Table 1. Regression Results for Agriculture Value Added.

|||All Countries<br>||Non-hig<br>|h-income Co<br>|untries<br>|
|---|---|---|---|---|---|---|
||(1)<br>FE|(2)<br>Diff GMM|(3)<br>Sys GMM|(4)<br>FE|(5)<br>Diff GMM|(6)<br>Sys GMM|
|First Lag of Log|0.642***|0.274**|0.891***|0.624***|0.046|0.960***|
|Agriculture GVA|(28.98)|(2.11)|(13.33)|(22.03)|(0.24)|(39.56)|
|Log Agriculture|-0.001|-0.256|0.024***|-0.012|-0.085|0.009|
|Electricity|(-0.13)|(-0.92)|(3.22)|(-1.52)|(-1.59)|(1.40)|
|Consumption|||||||
|Observations|1362|1253|1362|744|676|744|
|Countries|98|96|98|58|56|58|
|Hansen-p||0.740|0.627||0.505|1.000|
|AR2-p||0.680|0.216||0.129|0.044|
|AR3-p||0.072|0.265||0.369|0.351|

This table shows the regression results of six regressions: The fixed effects, difference GMM, and system GMM for all countries and non-high-income countries. The dependent variable is the log of contemporary Agriculture GVA. The independent variable of interest is the log of contemporary sector electricity consumption. t statistics are in parentheses and stars indicate significance: * p < 0.10, ** p < 0.05, *** p < 0.01. We exclude observations (years) with over 100% absolute change in electricity consumption or GVA growth year-on-year. The bottom of the table shows the number of observations (years times number of countries), the number of countries in the panel, the Hansen-p value, and the p-values of the tests of second- and third-order autocorrelation. We instrument for the lag of sector value added using the third and fourth internal lag, and for contemporary electricity consumption using the third and fourth internal lag in the Difference GMM and System GMM regressions. Included control variables are share of imports and exports in GVA, share of agriculture in GVA, and the log of capital and labor.

The dependent variable in Table 1 is the log of the agriculture value added, and the independent variable is the log of electricity consumption in the sector. In addition to all of the control variables discussed above, the regressions include the lag of the value added. The regressions are also weighted by the mean country-level value added over the analyzed period to ensure that countries with a small sectoral value added do not bias the results. The study instruments the lagged value added and the electricity consumption with their third and fourth lags in the two-step difference GMM and system GMM regressions to address the endogeneity and simultaneity bias. The choice of the lags for instruments is driven by second-order autocorrelations observed in the two-step system GMM estimation. After comparing various empirical approaches, the paper selects the two-step system GMM as a preferred methodology. Since the OLS and fixed effects estimations produce biased coefficients, the study focuses on the two-step difference and system GMM methods and uses various tests to identify the preferred strategy between them. In particular, it applies the Hansen test of overidentifying restrictions and secondand third-order autocorrelation tests for standard errors to check the validity or exogeneity of the chosen instruments for both the difference and system GMMs. If both the Hansen test of overidentifying 14