rafmacalaba/datause-encoder-data huggingface dataset:
contributions between the standard game and the agreement game are statistically insignificant at conventional levels using Wilcoxon sign-rank tests that take the match pair property of the data into account (p-value=0.753).

The proportion of free-riders, i.e., participants who contributed zero, is small in both games, i.e., 5.4% of the sample in the standard game and 8.1% in the agreement game. Although the fraction of free-riders is low compared with findings in public goods experiments with students, it is in line with findings using general population samples, such as Thöni _et al_ . (2012) in Denmark and Bortolotti _et al_ . (2015) in Italy. Logically, the average conditional contributions, i.e., the average contributions among those who contributed to the public good, are also similar between the standard game and the agreement game, with contribution levels of 65.2% and 66.5%, respectively. Finally, and interestingly, the proportion of subjects who contribute the whole endowment to the public good is very high relative to subject pools consisting of university students, and the levels are again almost identical in the two games, i.e., 27.0% and 27.9%, respectively. Using non-parametric tests, we cannot reject that the contribution behavior is the same in the standard game and the agreement game.

Figure 1 presents detailed information on how the possibility to form an agreement affects the contribution levels. Zero in the figure means that a subject did not change her contribution when the agreement option was introduced, while a positive (negative) number means that the contribution level increased (decreased). As can be seen in the left panel of Figure 1, almost 35% of the Nepalese subjects did not change their behavior at all when the agreement possibility was introduced. The remaining subjects changed their contributions in an almost symmetric way around zero, and as reported above, we cannot reject the hypothesis of no change in contributions when the agreement possibility was introduced.

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In a scenario with inclusive climate‐informed development that would provide universal access to basic services such as water and sanitation and achieve the World Bank goal of bringing extreme poverty to 3% in 2030 in the absence of climate change (the _Prosperity_ scenario), climate change reduces income in developing countries by between 0.5% and 2.2%, and increases the number of poor people by between 3 million and 16 million in 2030.

In a scenario where poverty persists, with 11% of the population in extreme poverty in 2030, and unchanged access to basic services (the _Poverty_ scenario), climate change impacts on aggregate income are similar (a reduction between 0.7% and 2.6% of aggregate income in developing countries), but poverty impacts are much larger with between 35 million and 122 million more people in extreme poverty. Development appears to reduce the impact of climate change on poverty much more than it reduces aggregated losses expressed in percentage of GDP.

In these two scenarios, the most important channel through which climate change increases poverty is through agricultural income and food prices, because agricultural impacts are the most severe in Sub‐ Saharan Africa and India, where most poor people live in 2030. In both the poverty and prosperity scenario, therefore, the regional hotspots are Sub‐Saharan Africa and India and the rest of South Asia.

# 2. Methodology

The first stage of our methodology is to build, for each country, many scenarios for possible future socioeconomic change. To do so, we explore a wide range of uncertainties on future structural change, productivity growth, demographic changes, and policies, to create several hundred scenarios for future income growth and income distribution in each country. We then explore the resulting space of possible future poverty and income distribution using indicators like income of the bottom 20%, number of people below $4 or $6 per day, income share of the bottom 40%, the Gini index, etc.

The second stage is to use scenario discovery techniques (Groves and Lempert 2007) to identify the conditions under which some poverty outcome is most likely. For instance, we can identify the demographic, economic, and policy conditions in which the average income of the bottom 20% is most likely to be higher than $2 per day in 2030 in Sierra Leone. These scenario discovery techniques tell us about the key determinants of various outcomes – such as eradicating extreme poverty – and therefore about the policies that can help achieve this goal.

## 2.1. Micro‐simulation model

We use a micro‐simulation model based on household surveys and, for each country, project the pathway of individuals in the economy (the Appendix gives all details of the model). Micro‐simulation models represent dozens of thousands of individuals per country, which are all assigned a weight so that the entire population is modeled. Each individual is characterized by age, sex, level of education, employment, income etc.

5
Measuring effects on financial knowledge is critical for understanding the potential impacts of financial literacy programs. Although financial literacy training has been promoted heavily around the world, research studies thus far find limited effects on financial behavior (Cole, Sampson, and Zia, 2011). This muted evidence may be due to several reasons. On the one hand, financial literacy programs may not be effective; financial behavior may be difficult to change, and the interventions, which typically involve a “generic” short course adapted to the local environment, may not be relevant, informative, or interesting for the target population.

On the other hand, it is also possible that financial education programs are effective, but measuring impacts on financial behavior is quite challenging. Financial knowledge has typically been measured using standard questions that rely heavily on the numeric and computational ability of respondents, for example asking them to compare two different loan repayment plans, one stated as a percentage APR and the other as a fixed cash payment. It is no surprise that nearly all surveys show a strong correlation between financial literacy score and mathematical ability. But is this really a measure of financial knowledge? Indeed, many financial choices we face in the real world require calculating interest rates and estimating returns, so perhaps financial knowledge and mathematical ability go hand in hand. However, financial literacy programs may also affect financial decision-making through other channels, for instance by making individuals and households more aware of product choices available to them, equipping them to ask the right questions of financial providers, encouraging them to seek professional and personalized financial advice, and changing their attitudes towards purchasing and recommending formal financial products and services. These alternate channels may be as important, if not more, than enhancing numeracy skills. Our study contributes to the literature by carefully examining how financial knowledge is measured, as well as the different layers of knowledge that may be affected by financial education.

Determining effects on financial knowledge is likewise important for establishing the channels between financial literacy training and financial outcomes. Since financial literacy programs are carried out in specific environments, uncovering mechanisms is crucial for generalizing research results to other settings. Nevertheless, the current impact evaluation literature, generally 3
work in Uganda, where educated farmers who received a diary were less likely to provide an entry (Deininger et al., 2012). An alternative explanation is that as farming practices are divided across a number of plots and responsibilities shared across individual household members, the response of our household heads and spouses may be less precise.

# 4 Cost-eectiveness Analysis

We further motivate the use of a knowledge exam to capture farmer awareness of the SLM techniques by comparing the total costs of adding the questions to the survey to the improved precision of the knowledge outcomes. We multiply the number of exam questions for a given technique by the price per survey question. The price per question is derived by dividing the cost of each indoor survey, which equals the total cost of the survey minus the cost of the eld survey, by the total number of questions asked on the indoor survey.

Our cost estimates for measuring objective adoption are computed using the additional time spent by the enumerator to collect information in the eld and the associated transportation costs (see Table A.2 for details). Since the objective measurement of SLM adoption is done for all techniques at once and the marginal cost of adding a technique to measure in the eld visit is minimal, we present a single estimate for the additional costs of adding objective adoption measures, but allow the improvement of the precision to vary by technique. By construction, we tend to underestimate the cost of adoption, as we exclude the additional cost of training the enumerators to identify the technique in the eld and cannot account for the exact time spent by the driver. In contrast, we will tend to overestimate the cost of asking additional knowledge questions as we assume that the marginal cost per survey question equals the average cost per survey question.

The cost-eectiveness estimates in Table 7 indicate knowledge exam questions produce substantial benets. Over the whole survey an additional $1,170 would improve the precision of self-reported strip tillage and mulching knowledge by 23-34 percent, while an additional $2,340 spent on intercropping knowledge questions would improve the precision by only 9 percent. The gains to including the intercropping questions are rather small, since the majority of respondents could identify the technique by name. If costs were divisible, this implies that an additional $100 spent on the use of 13
the last grade of primary education was only 33.2% in 2013, compared to a SubSaharan average of 57% (World Bank Education Statistics Data Bank, 2017).

While the net intake at Grade 1 of primary schooling is high for both boys (74.5%) and girls (73.1%), and secondary schooling is still restricted to an elite (17.9% net enrollment for both boys and girls), most children in Mozambique, and girls in particular, experience difficulties in completing primary school.<sup>8</sup> For upper primary schooling (Grades 6 and 7 or Ensino Primário de Segundo Grau “EP2”, which the present study focuses on), the official completion rate is abysmal, especially in rural areas where even at age 19 it is only about 14% for males and 8% for females (according to Figure 3.10 in Fox et al., 2012). In this context, a policy priority is to find ways to increase the school attachment of pupils, and girls in particular, in the higher grades of primary school.

We focused on one province of Mozambique where our implementation partner – the development NGO Magariro – is active and well-known: Manica. Manica Province is located in the Center Region of Mozambique and is home to 7.5% of the country’s population. It is close to the national average on a number of indicators, from population density (30.3 people per square meter compared to a national average of 31.3), poverty rate (41% in 2014 compared to a national average of 46.1%), to annual drop-out rates in primary schooling (6.8% in Manica and countrywide for EP1, 9.9% versus 8.8% nationwide for EP2) (INE, 2015; MPD-DNEAP, 2016). Mozambique in general, and Manica Province in particular, have low population density even for Sub-Saharan Africa standards (where the average was 42.6 in 2015), but not dissimilar to other countries in Eastern Africa (36.7% in Kenya, for instance). This may matter in our context because our study design is, as explained below, motivated by the hypothesis that there may be imperfect monitoring of the children’s actions by 8 The net intake equals the ratio of the total number of pupils in Grade 1 of the official starting age (6) divided by the number of children age 6. All figures are taken from World Bank Education Statistics Data Bank (2017).

10
Lederman, Daniel, and Laura Saenz. 2005. “Innovation around the World, 1960-2000.” World Bank Policy Research Working Paper 3774, The World Bank, Washington DC.

Mayer, Wolfgang. 1984. “The Infant-Export Industry Argument.” _Canadian Journal of Economics_ 17(2): 249-269.

Nordhaus, William D. 1969. _Invention, Growth and Welfare_ . Cambridge, Massachusetts: The MIT Press.

Rodrik, Dani. 2004. “Industrial Policy for the Twenty-First Century.” Harvard University Kennedy School of Government Faculty Research Working Paper Series RWP04-047, Cambridge, MA.

Vettas, Nikolaos. 2002. “Investment dynamics in markets with endogenous demand.” _The Journal of Industrial Economics_ 48(2): 189-203.

World Bank. 2004. _Doing Business in 2004: Understanding Regulation._ Washington, DC: The World Bank.

World Bank. 2005. _World Development Report 2005: A Better Investment Climate for Everyone_ . Washington, DC: World Bank & Oxford University Press.

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The assumption of this argument is that the rate of improvement would be more difficult to achieve among poor states, putting them at a competitive disadvantage. But this concern is attenuated by the fact that by design states would get more supplementary funds the greater their number of disadvantaged children. Besides, empirical results from PARE (Lopez-Acevedo, 1999c) indicate that intervention programs like it have produced rates of improvement among children with initially low learning achievement (specifically, indigenous students) that were remarkably higher than the average student.

The incentive system can also be designed so that the reward is earmarked for each specific state and kept until it has reached agreed benchmarks. This system could be combined with another approach that links rewards to number of graduates. The problem here, though, is that it is an incentive for passing students at lower learning standards. But this can be addressed by instituting some kind of Federal examination system in which graduates would be assessed, using national standardized tests. States would be rewarded for every graduate who passes the examination.

The above discussion point to the idea that a reasonable system of performance incentives based on learning outcomes for states can be developed. It will not be a simple task. It requires systematic experimentation. But it is worth a try.

Finally, there is the idea of using matching grants as an approach for allocating Federal transfers. This is often proposed as a method for providing incentives to states to allocate more of their budget to improve basic education. This is an important issue to consider in light of the need to mobilize additional resources for the education sector and the decline in states’ contribution to its finance. The use of matching grants, however, should be used carefully. First, if used alone, matching grants could widen inequality in education finance. Second, the approach suffers from the same criticisms mentioned above about the use of input-based incentives and allocation formula. Focusing on rewarding states’ outputs, as proposed above, would be a better approach because it would implicitly reward states that are able to achieve output benchmarks regardless of whether they did it through increased efficiency or mobilization of additional resources (both monetary and in-kind). Still, used within a broader equity-oriented resource allocation framework (e.g. the above multi-component formula), special matching grants program can be a useful tool for motivating local resource mobilization. To be effective, though, subnational governments should have ample authority to levy local resources. This issue, which could involved fiscal mechanisms, will have to be examined in the context of the government’s new federalism approach to governance.

11
Macedo, J (1982), " Exchange Rate Behavior with Currency Inconvertibility", _Journal of International Economics, Vol 12, pp. 65-81._ McDermott J. (1989), " Foreign Exchange Rationing and Employment in Industrializing Countries", _Journal of Internatlonal Economlcs, Vol. 27, pp. 24564._ Marion, H.P., "Empirical Evidence on European Dual Exchange Rates and its Relevance for Latin America", _Unpublished, Dartmouth College (Dartmouth, NE)._ Mart-in, L. and A.Panagartya (1984), " Smuggling, Trade and Price Disparity: A Crime Theoretic Approach", _Journal of International Economlcs, Vol. 19._ Phylakti, K. (1992), "The Black Market for Dollars in Chile", _Journal of Development Economics, Vol. 37, pp. 155-172._
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reduction is distributed across all sectors including autos. Given our focus on the labor market effects of protection, all remaining simulations assume an endogenous total labor supply. Also, most of our discussion will focus on the distortionary cost component of VERs.

# 4. Distortionary Costs of Protection vith Labor and Product Market Imperfections

We now come to the central issue: are labor market imperfections sufficient to affect significantly the distortionary cost estimates reported above? We contrast the estimated value of the distortionary cost in two dimensions. First, under the assumption of CRTS, we compute distortionary costs under the assumption of an exogenous wage premium, then under the assumption of an endogenously determined wage premium affected by union activity. Because of uncertainty about the relative weight unions attach to wages and employment, we report results for three combinations of weights to wages and employment in the utility function: low weight on wages _(7_ - 0.2, _6_ = 0.8); medium (7 - _6 -_ 0.5); high weight on wages (7 = 0.8; _6_ = 0.2). Second, we examine the sensitivity of these results on labor market imperfections to assumptions about market structure. Results for steel appear in table 3; results for autos in table 4.

Steel. Start with CRTS. The distortionary costs of rationing steel when the steel sector pays an exogenous wage premium of 26 percent is only about one-fourth of the cost when there is no wage premium. This derives from the second best effect of superior reallocation of labor. Given the high rent transfer component of VERs, however, the total costs are reduced by only 11 percent. The distortionary cost estimate reflects the potential lower cost of protection if an *equivalent" tariff (or a quota that captured rents) were employed rather than the VER.
countries are in Sub-Saharan Africa. With lower incomes and a late start in policy reforms and growth, IDA countries’ MDG performance tends to lag that of middle-income and non-IDA countries (figure 4 left panel). Despite the greater distance to the MDGs set by low starting points, the poverty target is within reach for more than 70 percent of IDA countries as a result of more recent economic growth and policy improvement. That is also true of the hunger target for 58 percent of IDA countries. Results are also good for gender parity in primary education.

Fragile conditions in conflict-affected countries are also associated with very poor MDG performance because these countries may experience growth collapses and disastrous policy and institutional environments (World Bank 2010, 2011b. Harttgen and Klasen 2010). In broad terms, the proportion of on-target countries tends to rise with declining state fragility (figure 4 right panel). Fragility in the graph is the index from the Center for Global Policy, which ranges from 0 (no fragility) to 25 (high fragility), divided into four categories ranging from little to extreme fragility (Marshall and Cole 2010).

**Figure 4. MDG performance lags in IDA and fragile countries**

<!-- Start of picture text --> MDG performance in IDA countries: countries on Countries on target to achieve the MDGs by level of<br>target State fragility<br>100%<br>100% 30<br>90% 57 90% 24 25<br>80%70% 35 46 5336 43 Non-IDA countriesIDA countries 80%70% 8 7 19 20 21 1814 2416 Little to LowModerateSerious<br>60%50% 12 25 60%50% 9 8 8 12 11 18 18 13 13 High to Extreme<br>40% 12 13 26 25 23 25 40% 5 9 8 8<br>30%20% 9 10 5 10 30%20%10% 1 1 4 5 6 5 4 6 4<br>10%<br>0%<br>0%<br>Proportion of developing countries<br>by level of state fragility<br>Proportion of developing countries<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from the World Development Indicators database and Marshall and Cole 2010. _Note:_ Figures above or beside each bar indicate the number of countries.

We also looked at several dimensions of trade--export sophistication and shipping connectivity, commodity versus noncommodity exporters as well as landlocked versus other countries. These associations are presented in detail in World Bank (2011a). In any case, export sophistication, shipping connectivity, and state fragility are likely to be correlated with a country’s level of 2009) close to 1.03 percent and a CPIA index of 3.00 in 2009. However, non-fragile states have grown, in per capita terms, at an average rate of 2.27 percent since 1990. The CPIA index for these countries is 3.68 in 2009.

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**Interactive term** : Suppose that two industries have the same level of competition. The industry that is farther away from the technological frontier will innovate less than the one that is closer to the frontier. The interactive term therefore evaluates the effects of competition on growth in labor productivity in a setting in which all industries are not equally close to the frontier. A negative sign suggests that competition is a necessary, but not a sufficient condition to motivate an increase in an industry’s innovative capacity. The impact of changes in the competition regime is stronger if industries are technologically more advanced or closer to the frontier but diminishes for industries located farther away from the technological frontier.

**<u>Table 5. GMM Estimation Results</u>**<sup>**21**</sup>

|_Dependent variable_:|Growth in value added per<br>worker|Log of value added per worker|
|---|---|---|
|Distance to thefrontier(lagged)|-0.12552*|- 1.026538***|
|Competition|0.113523***|0.8717822***|
|Distance to thefrontier*Competition|-0.060857***|-0.13327***|
|DIND80s|0.200927***||
|Constant|0.286821***|5.717783***|
|Observations|1045|1855|
|Groups|73|98|
|Observations pergroup|||
|Minimum|6|8|
|Average|14.32|18.93|
|Maximum|20|24|
|Numberof instruments|71|74|
|_Tipping point in distance beyond which_<br>_competition negatively affects innovative capacity_|_1.865411_|_6.541214_|
|_Source_:Authors’estimates usingISICRev2 –3 digi<br>***Significant at the1%level.|tindustrialdata, COMTRADE|data andTRAINS tariffdata.|
|Note:DIND80sis a dummyfor Indiainthe1980s.|||

GMM: System Generalized Method of Momentums using the Arellano-Bond Methodology and the xtabond2 command in STATA. The option collapse is used to avoid the excessive use of lagged instruments that diminishes <u>the efficiency of the estimates.</u> Our results indicate that several industries in the BICs remain very far from the Korean frontier. The model can be used to calculate how far they are in the following way. If the number 21 The first estimates present the results of using a panel data estimation procedure with fixed effects for every combination of industry and country in the sample. To overcome the possibility of endogeneity in the specification of our model and heteroskedasticity, we use the system GMM estimator because it is well suited for dynamic "small-T, large-N" panels that may contain fixed effects and idiosyncratic errors that are heteroskedastic and correlated within but, not across individuals. We use the two step variant of the system GMM estimator because it is asymptotically more efficient, compared with the one step variant. Nevertheless, two-step standard errors tend to be severely downward biased (Arellano and Bond 1991; Blundell and Bond 1998). To compensate for this, we use a finite-sample correction to the two-step estimation efficiency covariance matrix derived by Windmeijer (2005).

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