An additional dimension of variation lies in the treatment of value-added, where different functional forms and nestings of labor and other factors have been used.

5 Boeters and Savard (2013) review the literature on labor markets in CGE models; their discussion of labor market coordination and heterogeneity (pp. 1679-1706) covers some of the issues addressed here. Annabi (2003) reviews the endogenization of labor supply, and the modeling of unions and efficiency wages. CGE analyses that present and apply approaches that deviate from the standard competitive market model are found in Thierfelder and Shiells (1997), and Maechler and Roland-Holst (1997).

5
<!-- Start of picture text --> 55<br>Chart 4c: Coefficients component of the gender gap in Science<br>score: 2003 vs. 2007<br>45<br>35<br>G<br>e<br>n 25<br>Sience-2007<br>d<br>e 15 Science-2003<br>r<br>5<br>G<br>a<br>-5 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>p<br>Percentile<br><!-- End of picture text -->

# <u>Location gap</u>

In the absence of information on urban vs. rural school location, information on the size of community was used. In the data, size of community is grouped in four categories: under 3,000, 3,000-15,000, 15,000 to 500,000 and over 500,000. A dichotomous variable was created whereby large communities are defined as those with population over 15,000 and small communities those with population under 15,000. Approximately 46% of schools were located in large communities in 2003 and 42% in 2007. Over time, mean mathematics scores increased by 12% in schools in large communities, vs. 14% in small communities; similarly, science scores increased by 17% in large communities and 22% in small communities.

Estimates of the overtime change in the location gap over the entire distribution of scores are a lot more revealing. They suggest that while in 2003 the location gap was of similar magnitude across the distribution of scores (that is for both low and high performing students) at about 35 points for mathematics and between 30 and 50 points for science, in 2007 the location gap in both mathematics and science increased monotonically as one goes to higher points in the distribution of scores; for worse performing students it decreased drastically, while for better performing students correspondingly increased.

Furthermore, the decomposition results reveal that the structure of the components of the location gap have changed drastically for both mathematics and science. In 2003, most of the gap in favor of large communities was due to better returns to characteristics in large compared to small communities for the entire distribution of scores. In 2007, the narrowing of the gap among worse performing pupils is due to the improvement in the return to the characteristics of pupils in small communities compared to pupils in large communities. However, as one goes to higher points in the distribution, a build-up of characteristics in favor of large communities appears to have taken place over time. Above the median, the substantial increase in the characteristics component, along with an increasing coefficients component led to a widening of the location gap among well performing pupils.

17
150 rupees. The intervals then became increasingly wider to cover a range of extreme outcomes, with the last interval open-ended (5001 or more rupees). The self-anchored support is individual-specific. Respondents were first asked the maximum and the minimum value of catches that they would expect in a single day in the month of August. These responses are then used to construct four intervals of equal size. The left outer interval starts at the elicited minimum and the right outer interval ends at the elicited maximum.

The basic trade-off between the two methods is that while the self-reported support asks respondents about a range of values which are relevant to them, the disadvantage is that it requires real-time calculations by the interviewer when a paper survey is used, which can be time-consuming and subject to interviewer calculation error.<sup>6</sup> Moreover, since the bounds of the intervals are constructed based on the elicited maximum and minimum, they often are less rounded than those which one would use in a pre-determined support. For example, if the minimum and maximum answers are 100 and 2750, the intervals would be [100-762.5), [762.5-1425), [1425-2087.5), and [2087.52750]. Such intervals are likely to be harder for respondents to think about than intervals which are divisible by 10, 50 or 100.<sup>7</sup> This is also why the number of intervals has to be limited, since taking midpoints within these intervals requires more calculations and results in intervals which may not be easy for respondents to think about. In contrast, a pre-determined support enables the use of more intervals without increasing survey time or the risk that an interviewer makes a mistake. However, if the support is very heterogeneous across respondents, then a pre-determined support may require relatively wide intervals in order to cover everyone without having to employ too many separate

> 6 A few surveys in developing countries now use PDAs or ultra-mobile computers for data collection. In principle this offers the possibility of facilitating more intervals with a self-anchored design through automatic calculations and rounding. However, the only application we are aware of to do this is ongoing work by Fafchamps et al. (2010) which asks self-employed individuals in Ghana their expectations over future sales, using six self-anchored bins rather than the more common four.

> 7 Of course in practice there is no reason one could not have an additional step of rounding or mapping from these self-anchored intervals to intervals in a range which is divisible by 10 or 50. This has been done in some cases (e.g. Dominitz and Manski, 1997 and McKenzie et al. 2007), but it can further increase the interviewer burden.

10
Figure 1: Aspirational Videos: Take-up <!-- Start of picture text --> Region ID Screening 1 ID Screening 2<br>Kano 2120 GG 2019 GG<br>19 G 18 G<br>18 G 13 G<br>17 G 12 G<br>13 G 11 G<br>11 G 10 G<br>8 G 16 G<br>15 G 15 G<br>12 G 8 G<br>4 G 17 G<br>3 G 4 G<br>10 G 1 G<br>22 G 6 G<br>9 G 22 G<br>24 G 14 G<br>14 G 5 G<br>6 G 3 G<br>16 G 25 G<br>2 G 24 G<br>7 G 21 G<br>5 G 2 G<br>25 G 7 G<br>23 G 23 G<br>1 G 9 G<br>Jigawa 3127 GG 2721 GG<br>23 G 16 G<br>22 G 12 G<br>21 G 11 G<br>15 G 5 G<br>14 G 2 G<br>13 G 1 G<br>12 G 31 G<br>5 G 29 G<br>1 G 23 G<br>20 G 20 G<br>19 G 19 G<br>16 G 14 G<br>4 G 10 G<br>25 G 3 G<br>29 G 25 G<br>28 G 15 G<br>26 G 8 G<br>18 G 6 G<br>2 G 4 G<br>17 G 28 G<br>11 G 26 G<br>7 G 24 G<br>8 G 7 G<br>6 G 30 G<br>10 G 18 G<br>24 G 9 G<br>30 G 22 G<br>3 G 17 G<br>9 G 13 G<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Participation rate in % Participation rate in %<br>(a) Screening 1 (b) Screening 2<br><!-- End of picture text --> rates of the aspirational videos were, on average, above 90% for both screenings. Only 8 school catchment areas had a take-up between 80%-90% for Screening 1 (first weekend) and three of them remained with a take-up below 90% in the Screening 2 (second weekend). Figure 1 presents take-up rates for Screening 1 and 2 for all areas selected for treatment of aspirational videos ( _T_ 1 and _T_ 2). All families randomly selected for the mobile phones with m-Learning apps ( _T_ 2) picked up the mobile phone (100% take-up) when instructed and assisted the 2-hour-training to explain how to use the phone and the two m-Learning apps.<sup>8</sup>

# **5.1 Data Collection**

Using geo-spatial information provided by the Nigerian Federal Ministry of Education on the location of local schools in Kano and Jigawa states, the authors defined school catchment areas based on a 2-kilometer radius around the closest local school. We then created a sampling frame by producing a list of all households with children between

> 8This evaluation design was pre-registered prior to the baseline data collection at the The American Economic Association’s registry for randomized controlled trials `https://www.socialscienceregistry. org/trials/3619` , AEARCTR-0003619, on December 5, 2018. Ethical approval was given by Solutions IRB on December 5, 2018, and renewed with COVID-protocols to minimize infection risks prior to starting the second phase of the follow-up survey on September 3, 2020. Registration number: IRB Registration #: IORG0007116.

13
rates could rise as much as 20 percentage points in 2022-30, of which 6 percentage points would be due to such reforms. Advanced economies also have room for improvement, especially in higher level of education: tertiary enrollment rates would rise by 11 percentage points, on average during next decade, compared to the baseline scenario.

Rapid technological change and greater needs for interdisciplinary skills may also require new strategies for life-time education and retraining that enable workers to be more mobile and adaptable through their careers. For example, analysis of jobs posting suggests that a growing number of jobs across a range of industries required soft skills as well as communications and artificial intelligence-related skills (Liu and Lyu 2021; Squicciarini and Nachtigall 2021). Hence, an ability to acquire new skill sets may be a critical competency for workers to meet the demands of future labor markets (OECD 2018).

# Healthcare policies

Average life expectancy in EMDEs is still lower than in advanced economies: in 2011-21, they averaged 75 and 81 years, respectively. While life expectancy in some EMDEs, particularly in SAR and SSA, has risen significantly—by 4-7 years over the past two decades—it remains about one-fifth below advanced-economy levels in SSA and about one-seventh below in SAR.

Policies to improve public health, and to promote longer, healthier, and more productive working lives, range widely. In many EMDEs, better sanitation and access to clean water remain key to improvements in public health. The communities most affected by poor sanitation tend to be the poorest (Andres 2021). However, improvements in sanitation have to be accompanied by high sanitation usage and widespread handwashing to yield health benefits such as lower malnutrition and disease burdens (Carter 2017).

Improvements in healthcare provision can be spurred by well-defined and regularly monitored performance indicators (Bradley et al. 2010). In countries with higher per capita incomes, comprehensive provision of healthcare services has been followed by better health outcomes (Maeda et al. 2014). Programs carefully targeted at local health service providers or groups of patients have generated considerable improvements in healthcare services and outcomes. For example, in Rwanda, performance-based incentive payments helped significantly improve health indicators for children (Gertler and Vermeesch 2012). In India, enhanced training of primary healthcare providers led to better identification and treatment of ailments (Das et al. 2017).

In a stylized scenario of improved health outcomes, life expectancy is assumed to rise over the course of 2022-30 in each country by as much as its largest improvement over any historical ten-year period during 2000-21. This would imply an increase in average life expectancy in EMDEs of 1.4 years on top of the trend increase of almost 2 years, on average, but an additional increase of 4 years in SSA.

# Effects on potential growth

These stylized scenarios suggest that improvements in education and health outcomes—
**Table 2.** ITT effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package.

|||Number|of ISFM te|chnologies|adopted||Integrate<br>|d adoption<br>|
|---|---|---|---|---|---|---|---|---|
||OL|S|Pois|son|Opr|obit|of full ISF|Mpackage|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|T1 (extension)|0.683***|0.448***|0.688***|0.468***|0.542***|0.529***|0.103**|0.084***|
||(0.184)|(0.080)|(0.178)|(0.088)|(0.136)|(0.085)|(0.043)|(0.025)|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|0.016|0.001|
|T2 (extension + video)|0.840***|0.569***|0.822***|0.551***|0.671***|0.671***|0.137***|0.109***|
||(0.174)|(0.079)|(0.164)|(0.088)|(0.134)|(0.086)|(0.043)|(0.024)|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|0.002|0.000|
|Test T1 >= T2 (p-value)|0.216|0.087|0.216|0.177|0.206|0.080|0.242|0.185|
|Endline control mean|||2.2|22|||0.|152|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.071|0.531|0.016|0.122|0.025|0.223|0.023|0.271|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|2,160|2,160|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (Columns (3), (4), (7) and (8)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. Additional baseline control variables at household level are age, gender and education (in completed years) of HH head; whether HH participated in off-farm work or a nonfarm business activity; number of HH members above age 14; walking distances to nearest farmer training center, paved road and market (in minutes); number of local organizations involved; use of irrigation, total land size in ha, tropical livestock units (TLU), a basic assets score, a food insecurity score, whether HH is eligible for formal credit and has contracted a credit in the last farming season; whether HH had a below-average preceding farming season; number of times HH had contact with an extension agent and whether HH has participated in agricultural training; whether HH grew main crops (teff, wheat, barley, maize, sorghum) and used any kind of inorganic fertilizer. Community level covariates are rainfall, temperature, and distance to Woreda capital (in km). Two region dummies for Oromia and Amhara included. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

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# **1 Introduction**

Banks and stock markets operate in fundamentally different ways, with stock markets providing a public platform that directly connects investors with firms while banks serve as an intermediary between savers and borrowers. Such structural differences have inspired an extensive literature on the distinct roles of banks and stock markets in the real economy (e.g., Demirg ̈u ̧c–Kunt and Maksimovic 1998, 2002; Levine and Zervos 1998; Beck and Levine 2002; Levine 2002; Beck and Levine 2004; Shen and Lee 2006; Song and Thakor 2010; Demirg ̈u ̧c–Kunt, Feyen, and Levine 2013; Langfield and Pagano 2016). However, the evidence arising from this research is often inconclusive, especially when it comes to the stock markets (see, e.g., Zingales 2015).

This paper examines how domestic banks and stock markets help exporters survive in foreign product markets, moving beyond the traditional focus of existing literature on domestic output. Financial constraints are even more important in international trade than in domestic production due to additional costs like cross-border transport, customs clearance, conforming with foreign product market regulations, etc. The bilateral character of the data in international trade also allows us to exploit the variation in the strength of domestic banks and stock markets across exporting countries while at the same time holding constant the product market structure as well as the legal, regulatory and institutional environment by focusing on a single destination market (the United States). Last but not least, the highly disaggregated product-level data on international trade are available and directly comparable across many different countries at all stages of banking and stock market development.

The focus of our analysis is the different financing needs of exported products that can be addressed by well-developed financial markets and institutions. Certain products rely on extensive investment in physical capital (Rajan and Zingales 1998), high levels of working capital (Raddatz 2006), or a significant use of intangible assets that are less suitable to serve as collateral (Braun 2003; Claessens and Laeven 2003). Two additional transmission channels from finance to the real economy relate to the firm-to-firm trade credit as an alternative source of external finance (Fisman and Love 2003) and to the al1