rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4306

# **Abstract**

This paper uses the Ricardian approach to examine how farmers in 11 countries in Africa have adapted to existing climatic conditions. It then estimates the effects of predicted changes in climate while accounting for whatever farmer adaptation might occur.

This study differs from earlier ones by using farmers’ own perceptions of the value of their land. Previous research, by contrast, has relied on either observed sale prices or net revenues, sometimes aggregated over geographically large tracts of terrain. The study also makes use of high resolution data describing soil quality and runoff. Furthermore, it tackles the challenges involved in modeling the effect of climate on agriculture in a study that includes countries in the northern and southern hemispheres, as well as the tropics. The study confirms that African agriculture is particularly vulnerable to climate change. Even with perfect adaptation, regional climate change by 2050 is predicted to entail production losses of 19.9 percent for Burkina Faso and 30.5 percent for Niger. By contrast, countries such as Ethiopia and South Africa are hardly affected at all, suffering productivity losses of only 1.3 percent and 3 percent, respectively. The study also confirms the importance of water supplies as measured by runoff, which, being affected by both temperature and precipitation, may itself be highly sensitive to climate change.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the group to mainstream climate change research. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at d.maddison@ucl.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
effect of adult male education is actually perverse (children of more educated men spend less time outdoors).

We tentatively conclude that parents’ education and income do affect children’s pollution exposure, but only through the determinants of pollution. We extend the analysis by estimating separate regressions for fuel choice and structural determinants of pollution (cooking locations, construction materials). We combine information on fuel choices and structural characteristics into two separate indices, with weights determined by the regression coefficients for cooking-area pollution determinants in Table 7 of Dasgupta, et al. (2004). Table 6 presents the results, which suggest that female education, male education and family income all have large, highly-significant effects on pollution via fuel choice. Female education has an equivalent effect on the structural determinants, but male education and family income do not appear to be significant. As in the composite result (Table 5), female education appears to be the strongest and most pervasive determinant of arrangements that reduce indoor air pollution.

**Table 6:  Income, Education and Determinants of Indoor Air Pollution**

||Fuel|Construction Materials|
|---|---|---|
||Choice<br>|CookingLocation|
|Adult Education Level|||
|(Range: 0-4)|||
|Female|-8.687|-6.926|
||(12.00)**|<br>(3.85)**|
|Male|-7.555|-2.151|
||(12.14)**|<br>(1.39)|
|Income Per Capita|-12.760|1.547|
|($US Per Day)|(22.79)**|<br>(1.11)|
|Constant|15.511|22.031|
||(16.44)**|<br>(9.40)**|
|Observations|4,174|4,174|
|R-squared|0.36|0.01|

Absolute value of t statistics in parentheses significant at 5%; ** significant at 1% 18
(2) where _i_ indexes  the  individual, _j_ indexes  the  firm, _t_ indexes  time,  and ln _yijt_ denotes individual-­‐level  log  wages.  The  panel  of  linked  worker-­‐firm  data  allows  us  to  control  for  a rich  array  of  fixed  factors  that  may  influence  a  worker’s  wages,  in  addition  to  time-­‐varying, observable,  firm  characteristics  ( _! "#_ )  and  worker  characteristics  ( _!"#_ )  including  indicator variables  for  the  worker’s  occupation,  age,  and  education,  as  well  as  the  worker’s  tenure  at the  current  firm.  The  model  also  includes  individual  fixed  effects,  ,  which  allow  us  to  α _i_ control  for  any  are  time-­‐invariant  unobservable  worker  characteristics,  and  firm  fixed effects, ! _j_ ( _i_ , _t_ ) ,  for  firm _j_ at  which  worker _i_ is  employed  at  time _t_ ,  representing  firm heterogeneity.   Equation  (2)  could  then  be  augmented  to  include  sector  level  measures  of protection  interacted  with  the  firm’s  export  status  in  order  to  test  the  contribution  of unobservable  differences  across  workers  on  the  differential  response  to  trade  reform.

# **4.1   Endogenous   Worker   Mobility**

It  is  important  to  note  that  the  identifying  assumption  for  equation  (2)  is  that  the idiosyncratic  disturbance  term  in  each  period  is  independent  of  observable  worker  and  firm characteristics  as  well  as  firm  and  worker  fixed-­‐effects.

Often  referred  to  in  the  literature  as  the  assumption  of  “conditional  exogenous  mobility” (see,  for  instance,  Abowd,  Kramarz,  and  Margolis  (1999),  Woodcock  (2008)  and  Soerensen and  Vejlin  (2009)),  the  assumption  implies  that  employment  mobility  could  depend  on time-­‐varying  observable  worker  or  firm  characteristics  and  firm  and  worker  fixed  effects, but  not ! _it_ .  This  assumption  is  at  odds  with  many  well-­‐known  models  of  the  labor  market with  directed  search,  learning,  or  coordination  frictions.  For  example,  as  we  have  discussed before,  in  Helpman,  Itskhoki,  and  Redding  (2010),  workers  are _ex  ante_ identical  and  job 20
# 3 The database

Table 2 provides the emigration rates by educational category in 1990 and 2000. Countries are classi...ed by geographical area. We distinguish 4 American areas (North America, the Caribbean, Central America and South America), 4 European areas (Northern Europe, Western Europe, Eastern Europe and Southern Europe), 5 African areas (Northern Africa, Central Africa, Western Africa, Eastern Africa and Southern Africa), 4 Asian areas (Western Asia, South-Central Asia, South-Eastern Asia and Eastern Asia) and 1 Oceanian area. Between 1990 and 2000, the world con...guration changed. Czechoslovakia seceded into the Czech Republic and the Slovak Republic, the ex-USSR seceded into 15 countries (7 on the European continent and 8 on the Asian continent), Ex-Yugoslavia seceded into 5 countries, Eritrea emerged as a new country in 1993. To make comparisons possible, we compute information for ex-Yugoslavia, for the ex-USSR and ex-Czechoslovakia in 2000. The column ”reliability rate” provides, for each sending country, the proportion of emigrants for which accurate information about education attainment has been collected (from the total number of individuals declared as emigrants from that country). In a large majority of cases, the reliability rate is higher than 90 percent. Changes between 1990 and 2000 are given in the last two columns, but only for countries showing a high reliability rate in 1990 and 2000 (the minimal threshold is set at 70 percent).

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interactions of gender role attitudes with each of the nine (9) personality trait variables. For each specification, separate regressions are run for men and women.

# **5.1. Drivers of labor market participation and employment choice, no gender role attitudes and personality traits**

The MNL and LPM regressions of the determinants of labor market participation and employment choice, excluding personality traits and gender role attitudes, are presented in Tables 6 and 7 respectively. Here we use all individuals in the main sample (4,497) prior to restricting to only those that have information on gender role attitudes and personality traits. In the pooled sample results (Table 6), we see that the odds of employment, compared to not being in the labor force, do not differ significantly between men and women, although the odds of unemployment are lower for men (Glik and Sahn, 1997; Fadayomi et al., 2014; Naudé & Serumaga-Zake, 2010). This means that men are less likely to be unemployed, compared to not being in the labor force. One possible reason could be that fewer job opportunities are available to women. Formal job search channels are often more beneficial to men (Huffman & Torres, 2001), and informal channels contribute to the perpetuation of gender biases (Drentea, 1998; Affum-Osei et al., 2019). Another reason is that men may be taking over more jobs formerly thought to be in women’s domain (e.g., trading), and therefore crowding them out (Overa, 2005). Finally, supply side factors may also lead to higher unemployment among women including domestic responsibilities, own or spouses’ traditional attitudes about work outside the home, as well as culturally conditioned low work/career aspirations (Glick and Sahn, 1997).

The odds of employment are higher with age, though the relationship is non-linear, decreasing after a certain point (Glick and Sahm, 1997). Being married increases the odds of employment, compared to not being in the labor force. According to Glick and Sahn (1997), married women may be better able to secure capital through their husbands to start up small businesses, or use their husband’s connections to obtain wage employment. Marriage may not necessarily be an exogenous determinant of employment for men, as men may have secured employment before marriage. Other factors that are positively correlated with employment include education (Baah-Boateng, 2015) and residing in a household that is headed by a male (Fadayomi et al., 2014; Hussain et al., 2016). The presence of a spouse in the household reduces the odds of employment only among men, with no significant effect in the women sample. Individuals living in poor households have lower odds of being employed (Baah-Boateng, 13
whereas farmers in wet regions choose maize-beans, cowpea-sorghum, and maize-groundnut. Other crops, such as maize, are grown throughout Africa.

The study found that sometimes farmers choose only a single crop to grow, such as sorghum, cowpea or maize. However, farmers often select a crop combination that will survive the harsh conditions in Africa, such as maize-beans, cowpea-sorghum, and millet-groundnut. These combinations provide the farmer with more flexibility across climates than growing a single crop on its own.

The results have significant policy implications for climate change. This study has shown that African farmers adapt crop choice to climate. There is every reason to believe that they will continue to adapt in the future. Governments and farmers must anticipate the need to change crops rather than try to hold on to old crops that repeatedly fail.

The study strongly suggests that agricultural analyses of climate change impacts must take into account crop selection. Studies that treat crop choice as exogenous will seriously overestimate the damages from global warming. For example, agronomic studies or empirical studies that use weather as a proxy must be careful not to assume crop choices are exogenous. Farmers will probably change crops in response to a new climate rather than repeatedly grow crops that historically were successful but now fail. As a result, farmers will match future crops to future climates. Although this may still entail losses in agricultural income in Africa, the predicted losses will be much smaller than if one assumes crop choice is exogenous.

Finally, the paper examines crop choice only across the currently available selection of crops. Future research into new crops that are more suitable for higher temperatures could dramatically improve farmers’ welfare, especially in hot locations such as Africa. Although a great deal of progress has been achieved in making existing crops more productive, future research efforts need to move towards making them more resilient to higher temperatures.

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# **Table 4. R-Squared Shapley-Owen Decomposition for Brazil** **_Estimations Using Individual-Level Data_**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|||**Ever**|**Percent**||
|_Outcomes:_|**Graduation**|**employed**<br>**after**<br>**graduation**|<br>**of time**<br>**employed**|**Wages**|
|_Percent of explained variation attributable to:_|||||
|All quality determinants|39.98|19.54|24.62|18.89|
|Infrastructure|4.19|-|0.99|-|
|Costs|7.34|0.45|1.02|0.47|
|Training and curriculum|10.81|7.90|8.30|7.51|
|Faculty|5.49|3.32|2.82|3.48|
|Link with productive sector|8.05|2.37|4.73|2.16|
|Other Practices|4.11|5.50|6.77|5.27|
|Student characteristics (PCA Score)|0.25|1.30|1.63|1.32|
|Program characteristics (PCA Score)|1.03|1.51|1.18|1.51|
|HEI characteristics (PCA Score)|32.42|0.81|0.63|0.73|
|Student administrative variables (PCA Score)|0.41|14.33|24.23|18.09|
|Peer administrative variables (PCA Score)|1.83|18.17|15.36|18.03|
|Field of study fixed effects|23.92|7.34|5.67|7.64|
|State fixed effects|0.16|36.71|25.79|33.51|
|Graduation year fixed effects|-|0.29|0.90|0.27|
|R-squared|0.134|0.135|0.122|0.149|
|Obs.|22,663|7,177|6,827|7,089|

_Source_ : Own estimations using data from the WBSCPS and Brazil individual-level data. For variable definitions, see Section 3 and Appendix 1.

_Notes_ : This table present results from the R-squared Shapley-Owen decomposition for the regressions reported in Table A13, estimated with individual- and program-level data for Brazil (states of Ceara and Sao Paulo). For each regression, the table shows R-squared (net of the variation explained by survey noise variables) and the percent of the (net) explained variation attributable to each set of variables. “Obs.” indicates number of students (equal to the number of observations in the underlying regression).

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perhaps the word more frequently used is<sup>dumping; that is to say,</sup> that the trust or combine, having obtained command<sup>and control of</sup> its own market and finding that it will have<sup>a surplus of goods,</sup> sets out to obtain command of a neighboring<sup>market, and for the</sup> purpose of obtaining control of a neighboring market<sup>will put</sup> aside all reasonable considerations with regard to the cost or fair price of the goods; the only principle<sup>recognized is that the</sup> goods must be sold and the market obtained....

This dumping then, is an evil and we propose to deal<sup>with it. (quoted in</sup> U.S. Tariff Commission 1919, 22) Mr. Fielding's argument was not novel. Such<sup>alleged evil by foreigners</sup> had for several centuries been one of the familiar<sup>arguments for a higher</sup> tariff to restrict imports. Dumping, real or imagined,<sup>had for centuries been</sup> a target of opportunity for interests seeking protection.<sup>What was different</sup> this time was what Mr. Fielding proposed to do about<sup>it: this was the first</sup> time that antidumping became the _Instrument_ of opportunity.

The substance of the proposed antidumping regulation is in its<sup>fi.st</sup> paragraph: Whenever it appears to the satisfaction of the minister<sup>of</sup> customs, or of any officer of customs authorized<sup>to collect</sup> customs duties. that the export price or the<sup>actual aelling price</sup> to the importer in Canada of any imported dutiable article, of a class or kind made or produced in Canada, is less<sup>than the fair</sup> market value thereof, as determined according<sup>to the basis of</sup> value for duty provided in the Customs Act<sup>in respect of imported</sup> goods subject to an ad valorem duty, such articles<sup>shall, in</sup> addition to the duty otherwise established, be subject to<sup>a</sup> special duty of customs equal to the difference between<sup>such fair</sup> market value and such selling price. (quoted in U.S.<sup>Tariff</sup> C-mmission 1919, 21)

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# **Appendix Table 2: Attrition and Business Closure**

||||Attrition<br>||||Bu<br> <br>|siness Closure<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||Follow-up1|Follow-up2|Follow-up3|Follow-up4|All|Follow-up1|<br>Follow-up2|Follow-up3|Follow-up4|All|
|**Panel A: Full sample**|||||||||||
|Treatment 1: BRC|-0.020*|-0.005|-0.035**|-0.022|-0.020***|0.009|-0.013|0.031|-0.009|0.005|
||(0.012)|(0.015)|(0.016)|(0.016)|(0.007)|(0.016)|(0.020)|(0.019)|(0.019)|(0.009)|
|Treatment 2: BRC + TPIN|-0.007|-0.006|-0.017|-0.026|-0.014|-0.014|0.001|0.026|0.001|0.003|
||(0.016)|(0.020)|(0.022)|(0.021)|(0.010)|(0.020)|(0.027)|(0.026)|(0.026)|(0.012)|
|Treatment 3: BRC + IS + BBA|-0.006|0.002|-0.017|-0.012|-0.008|-0.013|-0.018|0.002|-0.016|-0.011|
||(0.011)|(0.014)|(0.015)|(0.015)|(0.007)|(0.014)|(0.018)|(0.017)|(0.017)|(0.008)|
|Control group mean|0.066|0.095|0.129|0.119|0.102|0.095|0.171|0.135|0.156|0.139|
|Sample size|3,002|3,002|3,002|3,002|12,008|2,830|2,720|2,664|2,686|10,900|
|p-value: Treatment 1 = Treatment 2|0.406|0.952|0.401|0.834|0.508|0.246|0.616|0.854|0.692|0.914|
|p-value: Treatment 1 = Treatment 3|0.176|0.603|0.194|0.465|0.059|0.118|0.753|0.104|0.682|0.062|
|p-value test of equality|0.351|0.947|0.194|0.472|0.044|0.392|0.729|0.287|0.784|0.223|

Notes: "All" includes dummies for survey round. Also, specifications include randomization strata dummies. Robust standard errors in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

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below results from the Heckman two-step estimator that aims to unpack them.<sup>39</sup>

# **5 Cross-border Tax Effects on Inward FAI**

This section presents baseline results from the use of the FAI data just described, looks at their robustness and some extensions, draws lessons, and then explores further the differences arising from the use of data on FAI rather than FDI.

## **5.1 Baseline Results**

Table 2 reports baseline results from the estimation of equation (8) on the full sample. The first column enters separately the various tax terms other than the parent _KF_ - _METR_ , which is taken up later. The coefficients are evidently significantly different from each other, so that sufficiency of the I-METR is rejected. In terms of its underlying components, the coefficient on the rate differential term is highly significant, has the expected sign, and is large. The host country _KF_ - _METR_ enters with the expected (negative) sign but is not statistically significant; the same is true of the squared rate differential term.<sup>40</sup> Contrary to expectation, the interaction term, intended to capture possible non-linear effects, enters negatively, and with some significance.

The remaining columns of Table 2 focus more closely on effects through the rate differential, ∆ _hpt_ and the host _KF_ - _METRh_ , with corresponding adjustments to fixed effects and control variables.

Columns (2)-(4) focus on possible spillovers from statutory corporate tax rates, in all cases including host country-year and parent country fixed effects. Column (2) shows that the rate differential term ∆ _hpt_ has a positive and statistically significant relationship with FAI. And the effect is large, with an implied semi-elasticity with respect to the rate differential, _β_ ∆, of

> 39One recent application of the PPML in a related context, for example, is Damgaard, Elkjaer and Johannesen (2019), which uses the Poisson model to estimate the differential impact of gravity-type variables on real versus phantom investment.

> 40This is in contrast to the finding of non-linear effects (typically focusing on the higher order terms of the tax rates) in, for example, B ́enassy-Qu ́er ́e, Fontagn ́e and Lahr`eche-R ́evil (2005), Dowd, Landefeld and Moore (2017) and Bratta, Santomartino and Acciari (2021).

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# 4. Sample Size and Design: Bigger and Simpler is Better

The CILSS had a sample size of only 1600 households each year. This restraint in sample size was necessary in view of the complexity of the questionnaire and the six hours needed for each interview. Resource constraints are such that there is a clear trade-off between sample size and the length of the questionnaire. In our experience a sample size of 1600 households is too small for genuinely policy-oriented analysis. It was not possible to distinguish more than five regions or six or seven socio-economic groups to disaggregate the analysis. Within urban areas, it proved very difficult to push the analysis beyond the differentiation between Abidjan and all other cities combined. Rural areas could only be split in three: East Forest, West Forest and Savannah. Socioeconomic classifications usually distinguished farmers from non-farmers. Farmers could be split further in export and food crop farmers, while non-farming households could be broken down according to sector (e.g. formal/informal).

While other disaggregations are possible, there is a general upper limit of six to seven groups because the number of observations per group becomes prohibitively small. The problem is that most policy interventions are targeted at groups which are narrower than this. In particular, it is extremely useful for policy to be able to consider jointly the regional and socio-economic classification of households. But this would yield 30-35 cells and that is clearly too much for a sample size of 1600 households. One recommendation would thus be to increase the sample size in future living standards surveys.3/ Within a given resource constraint, this could easily be accommodated if our earlier suggestion of reducing or eliminating the income modules of the questionnaire is adopted.

Before starting our analysis, we undertook a detailed scrutiny of several key variables in the survey, and we discovered several errors in the data related to sampling 3/The Ghana Living Standards Survey was conducted over a sample of 3200 households.

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