rafmacalaba/datause-encoder-data huggingface dataset:
sorghum yields by 0.002 tonnes per hectare. Temperature has the smallest effect on sorghum yields (coefficient of ‐0.07 tonnes per hectare) and the greatest on rice yields (coefficient of ‐ 0.28 tonnes per hectare). The effect on maize yield of roughly 12 percent loss per °C is consistent with estimates of 10 percent in the literature for Sub‐Saharan Africa (e.g. Jones and Thornton, 2003). The rice and sorghum sensitivities represent roughly 17 percent and 7 percent loss per °C, respectively. Time trends are significant only in the rice yield function, where they are significant and positive suggesting the presence of ongoing technological progress.

Table 3: Estimation Results for Tanzanian Grains Yield Functions; Dependent Variable is Yield <u>(tonnes/hectare)</u>

|Coefficients|Maize|Rice|Sorghum|
|---|---|---|---|
|Intercept|4.5705|‐87.5692|2.2699|
||(9.245)|(‐4.111)|(6.345)|
|Year||0.0476||
|||(4.402)||
|Precipitation (mm/month|0.0048|0.0049|0.0021|
|average for Jan‐June<br>growing season)|(5.597)|(4.166)|(3.909)|
|Temperature (°C average|‐0.1656|‐0.2817|‐0.0673|
|for Jan‐June growing<br>season)|(‐7.364)|(‐7.318)|(‐4.062)|
|Adjusted R‐Squared|0.209|0.181|0.074|

Source: Authors’ estimates Note: The t‐statistics are in parentheses and all estimates are significant at least at the 0.01 level of confidence.

We can now apply climate data to the coefficients estimated to determine climate‐ instrumented interannual variation in yields for each of the three grains under consideration. In addition to climate data based on the average of the values across the 22 sets of GCM results, we also quantify the envelope of yield predictions using output from GCM 02 (greatest increase in precipitation volatility and largest decrease in temperature volatility), GCM 06 (greatest increase in temperature volatility), and GCM 08 (largest decrease in precipitation volatility). We thus explicitly simulate complete climate scenarios for each time period (and GCM), by simulating the temperature and precipitation realizations for every year within each timeframe, and thereby capture both changes in the period mean and in the variance of each of these climate variables. The aggregate grains<sup>5</sup> yield series associated with each climate series is then obtained by taking the weighted average of the yields across the three crops, with the weights being the 2001 harvested area shares obtained from FAOSTAT (FAO, 2009).

In contrast to the exception of the grain yield series based on GCM 08, the predicted yield series from GCM 02 and GCM 06 exhibit higher volatility in the 21<sup>st</sup> Century compared to the volatility in the 20<sup>th</sup> Century (columns VIII, Table 2). Figure 1 illustrates these series, whose interannual differences we will now implement in our economic simulation analysis to determine poverty sensitivity to climate in Tanzania at the household level.

5 These three crops collectively proxy for the grains sector that we use in our CGE analysis, aggregated from the Paddy Rice, Wheat, and Other Grains GTAP sectors. Details on how the maize, rice, and sorghum yields are aggregated to grains can be found in Appendix A.

8
tance; and access to amenities. We also include the log of total population, population density, and average elevation as additional controls.

We begin by estimating (3) with<sup>�</sup> δ s −<sup>�</sup> δ i computed from the log income data. Results are shown in the fi rst column of Table 4. As discussed earlier, reported results include individual fi xed e ff ects and standard errors clustered by district of origin. The univariate analysis showed that income was signi fi cant on its own. Once we control for distance, population, prices and amenities, however, the di ff erence in expected income is no longer signi fi cant. Most of other variables are, though. Distance has the expected negative sign — on average the migration destination is closer to the district of origin than alternative destinations. The destination district also has a signi fi cantly larger population and population density, a lower elevation, and a lower rice price. Housing costs in contrast are higher in the destination district than in alternative destinations, possibly because they control for the availability of amenities and other public goods. We also see that the destination district has a signi fi cantly shorter average travel time to the nearest road. Once we control for road distance, travel time to the nearest bank is no longer signi fi cant.

The univariate analysis showed that migrants on average move to destinations where they are on average less likely to fi nd people like them. The results presented in Table 4 present a di ff erent picture. Conditional on the other regressors, the ethnicity and language proximity indices are signi fi cant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not signi fi cant. Taken together, these results suggest that, conditional on material bene fi ts from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network bene fi ts in terms of access to jobs and housing (Munshi 2003, Beaman 2006).

24
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the Mid-Age dummy (6-10 years) is negative and significant suggesting that young firms ≤ 5 years have higher productivity growth than mid-age firms (6-10 years) and as high or higher productivity growth than the mature firms (11+ years).

# **_F. Discussion_**

In this section, we discuss our findings in the context of the existing literature on firm size, and growth. The empirical literature on firm size and growth has largely focused on understanding the role of firm size and age for growth dynamics, and why Gibrats Law, the proposition that firm growth is independent of size, does not hold.<sup>19</sup> In the most recent evidence on this subject, Haltiwanger, Jarmin, and Miranda (2010a, 2010b) study U.S. census data and find that over the period 1992-2005, the large and mature firms (over 500 employees and 10+ years) account for about 45% of employment and most job creation (and destruction). They find that while size is inversely related to growth without controlling for age in the US, there is no systematic relation between size and growth once age is controlled for.

Our results, on the other hand, suggest that in developing economies small firms are significant contributors to employment and job creation. We do not have data on job destruction. In employment growth regressions, we find that size remains a significant predictor for employment growth even after controlling for age. The importance of small firms in developing economies is of significance since we know that in these countries, small firms face many institutional constraints such as limited access to finance (e.g. Demirguc-Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Beck, Demirguc-Kunt, and Maksimovic, 2005), poorly

> 19 See Sutton, 1997 for a review. Early studies such as Birch (1979, 1981, and 1987) found an inverse relation between growth and size and found small firms to be particularly important in job creation. Evans (1987), Dunne, Roberts, and Samuelson (1989), and Dunnes and Hughes (1994) focus on unraveling the roles played by firm age and size as determinants and find that larger firms have lower growth rates but are more likely to survive.

19
**Table 4.5.2: Singapore’s innovation infrastructure**

||High-tech|KIBS|Media/<br>creative|Overall|
|---|---|---|---|---|
|Availability of Innovators|4.4|4.1|4.6|4.4|
|Global Linkages|3.9|4.3|4.0|4.0|
|Venture Capital|3.8|3.6|4.1|3.8|
|Business Related Services|3.9|3.5|3.7|3.7|
|Research Institutes|4.1|3.3|3.6|3.7|
|Competitive Markets|3.6|3.2|3.4|3.4|
|IP Law Framework|3.3|2.9|3.9|3.4|

Measured on a Likert scale of 1(least needed) to 5 (most needed)

**Table 4.5.3a: Investments needed to attract and retain innovative people to Singapore**

|Are specific investments needed to attract and<br>retain innovative peopleto Singapore?|High-tech|KIBS|Media/<br>creative|Overall|
|---|---|---|---|---|
|No|6.3|35.7|14.3|18.2|
|Yes|87.5|64.3|85.7|79.5|
|Yes and No|6.3|0.0|0.0|2.3|
|Total|100|100|100|100|

**Table 4.5.3b: Investments needed to attract and retain innovative people to Singapore**

||Percentage of respondents|
|---|---|
|No|18.2|
|Yes|79.5|
|Yes and No|2.3|
|Type of investments?||
|VC, funding|18.2|
|Education and training|15.9|
|Arts and creative products/services|11.4|
|R&D institutions & companies|9.1|
|Adequate compensation|4.5|
|na|4.5|
|Other|18.2|
|Total|100.0|

From the interviews with the respondent firms, it is highlighted that human capital development is not just important for attracting innovative people to Singapore, but also for the firms’ continued business operations. Training and development is needed for the people at every level of the company, including innovators, investors, investees and management. The type of training specified also covers a 46
**Table 14. Descriptive Statistics Control Variables**

||No Violent<br>Shock|Violent<br>Shock|Difference<br>between means<br>(t-test)|All|
|---|---|---|---|---|
|Rural District Price Index|104.68<br>(6.05)|110.83<br>(8.80)|***|107.34<br>(7.97)|
|Livestock Income as Share of Total|0.39|0.44|***|0.41|
|Agricultural Income|(0.39)|(0.38)||(0.38)|
|_Dummy_Participation in Goverment|0.42|0.41||0.41|
|Program Familias in Acción (1 if has<br>acces to the program; 0 otherwise)|(0.49)|(0.49)||(0.49)|
|Wae er da|12,899.05|14,232.30|***|13,475.54|
|g p y|(5074.67)|(4123.08)||(4732.96)|
|_Dummy_problems with production do|0.31|0.32||0.31|
|to the quality of the land (1 if there|(0.46)|(0.47)||(0.46)|
|Area owned at the moment the|0.49|0.61|*|0.54|
|household was created (ha.)|(2.55)|(2.70)||(2.62)|
|A|43.77|43.73||43.76|
|ge|(13.06)|(13.33)||(13.17)|
|Education in ears|4.31|4.60|***|4.44|
|y|(3.29)|(3.46)||(3.37)|
|_Dummy_female household head (1|0.12|0.13|*|0.12|
|household head is a female, 0|(0.32)|(0.34)||(0.33)|
|standardized wealth index (principal|0.04|-0.06|***|0.00|
|components)|(1.09)|(0.87)||(1.00)|
|Number of children under 5 years of|0.45|0.46||0.46|
|age|(0.71)|(0.70)||(0.71)|
|Number of children between 5 and|1.48|1.48||1.48|
|18 years of age|(1.39)|(1.40)||(1.39)|
|Number of members 65 or older|0.25<br>(0.53)|0.29<br>(0.58)|***|0.27<br>(0.55)|
|Number of members of extended|0.77|0.71|**|0.74|
|family|(1.33)|(1.31)||(1.32)|
|Observations|3,690|2,811||6,501|

standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.1 Source: CLS-WILL: First wave – household questionnaire.

44
||CountryModel|HS6 Model|
|---|---|---|
||(1)|(2)|
|(Intercept)|_−_5.904<sup>_∗∗∗_</sup>|_−_5.968<sup>_∗∗∗_</sup>|
||(0.304)|(0.284)|
|log(seadistance)|0.116<sup>_∗∗∗_</sup>|0.069<sup>_∗∗∗_</sup>|
||(0.017)|(0.010)|
|log(shipsize)|0.007|0.001|
||(0.010)|(0.010)|
|log(value-to-weight)|_−_0.463<sup>_∗∗∗_</sup>|_−_0.459<sup>_∗∗∗_</sup>|
||(0.002)|(0.003)|
|AIC|816522.218|804847.066|
|BIC|816835.895|805840.379|
|Log Likelihood|_−_408231.109|_−_402328.533|
|<br>Num. obs.|256,766|256,766|
|Num. groups: country|66||
|sd: country (Intercept)|0.63058||
|<br>sd: country_×_log(seadistance)|0.07421||
|sd: Residual|1.18527|1.14571|
|Num. groups: HS6||3973|
|sd: HS6 (Intercept)||0.32145|
|<br>sd: HS6_×_log(seadistance)||0.01625|
|Included Fixed Effects:|Year,District|Year,District,Country|
|Distance ElasticityCV:|0.466|0.079|

> _∗∗∗ p <_ 0.01; _∗∗ p <_ 0.05; _∗ p <_ 0.1, sd - standard deviation

Table 2: Heterogeneity of Distance Elasticity (based on US imports) maritime transport) for 223 importers and 23 partners annually for 2006 and 2007.

# **4.4 Port Infrastructure**

Ports infrastructure is vital to the integration of countries in the global economy via international trade. Consequently, there is an ongoing discussion of port reform and challenges associated with it (Haralambides, 2017). This discussion requires careful estimation of port efficiency, its sources, and its effect on transportation costs. Blonigen and Wilson (2008) classify the literature on evaluating port efficiency into three major groups: (i) Production function approaches, (ii) Surveys of port efficiencies, and (iii) Estimating port efficiencies from international trade data. In what follows, we will 36
Chao, Chi-Chur, Win-Lin Chou, and Eden S.H. Yu, 2001. “Export Duty Rebates and Export Performance: Theory and China's Experience.” _Journal of Comparative Economics_ 29(2): 314-26.

Chao, Chi-Chur, Eden S.H. Yu, and Wusheng Yu, 2006. “China's Import Duty Drawback and VAT Rebate Policies: A General Equilibrium Analysis.” _China Economic Review_ 17(4): 432-48.

Chen, Chien-Hsun, Chao-Cheng Mai, and Hui-Chuan Yu. 2006. “The Effect of Export Tax Rebates on Export Performance: Theory and Evidence from China.” _China Economic Review_ 17(2): 226-35.

Clausing, Kimberly A., 2003. “Tax-motivated Transfer Pricing and US Intrafirm Trade Prices.” _Journal of Public Economics_ 87(9-10): 2207-23.

Clausing, Kimberly A., 2006. “International Tax Avoidance and U.S. International Trade.” _National Tax Journal_ 59(2): 269-87.

Cui, Zhiyuan, 2003. “China’s Export Tax Rebate Policy.” _China: An International Journal_ 1(2): 339-49.

Feenstra, Robert, and Gordon Hanson, 2004, “Intermediaries in Entrepôt Trade: Hong Kong Reexports of Chinese Goods,” _Journal of Economics & Management Strategy_ 13(1): 3–35.

Felbermayr, Gabriel J., and Benjamin Jung (2011). "Trade Intermediaries, Incomplete Contracts, and the Choice of Export Modes". _Review of International Economics_ 19: 634-48.

Feldstein, Martin, and Paul Krugman, 1990. “International trade effects of value-added tax.” In: Razin, A., Slemrod, J. (Eds.), _Taxation in the Global Economy_ . University of Chicago Press, pp. 263–282.

Ferrantino, Michael, Robert Koopman, Zhi Wang, and Falan Yinug, Ling Chen, Fengjie Qu, and Haifeng Wang, 2007. “Classification and Statistical Reconciliation of Trade in Advanced Technology Products: The Case of China and the United States,” Bookings-Tsinghua Center for Public Policy Working Paper Series – WP20070906EN.

Ferrantino, Michael, Xuepeng Liu, and Zhi Wang, 2012. “Evasion Behaviors of Exporters and Importers: Evidence from the U.S.-China Trade Data Discrepancy.” _Journal of International Economics_ 867(1): 146-57.

Fisman, Raymond, Peter Moustakerski, and Shang-Jin Wei, 2008. “Outsourcing Tariff Evasion: A New Explanation for Entrepôt Trade.” _Review of Economics & Statistics_ 90(3): 587-92.

Fisman, Raymond, and Shang-Jin Wei, 2004. “Tax Rates and Tax Evasion: Evidence from ‘Missing Imports’ in China.” _Journal of Political Economy_ 112(2): 471-96.

Gourdon, Julien, Stéphanie Monjon, and Sandra Poncet, 2014. “Incomplete VAT rebates to exporters : how do they affect China's export performance?” Working Paper.

31
- 10 -

Our identification strategy is based on the difference-in-differences (DID) method with other covariates taken into account. _OPRC_ is a dummy variable for the adoption of OPRC. There are two periods of time: before ( _t=_ 0) and after ( _t=_ 1) the project. The impact of OPRC is expected to be captured by _β_ 3 in the equation. An advantage of the DID estimator is that it allows to control unobserved heterogeneity between the treatment and control groups and mitigate the self-selection bias, as long as they are time-invariant. However, as often discussed, it is a strong assumption that no time-variant factor affects the outcome indicators. To mitigate this risk, time-variant household characteristics are included on the right-hand side of the regression equation (e.g., Jalan and Ravallion 1998).

Following the agricultural economics literature (e.g., see Gyimah-Brempong (1987) and Bravo-Ortega and Lederman (2004)), six inputs are considered for _xk_ : _k_   _L_ , _H_ , _S_ , _F_ , _P_ , _TR_  , where _L_ and _H_ denote labor and land, respectively. Three advanced inputs are included: improved seeds ( _S_ ), fertilizer ( _F_ ), and pesticides ( _P_ ). _TR_ is the amount of spending on transportation services to take outputs to market.

Agricultural production must depend on underlying agro-climatic conditions. _POT_ is an estimate of potential crop production based on agro-ecological and soil conditions. It is defined by  _j p j H ijtY j_ where _Hijt_ is household _i_ ’s land area used for production of crop _j_ at time _t_ . _Yj_ is a yield estimate (kg/ha) of crop _j_ at this location under the rainfed high inputs assumption. This comes from a global spatial data set, Global Agro-Ecological Zones (GAEZ).

Other observable household characteristics, such as access to utility services and household assets, are also included in _zm_ , and _u_ is an error term.

One empirical challenge is that many input variables are likely to be zero, when analyzing data from developing countries. As will be seen below, only half of the farmers actually use fertilizer (both basal and surface dressing included) in Zambia. About 15 percent and less
36 members' ability to fully capitalize on the agreement's trade preferences.

One way of placing the importance of NAFTA in perspective is to compare the projected East AsiAS NAFTA-induced losses with estimates of the export gains this region should achieve as a result of a siuccessful completion of the Uruguay Round. The World Bank (1992, p. 52) estimates that exports from East Asian low- and middle-income economies (i.e., all East Asia less Hong Kong, Singapore, Taiwan (China) and OECD Asia) would increase by $16.3 billion annually under a 30 percent Uruguay Round reduction in trade barriers; an increase of $27.1 billion is forecast for a 50 percent liberalization. For these East Asian economies, the gains from the Uruguay Pound results (which are estimated to fall in the above mentioned range) are 60 to more than 100 times the NAFTA-induced losses estimated in this study.' Finally, two qualifications should be noted conceniing the results that are reported in this study. We have assumed that NAFTA does not raise trade barriers against third countries. In other words, we assumed that the member countries comply with GATr Article XXIV that requires 'the igeneral incidence of duties and regulations affecting third parties is no higher after than it was Pefore the establishment of the agreement."<sup>3</sup> ' If this is not the case (as the adoption of more restrictive riles of origin suggests), then the third country impact could be more important than' indicated by our findings. Second, it should be noted that we have analyzed the influence :of NAFTA in isolation of potentially related developments, and in 30 Actually, these conparisons overstte the relative importance of NAFTA since the implementation of the Uruguay Round results will lower North American trade barriers and the preferences that can be extended to NAFTA members. This will. in turn, reduce the value of third countries' exports that might be diverted.

" Artice XXIV contqins twro other important pmvisions relating to the formation of FTAs: *_ duties and other restrictive regulations are eliminated on subsantially ali trade between partner countries; and the agreement contins a plan and schedule for its complete formation within a reasonable Legth of iinme.'
M. Regulatory substance- market entry index for Kenya and comparators, 2015

||**Regulatory substance**|**Kenya**|**Senegal**|**Tanzania**|**Uganda**|**International**<br>**benchmark**|
|---|---|---|---|---|---|---|
||**Market entry regulation**|100%|100%|100%|83%|77%|
||**Permitting new entrants**|100%|100%|100%|100%|90%|
||Responsible for monitoring compliance<br>with the terms of the license orpermit|||||88%|
||Authority to impose penalties for<br>violatinglicense orpermit terms|||||100%|
||Penalties are formally written and<br>publiclyavailable|||||80%|
||Provisions to force companies to<br>relinquish licenses or permits for<br>violation|||||100%|
||**PPA approvals**|100%|NAP|100%|67%|57%|
||Legally required to approve all power<br>sales contracts either directlyor indirectly|||||59%|
||Approve or refuse a proposed PPA in a<br>legallyspecifiedperiod of time||NAP|||60%|
||Authority over the process by which<br>utilities can select or procure power from|||||50%|
||IPPs||||||
||Satisfactoryresult<br>Unsatrifactoryresult||||||
|NAP<br>NAV|Not applicable<br>Not available||||||

51
Sen, I. Zeina Afif, Varun Gauri, and Gohdar Mohamed. "Women’s Labor Force Participation in the Kurdistan Region of Iraq." (2022).

Shrestha, Maheshwor. "The impact of large-scale migration on poverty, expenditures, and labor market outcomes in Nepal." _World Bank Policy Research Working Paper_ 8232 (2017).

USAID. “Advocacy for the promotion of women’s entrepreneurship in Nepal. Study Brief.” (2019).

World Bank. “Reshaping Norms: A New Way Forward”. (2022). South Asia Economic Focus (April), World Bank, Washington, DC.

33
One other paper is relevant here too, although it is not focused on _SP_ itself. Rather, Juarez, L. (2008) looks at a different program rolled out only in the Federal District (DF) to provide universal free health insurance in 2001. She constructs a difference-in-difference approach by comparing results with 3 neighboring states that did not have the program. The paper‘s focus is the effect on wages, but she uses the program as an instrument for participation in the formal sector so the first stage results are relevant here.

She uses quarterly individual data from Encuesta Nacional de Empleo 2000-2004 taking the subsample of unskilled 18-60 years old ―salaried‖ women outside of the government sector. She is able to control for a number of individual and household characteristics. While it is a panel data, she does not use panel estimation techniques. Rather, she pooled the cross sections and clustered the standard errors at the individual level. She finds that a woman with at most high school education in DF is 4 to 9.6 percentage points less likely to have a formal job after the policy change.

Juarez (2008) is only testing a single district, and the change is over 1 period, so it makes it harder to exclude the possibility of another trend in the DF contributing to the results. Moreover, at least part of the estimated effects could be driven by migration to covered areas. However, it is certainly suggestive that these types of effects may be at work in the larger _SP_ program.

In addition to _Seguro Popular_ , two other programs in Argentina and Colombia have been introduced and evaluated for their effects on formal employment. The programs do not share all the same features as _SP_ , but again they indicate that it is worth evaluating the potential effect they may have on incentives to work in the formal sector.

Gasparini et al. (2007) assess the impact of a poverty alleviation program, ― _Programa Jefes de Hogar_ ‖ (PJH), on labor informality in Argentina. The main benefit of the program was a conditional cash-transfer of 150 pesos/month (below average market wage for unskilled workers) to each eligible individual (unemployed household-heads with dependants under 18 who are enrolled in school). Participants were required to perform 20 hours a week of training, school, or subsidized work in a private company for 6 months. However, it is unclear how much these work 9