rafmacalaba/data-use-mentions-extended huggingface dataset:
- 29 -

multipliers occurs because while large farmers are more dependent on purchased inputs (as reflected in their lower value added to gross output ratios) and hence have potentially stronger links to the nonfarm economy, the potential does not materialize because many of those inputs are produced in urban areas or overseas. But this result assumes identical consumption patterns among small farmers and estateholders. If estate families are found to have more urbanized expenditure patterns--and unfortunately consumption data are simply unavailable indicating whether or not this is so--the estate-dominated regions would generate regional growth multipliers of only about 1.31, substantially below those of smallholders. Finally, the model has been used to separate out the contribution of consumption and production linkages in overall regional multipliers (Haggblade, Hazell and Brown, 1987). Under conditions prevailing in SubSaharan Africa, that work indicates that consumption linkages account for about 80 percent of total agricultural growth multipliers. In Asia, on the other hand, the relative importance of consumption linkages appears much smaller. 1'a the Muda region of Malaysia, consumption linkages account for about 60 percent of the total multiplier, while in North Arcot the consumption share drops to 50 percent (Bell, Hazell and Slade 1982; Hazell, Ramasamy and Rajagopalan, forthcoming). This does not arise because the consumption linkages are more powerful in Africa than in Asia; the marginal budget share for nontradables is smaller in Africa. Rather, the different relative importance of consumption and production in overall growth multipliers reflects the much weaker inter-industry linkages in Africa. The weak African production linkages likely stem, as discussed earlier, from the much lower level of mechanical input use in Africa along with the absence of important construction and maintenance associated with Asian irrigated agriculture. Reiterating inferences drawn from the Togo and Sierra Leone direct multiplier estimates above, the modeling efforts
2 Motivation and literature review**

The importance of social capital for economists is evident from the variety of economic phenomena that have received better explanation with the use of the concept of social capital, such as economic growth (Knack and Keefer 1997), and, among others, individual self-reported life satisfaction and happiness.

The literature on what is now called ‘happiness economics’ has been growing rapidly, often by basing the analysis on psychology, and by yielding implications for public policy. A variety of evidence on the validation and reliability of subjective data of this kind has 3
and waste management (E), construction (F), transportation (H), and real estate (L).<sup>8</sup> Using a smaller sample, for which a more detailed breakdown into 89 sectors (NACE 2) is available, we obtain similar results, finding that 33 percent of guarantees are toward transition sensitive sectors.

Figure 2 – Sectoral breakdown of CGSs’ guarantee portfolio (NACE1 sample, 2020) Source: Authors based on AECM survey.

**Most of the exposures of CGSs are towards sectors that are in the 30-60 percentile of GHG intensity.** Table 2 shows the shares of GHG emissions, CO2 emissions, CGS exposures, and value added per sector for our sample for which 2-digit sectoral data is available. Sectors are grouped into deciles based on GHG intensity. We find that CGSs are not highly exposed to the most GHG intensive sectors, such as coke and refined petroleum products, chemicals, basic metals, utilities, and air transport. This also holds when comparing exposures to value added. In the second decile, CGSs have a higher exposure share compared to the share of value added. This is mostly driven by a relative high exposure of CGSs to crop and animal production and land transport. The majority of CGS exposures are however found in the fourth, fifth and sixth deciles, with a

> 8 Since not all manufacturing is equally climate policy relevant, we weight this sector using a factor of 0.5. All other sectors are weighted using a factor of 1.0.

11
. The surveys were collected from 562 slum households that represent the slum households in the entire city of Pune. This paper also retrieves slum-level information from the _Pune Slum Atlas_ , prepared by the Maharashtra Social Housing and Action League, or MASHAL (MASHAL, 2011), and combines it with the household survey data. The dataset, constructed in this way, contains information about detailed housing, households, and slum and locational characteristics.

The series of analyses performed in this paper clarify that even in the presence of other legal and non-legal factors, slum residents highly evaluate the benefits from slum declaration as to what improves their tenure security. The hedonic analysis reveals that slum declaration status is associated with approximately 19% of the average housing rent in Pune’s slums. The estimation result is robust against a bias stemming from spatial effects and selectivity in survey responses. The calculated MWTP is 600 Indian rupees, which is equivalent to approximately 6% of the average monthly expenditure of slum households in Pune. This is a strong premium, considering that a majority of slum residents enjoy _de facto_ tenure security to some degree, as demonstrated by their fairly long average duration in their current 3
Survey (FIAS) that followed a subset of households surveyed by the IHPS 2013 and that were located in districts where flooding was most pronounced; (iii) monthly data on humanitarian aid distribution by the World Food Programme and partners over the period of 2012-2017 aggregated at the traditional authority (TA)-level<sup>2</sup> and matched to

> 2 In Malawi, TAs are in charge of administration of traditional land within a particular territory, and they perform various other cultural and administrative roles. There are around 250 TAs in Malawi, ranging from 3-15 TAs per district (NSO, 2008).

4
activity and other functions|196|383|449|
|Family of two adults and two children - Including physical activity and other functions|690|1,349|1,581|

_Source: Own estimates based on EC reference food baskets, official Food CPI estimates, and a 2015 exchange rate of 0.5114 EUR/BGN._

**While significantly less restrictive, the new income eligibility thresholds resulting from the reform are still below the Bulgarian food basket for all household types; this means that Bulgarian families who have insufficient income to satisfy adequate food intake but are still above the current eligibility thresholds are not eligible for the MSA benefit.** We compare the food basket's cost with the family's differentiated minimum income for different family types before and after the reform (Figure 8). The comparison shows a substantial increase in the minimum income thresholds; however,  the food basket cost is still significantly higher than the eligibility thresholds for different family types, suggesting that the post-reform income thresholds are still restrictive. For example, a Bulgarian family of two parents with two children earning more than 605 BGN per month in 2020 is not eligible for the MSA benefit, given that their income surpasses the maximum income threshold. However, the monthly budget in 2020 needed for an adequate food intake for this family is estimated to be 1,455 BGN (Figure 8), while the relative poverty line (AROP) for this type of family was 1,059 BGN (in 2020).<sup>25</sup> Despite being at risk of poverty and unable to buy a minimum food basket, this family cannot cover the shortfall with the MSA benefit under the updated formula. Figure 8 below shows that these restrictive income thresholds disproportionately affect families with more children (2 or 3 children), which are overrepresented among the poor. There is scope for increasing these income eligibility thresholds closer to the levels of the Bulgarian food basket to

> 23 Source: EC, Directorate-General for Employment
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4 liquid form. Moreover, as we show below, price stabilization has proven expensive to farmers.

Even if nominal price stabilization limited price volatility to producers it became fiscally unsustainable after the mid- 1980s. The objective of producer price stabilization then changed from inter-annual stabilization to intra-annual stabilization (within seasons). Since 1990, Cote d'Ivoire has not attempted inter-annual price stabilization and it is unlikely that it will try to do so again.

The objective of remunerative producer prices has not been well met. Figure 1 compares Ivorian producer prices to world prices in real terms; the latter are represented by the International Cocoa Organization (ICCO) price converted to FOB Cote d'Ivoire. Following the collapse in 1990 from CFAF 400 to CFAF 200 per kg, producer prices remained low as a share of FOB. Though nominal prices rose after the 1994 devaluation and the (unrelated) recovery of world prices that began in 1993, nonetheless, real producer prices during the 1990s have been less than one-half of their 1980 levels. The difference between producer and FOB prices was enormous before 1987, became negative in 1988 and 1989, and has widened again since 1993.

_Figure 1. Cote d'Ivoire -- Quarterly cocoa prices (1990 US $/mt)_ <!-- Start of picture text --> 3000<br>2500<br>X Producer -FOB<br>2000<br>1500<br>1000<br>500<br>0<br>83 83 84 85 86 86 87 88 89 89 90 91 92 92 93 94 95 95 96 97<br><!-- End of picture text -->

**_Source:_** Authors'calculations from World Bank (1998).

As a share of FOB, producer prices in Cote d'Ivoire have been low compared to those in other major producing countries. Data from 1995 (Figure 2) show that the share of the producer price in Cote d'Ivoire was smaller
For East Africa, AGOA has a growing positive impact on apparel exports, the impact is significantly lower in the early AGOA period than after the MFA ended. In contrast, for Southern Africa the average impact of AGOA on apparel exports is substantially higher in the early AGOA period and it decreases after the MFA ended in 2005 until it becomes insignificant. While Southern African countries initially took advantage of the opportunities created by AGOA, they suffered more after the MFA was phased out. These patterns are largely consistent with the four apparel stories in Section 4.

Countries that did not take any advantage of the US trade preferences probably faced other binding constraints. The boom-bust or boom-stagnation dynamics observed in many other African countries suggest that while trade preferences helped jump-start their exports, they did not create durable comparative advantage that survived increased competition after 2005 from previously quota-constrained countries such as China. Specific country events like Madagascar’s political instability after 2009 show that any benefits of infant industry preferential access could be eroded by broader political and economic shocks. Late success stories may be because the initial inadequacy of domestic conditions was remedied by domestic reforms.

# **8. Firm-level evidence on the impacts of AGOA**

We now consider the firm-level dynamics underpinning the country-level impact of AGOA on apparel exports. To this end, we use customs transaction data for four African countries that represent a spectrum of apparel export experiences: Madagascar, boom-and-bust; Mauritius, boom-and-stagnation; Kenya, reasonably sustained growth; and Ethiopia, late take-off. In each case, we seek evidence of durable benefits of preferential access to the US market.<sup>42</sup>

> 42 The customs transaction data was collected in the context of the Exporter Dynamics Database described in Fernandes et al. (2016). Details on the data are provided in Online Appendix D.

28
00487)|
|VIX Index|-0.101**|0.0108|-0.00396|-0.0941***|-0.121**|0.0102|-0.00899|-0.117***|
||(0.0468)|(0.0192)|(0.0253)|(0.0321)|(0.0542)|(0.0218)|(0.0302)|(0.0374)|
|US Economic Policy|-0.00109**|-0.000531***|-0.000202|-0.000485|-0.000838*|-0.000384**|-7.68e-05|-0.000377|
|Uncertainty|(0.000449)|(0.000187)|(0.000248)|(0.000308)|(0.000460)|(0.000186)|(0.000259)|(0.000318)|
|Commodity Terms of|-0.745|0.113|-0.424|-0.319|-0.717|0.0571|-0.438|-0.257|
|Trade|(0.519)|(0.216)|(0.287)|(0.356)|(0.509)|(0.206)|(0.286)|(0.352)|
|Oil Price|-0.0360|-0.00605|0.0256|-0.0550|-0.0656|-0.0109|0.0158|-0.0721**|
|_(in logs)_|(0.0497)|(0.0205)|(0.0272)|(0.0341)|(0.0495)
largest randomized evaluation in the financial education literature, covering 892 public high schools in six Brazilian states and approximately 25,000 students.

As part of the original study, schools in the study sample were assigned their treatment status through stratified and matched randomization, with schools within each state matched into pairs based on their pre-existing school- and community-level characteristics. Within each pair, one school was then randomly assigned as treatment while the other served as control.

Treated schools received financial education material and teacher training. Control schools did not receive any material or training but participated in surveys and testing in the same manner as the treated schools. One eleventh grade class in each school participated in our study. Treated classes received the first semester of financial education during the second semester of eleventh grade (Fall 2010) and the second and third semesters of financial education throughout twelfth grade (Spring 2011 and Fall 2011), the last year of high school. Most students in the sample were between 15 and 17 years of age at the start of the intervention. The rationale for choosing this age group was to engage with students who were already making some personal financial decisions, for example, when purchasing consumer products. Many students in this cohort also worked and earned income so there was an opportunity to apply newly learned financial concepts to their concurrent decisions.

The short-term analysis was based on three rounds of data collection: baseline (August 2010), follow-up 1 (December 2010), and follow-up 2 (December 2011). In addition, complementary administrative data on school graduation and dropout rates was compiled for the study period. Finally, teachers and principals were interviewed for feedback on the program.

# **_2.b. Short-Term Results_**

The empirical analysis of short-term impacts used the three surveys described above and applied the following intent-to-treat OLS specification: where yi,s,f is a measure of the financial knowledge, attitude, or behavior, of student _i_ in school pair _s_ at follow-up _f_ . The variable Treatmenti,s
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systematically more likely to implement Basel III. We discuss the sample selection issues and then introduce our baseline framework. Given that our panel contains only two periods, we take the first difference of our dependent variable and regress it on the pre-treatment values of the explanatory variables.

Therefore, our baseline panel specification in its differenced form is as follows: Δa2f_sme�,�,� denotes the measure of access to finance constraints for firm _f_ in country _c_ . The difference, Δ, is taken between the values before and after the treatment—the exact years are specified in table A.1, in the appendix. B3A�0/#�� represents our variable capturing the effect of Basel III implementation, with β assessing the estimated effect of Basel III on SME access to finance. The symbol # captures the gestation lag at the time of the availability of the Enterprise Survey data after Basel III implementation and is distributed between (0,1] . X�,�,�� controls for firm-level and Z�,�� for bank and/or banking system characteristics before the treatment. W�,�� captures the country-level variables (such as the initial GDP per capita or contract enforcement levels). ES_gap� is a variable controlling for the time between the spells of the Enterprise Surveys across countries. This could be important for trending dependent variables, such as progressing financial inclusion. λIC�,�� represents initial conditions, that is, the level of access to finance constraints prior to Basel III implementation (in the initial period). ε�,� is a residual.

For simplicity, we present the above equation in linear form, which is accurate in case we were to estimate it using ordinary least squares. However, because of the limited dependent variable (access to finance as a constraint), the equation translates accordingly to a nonlinear form and is estimated using the ordered logit model. The standard errors are clustered at the country level, and the fixed effects disappear because of
weak instruments, I cannot enter all of these in the same specification. However, as Appendix Table A.4 shows, their individual inclusion has negligible effects on the main point estimates and their statistical significance. My findings seem to come from what income and inequality imply for the number of people wanting public pre-primary education, and from their political influence, rather than one of these more mechanical channels.

> 43The same analysis, applied to pre-primary quality, also reveals almost no change in the size of the effects.

> 44E.g., investment in roads and public transit might boost enrollment by reducing the cost of getting to school.

> 45Urbanization is as classified by IBGE (2000). Labor force participation is the fraction of the adult population that is economically active. Racial fractionalization is an ethnolinguistic fractionalization (ELF) index (an HHI), computed using data on the population in each of four categories: black or brown, white, asian, and indigenous.

34
-20<br>-40 60<br>60 -40<br>-60 40<br>40 -60<br>-80 -80 20<br>20<br>-100 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

37
Population density _×_ COVID|||-11.9***|-12.2***|-12.1***|
||||(0.83)|(0.89)|(0.98)|
|Constant|8.77***|8.84***|0.63|0.70|-4.78***|
||(0.88)|(0.89)|(0.91)|(0.90)|(1.12)|
|Country fxed efects|Yes|Yes|Yes|-|Yes|
|Date fxed efects|Yes|Yes|Yes|Yes|Yes|
|2017Q1 adjustment|-|-|-|Yes|-|
|Starting period|full sample|full sample|full sample|Full sample|_≥_2017Q1|
|Obs|39176|39176|39176|39176|23301|
|Adjusted _R_<sup>2</sup>|0.27|0.27|0.32|0.33|0.41|

_Note_ . The sample includes 2165 first administrative regions of 116 EMDEs between 2020Q1 and 2020Q4. Nighttime light growth is in percent. Both average nighttime light per capita and population density are in logs. Throughout, country and date fixed effects are included. Standard errors are clustered at the country level and in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# **8 Conclusion**

Annual DMSP nighttime light data have already contributed greatly to deepening our economic understanding (Donaldson and Storeygard, 2016). With the availability of monthly VIIRS data
Due to the small sample sizes and evidence of inconsistent matching of suitability classification relative to the MAPS plot-level soil sample classification, results are not reported in Table 4 for highly suitable (S1) or marginally suitable (S3) categories for AFSIS. Analysis on these categories, available in annex tables for all tables from this point forward, revealed highly-sensitive results contrary to expectations, including those expectations rooted in agronomic science, and, depending on the specification, implied potential yields far greater than agronomically feasible (for S1 plots). These unattainable potential yields, which are a function of low technical efficiency scores, support the descriptive finding that the geospatial data fails to sufficiently and consistently distinguish between maize-specific soil suitability relative to ground-based measures.<sup>17</sup> Coefficients in the overall specification are generally in line with those reported for moderately suitable (S2) plots, both for MAPS- and AFSIS-based specifications.

While AFSIS results for S1 and S3 are not reported here due to the sensitivity and inconsistency of findings (see Annex Table A3 for full reporting), there are takeaways from the comparison of MAPS S1 and S3 categories. Most apparent is the insignificant effect of cultivation pattern on S3 plots. In this category, neither the binary indicator on pure stand cultivation nor the intercropping seed rate is significant, suggesting that production on S3 plots is unchanged with cultivation pattern. On S1 plots, however, returns to cultivating pure stand maize are positive and significant, and higher in magnitude than the returns to pure stand cultivation on S2 plots. Related to seeding, the S3 specification exhibits a negative and significant coefficient on quantity of seed planted. A negative coefficient on seed application is contrary to expectations. One potential explanation could be over-seeding on these lower-suitability plots in an attempt to encourage greater production, while serving only to crowd out successful plants.

Across all specifications reported, technical inefficiency is largely unexplained by observable factors. While not statistically significant, coefficients on the technical inefficiency predictors are generally in the expected direction (Table 4)