rafmacalaba/datause-encoder-data huggingface dataset:
Table 5. Alternative PC provisions and Their Associated Productivity Level and Growth Rate Effects: a simulation based on the FE estimators of **table 4 for the sample of PC-participants**

|PC provisions:<br>Bare bones PC without other provisions<br>(evaluated at the wage elasticity of zero and the mean markup ratio)|(1) % firms in this<br>category for the<br>participants used in the<br>estimation<br>10.7|Before-A<br>Level<br>Rate|fter estimates<br>-. 120**<br>-.022|(FE)<br>(.053)<br>(.022)|
|---|---|---|---|---|
|Bare bones PC + CEO by bidding<br>(evaluated at the wage elasticity of zero and the mean markup ratio)|2.2|Level<br>Rate|-.279***<br>.094**|(.101)<br>(.047)|
|Bare bones PC + Primary PC target being profit<br>(evaluated at the wage elasticity of zero and the mean markup ratio)|25|Level<br>Rate|-.056<br>-.022|(.056)<br>(.022)|
|Bare bones PC + Manager posting bond<br>(evaluated at the wage elasticity of zero and the mean markup ratio)|9.2|Level<br>Rate|-. 120i*<br>-.017|(.053)<br>(.031)|
|Bare bones PC + mean wage elasticity<br>(evaluated at the mean wage elasticity of 0.42 and the mean markup ratio)|23 9S|Level<br>Rate|-.039<br>-.022|(.047)<br>(.022)|
|Bare bones PC + wage elasticity a standard deviation above mean<br>(evaluated at the mean wage elasticity of 0.75 and the mean markup ratio)|3.1|Level<br>Rate|.024<br>-.022|(.052)<br>(.022)|
|Bare bones PC + Reducing markup ratio by one standard deviation:<br>(evaluated at the wage elasticity of zero and the mean markup ratio)|.1|Level<br>Rate|-.071<br>-.022|(.058)<br>(.022)|
|Bare bones PC + CEO by bidding + Primary PC target being profit + Manager<br>posting bond + contract being three years + Reducing the markup ratio by one<br>standard deviation (evaluated at the mean wage elasticity of 0.42 and the mean<br>markup ratio)|2.5|Level<br>Rate|-.134<br>.100**|(.099)<br>(.050)|
|Bare bones PC + CEO by bidding + Primary PC target being profit + Manager<br>posting bond + contract being three years + Reducing the markup ratio by one<br>standard deviation (evaluated at the mean wage elasticity of 0.75, and, the<br>markup ratio of one standard deviation below the mean)|.0|Level<br>Rate|-.022<br>.100**|(.104)<br>(.050)|

Note. Reported in the parentheses are the standard errors for the estimated coefficients. *, ** and * represent significance levels of 10, 5 and I per cent respectively..

t. Here the mean or the percentile of wage elasticity is based on the PC-participants sample.

9R. When evaluated at a positive wage elasticity, the percentage of firms refers to that with wage elasticity equal to or greater than the specified threshold. Note that when evaluated at 0 wage elasticity, however, it refers strictly to firms with zero wage elasticity.

38
schemes vs -3.7%) but less well than the public transport subsidy. In terms of welfare, construction subsidies, targeted or generalized, are a poor substitute to transit subsidies.

# 6.3.2 Beyond consumption-related welfare: Sprawl, distance travelled and emissions

In this sub-section we look into features that are not captured by our welfare function such as the impact of alternative policies on the extent of the urban area, the average distances travelled and the emissions that stem from commuting.

Map 5 provides a visual representation of how different policy packages would modify the size of the urbanized are in 2050 in our simulations. We selected three different scenarios: the baseline in which transit subsidies exist and amount to two-thirds of the full cost of a trip, the counterfactual in which the transit subsidy is removed, resulting in a tripling of the monetary costs of riding public transport and finally a scenario whereby subsidy removal is coupled with the redistribution of the savings in the form of construction subsidies.

It can be visually seen from Map 5 that the removal (compensated or not) of public transit subsidies has an overall limited impact on the size of the urbanized area. In 2050 the urbanized area decreases by only 11 km<sup>2</sup> (2739 km<sup>2</sup> vs 2750 km<sup>2</sup> ) for the uncompensated removal of the public transport subsidy (orange in the map) and by 14 km<sup>2</sup> (2736 km<sup>2</sup> ) for the scenario in which public transport subsidy removal is accompanied by a generalized construction subsidy (green in the map). The orange shade is virtually indistinguishable because it is overlayed with the green layer.

The lump sum transfer scenario yields an urbanized area in 2050 which is slightly larger compared to the baseline: 2797 km, i.e. a 1.7% increase. And finally the targeted construction subsidy scenario results in a slight decrease of the urbanized area of -0.8% (2729 km<sup>2</sup> vs 2750 km<sup>2</sup> ). The conclusion which we draw from these simulations is that the potential to curb urban sprawl through removing the public transport subsidies exists but is limited and therefore so would the savings in terms of infrastructure costs associated to increased urbanization.

27
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

# Annex D Water tariffs in African utilities

|Country|Utility|Fixed<br>charge|Fixed charge as<br>% of 10m<br>3|Number of<br>blocks|Size of 1st<br>block|Size of nth<br>block|Price of<br>1st<br>block|Price of<br>nth block|
|---|---|---|---|---|---|---|---|---|
|BEN|SONEB|0.00|0|2|5|5+|0.41|0.85|
|BFA|ONEA|2.05|27|3|6|30+|0.39|2.13|
|CIV|SODESI|0.00|0|3|8|300+|0.00|1.20|
|CPV|ELECTRA|0.00|0|2|6|11+|2.67|4.67|
|DRC|REGIDESO|0.00|0|4|10|40+|0.05|0.12|
|ETH|AWSA|0.16|7|5|7|20+|0.19|0.42|
|ETH|ADAMA|0.00|0|3|5|30+|0.26|0.44|
|ETH|Dire Dawa|0.00|0|4|5|50+|0.14|0.34|
|GHA|GWCL|0.00|0|4|20|20+|0.52|0.73|
|KEN|NWASCO|0.00|0|2|10|60+|0.18|0.52|
|KEN|KIWASCO|0.00|0|4|10|60+|0.60|0.60|
|LSO|WASA|0.00|0|5|5|24+|0.29|1.18|
|MDG|JIRAMA|0.43|68|4|10|10+|0.03|0.08|
|MOZ|ADeM Beira|3.83|100|3|9|30+|0.00|0.66|
|MOZ|Adem Maputo|3.83|100|3|9|30+|0.00|0.71|
|MOZ|AdeM Nampula|3.83|100|3|9|30+|0.00|0.58|
|MOZ|AdeM Pemba|3.83|100|3|9|30+|0.00|0.57|
|MOZ|AdeM Quelimane|3.83|100|3|9|30+|0.00|0.57|
|MWI|LWB|0.30|5|2|10|30+|0.30|0.61|
|MWI|BWB|2.42|83|1|4|40+|0.00|0.52|
|MWI|CWB|0.48|20|3|nav|nav|0.00|0.00|
|NAM|Walvis Bay|2.33|33|0|15|85+|0.71|3.48|
|NAM|Windhoek|0.00|0|4|6|45+|0.80|2.46|
|NAM|Oshakati|2.58|18|3|6|40+|1.01|1.94|
|NER|SEEN|3.85|106|4|10|40+|0.26|0.92|
|NGA|FCT WB|1.02|26|3|nav|nav|0.39|0.39|
|NGA|Kaduna WB|0.00|0|1|30|30+|0.16|0.19|
|NGA|Katsina WB|0.00|0|2|30|1000+|0.19|0.28|
|RWA|Electrogaz|0.00|0|3|5|500+|0.44|1.09|
|SDN|NWC Khartoum|0.00|0|6|20|60+|0.37|0.73|
|SDN|NWC South Darfur|0.00|0|4|nav|nav|0.64|0.64|
|SDN|NWC Upper Nile|0.00|0|1|nav|nav|0.59|0.59|
|SEN|SDE|0.00|0|1|20|40+|0.37|1.46|
|TCD|STEE|0.00|0|3|15|15+|0.22|0.47|
|TZA|DAEASCO|0.00|0|2|5|5+|0.39|0.52|
|TZA|DUWASA|3.95|100|3|14|25+|0.00|0.51|
|TZA|MWSA|1.11|32|3|24|75+|0.24|0.28|
|UGA|NWSC|0.92|12|1|nav|nav|0.65|0.65|
|ZAF|Drakenstein|1.52|60|7|6|1000+|0.00|1.86|
|ZAF|Tygerberg|2.02|58|6|6|50+|0.00|1.86|
|ZAF|eThekwini|6.89|66|3|6|30+|0.00|1.77|

32
# **Gender Differences in Agricultural Productivity in Côte d’Ivoire: Changes in Determinants and Distributional Composition over the Past Decade**

Aletheia Donald, Gabriel Lawin and Léa Rouanet<sup>♣</sup> Keywords: gender gap, agricultural productivity, households, Côte d’Ivoire JEL codes : J16, J24, O13, Q12, Q18

> ♣ Donald: World Bank; adonald@worldbank.org; Lawin: World Bank; kotchigab@gmail.com; Rouanet: World Bank; lrouanet@worldbank.org. This paper is a product of the Gender Innovation Lab, Office of the Chief Economist, Africa Region. The authors would like to thank Jacques Morisset and participants of the 2018 International Association for Feminist Economics conference for valuable comments. We are grateful to UN Women and the World Bank Group’s Umbrella Facility for Gender Equality for financial support.
There are 129 countries and regions in the GTAP database, but we have data for only 105 countries. The GTAP data set, however, includes the EU countries as independent countries in the model. We assume, as Kee does, that there is a unique ad valorem equivalent of the non-tariff measure for all EU countries. For the single countries of the GTAP data set, which are also available in the Kee et al. (2009) data, we simply assign the calculated AVEs for the EU **.** Several GTAP regions are aggregates of smaller countries. These GTAP regions, the countries that comprise the regions and data availability for the countries within the region from Kee et al. are listed in table B1. We obtain estimates for these regions as follows. For Rest of Oceania, Rest of East Asia, Rest of South Asia, Rest of Central America, Caribbean, Rest of Europe and Rest of South African Customs Union, the AVEs are zero for all countries within these regions for which estimates from Kee et al. (2009) are available. Consequently, we report zero AVEs for these countries. For Rest of EFTA, Rest of North Africa and Central Africa we have estimates for only one country in the region from Kee et al. (2009); for these regions, we assume the values of the one country apply to the region. We compute simple averages for Rest of Western Asia, Rest of Western Africa and Rest of Eastern Africa<sup>30</sup> using AVEs for the available countries. There are no data available for several of the smaller regions and countries in the GTAP aggregation. After this aggregation step, we receive positive AVEs for 90<sup>31</sup> GTAP countries, zero AVEs for 27 countries and 18 missing values. A detailed description of our results is given in Table B2.

In the final step we aggregate the GTAP countries to the model specific regions using the mapping described in Appendix A. For the single countries we simply assign the calculated values. For SADC, COMESA, EU and ROW we compute weighted averages using GTAP countries’ total imports at market prices as weights. The AVEs for SADC are calculated without taking Madagascar, Zimbabwe and rest of South Central Africa into account as these values are missing. Table B3 presents the resulting AVEs applied in our model.

# **Table B1: Data availability for the GTAP regions**

|**No.**<br>**Code**<br>**Description**<br>**Countries**<br>**No.**<br>**Code**<br>**Description**<br>**Countries**|
|---|
|**available**<br>**available**|

> 30 Kenya, Tanzania, Rwanda and  Uganda are all regions of our model. The aggregation for Rest of Eastern Africa is based on the available data for Burundi, Djibouti and Sudan.

> 31 As the EU is a single region in the data set of Kee et al. (2009), we assume the same value for all EU member countries which apply a common trade policy.

88
- **Sack** , Alexander Nahum, Les Effets des Transformations des États sur leurs dettes publìque et autres obligations financières, 1927.

- **Sack** , Alexander Nahum, The Juridical Nature of the Public Debts of States, _New York University Law Quarterly Review_ 10 (1932-33), 127 and 341.

- **Stiglitz** , Joseph E., Odious rulers, odious debts, _The Atlantic Monthly_ , November 2003, available at : <u>www.odiousdebts.org/odiousdebts/index.cfm?DSP=content&ContentID=8577.</u>

- **Stiglitz** , Joseph E., Ethics, Markets and Government Failures, and Globalization: Perspectives on Debt and Finance, in Jochnick, Chris / Preston, Fraser A. (eds.), _Sovereign Debt at the Crossroads – Challenges and Proposals for Resolving the Third World Debt Crisis_ , 2006, p. 158.

- **Tamen** , Anaïs, La Doctrine de la Dette « Odieuse » ou : L’utilisation du Droit International dans les Rapports de Puissance, 2004 (Dissertation présentée le 11 décembre 2003 lors du 3<sup>ème</sup> colloque de Droit International du Comité pour l’Annulation de la Dette du Tiers-Monde, à Amsterdam), available at : <u>www.dette2000.org/data/File/detteillegitime/ladoctrine/ladoctrinedelado_anaistamen_ janv04.pdf#search=%22tamen%20la%20doctrine%22.</u>

**Weisburd** , A. Mark, _Reflections on_ A Convenient Untruth _, Virginia Journal of International Law_ 48 (2008), 641.

**Winters** , Jeffrey A., Criminal Debt in the Indonesian Context, 2000, available at: <u>www.probeinternational.org/pi/documents/odious_debts/Criminal_Debt.html.</u>

- **Yianni** , Andrew / **Tinkler** , David, Is There a Recognized Legal Doctrine of Odious Debts?, _The North Carolina Journal of International Law and Commercial Regulation_ 32 (2007), 749.

***********

- Page 42 -
the point estimates are larger.<sup>17</sup> The marginal impact of these new estimates now range from 6.2% (girls math) to 10.6% (girls Portuguese) of a standard deviation decline in test score. Interpreting these in terms of average annual increases in test scores reveals that the impact of starting to work is equivalent to roughly one half to a whole year of learning loss. The coefficient estimates for the child labor variable presented in Table 13 for the ’outs’ are all negative and significant at the one percent level, similar to Table 9, but again the point estimates are larger. The marginal impact of the estimates now range from 6.7% (boys math) to 19.0% (girls Portuguese) of a standard deviation decline in test score of roughly one half to almost two years of learning loss for those that remain in the labor market compared to those who exit.

The ‘In’ and ‘Out’ coefficient estimates have similar magnitudes and we cannot reject that they are statistically equal to each other. Therefore there is no evidence of heterogeneity between moment into or out of the labor market. This could suggest that transitions into and out of the labor market may be due to individual idiosyncratic shocks that are orthogonal to proficiency.

# **5.3 Exposure and lingering e↵ects of child labor**

We now turn to two questions that demand the use of the third ’three period’ sample. Recall that this sample takes all of the children we observe in 6<sup>_th_</sup> grade in either 2007 or in 2008 and whom we observe in the next two consecutive years (regardless of progression through the grades) and we compare scores in _t_ and _t_ + 2. Table 14 presents estimates of ’exposure e↵ects’ and takes all children who are not working in the first observation year and compares

> 17It is important to understand that these estimates are from an entirely di↵erent model and are thus not directly comparable. Whereas the baseline model imposes homogeneous e↵ects and estimates the average impact, this model estimates the impact of working conditional on not working in period _t_ .

21
**16**

Table 2: Incidence of Multiple Currency Practices in Developing Countries

|Country<br>|DifferentRates<br>for<br>Exports andImports|Exports<br>MoreThan1Rate|Imports<br>MoreThan1Rate|
|---|---|---|---|
|*Afghanistan<br>|**x**|**x**|**x**|
|*Argentina<br>|**x**|**x**||
|*Bangladesh<br>|**x**|**x**|**x**|
|Bolivia<br>|**x**|**x**||
|Brazil|**x**|**x**|**x**|
|Colombia<br>|**x**|**x**|**x**|
|Dominica<br>|**x**<br>|**x**|**x**|
|*DominicanR|ep<br>**x**|**x**|**x**|
|Ecuador|**x**|**x**|**x**|
|***Egypt**<br>|**x**|**x**|**x**|
|***El**Salvador|**x**|**x**|**x**|
|Gambia|**x**|**x**|**x**|
|Ghana<br>|**x**|**x**|**x**|
|Grenada|||**x**|
|*Guatemala|**x**|**x**|**x**|
|GuyanA<br>|**x**|**x**||
|Honduras<br>|**x**|**x**|**x**|
|*Hungary|**x**|**x**|**x**|
|*Jamaica<br>||**x**|**x**|
|Kenya<br>|**x**|**x**||
|*Lao|**x**||**x**|
|Mexico<br>|**x**|**x**||
|Nicaragua<br>|**x**|**x**|**x**|
|Nigeria|**x**|**x**|**x**|
|*Paraguay|**x**|**x**|**x**|
|*Peru<br>|**x**|**x**|**x**|
|*Poland|**x**|**x**|**x**|
|*Romania|**x**|**x**|**x**|
|*Somalia<br>|**x**|**x**|**x**|
|*Sudan|**x**|**x**|**x**|
|Syria|**x**|**x**|**x**|
|Uruguay|**x**|**x**||
|*Venezuela|**x**|**x**|**x**|
|Vietnam|**x**|**x**|**x**|
|W.Samoa|**x**||**x**|
|*YemenAR|**x**||**x**|
|Zambia|**x**||**x**|
|Total|34|**31**|**32**|

> Source: Extracted from IMF, Annual Report on Exchange Arrangements and Exchange Restrictions (Washington **DC:1989)**

* Principal, secondary, tertiary rates, and their applicability to types of transactions are found in **IFS.**
reconstruct food consumption without transfers. Standard errors are clustered at the district level. Standard errors in parentheses: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

As mentioned earlier, removing self-reported aggregate food aid, as reported in the social safety net module of the IHS, from consumption per capita does not fully account for the total amount of support received by households. Instead of relying on this aggregate estimate of in-kind transfer, we create an alternative measure of food consumption per capita without transfers by excluding transfer of individual food items in the consumption module of IHS 3 and IHS 4. The aggregate transfer constructed from individual food items is a more accurate measure of food transfer than the aggregated aid reported by households for all food items. A downside is that we were able to build this measure only for IHS 3 and IHS 4 but not for IHS 2. Column (2) of Table 3 displays results from model (3) of Table 2 applied to IHS 3 and IHS 4 (excluding IHS 2) and the same model used on food consumption when transfers are removed.<sup>14</sup> Table 4 shows food consumption per capita in columns (1) and (2) and the same model in column (3). Thus, the difference between columns (2) and (3) is related to the removal of food transfers only. It confirms that the coefficients of excess rain later in the growing season, lack of employment, and MASAF or education aid are all likely to pick up assistance that is unaccounted for in Table 2.

# **Table 4: Association between Shocks and Consumption per capita, without Transfers and Aid**

|% change in<br>ex<br>_IHS2-4_|total con<br>cluding ai<br>_I_|sumption<br>d<br>_HS3-4_|,<br>%<br>co<br>tra<br>_IH_|change in foo<br>nsumption, ex<br>nsfers<br>_S3-4_|d<br>cluding|
|---|---|---|---|---|---|
|**1. Weather hocks**||||||
|Drought in growing season<br>-2.7<sup>∗∗</sup>|(1.0)|-3.2<sup>∗∗</sup>|(1.3)|-4.3<sup>∗∗∗</sup>|(1.3)|
|Days with excess rainfall (Nov-Dec)<br>-0.7|(0.7)|-1.2|(0.8)|-1.8<sup>∗∗</sup>|(0.8)|
|Days with excess rainfall (Jan-Feb)<br>1.4<sup>∗∗</sup>|(0.5)|1.5<sup>∗∗</sup>|(0.6)|0.7|(0.5)|
|Days with excess rainfall (Mar-Apr)<br>1.5<sup>∗</sup>|(0.8)|1.0|(1.1)|1.3|(1.2)|
|TA’s median Palmer Index<br>-6.4<sup>∗</sup>|(3.7)|-2.7|(3.6)|-7.9<sup>∗</sup>|(4.3)|
|TA’s 98th percentile of rainfall<br>0.1|(0.1)|0.2<sup>∗</sup>|(0.1)|0.2|(0.2)|
|**2. Self-reported shocks**||||||
|Job loss<br>2.9<sup>∗</sup>|(1.7)|0.5|(3.3)|0.7|(3.6)|
|Food price shock<br>-2.0|(1.4)|-5.5<sup>∗∗∗</sup>|(1.6)|-6.1<sup>∗∗∗</sup>|(1.9)|
|Death<br>3.4<sup>∗∗</sup><br>**3. Household Head**|(1.4)|0.4|(2.1)|-0.2|(3.3)|

14 The difference between columns (1) and (2) is only due to the exclusion of IHS 2. The difference between columns (2) and (3) is due to the new dependent variable, which accounts better for transfers. As explained earlier, the coefficients on excess rainfall, as well as the one on job loss, death, and no employment, become smaller.

20
|Somalia|High<br>Frequency<br>Survey<br>(2017)|Multi-stage<br>stratified random<br>sample|Somalia (within secure<br>areas)|IDPs and host<br>communities|27,287<br>(82.3%)|
|---|---|---|---|---|---|
|Sudan|IDP<br>Profiling<br>Survey<br>(2018)|Stratified<br>cluster<br>sampling|Abu Shouk and El<br>Salam<br>camps,<br>and<br>neighboring and non-<br>neighboring Al-Fashir|IDPs and host<br>communities|17,645<br>(95.2%)|
|South<br>Sudan|High<br>Frequency<br>Survey<br>Wave<br>4<br>(2017)|Stratified<br>two-<br>stage<br>cluster<br>design|Urban areas of seven of<br>the ten pre-war states<br>(Western<br>Equatoria,<br>Central<br>Equatoria,<br>Eastern<br>Equatoria,<br>Northern<br>Bahr-El-<br>Ghazl, Western Bahr-<br>El-Ghazal,<br>Warrap,<br>and Lakes state).|IDPs and host<br>communities|4,554<br>(92.8)|

Data were available for all selected MPI indicators in every country, except for access to a bank account in South Sudan, where the remaining indicators in the financial security dimension are re-weighted accordingly.

# **_4.2 Description of the data_**

The relative sizes of different comparator groups – displaced and non-displaced populations, adults and children, and displacement status groups by gender – are shown in Figure 1 and Table 3.

_Figure 1: Population shares by age and displacement status by country_ 11
**<u>Table 6. Fixed-effect panel regression on household crop production income</u>**

|<br>|<br>Coef.|<br>Std.Err.||<br>Coef.|<br>Std.Err.||
|---|---|---|---|---|---|---|
|D<sup>RSDP5km</sup><br>|0.641|(0.275)|**||||
|D<sup>URRAP5km</sup><br>|0.175|(0.271)|||||
|D<sup>RSDP</sup>•KM<br>||||-0.037|(0.077)||
|D<sup>URRAP</sup>•KM||||-0.152|(0.058)|***|
|ln_L_|0.052|(0.079)||0.050|(0.079)||
|ln_R_<br>|1.027|(0.073)|***|1.025|(0.072)|***|
|ln_I_|0.418|(0.095)|***|0.424|(0.095)|***|
|ln_P_|0.099|(0.042)|**|0.095|(0.043)|**|
|ln_F_|0.044|(0.043)||0.042|(0.043)||
|ln_SIZE_<br>|0.336|(0.222)||0.343|(0.222)||
|ln_MALE_|0.558|(0.294)|*|0.579|(0.295)|**|
|ln_AGE_<br>|0.626|(0.361)|*|0.638|(0.363)|*|
|EDU2<br>|-0.091|(0.325)||-0.069|(0.324)||
|EDU3|-0.485|(0.304)||-0.444|(0.302)||
|EDU4<br>|-0.105|(0.646)||-0.044|(0.648)||
|EDU5<br>|-0.638|(0.451)||-0.593|(0.453)||
|EDU6<br>|-0.695|(0.586)||-0.668|(0.588)||
|EDU7<br>|0.019|(0.405)||0.080|(0.409)||
|COOK|0.231|(0.453)||0.203|(0.451)||
|LIGH<br>|-0.657|(0.304)|**|-0.631|(0.306)|**|
|T2013<br>|-0.433|(0.191)|**|-0.409|(0.187)|**|
|T2014|-0.535|(0.225)|**|-0.390|(0.207)|*|
|T2015<br>|0.664|(0.226)|***|1.074|(0.211)|***|
|T2016|-0.517|(0.220)|**|-0.044|(0.204)||
|Constant|4.736|(1.634)|***|4.700|(1.632)|***|
|Obs.|4639|||4639|||
|No. of groups|1004|||1004|||
|R-square|||||||
|within|0.2163|||0.2166|||
|between|0.8282|||0.8269|||
|overall|0.6278|||0.6255|||
|F-stat.|22.33|||23.16|||

The dependent variable is the log of total crop production value. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

For other covariates, the estimated results are generally consistent with our prior expectation: The coefficient of _L_ is positive but small and indifferent from zero. Thus, labor is not so productive. This may be attributed partly to our data issue that we assume the whole household members would engage in agricultural activities. But this may not be the case,