rafmacalaba/datause-encoder-data huggingface dataset:
inclusion of the fixed effects models is important for demonstrating the robustness of our main findings.<sup>21</sup>

# _5.2. Results_

# _5.2.1 Loan-level regressions_

Loan-level regressions that use the full set of 206,785 loans extended through CFPA loan officers from 2006 to 2010 appear in Table 5. In column 1, the dependent variable is the size of each loan, our proxy for depth of outreach to poorer borrowers. Again, larger loans signify wealthier borrowers.<sup>22</sup> The coefficient for _Year 2008 and after_ is positive and highly significant indicating that loans were 1,710 yuan larger after CFPA separated from the government and began relying more heavily on commercial sources of funding. The pattern is consistent with loan officers drifting from their mission to serve the poor as commercial incentives took hold.

The tendency to make larger loans over time was more pronounced for loan officers with a career background in commercial business (the omitted category) than those with a background in local government. This is reflected in the significant positive coefficient for _Months as a CFPA loan officer_ and the significant negative coefficient for its interaction with the government background dummy ( _GOV)_ in column 1. In fact, the negative coefficient for the interacted variable is larger (in absolute value) than that for _Months as a CFPA loan officer_ , indicating that officers with local government experience made smaller loans over time (controlling for the other explanatory variables in the regression, including _Year 2008 and after_ ). In contrast, loan officers with farming backgrounds increased their typical loan size at the same rate as those with commercial business backgrounds, as indicated by the insignificant coefficient for the interaction between months of CFPA experience and the dummy

> those presented in the paper. In part, county fixed effects could help control for any differences in the way that the credit committees of the different branches operate (though again, lending guidelines are uniform throughout CFPA). To conserve space, we present only models that use the more restrictive loan officer fixed effects.

> 21 A similar approach is to run regressions after taking first differences of the dependent and explanatory variables, which we undertook as a robustness check, Results are almost identical for the two approaches when average loan size is the dependent variable in the regression. Results are also similar when the portfolio quality measures based on the ratio of delinquent payments to total disbursements are used as dependent variables, though coefficients in the first differences models are estimated less precisely and thus larger standard errors imply less significance for some explanatory variables.

> 22 As shown in Appendix B, a handful of loans were much larger than the typical ones made by CFPA Microfinance. In an unreported robustness check, we therefore replaced loan size with the log of the loan amount to test whether outliers (very large loans) were driving our results. The sign and significance of the explanatory variables were very similar to those that we present here.

20
Figure 5c is similar to the results from the CCC scenario in Figure 5b but there are slightly higher damages in the Amazon basin, Ecuador, and Peru.

# 6. **Conclusion**

We examine the vulnerability of Latin American agricultural production to climate change. Surveys were collected across 7 countries in South America. Four countries are from the Southern Cone region and three countries are from the Andean region. Using cross sectional analyses, we test the effect of climate, soils, and other control variables on both land value and net revenue.

Our results indicate that many of the control variables explain the geographic variation in land value and net revenues across Latin American farms. Many soils are relatively beneficial to farms, whereas a few soils are harmful. Soils with some mix of sand and clay command relatively higher profits whereas clay soils are relatively harmful. Farms with electricity earn significantly higher profits and farms with computers have higher land values.

However, the main focus of this study concerns the sensitivity of farm values to climate. Both land value and net revenue are sensitive to temperature and precipitation. The land values and net revenues of farms have a hill-shaped relationship with temperature. Farms at moderate (temperate) temperatures earn more profits compared to farms at extreme (tropical) temperatures. Small farms and large farms have very similar responses to temperature and summer precipitation. However, small and large farms did have different responses to winter precipitation. Large farms located in places with higher winter precipitation had lower land values but winter precipitation had no effect on the land value of small farms. Rainfed farms and irrigated farms have 19
# **Appendix A3: Economic and Political Variables**

**Table A3: Descriptive Statistics**

||**Mean**|**Standard**<br>**Deviation**|**Minimum**|**Maximum**|
|---|---|---|---|---|
|Political Instability|3.76|1.04|2.31|7.02|
|ln Industrial Production|20.29|4.50|4.36|23.59|
|Δ ln Industrial Production|0.00|0.15|-1.86|1.38|
|Inflation|0.02|0.02|-0.05|0.12|
|Δ Exchange Rate|2.39|3.09|-1.32|9.41|

Note: Inflation and Δ Exchange Rate are Winsorized at the 1% level.

40
Unfortunately, these suggestions may be of limited usefulness-or will require some creativity to use-in in-person surveys conducted for the purpose of building a large dataset.

Not surprisingly, respondents are less likely to admit to incriminating behavior in a faceto-face interview than in a self-administered survey. Tourangeau and Smith (1996) note that the "literature on sensitive questions demonstrates that the method of collecting the data can affect the answers that are obtained.... Several of these studies have demonstrated that selfadministration of sensitive questions increases levels of reporting relative to administration of the same questions by an interviewer. Respondents are apparently reluctant to admit to an interviewer that they have engaged in illegal or otherwise embarrassing activities. Studies comparing self-administered questionnaires with conventional paper-and-pencil interviewer administration have shown that self-administration increases reporting of abortions, alcohol consumption, and illicit drug use." Although most World Bank firm surveys are conducted in-person, it may be possible to combine a self-administered component with the in-person interview. For exarnple, the interviewer could hand each respondent a package containing additional questions on sensitive topics and an addressed, starnped envelope that the respondent can use to retuni the survey. The self-administered fbrm would have to be coded so the self-administered section can be matched to the in-person section. This combination raises two issues that are not addressed in the literature. First, would respondents be more likely to be truthful on a self-administered questionnaire if they know that, despite answering the questions in private, thei.r answers will not be anonymous? On one hand, they can answer the questions in private and not admit their behavior verbally.<sup>8</sup> On the other hand, if the answers can be linked back to their firm it may not matter whether the answer is given in written or oral form. This is, however, an open question and could be tested empirically during the survey pretest phase.

_Randomized responses_ are another way to increase reporting of such behavior (Sudman and Bradburn 1982, Fowler 1995). As will be clear, this method is especially useful for learning the percentage of a population engaged in certain activities, but by its nature acids substantial measurement error, making hypothesis testing difficult. The method involves presenting the s It is conceivable that speaking out loud may affect truthfulness, especially since the interv.iewer is likely to be a citizen of that country, and most likely a local resident.

34
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# **CENTRAL-BANK BALANCE SHEET**

|**Assets**||**Liabilities**||
|---|---|---|---|
|Gross external assets|(A)|External liabilities|(L)|
|**Domestic assets:**||**Domestic liabilities:**||
|Government obligations|(F)|Monetary base:|(B)|
|Commercial-bank obligations|(H)|Currency in circulation|(C)|
|||Bank reserves|(R)|
|||Government deposit account|(Q)|
|||Non-monetary domestic liabilities|(U)|
|||Net worth:|(V)|
|||of which, government capital|(K)|

The model determines the flow changes in the values of L, B, Q, U, H, and A, then solves simultaneously for the flow changes in the values of F and V.

The model sets out by calculating the economy's average holding of broad money over the year, and from this computes the change in broad money outstanding over the year: and The period-end value of M is then calculated as The flow increases in the monetary base and its components are given by and 15
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**Appendix Table 3: Impacts over time on having BRC**

||||BRC<br>|||
|---|---|---|---|---|---|
||Follow-up1|Follow-up2|Follow-up3|Follow-up4|All|
|Panel A: Full sample||||||
|Treatment 1: BRC|0.544***|0.543***|0.467***|0.504***|0.515***|
||(0.021)|(0.021)|(0.022)|(0.022)|(0.016)|
||0.000|0.000|0.000|0.000|0.000|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Treatment 2: BRC + TPIN|0.569***|0.538***|0.487***|0.534***|0.532***|
||(0.031)|(0.031)|(0.032)|(0.031)|(0.024)|
||0.000|0.000|0.000|0.000|0.000|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Treatment 3: BRC + IS + BBA|0.680***|0.644***|0.610***|0.612***|0.636***|
||(0.016)|(0.017)|(0.018)|(0.018)|(0.013)|
||0.000|0.000|0.000|0.000|0.000|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Control group mean|0.061|0.066|0.105|0.093|0.081|
|Sample size|2830|2720|2664|2686|10900|
|p-value: Treatment 1 = Treatment 2|0.481|0.885|0.584|0.381|0.519|
|p-value: Treatment 1 = Treatment 3|0.000|0.000|0.000|0.000|0.000|
|p-value: Treatment 2 = Treatment 3|0.000|0.001|0.000|0.016|0.000|
|p-value test of equality|0.000|0.000|0.000|0.000|0.000|
|p-value test of equality of treatment effects<br>over time||||||
|Treatment 1|n.a.|n.a.|n.a.|n.a.|0.002|
|Treatment 2|n.a.|n.a.|n.a.|n.a.|0.098|
|Treatment 3|n.a.|n.a.|n.a.|n.a.|0.001|

Notes: Specifications include strata dummies, a variable representing the initial outcome at baseline, and a variable indicating missing data at baseline. "All" has data pooled for all follow-up surveys. "All" includes dummies for survey round. Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q- values are reported below standard errors (q-values in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

49
# **1. Introduction**

The purpose of this paper is to investigate the link between sectoral concentration in the search for innovations and export booms and overall performance in these search processes at the country level. In order to inform the modeling of these search processes as well as the debate as to whether national innovation policies should be focused on certain sectors or diversified, we ask the question: Do countries that are high performers in these search processes specialize in a narrow range of sectors, or are their successes spread across a wide range of industries?

Previous research has focused on cross-country patterns and trends in the concentration of patenting activity of OECD countries. We expand this country coverage from the OECD to a much larger worldwide dataset, and extend the focus from only technologically complex on-the-frontier innovation to also include inside-the-frontier innovation (the emergence of new industries, or discoveries) and product-level export booms. All of these forms of innovation are important for growth, involve search across a wide array of potential sectors, and are frequently supported by government in some fashion. As such, this paper assesses the degree to which success in these search processes is focused or diversified in order to inform both public support strategies and modeling in theoretical work.

The question of whether innovation is persistent and concentrated in particular industries or distributed across a wider range of technologies arises from two very different characterizations of innovation. One view, attributed to Schumpeter 1934 and subsequently labeled the Mark I model, is a model that describes innovation as a stochastic process where relatively homogeneous firms fish for innovations in a pool of 1
**<u>Table 5: Area allocation and changes in aggregate crop mix</u>**

||<br>**Total **|<br>**Center **|<br>**North **|<br>**South **|**East**|**West**|**<50**|**50-120**|**120-500**|**>500**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A: 2021**|||||||||||
|Average area cult. (ha)|346.46|269.29|676.69|248.54|438.45|410.76|21.52|77.93|225.19|1713.87|
|% of farmers cultivating|||||||||||
|... wheat (ha)|0.70|0.67|0.58|0.76|0.81|0.77|0.53|0.71|0.78|0.81|
|... barley (ha)|0.30|0.28|0.15|0.46|0.33|0.24|0.22|0.29|0.33|0.41|
|... sunflower (ha)|0.59|0.64|0.55|0.70|0.71|0.18|0.38|0.60|0.68|0.76|
|<br>... maize (ha)|0.46|0.56|0.66|0.20|0.42|0.33|0.25|0.46|0.52|0.72|
|... soybean (ha)|0.24|0.27|0.28|0.01|0.12|0.59|0.16|0.28|0.25|0.30|
|<br>... rapeseed (ha)|0.10|0.08|0.07|0.11|0.01|0.23|0.00|0.05|0.12|0.33|
|... other (ha)|0.19|0.14|0.28|0.11|0.18|0.39|0.13|0.16|0.22|0.29|
|<br>Total area grown with|||||||||||
|... wheat (ha)|176,082|61,484|29,619|42,179|19,128|23,672|3,029|15,569|31,615|125,869|
|... barley (ha)|34,175|12,326|2,533|14,496|2,025|2,795|986|3,595|7,405|22,189|
|<br>... sunflower (ha)|171,289|74,257|37,624|29,469|17,483|12,456|2,945|14,364|27,197|126,783|
|... maize (ha)|172,414|68,517|71,189|4,752|6,714|21,242|1,651|11,401|20,846|138,516|
|<br>... soybean (ha)|<br>48,211|<br>13,042|<br>18,631|<br>105|<br>2,098|<br>14,335|<br>1,088|<br>5,487|<br>6,984|<br>34,652|
|... rapeseed (ha)|33,570|7,913|4,490|7,830|490|12,847|0|766|3,997|28,807|
|... other (ha)|29,117|8,858|11,853|2,574|1,168|4,664|544|2,824|5,542|20,207|
|**Panel B: 2022**|||||||||||
|Average area cult. (ha)|370.70|301.35|716.61|261.69|400.48|436.18|22.60|83.67|242.02|1832.09|
|% of farmers cultivating|||||||||||
|<br>... wheat (ha)|0.75|0.74|0.60|0.82|0.89|0.79|0.56|0.78|0.83|0.88|
|... barley (ha)|0.32|0.32|0.15|0.49|0.32|0.26|0.22|0.30|0.38|0.45|
|<br>... sunflower (ha)|0.65|0.70|0.66|0.75|0.71|0.21|0.40|0.69|0.75|0.80|
|... maize (ha)|0.48|0.60|0.63|0.22|0.38|0.39|0.25|0.50|0.55|0.73|
|... soybean (ha)|0.30|0.34|0.37|0.01|0.12|0.64|0.19|0.33|0.30|0.38|
|... rapeseed (ha)|0.12|0.10|0.10|0.13|0.02|0.27|0.01|0.07|0.15|0.38|
|... other (ha)|0.21|0.16|0.32|0.13|0.21|0.42|0.15|0.18|0.25|0.31|
|<br>Area/farm grown with|||||||||||
|<br>... wheat (ha)|183,742|65,695|29,231|40,966|23,887|23,963|3,072|16,116|32,644|131,910|
|<br>... barley (ha)|<br>33,119|<br>12,581|<br>2,416|<br>13,855|<br>1,376|<br>2,891|<br>863|<br>3,691|<br>6,922|<br>21,643|
|... sunflower (ha)|201,675|88,306|59,642|30,983|10,955|11,789|3,096|16,282|31,800|150,497|
|<br>... maize (ha)|<br>154,938|<br>70,159|<br>53,187|<br>5,693|<br>5,660|<br>20,239|<br>1,607|<br>10,481|<br>18,841|<br>124,009|
|... soybean (ha)|68,504|19,754|24,823|477|947|22,503|1,502|6,750|9,238|51,014|
|... rapeseed (ha)|41,068|11,366|5,977|11,295|983|11,447|70|1,354|4,677|34,967|
|<br>... other (ha)|28,331|7,870|11,042|3,501|1,046|4,872|549|3,310|7,207|17,265|
|No. of obs. (farms)|1,919|915|260|408|112|224|476|693|460|290|

_Source:_ Own computation from MAPF/WB/EU small producer survey _Note:_ Data are for the balanced panel of farms that report cultivating any crop in both the 2021 and 2022 agricultural seasons.

24
receiving transfers from employers may simply capture a fall in having an employer. In order to assess whether the effect is driven by this, in Panel B of Table 9 I restrict the sample to ultra-poor households who are engaged in wage-employment at baseline as well as at the two followup surveys. Similarly, I find that households within this subsample are less likely to receive transfers from their employers. Note that this analysis has a caveat (since it is based on a selected sample), as such, it should be interpreted as suggestive evidence.<sup>31</sup> Overall, the findings in this section imply that the program led to a change in the mechanisms through which ultra-poor households reciprocate transfers from others. The reduction in their poverty level enabled the ultra-poor to have reciprocal transfer connections with other households in their communities, and reduced their likelihood to receive transfers from their employers. The latter suggests that there was a reduction in the interlinkage of labor and insurance arrangements of the ultra-poor.

# **6 Discussion**

## **6.1 Outside-village Transfers**

One of the emerging findings is that the program crowded out outside-village transfers. Unfortunately, I have limited information on the identify of outside village transfer sources/recipients. One important piece of information about outside-village transfers: I know if the transfers are given/received to/from the respondent’s first-degree family members.<sup>32</sup> Moreover, at baseline, respondents were asked to classify the wealth status of their members’ households relative to their own first-degree family (whether the family members are better-off, same or worse-off in terms of their wealth). I exploit this information to shed some light on the changes in transfers outside the village. First, note that a large share of the outside-village transfers consists of transfers from first-degree family members. In particular, 63% (81%) of the outside-village transfers received (given) by ultra-poor in the control group at midline/endline were from (to) their first-degree family members living outside the village. Second, I anthe effects on transfers for versus alyze outside-village separately family non-family transfers, Appendix Table S1.9 displays the results. On the extensive margin, ultrahouseholds were less to be net receivers of transfers from poor significantly likely outside the village and this seems to be driven mainly by non-family transfers. On the intensive margin, I find that the effects on within-family transfers display a similar pattern as outside-family transfers. Treated ultra-poor households received BDT 74 less in net transfers from their first-degree family members who live outside the

> 31In Appendix Table S2.6 I assess the differences in treatment effects on probability of having transfers from employers at endline relative to midline. None of the differences are precisely estimated and the treatment effects were similar at the two followup surveys.

> 32Parents, children, siblings and siblings-in-law.

22
countries.<sup>10</sup> Since demand for skilled labor is assumed to be less elastic than demand for unskilled labor, the initial downward pressure on skilled wage rates in the host countries is more pronounced than that on unskilled labor. Skilled workers are also complements to capital, and as capitalintensive sectors expand, the demand for skilled workers also rise and push up their wages. However, the greatest detrimental wage effects are experienced by the incumbent LMI migrants in the receiving countries (Figure 7).

New LMI migrants are assumed to be perfect substitutes for incumbent LMI migrants, whereas they are imperfect substitutes for natives of and migrants from high-income countries.<sup>11</sup> This assumption is consistent with Ottaviano and Peri (2012) who examine the substitutability between natives and immigrants of similar education and experience levels in the USA. That paper found a small but significant degree of imperfect substitutability between natives and immigrants which, when combined with the other estimated elasticities, implies that in the period from 1990 to 2006 immigration had a small effect on the wages of native workers with no high school degree (between 0.6% and +1.7%). It also had a small positive effect on average native wages (+0.6%) and a substantial negative effect (−6.7%) on wages of previous immigrants in the long run. Manacorda, Manning, and Wadsworth (2012) find similar evidence on imperfect substitutability between native and migrant workers for the UK.

Remaining workers in LMI sending regions where the labor supply drops significantly enjoy pronounced real wage increases as the marginal productivity of remaining labor rises with the increase in capital-labor ratios (Figure 3 and Figure 4). An exception is the real wage effect for unskilled labor in South Africa. This exception arises from the fact that the skill composition of South African migrant workers is atypical among the LMI regions. Only 35 percent of South Africa’s total out-migrant workers were unskilled in the 2007 benchmark year, while for most other LMI sending regions unskilled workers dominate in headcount terms. Thus, in the SouthNorth migration scenario, South Africa sends out more skilled than unskilled workers, leading to unskilled workers becoming relatively more abundant in the total labor force.

> 10 Comparative-static CGE analyses – such as Walmsley, Winters, and Ahmed (2010) – would miss this induced accumulation effect, and are thus bound to seriously underestimate the gains from migration for host nations. 11 The size orders for the real wage effects are obviously sensitive to the values for the substitution elasticities σl and σlz between workers by origin in equations (1) and (3) above. The simulation results presented here assume σl = 4 and σlz = 5.

15