rafmacalaba/datause-encoder-data huggingface dataset:
_Table 18. Estimated combined impacts of drought or flood events captured in LSMS-ISA_

|Country|Year|Number of<br>individuals<br>affected by<br>droughts<br>and/or<br>floods|Share of<br>total<br>population<br>affected by<br>droughts<br>and/or<br>floods|Value of<br>damages<br>from<br>droughts<br>and/or<br>floods|Number of<br>individuals<br>affected by<br>droughts<br>and/or<br>floods|Share of<br>total<br>population<br>affected by<br>droughts<br>and/or<br>floods|Value of<br>damages<br>from<br>droughts<br>and/or<br>floods|
|---|---|---|---|---|---|---|---|
|||LSMS-ISA|LSMS-ISA<br>|LSMS-ISA|EM-DAT|EM-DAT|EM-DAT|
||2011 -<br>2012|7.4 million<br>[ 5.4 ; 9.5 ]|7.9 %<br>[ 5.7 % ; 10.0<br>% ]||5.846 million|6.30 %||
||2013 -<br>2014|11.4 million<br>[ 8.8 ; 14.0 ]|11.4 %<br>[ 8.8 % ; 14.1<br>%]|589.6 million<br>[ 280.9 ; 898.2<br>]|52 thousand|0.10 %|3.5 million|
|Ethiopia|2015 -<br>2016|34.2 million<br>[ 28.8 ; 39.6 ]|32.5 %<br>[ 27.4 % ;<br>37.6 % ]|2,981.3<br>million<br>[ 1,999.0 ;<br>3,963.7]|10.904<br>million|10.40 %|2,134.4<br>million|
||2018 -<br>2019|16.3 million<br>[ 13.1 ; 19.5<br>]|14.3 %<br>[ 11.5 % ;<br>17.1 % ]|1,355.7<br>million<br>[ 227.9 ;<br>2,483.4]|200<br>thousand|0.20 %||
||2009 -<br>2010|3.5 million<br>[ 3.0 ; 4.0 ]|23.8 %<br>[ 20.5 % ;<br>27.1 % ]||38 thousand|0.30 %||
|Mli|2012 -<br>2013|2.6 million<br>[ 2.2 ; 3.1 ]|16.5 %<br>[ 13.5 % ;<br>19.5 % ]||2.05 million|13.00 %||
|aaw|2015 -<br>2016|4.6 million<br>[ 3.5 ; 5.7 ]|26.4 %<br>[ 20.1 % ;<br>32.8 % ]||7.341 million|42.40 %|594.6 million|
||2018 -<br>2019|5.4 million<br>[ 4.1 ; 6.7 ]|28.5 %<br>[ 21.5 % ;<br>35.4 %]|379.9 million<br>[ 123.0 ; 636.7<br>]|1.001 million|5.40 %||
||2014|2.8 million<br>[ 2.4 ; 3.2 ]|15.9 %<br>[ 13.4 % ;<br>18.4 % ]|70.0 million<br> [54.7;85.3]|No entry|||
|Mali|2017|4.7 million<br>[ 4.2 ; 5.3 ]|24.5 %<br>[ 21.7 % ;<br>27.4 %]|164.7 million<br>[ 135.4 ;<br>194.0]|No entry|||
|Niger|2011|9.8 million<br>[ 8.5 ; 11.1 ]|56.7 %<br>[ 49.1 % ;<br>64.4 % ]|1,589.9<br>million<br>[ 1,298.4 ;<br>1,881.3]|3.041 million|17.40 %||
||2014|4.7 million<br>[ 3.6 ; 5.8 ]|24.1 %<br>[ 18.4 % ;<br>29.8 %]|265.6 million<br>[ 129.5 ; 401.7<br>]|166<br>thousand|0.90 %|3.1 million|
|Nigeria|2019|13.6 million<br>[ 10.5 ; 16.7<br>]|6.8 %<br>[ 5.3 % ; 8.4<br>%]|427.5 million<br>[ 274.4 ;<br>580.6]|71 thousand|0.00 %|0.0 million|
||2008|5.3 million<br>[ 4.5 ; 6.1 ]|12.4 %<br>[ 10.6 % ;<br>14.2 % ]||10 thousand|0.00 %||
|Tanzania|2010|8.6 million<br>[ 7.5 ; 9.7 ]|19.0 %<br>[ 16.5 % ;<br>21.4 % ]||50 thousand|0.10 %||
||2012|7.3 million<br>[ 6.3 ; 8.4 ]|15.4 %<br>[ 13.1 % ;<br>17.7 % ]||1.0 million|2.10 %||

39
the difficulty in studying these effects beyond the local level is that women’s market-based work often varies strongly with geography, with very low participation rates in some areas, and higher degrees of concentration in other localities (see de Janvry and Sadoulet, 2000; Fafchamps and Shilpi, 2003; Kuiper et. al., 2006; McCarthy and Sun, 2009). As a result, the effects of other variables on labor supply, including education and health, are often subsumed by geographic factors (Phillips, 1987; Fafchamps and Quisumbing, 1998).

Infrastructure investments are also typically guided by geographically-correlated community characteristics, including agro-climatic factors and potentially unobserved features such as local political influence. By way of offering access to markets and resources, poor infrastructure might fall among the potential economic constraints facing women’s labor supply. Confirming this, however, requires untangling the geographic effects on women’s labor supply from infrastructure placement. In addition, observed and unobserved factors at the individual and household level can affect both access to infrastructure and women’s participation in market activities within communities.

The few studies that examine this question with respect to water use various approaches and find mixed effects on women’s market-based activities. Ilahi and Grimard (2000) use 1991 data from Pakistan and a simultaneous-equation reduced form analysis to model women’s choices over time spent on water collection, market-based activities, and leisure, as a function of household access to water.<sup>4</sup> They find that greater distance to a water source raises water-collection rates for women, and lowers their participation in income-generating activities; however, in households with private water technology (as opposed to poorer public infrastructure outside the home), women are more likely to spend time on leisure than on market-based work. Menon (2009) uses 1995-96 household data for Nepal to construct a logit model of occupational choice in the context of rainfall uncertainty, and finds that household members, including women, are less likely to work in agriculture when rainfall is less predictable, even if the head is self-employed in agriculture. Finally, Lokshin and Yemtsov (2005) find that women’s wage employment is not significantly affected by rural water supply improvements in Georgia between 1998 and 2001. Creating a panel of villages across two rounds of data, they apply a double-

> 4 Specifically, they use average distance to the nearest external water source in the community, and separately whether or not the household has access to water in the home.

6
24 where y represents growth, _X_<sup>_1_</sup> represents a set of lagged explanatory variables and _X_<sup>_2_</sup> a set of contemporaneous explanatory variables, C is an unobserved country-specific effect, T is a timespecific effect, u is the time-varying error term, and _i_ and _t_ represent country and (5-year) time period, respectively.

The first benefit from moving to a panel is the ability to exploit the time-series and crosssectional variation in the data. LLB construct a panel that consists of data for 77 countries over the period 1960-95. The data are averaged over seven non-overlapping five-year periods. Moving to a panel incorporates the variability of the time-series dimension, which allows the panel approach to exploit substantial additional variability.

A second benefit from moving to a panel is that in the purely cross-sectional regression, the unobserved country-specific effect is part of the error term so that correlation between C(i) and the explanatory variables results in biased coefficient estimates.<sup>2</sup> To control for the presence of unobserved country-specific effects, Arellano and Bond (1991) propose to first-difference the regression equation to eliminate the country-specific effect and then use instrumental variables to control for endogeneity. This approach eliminates biases due to country-specific omitted variables.

The third benefit from using a panel is that it overcomes the problem that the pure crosscountry instrumental variable work does not control for the potential endogeneity of all the regressors. This can lead to inappropriate inferences on the coefficient on financial development. The panel estimator uses instruments based on previous realizations of the explanatory variables to consider the potential endogeneity of the other regressors.

> 2 Furthermore, if the lagged dependent variable is included in _X1_ (which is the norm in cross-country regressions), then the country-specific effect is certainly correlated with _X_<sup>_1_</sup> .
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Table 2 Earnings **Functions (2SLS): Primary and Secondary Labor Markets (Dependent Variable: Log Annual Male Earnings)**

|Independent||Regressio<br>|n (3)__<br>||
|---|---|---|---|---|
|Variables|Primary<br>|Sector|Secondary<br> <br>|Sector|
||b||b<br>b|t|
|(a) Human|||||
|capital<br>variables|||||
|SCH|0.0465|2.83|-0.0088<br>|-0.30<br>|
|EXP<br>|0.0036<br>|0.22<br>|0.0120<br>|0.59<br>|
|EXp2|-0.0002|-1.24|-0.0003|-1.24|
|EXP.SCH|0.0002|0.31|-0.00007<br>|-0.08<br>|
|PRESWK|0.0108|3.49|0.0049|1.13|
|(b) Control|||||
|variables|||||
|RESID|0.0036|1.90|0.0076|2.68<br>|
|WEEKS|0.0547|2.18<br>|0.0148<br>|0.72<br>|
|HOURS|0.0083|2.78|0.0032|0.63|
|ETHNIC|-0.0885|-2.09|-0.1245|-2.15|
|Economic Sector:|||||
|IND|0.2425<br>|1.48<br>|0.2611<br>|2.50<br>|
|ELEC|0.2774<br>|1.37<br>|*****<br>|*****<br>|
|CONSTR|0.2567|1.38<br>|0.2170<br>|1.65<br>|
|COMM|0.1077<br>|0.63<br>|0.1625<br>|1.30<br>|
|TRANS|0.1827<br>|1.06<br>|0.1292<br>|0.77<br>|
|FINANCE|0.2430|1.44|*****|*****|
|PRIVATE|0.0624|0.36|0.1144|0.57|
|(c) Characteristic|||||
|of marriage<br>|||||
|variables|||||
|WSCH|-0.1078|-1.04|0,0145|0.07|
|MARR|0.0103|1.17|-0.0071|-0.91|
|WSCH.MARR|0.0113<br>|2.17<br>|0.0003<br>|0.04<br>|
|SCH.MARR|-0.0014|-1.54|0.0006|0.74|
|CONSTANT|5.8545|5.34|8.2728|8.64|
|R2|0.3199||0.2330||
|n|488||198||

# **Notes:**

b: Coefficient t: t-statistic The following variables are included in the<sup>constant term:</sup> Ethnic origin: western Economic sector: agriculture Wife's education: less than 12 years of

# schooling

***** No observations in these categories.
Table F.1. Survey Experiment: Successful Covariate Balance

|Variable|Control|Treatment|P- value|
|---|---|---|---|
|Age|51.007|49.362|0.106|
|Male [=1]|0.476|0.526|0.107|
|Ed: Primary school or less [=1]|0.163|0.172|0.696|
|Ed: Secondary school or less [=1]|0.364|0.370|0.842|
|Ed: Tchnician, university or more [=1]|0.473|0.457|0.628|
|Married or Cohabitating [=1]|0.535|0.526|0.785|
|Economic Strata: Low [=1]|0.498|0.500|0.953|
|Economic Strata: Medium [=1]|0.308|0.314|0.833|
|Economic Strata: High [=1]|0.194|0.186|0.746|
|Employed [=1]|0.899|0.872|0.293|
|Labor Contract [=1]|0.404|0.440|0.418|
|Student [=1]|0.132|0.123|0.686|
|Political Interest [=1]|0.771|0.743|0.291|
|Voted in mayoral 2019 elections|0.731|0.717|0.610|
|Voted inpresidential 2022 elections|0.789|0.765|0.349|
|Join F-Test|||0.394|
|Observations|546|494|1,040|

_Notes:_ Column (1) presents the control sample mean and Column (2) the treatment sample mean. Column (3) depicts the p-value of the t-test regression. We performed a joint orthogonality test by running a multinomial logit where the dependent variable is the assigned treatment, the explanatory variables are all the covariates in this table, and the base group is the control group. The joint orthogonality test p-value is 0.217. Definition dependent variables: labor contract is an indicator [=1] if the respondent reported a labor contract in his actual job. Political interest is an indicator [=1] if the respondent reported awareness of the current political situation in the country.

89
Table 2: Age at first birth and fertility by cohort

|Birth<br>Cht||%<br>|whogave fi<br>|rst birth at ag<br>|e<br>||% who<br>never gave|Number of<br>obser-|
|---|---|---|---|---|---|---|---|---|
|oor|15|16|17|18|19|20|birth|vations|
|1956-60|0.07|0.10|0.12|0.12|0.12|0.10|0.27|3269|
|1961-65|0.06|0.09|0.12|0.14|0.12|0.11|0.28|4402|
|1966-70|0.05|0.08|0.12|0.12|0.12|0.10|0.34|4790|
|1971-75|0.04|0.07|0.09|0.09|0.08|0.07|0.52|5542|
|1976-80|0.03|0.04|0.08|0.08|0.08|0.07|0.59|4333|

27
3,466 are from rural areas. The second round (ESS2) was collected in 2013/14. In this round, the survey was expanded to cover large urban centers by including an additional 100 EAs, and the number of sample households grew to 5,262, coming from all 11 regions of the country. Of these households, 3,323 are from rural areas. The urban sample households became nationally representative of all urban households of the country since this round, making the whole sample of households nationally representative of households from all areas of Ethiopia,<sup>12</sup> as the rural sample households have been nationally representative since ESS1. The attrition rate across these two rounds was about 5%. The final round (ESS3) was collected in 2015/16, successfully tracking and interviewing 4,954 of the sample households in the ESS2, implying that the attrition rate from the ESS2 was about 6%. Of these households, 3,272 resided in rural areas. One of the rural EAs was not visited in the ESS3 for security reasons.

The sample households were selected following a stratified, two-stage sampling design. The sample frames are the 2011/12 Agricultural Sample Survey of Ethiopia and the 2007 Population and Housing Census of Ethiopia (PHC) for rural and urban households, respectively. Each of the regions of Ethiopia served as a stratum. The first stage of the two-stage sampling process involved the selection of sample EAs, which is undertaken under the condition of drawing at least a prespecified number of EAs per stratum. The second stage involved selecting an equal number (15 from large urban EAs while 12 from others) of households from each sample EA, using a simple random sampling method (CSA and World Bank, 2015, 2017).

The ESS contains rich individual-level information including basic demographics (age, gender, and so forth), employment condition, and participation in labor-sharing arrangements. It further has household-level information including consumption expenditure, asset ownership, housing conditions, ownership of non-farm enterprises, total annual rainfall, and total rainfall during the wettest quarter. It also has EA-level information such as access to healthcare facilities and zone-level information such as change in greenness in the main growing season ( _Meher)_ .

All the information used in this study comes from the household module, which is fielded from January to March in 2012 for ESS1, and from February to April in 2014 and 2016 for ESS2 and ESS3, respectively. In addition, I use four environmental covariates (rainfall and greenness variables) that are merged with the ESS from external sources by the LSMS-ISA team.

> 12This excludes three zones in the Afar and six zones in the Somali regional states (CSA and World Bank, 2015, 2017). Zone is the third largest administrative region in Ethiopia.

9
density in 1900, Railroad density in 1900, Population density in 1900, and a set of geographical controls. Table 3 shows that engineering in 1900 appears significantly in explaining today’s income per capita despite controlling sequentially and then together for population density and railroad density. Were we to include Denmark and Sweden, whose density we have confidence in, not to mention France and Germany, the results would be even stronger. Columns 4 suggests that with few observations, it is impossible to separate the impact of engineering and literacy although in subsequent exercises we can.

# **4 Establishing the Importance of Engineering and Patenting Capacity: US County Level Data**

We construct engineering and patenting density data for 2380 US counties.<sup>17</sup> Table 4 presents the OLS estimates including the core controls for geography, economic activity and education which reduce the number of observations to 1905. All specifications have robust errors and are clustered at the state level and include both OLS and fixed effect estimators. We estimate: _′ ′ ′ Y_ 2000 _,i_ = _α_ + _γEEng_ 1900 _,i_ + _γP Pat_ 1900 _,i_ + **Geoi** _γ_ **Ge** + **Y1900** _,_ **i** _γ_ **Gr** + **HC1900** _,_ **i** _γ_ **H** + _μstate_ + _εi_ (2) To be consistent with the other samples, we begin with an OLS specification of engineering density along with a vector of geographical controls. Errors are robust and clustered by state. In column 1, engineering emerges strongly significantly and of expected sign in the OLS specification. Temperature and distance from the coast both enter negatively and significantly. Column 2 replicates the specification but with the smaller sample lim-

> 17A small number of counties change their borders in the last century and we drop them. 15
- 24 -

# **Table 2.** **<u>Conditional logit model</u>**

||Choice-<br>specific|Individual-spe|cific variables:|||||
|---|---|---|---|---|---|---|---|
||variables|Maize|Rice|Groundnuts|Tobacco|Cotton|Sesame|
|Transport cost|-0.009 ***<br>(0.002)|||||||
|Crop suitability|0.212 ***<br>(0.053)|||||||
|Crop price|-0.620 ***<br>(0.147)|||||||
|Access to fertilizer|0.067<br>(0.139)|||||||
|Access to irrigation|0.101 ***<br>(0.020)|||||||
|Access to improved<br>|0.810 ***<br>|||||||
|seed|(0.102)|||||||
|Area of plot||2.443 ***|2.334 ***|2.216 ***|<br>2.480 ***|<br>2.538 ***|<br>2.470 ***|
|||(0.666)|(0.668)|(0.669)|(0.667)|(0.668)|(0.672)|
|Steep slope||0.237|-1.225 ***|-0.216|0.350|-1.066 **|0.362|
|||(0.346)|(0.377)|(0.371)|(0.481)|(0.492)|(0.452)|
|Land title||-0.820 *|-1.369 ***|-1.491 ***|<br>-0.788|-1.073 *|-1.763 **|
|||(0.456)|(0.497)|(0.540)|(0.724)|(0.614)|(0.858)|
|Electricity access||0.743|0.950|0.466|0.951|-13.184 ***|<br>-0.420|
|||(1.111)|(1.122)|(1.162)|(1.360)|(1.113)|(1.516)|
|Mobile access||0.375|0.902 **|0.739 **|0.469|0.501|0.509|
|||(0.353)|(0.364)|(0.372)|(0.516)|(0.430)|(0.456)|
|Distance from road||-0.099 **|-0.044|-0.069 *|-0.151 *|-0.161 *|-0.032|
|||(0.041)|(0.041)|(0.043)|(0.087)|(0.089)|(0.045)|
|Male household||-0.349|-0.669|-0.567|-0.007|-0.240|-0.752|
|head||(0.484)|(0.493)|(0.501)|(0.706)|(0.564)|(0.586)|
|HH head age||0.038 ***|0.030 ***|0.030 ***|<br>0.019|0.017|0.027 **|
|||(0.010)|(0.010)|(0.011)|(0.017)|(0.012)|(0.012)|
|HH head's year of||0.115 **|0.103 *|0.017|-0.008|-0.059|0.187 **|
|schooling||(0.057)|(0.059)|(0.061)|(0.101)|(0.072)|(0.073)|
|Household size||0.046|0.029|0.083|0.118 *|0.156 **|-0.049|
|||(0.062)|(0.064)|(0.065)|(0.072)|(0.067)|(0.086)|
|HH wage income||-0.036|0.014|-0.081|-0.171|-0.069|-0.022|
|||(0.037)|(0.036)|(0.085)|(0.363)|(0.063)|(0.056)|
|Obs.|17325|||||||
|Wald chi2|26724.8|||||||
|Pseudo R squared|0.5086|||||||
Policy Research Working Paper 8720

# **Abstract**

An earlier paper titled “Non-linear effects of tax changes on output: The role of the initial level of taxation,” estimated tax multipliers using (i) a novel dataset on value-added taxes for 51 countries (21 industrial and 30 developing) for the period 1970–2014, and (ii) the so-called narrative approach developed by Romer and Romer (2010) to properly identify exogenous tax changes. The main finding is that, in line with existing theoretical distortionary and disincentive-based arguments, the effect of tax changes on output is highly non-linear. The tax multiplier is essentially zero under relatively low/moderate initial tax rate levels and more negative as the initial tax rate and the size of the change in the tax rate increase. This companion paper first shows that these findings have important policy implications, given that the initial level of taxes varies greatly across countries and thus so will the potential output effect of changing tax rates. The paper then turns to some specific policy applications. It focuses on the relevance of the arguments for revenue mobilization in countries with low levels of provision of public goods and social and infrastructure gaps, as well as in commodity-dependent countries. The paper then considers some practical implications for the standard debt sustainability analysis. Lastly, it evaluates the implications of the findings for the Laffer curve.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ research. The authors may be contacted at gvuletin@worldbank.org.

_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
arable lands is more elastic through new land opening. To that extent, the disparity problem could be less severe to emerge in such economies than in Japan, Korea and Taiwan even at the same speed of industrialization.

Such a difference could well be significant, but it is unlikely that even newly-settled land-abundant economies where land markets are usually more active can totally escape from the trap of the disparity problem in the process of industrialization. Unlike the manufacturing sector where adjustments in factor combination necessary for the borrowing of foreign technology can easily be done based on the efficient supply of capital from both domestic and foreign sources through generally well-functioning capital markets, adjustments in land input needed for introducing foreign agricultural technology is much more difficult and costly, given the typical absence in developing economies of institutions to support land markets such as cadastral surveys and land registry, not to speak of the total absence of “international markets for lands” unlike the case for capital. Furthermore, as emphasized in section 4, the importation of foreign technology is usually much more difficult for agriculture than for manufacturing because of the location-specific nature of agricultural technology that is constrained by natural environments (Hayami and Ruttan 1985). For these reasons it is just unlikely that even the economies most favourably endowed with agricultural resources can be immune from the trap of the disparity problem when they are able to achieve rapid industrialization based on the success in industrial technology borrowing, In fact, Thailand represents a good example in this regard. Within East Asia, Thailand has been characterized by relative abundance in the endowment of land where land frontiers had been open until very recently, especially in its North East. Nevertheless, Thailand could not escape from the disparity problem when it joined the East Asian Miracle of rapid industrialization and economic growth, as explained in the previous section. The policy response of Thailand to the disparity problem is also representative of land abundant economies. As a means to support farmers, Thailand relied heavily on the reduction of rice export tax, unlike the cases of Japan, Korea and Taiwan which relied on border protection measures such as tariffs and quotas. As a major exporter of agricultural commodities, it was not a viable option for Thailand to support farmers by raising domestic prices by the combination of border protection and domestic price support programs. If domestic prices may be raised above the international market prices, Thailand’s exports of agricultural commodities should have stopped, resulting in the accumulation of surplus agricultural products in domestic markets to the point that 16