rafmacalaba/datause-encoder-data huggingface dataset:
suggest that an extra dollar increase in government investment crowds-in private investment by approximately 1.8 dollars after one-year and by 2.1 after four-years.

**Table 2:** The response of private investment to government investment

||||Cumulative<br>|Multipliers(horizo<br>|n h inyears)<br>||
|---|---|---|---|---|---|---|
||Baseline<br>(1)|h=0<br>(2)|h=1<br>(3)|h=2<br>(4)|h=3<br>(5)|h=4<br>(6)|
|Panel A: OLS|||||||
|Change in government<br>investment|-0.041|-0.069|0.169**|0.165*|0.211**|0.297***|
||(0.057)|(0.051)|(0.081)|(0.097)|(0.099)|(0.097)|
|Panel B: 2SLS|||||||
|Change in government|||||||
|investment|1.843**|1.623**|2.394**|2.239***|2.067**|1.351|
||(0.821)|(0.698)|(0.944)|(0.829)|(0.881)|(1.149)|
||[0.706, 5.06]|[0.60, 4.36]|[0.71, 5.57]|[0.43, 4.44]|[ 0.22, 4.20]|[-1.24, 4.03]|
|Panel C: First-stage estimates|||||||
|Change in predicted disb.|0.212***|0.231***|0.212***|0.230***|0.255***|0.252***|
||(0.070)|(0.072)|(0.072)|(0.073)|(0.071)|(0.074)|
|Observations|3,437|3,349|3,242|3,136|3,029|2,922|
|F-statistic|9.340|10.32|9.268|10.03|12.83|11.63|
|KP Underid stat|0.00454|0.00295|0.00457|0.00326|0.00115|0.00175|
|No. of countries|109|109|109|109|109|109|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence a ́ la Boehm (2020). All estimations include country-fixed effects and year effects. “Baseline” specification does not include the lagged dependent while all the estimations in columns (2)-(6) include one year lag of the dependent variable.“KP” is Kleibergen and Paap underidentification test.

_F_ -statistic follows Olea The 2SLS effective F-statistic lies between 9.3 and 12.8. The effective and Pflueger (2013) and has recently been advocated by Andrews et al. (2019) as the more Similar reliable metric of first stage strength in the presence of heteroskedastic residuals.<sup>13</sup> to the standard rule-of-thumb value for the standard _F_ -statistic, in a just-identified model, the yardstick value for the effective _F_ -statistic is 10 (Andrews et al. 2019). A welldocumented issue of using instrumental variables is that when they turn out weak, the estimated coefficient of interest can be biased and unreliable; a possibility here with some F-statistics below the benchmark. Hence, the Andersen-Rubin instrument consistent 95 percent confidence intervals for the baseline 2SLS are 0.73 to 5.19 and for h=0 is 0.6 to 4.36 (and slightly smaller for Driscroll-Kraay standard errors). Indeed, Eden and Kraay (2014), who focus on 39 LICs countries, acknowledge that although their reported weak-instrument

> 13 This statistic coincides with the conventional F-statistic when residuals are homoskedastic.

12
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# **Table 3 Main Results: Estimated MPS Commodity Revenues**

Two-Stage Least Squares, Parsimonious Specification Dependent variable: Annual change in expenditure as a share of 3-year MAGDP

|Dependent variable: Annu<br>ITERACTIONS WITH<br><br><br><br>|al chang|e in expen<br>A. EMDEs|diture as<br>|a share of<br>B. SUB-|3-year M<br>SAHARAN|AGDP<br>AFRICA|
|---|---|---|---|---|---|---|
|ΔCRCit<br>y:<br><br>|(1)|(2)|(3)|(4)|(5)|(6)|
|β0<br>pp<br><br>|0.25***|||0.31*|||
|_(Overall MPS)_<br><br>|(0.05)|||(0.18)|||
|β0<br>FFFxFFFi<br><br>||0.30***|0.39***||0.52**|0.82***|
|_(MPS of Floating ER)_<br><br>||(0.07)|(0.07)||(0.23)|(0.14)|
|β0<br>PPxPPi<br><br><br>||0.15***|0.15***||0.15***|0.19*|
|_(MPS of Fixed ER)_<br><br><br>||(0.04)|(0.04)||(0.04)|(0.10)|
|β1<br>FFFxpl(HFiHF<br>����)<br>⁄<br>xFFFi<br><br><br>|||0.60***|||0.88***|
|_(MPS slope of Floating ER)_<br><br><br>|||(0.11)|||(0.16)|
|β1<br>PPxpl(HFiHF<br>����)<br>⁄<br>xPPi<br>_(MPS slope of Fixed ER)_|||0.12<br>(0.12)|||0.32<br>(0.58)|
|**Controls**|NO|NO|NO|NO|NO|NO|
|(Interactions with IQ, LLY, GNI)|||||||
|P-VALUE (Prob > F)|||||||
|H0: MPS Float = MPS Fixed||0.02**|0.00***||0.07*|0.00***|
|H0: [Slope] MPS Float=MPS F|ixed||0.00***|||0.32|
|First stage F-statistic|91.7|28.7|18.6|40.6|19.8|8.8|
|Control for non-res revenue|YES|YES|YES|YES|YES|YES|
|Country & year fixed effects|YES|YES|YES|YES|YES|YES|
|Observations|917|917|917|322|322|322|
|R-squared|0.20|0.20|0.21|0.16|0.17|0.20|
|Number of countries<br>Notes: Standard errors are in pare|54<br>ntheses: *|54<br>**_p_< 0.01|54<br>, **_p_< 0.|22<br>05, *_p_< 0|22<br>.1. Standard|22<br>errors are|

<mark>Notes: Standard errors are in parentheses: ***</mark> _<mark>p</mark>_ <mark>< 0.01, **</mark> _<mark>p</mark>_ <mark>< 0.05, *</mark> _<mark>p</mark>_ <mark>< 0.1. Standard errors are</mark> clustered at the country level. In the first stage, commodity revenues are instrumented with the countryspecific commodity price index scaled with the average commodity revenues as a share of GDP in 2008–2012.

Column (3) of Table 3 reports the full specification, which allows the MPS to vary with the ER regime and the half-life of the commodity price shock. For a typical developing commodity exporter (with average half-life), the MPS is 0.39 for floating ERs and 0.15 for fixed ERs, and we reject the null hypothesis that 0 0P at the 1% significance level (also in line with the NK model’s first FFLLNNAAFF GG prediction). The point estimate of the semi-elasticity of the MPS to years of halfββ = ββ life, 1 , is 0.6 for floating ER and 0.12 for fixed ER. Moreover, 1 is jj FFLLNNAAFF statistically greater than zero at the 1% level, supporting the NK model’s second ββ̂ ββ̂ prediction (the MPS increases with persistence for countries with a floating ER). We also reject the null hypothesis that 1 1P at 1% significance level, FFLLNNAAFF GG 39 ββ = ββ
Farr W. K.; Lord R. A.; and Wolfenbarger, J. L., 1998. “Economic Freedom, Political Freedom, and Economic Well-Being: A Causality Analysis.” Cato Journal Vol.18 No. 2.

Frankel, Jeffrey and David Romer, 1999. Does Trade Cause Growth? American Economic Review 89 (3): 379-99 Freeman, R,. 2002. “Institutional Differences and Economic Performance among OECD Countries.” CEP Discussion Papers Friedman, Benjamin M., 2005. “The Moral Consequences of Economic Growth”, Knopf Publishing Group Friedman, Milton, 1962. Capitalism and Freedom. Chicago: University of Chicago Press.

Gallup, John Luke, Sachs, Jeffrey D. and Mellinger, Andrew D., 1998. Geography and Economic Development, NBER Working Paper Series, Vol. w6849, pp. -, 1998 Ghosh, Sugata & Andros Gregoriou, 2006. "On the Composition of Government Spending, Optimal Fiscal Policy, and Endogenous Growth: Theory and Evidence," Economics and Finance Discussion Papers 06-19, Economics and Finance Section, School of Social Sciences, Brunel University Giavazzi F. and G. Tabellin (2005) Economic and political liberalizations. Journal of Monetary Economics 52(7):1297–1330 Glaeser, E. L., R. La Porta, F. Lopez-de-Silane and A. Shleifer (2004), Do Institutions Cause Growth?, NBER Working Paper No. 10568.

Grossman, Philip J., 1990, “Government and Growth: Cross-sectional Evidence,” Public Choice Vol.65: 217-227.

Gupta, Sanjeev, Robert Powell and Yongzheng Yang, 2005. “The Macroeconomic Challenges of Scaling Up Aid to Africa”, IMF Working Paper 05/179 (Washington: International Monetary Fund) Gwartney, James and Lawson, Robert, 2004. Economic Freedom of the World Gwartney, J., Lawson, R. and Block, W. 1996. Economic Freedom in the World, 1975–1995. Vancouver: Fraser Institute.

Gwartney, J., Holcombe, R. G. and Lawson, R. 2004. Institutions and the impact of investment on growth. Paper presented at the Conference of the Association Private Enterprise Education (APEE).

Hanke, Steve and Walters, Stephen, 1997. Economic Freedom, Prosperity, and Equality: A Survey, Cato Journal, Vol. 17, No. 2.

Hayek, Friedrich A., 1960. “The Constitution of Liberty”, (Chicago: University of Chicago Press).

Hayek, Friedrich A., 1973. Law, Legislation and Liberty, Vol. 1: Rules and Order, (Chicago: University of Chicago Press).

28
welfare distribution, without expressing an opinion on whether the present welfare level of the household is satisfactory. For instance, two respondents can place their households at step 3 of the ladder, but one of them could be fully satisfied with that placement (perhaps due to a recent improvement in finances), whereas the other could be completely dissatisfied (perhaps due to higher aspirations or feelings of loss resulting from some recent negative income shock). The key question is then whether the placement of households in the country’s welfare distribution based on the economic ladder question is consistent with their placement in the country’s welfare distribution based on measured expenditures or assets.

**Figure 1: Mean asset index by subjective relative income position**

<!-- Start of picture text --> Current welfare ladder and assets: Western Europe Current welfare ladder and assets: New EU states<br>France Germany Italy Bulgaria Czech Estonia Hungary<br>1 1 1<br>2 2 2<br>3 3 3<br>4 4 4<br>5 5 5<br>6 6 6 10 987654321 10 987654321 10 987654321 10 987654321<br>7 7 7<br>8 8 8<br>9 9 9 Latvia Lithuania Poland Romania<br>10 10 10<br>-2 -1 0 1 2<br>Asset index<br>Sweden UK 10 987654321 10 987654321 10 987654321 10 987654321<br>1 1 -3 -2 Asset index-1 0 1 2 -3 -2 Asset index-1 0 1 2<br>2 2<br>34 34 Slovakia Slovenia<br>5 5<br>6 6<br>7 7<br>8 8<br>9 9<br>10 10<br>10 987654321 10 987654321<br>-2 -1Asset index0 1 2 -2 -1Asset index0 1 2 -3 -2 Asset index-1 0 1 2 -3 -2 Asset index-1 0 1 2<br>Source: LiTS II, 2010. Source: LiTS II, 2010.<br>Current welfare ladder and assets: South-East Europe Current welfare ladder and assets: FSU countries<br>Albania BiH Croatia Armenia Azerbaijan Belarus Georgia<br>10 987654321 10 987654321 10 987654321 10 987654321 10 987654321 10 987654321 10 987654321<br>FYROM Kosovo Montenegro Kazakhstan Kyrgyzstan Moldova Russia<br>10 987654321 10 987654321 10 987654321<br>10 987654321 10 987654321 10 987654321 10 987654321<br>-2 -1Asset index0 1 2 -2 -1Asset index0 1 2 -2 0Asset index2 4 6<br>Serbia Tajikistan Ukraine Uzbekistan<br>10 987654321<br>10 987654321 10 987654321 10 987654321<br>-2 -1Asset index0 1 2 -2 0Asset index2 4 6 -2 0Asset index2 4 6 -2 0Asset index2 4 6<br>Source: LiTS II, 2010. Source: LiTS II, 2010.<br><!-- End of picture text --> Figure 1 reports for each country the mean of the asset index for each of the 10 steps of subjective positions on the welfare ladder. Except for the highest steps (9<sup>th</sup> and 10<sup>th</sup> ) of the ladder (for which estimates are imprecise as there are very few observations at the top), welfare ladder assessments exhibit a clear wealth gradient in most countries. The only exceptions are several countries in Central Asia (Kyrgyzstan, Tajikistan, and Uzbekistan) where the graph is essentially flat throughout most of the ladder, increasing only at the very top.

6
<!-- Start of picture text --> 10 8 6 4 2 0 -2 -4 -6 -8 -10 10 8 6 4 2 0 -2 -4 -6 -8 -10<br>G.D.P. (H&P) Right Scale<br>G.D.P. (H&P) Right Scale<br>F<br>I, S.E.<br>I (H&P)<br>F (H&P)<br>S.E. (H&P)<br>(except in the case of the panel “Levels”) and smoothed using a 3<br>5<br>2.0 1.5 1.0 0.5 0.0<br>-0.5 -1.0 -1.5 0.6 0.4 0.2 0.0<br>-0.2 -0.4 -0.6<br>2.5 2.3 2.1 1.9 1.7 1.5 1.3 1.1 0.9 0.7 0.5 10 5 0 -5 -10 10 5 0 -5 -10<br>I (right)<br>G.D.P. (H&P) Right Scale<br>G.D.P. (H&P) Right Scale<br>U (right)<br>Levels<br>F<br>U and O.L.F.<br>U (H&P)<br>O.L.F. (H&P)<br>S.E.<br>16 14 12 10 8 6 4 2 0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 0.20 0.10 0.00 -0.10 -0.20<br>Note: Mean duration inferred from the continuous time transition matrix for each period using monthly data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002 following the procedure by Geweke et al. (1986) outlined in Annex 1. Computations are based on 10.000 Monte Carlo replications. The series have been averaged per quarter, de-trended using a HP filter with smoothing parameter 10 quarter moving average smoother. OLF=Out of the Labor Force, U=Unemployment rate, I=Informal Salaried, SE=Informal Self-employed, F=Formal Sector.<br>2002 2002<br>2001 2001<br>2000 2000<br>1999 1999<br>1998 1998<br>1997 1997<br>1996 1996<br>1995 1995<br>1994 1994<br>1993 1993<br>1992 1992<br>1991 1991<br>1990 1990<br>1989 1989<br>1988 1988<br>1987<br>1987<br>1986<br>1986<br>1985 1985<br>1984 1984<br>1983<br>1983<br>2002 2002 2002<br>2001 2001 2001<br>2000 2000 2000<br>1999 1999 1999<br>1998 1998 1998<br>1997 1997 1997<br>1996 1996 1996<br>1995 1995 1995<br>1994 1994 1994<br>1993 1993 1993<br>1992 1992 1992<br>1991 1991 1991<br>1990 1990 1990<br>1989 1989 1989<br>1988 1988 1988<br>1987<br>1987 1987<br>1986 1986 1986<br>1985<br>1985 1985<br>1984<br>1984 1984<br>1983<br>1983 1983<br><!-- End of picture text -->
intensities for new Chinese manufacturing exporters between 1999 and 2005. In 2005, 58% of domestic new exporters reported an export intensity of between 0 and 10%, while this percentage was 36% for foreign new exporters. Although these percentages were somewhat lower in the preceding years, at least a third of domestic new exporters and at least a quarter of foreign new exporters export 10% or less in their first year. We assume that a similar distribution holds for our sample. Given that the average export intensity of all direct exporters in our sample is 48.3%, this reflects that old exporters usually export a larger share of their sales.

(ii) Firms that exported in the previous period and stop exporting now (exiting exporters) have an export intensity below threshold _ζ_ . Using Colombian customs data from 1996 to 2005, Eaton, Eslava, Kugler, and Tybout (2007) find that almost half of all exporters did not export in the previous year, but their contribution to export revenues is extremely small and most new exporters leave the export market in the following year. While firms exiting the export market do not necessarily have to be new exporters, it is much more likely that firms with a smaller export intensity stop exporting as opposed to firms with a high export share.

(iii)We relax the assumption that new exporters have to carry the whole burden of sunk costs upon entry into the export market. This assumption is in line with Eaton et al. (2007) who suggest that new exporters and their buyers undergo a period of learning about one another before locking in major exporting contracts. Surviving exporters typically start to export into a single foreign market and gradually expand into additional markets, suggesting that “the costs of “testing the waters” may be substantially less than the cost of locking in major exporting contracts” (Eaton et al., p. 17).

We will attribute an export dummy to firms only that exceed a certain threshold _ζ_ of export intensity. Assumptions (i) and (iii) ensure that applying a threshold _ζ_ covers established exporters only that have paid the bulk of sunk entry costs, but not new exporters. Assumption (ii) makes sure that exiting exporters wouldn’t fall under the category exporter if their export intensity is below the threshold _ζ_ . If all three assumptions hold, lagged exports – the measure for sunk entry costs – and the dependent variable would be identical. Since lagged exports would perfectly predict exports in such a case, lagged exports as a measure of sunk entry costs couldn’t be included into the regression analysis.

Equation (2) then translates into: Pr( _exit_  1)  Pr(  _it_   _firmit_  _regrt_  _aggrt_  0) (3) 17
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure A-2: Cote d’Ivoire - Sensitivity of Capital Stock Estimate to plus or minus $1,000 in Initial Capital Stock (in 1995 CFA equivalent)**

<!-- Start of picture text --> Sensitivity of Capital Stock Estimate<br>To Initial Capital Assumptions - In CFA Francs<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Years 1960-2002<br>Initial Capital $2200 Initial Capital $1200 Initial Capital $3200<br>Billions<br>Constant 1995 CFA Francs<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->

**Figure A-3: Cote d’Ivoire – Sensitivity of Capital Stock Estimate to the Depreciation Rate Assumption – 4%, 6%, 8% and 10%**

<!-- Start of picture text --> Sensitivity Of Capital Stock Estimate<br>To the Depreciation Rate Assumption<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Years 1960-2002<br>Depr=10% Depr=8% Depr=6% Depr=4%<br>Billions<br>Constant 1995 CFA Francs<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Next, we considered four different depreciation rates (Figure A-3). Here the difference in levels persists throughout the time series. In particular, the peak capital stock level in 24
the United Kingdom; Turkish, Croatian and Serbian diasporas in Germany; Algerians and Moroccans in France; and large pools of migrants from India, Pakistan, the Philippines, Bangladesh, Indonesia and Africa in the oil-rich Gulf countries.

Table 4: Countries with Large Diasporas in the High-income OECD Countries

|||High-skilled<br>emigrant stock<br>(thousand)|High-skilled emigrant<br>stock<br>(% of population)|Emigrant stock<br>( % of<br>population)|Governance<br>indicator|
|---|---|---|---|---|---|
|1|Philippines|1,126|1.49|2.22|-0.52|
|2|India|1,038|0.10|0.17|0.09|
|3|Mexico|923|0.94|6.56|-0.48|
|4|China|817|0.06|0.13|-0.47|
|5|Vietnam|506|0.64|1.61|-0.45|
|6|Poland|449|1.16|2.94|0.32|
|7|Iran, Islamic Rep.|309|0.48|0.83|-0.76|
|8|Jamaica|291|11.24|26.30|-0.55|
|9|Russian Federation|289|0.20|0.39|-0.84|
|10|Ukraine|246|0.50|1.51|-0.60|
|11|Colombia|234|0.55|1.33|-0.71|
|12|Pakistan|222|0.16|0.42|-0.81|
|13|Romania|176|0.79|2.51|-0.29|
|14|Turkey|174|0.26|2.92|0.07|
|15|Brazil|168|0.10|0.22|-0.41|
|16|South Africa|168|0.38|0.61|0.19|
|17|Peru|164|0.63|1.35|-0.77|
|18|Dominican Republic|155|1.88|7.08|-0.66|
|19|Egypt, Arab Rep.|149|0.22|0.38|0.02|
|20|Serbia and Montenegro|148|1.82|8.78|-0.81|
|21|Morocco|141|0.51|3.93|-0.10|
|22|Lebanon|138|4.07|9.15|-0.36|
|23|El Salvador|128|2.03|10.67|-0.37|
|24|Hungary|124|1.22|3.12|0.70|
|<br>25|<br>Trinidad andTobago|120|9.37|18.35|-0.07|
||_Cuba_|_333_|_2.99_|_7.76_|_-1.14_|
||_Haiti_|_153_|_1.92_|_4.93_|_-1.62_|
||_Nigeria_|_149_|_0.13_|_0.20_|_-1.38_|

_Source: Governance data from Kaufman, Kraay and Mastruzzi; high-skilled migrants abroad in highincome OECD countries as of 2000 from Docquier and Marfouk (2004)._ But for diaspora investors to purchase hard currency bonds issued by their countries of origin, it would seem that there has to be a minimum level governability. Absence of governability, as reflected in civil strife, is clearly a big negative for diaspora bonds. While this requirement would not disqualify most countries in the Far East and many in Eastern Europe, countries such as Cuba, Haiti and Nigeria (and several others in 17
**Figure 4. Wage Bill vs. Fiscal Balance (2000-2011 averages)**

<!-- Start of picture text --> 6<br>AZE<br>4<br>RUS<br>KAZ<br>2<br>KSV BLR EST<br>0<br>MKD BGR<br>0 2 4 6 8 MDA 10 12 14<br>SRB MNE<br>BIH<br>-2 TUR LTU<br>GEO<br>HRV<br>KGZ UKR SVN<br>TJK<br>-4 SVK ROM LVA<br>ALB CZE<br>HUN<br>POL<br>-6<br>Wage Bill (% of GDP)<br>Fiscal Balance (% of GDP)<br><!-- End of picture text --> Source: ECA Fiscal Database, Country sources

# **3. Data and Empirical Strategy**

The data set contains public wage bill data for 26 ECA<sup>3</sup> countries, covering the period 20002011 (see Table A5 for a complete list of countries used in the analysis).<sup>4</sup> Nineteen Western European countries are subsequently used as a comparator group to determine if the effects are less significant in non-transition economies. Table A1 in the annex reports the summary statistics for all variables used in the analysis. There is considerable variation, both between and within countries, which justifies the panel estimation method.

We test three related hypotheses. The first hypothesis is that wage bill growth pro-cyclically responds to the business cycle and the electoral cycle. The second hypothesis is that the wage bill spending is characterized by downward rigidity, e.g. that spending increases are stronger in boom periods than wage bill decreases in downturns. The third hypothesis is that wage bill expansions are associated with a deterioration of the overall fiscal position.

# **_H1: Cyclicality of Wage Bill Spending_**

The first hypothesis explores the cyclical behavior of wage bill spending. Periods of high growth tend to increase wages in the private sector, and lead to calls for higher wages or benefits in the

> 3 Turkmenistan and Uzbekistan were not included due to data availability, and Armenia and Azerbaijan were dropped due to spurious data.

> 4 The sample period reflects the availability of data. Public wage bill data does not exist for most ECA countries prior to the year 2000. Data on public employment is not comprehensively available for most countries in the sample. Therefore, it is not possible to decompose changes in wage bill expenditure into changes in public employment and in pay policies.

7
<!-- Start of picture text --> Distribution of diposable income, Belarus 2014-2017<br>1<br>&<br>!<br>2s:' ! !<br>'<br>!<br>g! \<br>SY» & 45 6 AX D8 & _& !<br>RS Nod !<br>& w<br>S<br>; i<br>—_ —_ 0 200 400 606<br>—— 2014 — 2015<br>——— 2016 ——— 2017<br><!-- End of picture text -->
Table 1: Descriptive statistics

|Variable|Obs|Mean|Std. Dev.|
|---|---|---|---|
|ln simple tarifs|41,043|2.034|1.221|
|ln weighted tarifs|40,606|1.582|1.680|
|ln rain|41,043|2.469|0.757|
|agric|41,043|0.129|0.335|
|agric2|41,043|0.209|0.406|
|irrig|18,812|13.626|17.476|
|North America|41,043|0.046|0.211|
|Latin America and Caribbean|41,043|0.369|0.482|
|Europe|41,043|0.053|0.225|
|Africa|41,043|0.200|0.400|
|Asia-Pacifc|41,043|0.329|0.469|
|Number of countries||70||
|Number of products||65||

Country-level data on the proportion of agricultural land that is irrigated are available for 32 countries.

Table 2: Baseline estimates

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.064<br>[0.076]|0.035<br>[0.076]|0.125<br>[0.094]|0.082<br>[0.093]|
|ln rain_jt ∗_agric_i_||0.230***<br>[0.047]||0.338***<br>[0.064]|
|Observations|41,043|41,043|40,606|40,606|
|Number of countries|70|70|70|70|
|Number of products|65|65|65|65|
|R-squared|0.572|0.574|0.401|0.403|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 1