rafmacalaba/datause-encoder-data huggingface dataset:
**Table 11c: Predicted change in number of each animal per farm from AOGCM climate scenarios for small farms (animals/farm)**

|||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|---|
|<br>Baseline|number|1.48|2.92|7.09|5.83|31.10|
|2020|||||||
||CCC|0.04|-0.01|-0.57|-0.39|-3.98|
||CCSR|0.05|-0.02|-0.70|-0.57|-4.28|
||PCM|0.21|-0.01|0.01|0.34|-2.81|
|2060|||||||
||CCC|0.25|-0.01|-0.06|-0.24|-4.67|
||CCSR|0.07|-0.02|-0.12|-0.57|-6.82|
||PCM|0.04|0.00|-1.10|-0.99|-5.63|
|2100|||||||
||CCC|-0.09|0.19|-0.27|-1.96|-5.26|
||CCSR|0.40|0.20|-0.23|-1.31|-4.55|
||PCM|0.34|-0.03|-0.17|-0.49|-6.14|

**Table 11d: Predicted change in expected income from AOGCM climate scenarios for small farms (US$)**

|||**Mean**<br>**(US$/farm)**|**Total**<br>**(billions US$)**|**% change**|**Bootstrap**<br>**lower 95 %**|**Bootstrap**<br>**upper 95%**|
|---|---|---|---|---|---|---|
|<br>Expected|income|106|6.00||||
|2020|||||||
||CCC|-6|-0.34|-5.7%|-13|1|
||CCSR|-13|-0.74|-12.3%|-26|-1|
||PCM|13|0.74|12.3%|3|23|
|2060|||||||
||CCC|5|0.28|4.7%|-5|21|
||CCSR|16|0.91|15.1%|-2|23|
||PCM|-3|-0.17|-2.8%|-12|14|
|2100|||||||
||CCC|36|2.04|34.0%|-5|110|
||CCSR|-6|-0.34|-5.7%|-24|54|
||PCM|9|0.51|8.5%|-9|36|

37
outside the home, and since they revert back to the low labor supply after the program ended, we infer that the main barrier to these women’s labor force participation is not the prevailing social norms about women and work, but rather the lack of suitable employment opportunities. Suitability itself is defined by norms related to provision of childcare and doing household chores. Alaref et a. (2024) have similar conclusions in their study of women’s employment in Nepal which measures numerous dimensions of norms. They find that specific norms related to domestic work and care giving, and not those related to women working per say, drive low rates of female employment.

Although a lack of opportunity plays a greater role than social norms in deterring women from entering the labor force for this particular job offer, we do not claim that the former is the limiting factor in developing countries at large. Policies that focus exclusively on the role of social norms or, more generally, on the social and cultural determinants of women’s labor supply, could, however, leave unexploited an important policy margin. Still, our results suggest that policies that increase work opportunities, especially to women at bottom of the skill distribution are important in promoting women’s labor force participation. Future research is needed to identify the extent of the influence that the wage rates, job features, and benefits have in increasing women’s labor supply in different contexts. Tailoring programs to women’s needs, as opposed to more complex attempts at understanding and attempting to externally shift social norms, might play a substantial and effective role in increasing women’s labor force participation.

27
One of the contributions of this paper is to update previous results with more recent data. Using data between 1982 and 2002, we confirm the findings of previous studies: we find that longitudinal wage changes due to either changes in import competition or offshoring within the same industry range from zero to _positive_ and significant, albeit small in magnitude. For example, a 10 percent increase in the number of workers employed by US firms in low-income countries had virtually no impact on wages across all skill groups. However, a 10 percent increase in offshoring to high-income countries is associated with a small increase in wages of less-skilled workers of between 0.1 and 0.2 percent. Likewise, the impact of an increase in import penetration is negligible and not statistically significant when we focus only on manufacturing workers.

However, we also find significant employment reallocation across sectors in response to import competition and offshoring. This finding is consistent with a number of studies, which show that the intensified competition brought about by trade liberalization has led to industry rationalization: the least productive firms exit the industry, and remaining firms shed excess labor.<sup>1</sup> The question left unanswered by these studies is what happens to the workers that are displaced. If most of the downward pressure from globalization on wages occurs in general equilibrium, then the downward pressure on wages resulting from workers leaving manufacturing and entering the service sector would be missed by looking only within manufacturing.

This is a key limitation of the existing literature. Using individual-level data we are able to examine the impact of globalization on wages both within the manufacturing sector and across sectors and occupations. We begin by identifying the within- _occupation_ effect of trade on wages. If it is more costly for workers to change occupations than industries, then the wage impact of traderelated job switches could be significant at the occupation level while being negligible within manufacturing. We find a significant divergence between results for occupational exposure and

> 1 See for example Pavcnik (2002), Paus et al. (2003), Ferreira and Rossi Junior (2003), Fernandes (2007), and Esclava et al. (2009).
It is also worth noting that FSAPs’ content has tended to follow a natural product cycle over time, which reflects basic sequencing constraints, increasing layers of complexity, and evolving international trends and priorities. For example, recommendations on the introduction of (all types of) registries peaks during 2002-2004 while those on the strengthening of those registries keeps rising (Figure 1). Recommendations on the strengthening of Basel Pillar I-type supervision (supervisory-based) account for a dominating share of FSAPs’ total recommendations on the supervision of all financial institutions (banks, insurance companies and pension funds) but are smoothly declining over time (Figure 2). Instead, Pillars II and III-type recommendations started from a lower base but have continued to increase steadily. This conforms well to ongoing supervisory trends.

The results of the regression analysis of FSAP breadth and concentration (the total number of recommendations and their variance across rows) appear in Table 11. Although the effect is not very strong, the variance of FSAP recommendations has tended to decline over time, suggesting that the scope of FSAPs has become broader.

**Figure 1. FSAP Recommendation on Registries**

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which farmers demonstrated each crop. These administrative data align remarkably well with our self-reported household survey data; 5% and 15% of non-demonstrators (defined at the household-crop level) report adoption of improved seed and cultivation, respectively, for promoted crops during the demonstration year, in contrast to 80% and 94% of demonstrators.

**Restriction to promoted crops** Our main analysis leverages data at the farmercrop-survey wave-level and restricts to what we refer to as “promoted crops”. A key choice made by extension agents for treatment villages is the selection of pulses for demonstration of improved seeds; as we do not expect impacts of demonstration on non-demonstrated pulses, pooling demonstrated and non-demonstrated crops together in analysis would meaningfully reduce power. However, no crops were demonstrated in control villages. We therefore identify comparison crops in control villages based on the observation that extension agents were instructed to select crops for demonstration packages based on suitability in their Union (Section 3.1); specifically, we select the crops that were demonstrated in the other sampled village in their Union (and therefore selected by the same extension agent) as comparison crops.<sup>15</sup> We refer to comparison crops in control villages, and demonstrated crops in treatment villages, as “promoted crops”, and non-promoted pulses as “placebo crops.”

The restriction to promoted crops would result in bias if promoted crops were endogenous to treatment assignment; for instance, extension agents could have been aware of the village’s treatment status (regular, shared, or decentralized) before selecting the demonstration package, and this information could have informed their choice of demonstrated crops.

We consider four tests for this bias, which we discuss and implement in Section 4.6. We compare, across treatment arms, the choice of promoted crops, baseline outcomes, and outcomes on placebo crops, and we estimate impacts controlling for crop-survey wave fixed effects. The results of these tests lead us to conclude that potential endogeneity of promoted crops to treatment assignment does not meaningfully bias our results.

> 15In 2 Unions, both villages were randomly selected to be control villages; these 4 villages are excluded from all analysis, as comparison crops are not defined.

19
<!-- Start of picture text --> So<br>N<br>oO<br>o<br>€<br>S<br>no}<br>r=}<br>20<br>c=<br>2<br>3<br>=oO<br>6<br>ro<br>82<br>o<br>a<br>o<br>D<br>£<br><o=<br>wo<br>0 1 2 3 4<br>Child Age (Years)<br><!-- End of picture text --> <!-- Start of picture text --> BR<br>c<br>2a<br>><br>2<br>o>ae<br>c—<<br>2<br>32<br>=oO<br>Eoor2<br>oa<br>o<br>2D Cf<br>£<br><oo=><br>0 1 2 3 4<br>Child Age (Years)<br><!-- End of picture text -->
administrative penalties, and other citations for illegal activity. For each indicator, the system specifies a link to ratings that is clear, unambiguous and publicly available.

The first Green Watch ratings were disclosed through the media in 1999. The program was extended from Zhenjiang to all of Jiangsu Province in 2001, and to eight other provinces during 2003-2005. Nationwide implementation of Green Watch has been promoted since 2005.

Overall, the available evidence suggests a positive impact for the program. Table 1 shows that in Zhenjiang, the percentage of firms with positive ratings (green, blue and yellow) increased from 75% in 1999 to 85% in 2000. The most significant changes were in the extremely-noncompliant black group, whose percentage dropped from 11% in 1999 to 2% in 2000, and a major shift from the partially-compliant yellow group (44% to 22%) to the fully-compliant blue group (27% to 61%).

Evidence for the Green Watch program in Jiangsu Province indicates both increasing participation by firms and improvement in their compliance rates. As shown in Table 1, the number of rated firms increased more than tenfold, from 1,059 in 2001 to 11,215 in 2006; and the percentage of firms with positive ratings (green, blue, and yellow) increased from 83% in 2001 to 90% in 2006. Furthermore, Table 1 suggests that Green Watch ratings provide a strong improvement incentive for noncompliant (red and black) firms, with stronger effects on firms with red ratings (moderate noncompliance) than those with black ratings (extreme noncompliance).

# **4. Data**

This study utilizes a pollution dataset for both rated and unrated firms during the period 1996-2001 in four cities of Jiangsu province (Huaian, Wuxi, Yangzhou, and Zhenjiang). Following the success of the pilot program in Zhenjiang, Huaian, Wuxi and Yangzhou adopted
Formal Sector Migration in Brazil.” _World Development_ 38 (6): 840–56. Anderson, J. E., and E. van Wincoop. 2003. “Gravity with Gravitas: A Solution to the Border Puzzle.” _American Economic Review_ 93 (1): 170–92.

Autor, D. H., D. Dorn, and G. H. Hanson. 2013. “The China syndrome: Local Labor Market Effects of Import Competition in the United States.” _American Economic Review_ 103 (6): 2121–68.

Berry, S. T. 1994. “Estimating Discrete-Choice Models of Product Differentiation.” _RAND Journal of Economics_ 25 (2): 242–62.

Bertoli, S., and J. Fernández-Huertas Moraga. 2013. “Multilateral Resistance to Migration.” _Journal of Development Economics_ 102 (C): 79–100.

Bertoli, S., J. Fernández-Huertas Moraga, and F. Ortega. 2013. “Crossing the Border: Self-Selection, Earnings and Individual Migration Decisions.” _Journal of Development Economics_ 101 (C): 75–91.

Chaney, T. 2008. “Distorted Gravity: The Intensive and Extensive Margins of International Trade.” _American Economic Review_ 98 (4): 1707–21.

Corseuil, C. H., M. Foguel, G. Gonzaga, and E. P. Ribeiro. 2013. “Youth Labor Market in Brazil Through the Lens of the Flow Approach.” Proceedings of the 41st Brazilian Economics Meeting, ANPEC.

Cunha, J.-M. P. d. 2002. “O uso das PNADs na análise do fenômeno migratório: Possibilidades, lacunase desafios metodológicos [Using PNAD Surveys to Study Migrations: Potentialities, Shortcomings and Methodological Challenges].” IPEA Working Paper 875.

Dahl, G. 2002. “Mobility and the Return to Education: Testing a Roy Model with Multiple Markets.” _Econometrica_ 70 (6): 2367–420.

Dustmann, C., T. Frattini, and I. Preston. 2013. “The Effect of Immigration along the Distribution of Wages.” _Review of Economic Studies_ 91 (1): 154–63.

Eaton, J., and S. Kortum. 2002. “Technology, Geography, and Trade.” _Econometrica_ 70 (5), 1741–79.

Fally, T., R. Paillacar, and C. Terra. 2010. “Economic Geography and Wages in Brazil: Evidence from Micro-Data.” _Journal of Development Economics_ 91 (1): 32
# 1S. PRIMARY EDUCATlON$ INVSMTMEM OBRJCTIVES, STRATEPAIS AND LESSONS

Leording for primary education 10 has supported four categories of objectives and related educational inputs:

- eduiattonal-ojortuniti2s through s-hool construction,_xaan_ina

- provision of pre-service teacher training, and strongthening the demand for schooling by helping people overcome fLnancLal, cultural or religLous barrlers to particLpation;

- imnrovina the aualitv of instructLon through curriculum development (such as revislon of syllabi, ruralizatLon of the curriculum, and lntroduction of practical subjects), and Lnstructional development (such as provision of textbooks, Ln-servLce teacher trainlng, and distance educatLon)s

- increasina the efficlency of deliverv by containing cost per pupil, manlpulatlng student flows, and reallocatLng avaLlabl- resources; and

- strengthenLna eSucatlon sector management by developlng the institutLonal capaclty for decLeion maklng, plannlng, and evaluatlon and research.

- These strategies and lessons learned wlll be dlscussed below.

# QbWective Is Expanding Educational gooortunities

Since the early 1970s, Bank prlmary education projects have provlded strong support for the expansion of educational opportunlties. Initlally, thle support focused mainly on the constructlon of school buildings. Between 1970 and 1980, the Bank provided flnancing for the supply of more than four million school places. In addition, extensive support has been provlded for increasing the supply of qualified teachers. Finally, in recent years, considerable attention has been glven to the need to address constraints on the demand for schooling, especially for girls.

School construction is a major part of educatlonal investment. In some countries the annualized capital cost represents 80% of the annual recurrent cost From the outset, the Bank emphasized the need to adapt the school deslgn to local resources, buildlng traditlons, and climate. Throughout the 1970s, Bank projects tested a wlde range of low-cost methods for the bulldlng of durable and functional schools. In the maln, three approaches have been tried, each with varylng degrees of success.

10 ThL chapter draws heavily on Romain, "LendLng ln Primary Education." World Bank, "Education in Sub-Saharan Africa." 8
# **6 Conclusions**

This paper contributes a systematic portfolio review of the International Development Association (IDA) funded projects to FCS over the period 2001 to 2013 and a detailed empirical analysis of the correlations between project and country-level characteristics with project outcome ratings. The portfolio review identifies a decline in proportional amount of IDA resources directed to FCS countries as well as a decline in the number of internationally recruited staff based in these countries.

The empirical analysis finds there is no statistical difference in whether projects obtain at least a moderately satisfactory outcome ratings between FCS and non-FCS countries. This finding is robust to nonlinear model specification checks. In addition some project characteristics that are often thought to be important, such as the experience level of project leaders or the time spent on projects by internationally recruited staff or higher grade level staff, are not significantly correlated with outcome ratings for projects in FCS and non-FCS countries. Factors that are correlated with outcome ratings include project supervision and preparation time and project early warning indicators, such as implementation flags raised during the first half of the project by project leaders. These are all negatively correlated with outcome ratings most likely indicating that these are complicated and difficult projects to implement. This latter finding is consistent with the results presented in Denizer et al [3], which used a larger sample of projects and included IBRD funded projects as well as IDA funded projects. Some factors within managerial control that are positively correlated with project outcomes are office size - it appears important to have a country office of at least 2-5 GF+ level staff, and the project leader appointed to the project - TTLs that have a greater propensity to lead successful projects continue to obtain better ratings on average. This is true even when controlling for country fixed effects and in FCS countries, although to a lesser degree. This finding further demonstrates that project outcome ratings can be difficult to influence in FCS environments. Last, a new approach to control for unobservable project characteristics, such as inherent complexity or ambitious, shows preliminary evidence that changes to the project leader and increases to supervision budget are correlated with improvements in project performance in both FCS and non-FCS countries.

44
with both each other and with the formal sector. An ideal data set would provide details on sourcing and selling patterns by sector, geography, and formality status. In the absence of such data and given the strong correlation of size and formality status, our default approach will be to assume that informal firms exhibit linking preferences _p_ ( _θ, θ_<sup>_′_</sup> ) similar to those of small formal firms, conditional on sector and geography.

This approach is particularly attractive given its straightforward implications for the sectoral and geographic composition of linking patterns. It further allows for informal and small formal firms to experience different wedges in link formation, provided these wedges generate aggregate linking patterns that are still comparable to those of small formal firms at the sector-countysize level. Consider, for instance, a small formal retailer and an informal retailer both seeking to purchase soap. While neither can source directly from manufacturers in Nairobi, the small formal retailer might purchase from a large formal wholesaler locally, whereas the informal retailer—potentially excluded due to their tax status—might source from an alternative local wholesaler. Despite sourcing from different individual suppliers, both retailers exhibit the same patterns once their sourcing is aggregated at the sectoral and geographic levels. The similarity assumption is also motivated by findings in the administrative data, where we document that small formal firms tend to source more locally and from intermediaries (see Table B1).

Nevertheless, this assumption may not fully capture the implications of challenges specific to informal firms for their linking patterns. Therefore, we subsequently introduce modifications to our approach, drawing on stylized facts from the existing literature, to assess the sensitivity of our results to alternative assumptions about the linking patterns of informal firms.

# **5.3 Characteristics of the Revised Network**

How does the revised network that accounts for informal firms compare to the baseline network? First, we find that firm types in sectors and counties with a high incidence of informality are predicted to have a relatively larger increase in outdegrees (Figure C2). Second, the share of total outlinks attributed to Nairobi- and Mombasa-based firms declines from 80% to 75% (see Table 5). While the two cities maintain their prominent role in the network, this shift is meaningful for the remaining counties in relative terms as it represents a 25% increase in their outdegrees. Third, accounting for informal firms reduces the variation in outdegrees across counties by 7.5% (Table 4). We visualize this reduction in inequality by plotting the Lorenz curve for county-level outlinks in Figure 8.

What drives the reduction of outdegree inequality? First, Nairobi and Mombasa become less important as a destination for products and services from other counties. In Figure 9 we plot the row-normalised adjacency matrices, before and after accounting for informal firms, at the county 25
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|---|---|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Guyana SecondaryEducation Improvement|Ingrid Bjerke|
|Youth,Employment,Skills Project Additional Financing|Suleiman Namara|
|Youth,Employment,Skills Project Additional Financing|Suleiman Namara|
|Youth,Employment,Skills Project Additional Financing|Suleiman Namara|
|BR Amazonas DPL|Laura De Castro Zoratto|
|BR Amazonas DPL|Laura De Castro Zoratto|
|BR Amazonas DPL|Laura De Castro Zoratto|
|BR Bahia DPL|Antonio Nucifora|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries<br>|Gibwa A. Kajubi<br>|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|JM Youth Employment in Digital and Animation Industries|Gibwa A. Kajubi|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|MA-Health Sector Support|Aissatou Diack|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|
|National and Regional Roads Rehabilitation|Liljana Sekerinska|