rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8 Projected and Actual Real GDP per Capita Growth Rates, 1965–2010**

# <u>(annual percent change, except where otherwise indicated)</u>

|||**_History_**||**_Project_**<br>**_19_**|**_ions for_**<br>**_90s_**||**_Actual_**|
|---|---|---|---|---|---|---|---|
||_Per capita_<br>_income_<br>_(dollars)_|_Trend_<br>_1965–87_|_Then recent_<br>_experience_<br>_(1980–88)_|_Baseline_|_Low case_|_1990–_<br>_2000_|_2000–10_|
|_Region or income group_||||||||
|All low- and middle-income countries|690|3.0|2.1|3.1|2.4|3.2|4.4|
|Excluding China and India|1,110|2.4|0.2|2.0|1.4|1.7|2.0|
|Sub-Saharan Africa|310|2.6|2.2|2.6|2.0|–0.1|2.4|
|High-income OECD countries|16,530|2.6|2.2|2.6|2.0|1.9|0.7|
|Memo items:||||||||
|Population growth||||||||
|All low- and middle-income countries||2.2|2.0|1.8|1.8|1.7|1.3|
|High-income OECD countries||0.8|0.6|0.4|0.4|0.7|0.6|

_Source_ : This is table 15 of _A Long-Term Outlook (_ Fardoust and Dhareshwar 1990), to which actual figures from World Development Indicators for the 1990s and the 2000s were added.

53
- McKinnon, B., S. Harper, J.S. Kaufman and Y. Bergevin. 2014. “Socioeconomic inequality in neonatal mortality in countries of low and middle income: a multicountry analysis.” _The Lancet Global Health_ , 2( 3 ): e165 - e173.

- Mirkuzie, A. H. 2014. “Exploring inequities in skilled care at birth among migrant population in a metropolitan city Addis Ababa, Ethiopia; a qualitative study.” _International Journal for Equity in Health_ , _13_ (1), 110.

- Moser, K, C. Frost and D.A. Leon. 2007. “Comparing health inequalities across time and place rate ratios and rate differences lead to different conclusions: analysis of cross-sectional data from 22 countries 1991- 2001.” _Int J Epidemiol..,_ 36: 1285–1291.

- O’Donnell O., E. van Doorslaer , A. Wagstaff A, and M. Lindelow. 2008. _Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their Implementation._ Washington, DC: World Bank.

- Onarheim, K. H., S. Tessema, K. A. Johansson, K. T. Eide, O. F. Norheim, and  I. Miljeteig. 2012. “Prioritizing Child Health Interventions in Ethiopia: Modeling Impact on Child Mortality, Life Expectancy and Inequality in Age at Death.” _PLoS ONE_ , _7_ (8), e41521.

- Quentin, W., O. Abosede, J. Aka, P. Akweongo, K. Dinard, A. Ezeh and L.  Sundmacher. 2014.  “Inequalities in child mortality in ten major African cities.” _BMC Medicine_ , _12_ , 95.

- Rutstein, S. O. and K. Johnson. 2004.  “The DHS Wealth Index.” _DHS Comparative Reports,_ No. 6. Calverton, Maryland: ORC Macro.

- Rutstein, S. O. and G. Rojas. 2006. _Guide to DHS Statistics. Demographic and Health Surveys_ .  Calverton, Maryland: ORC Macro.

- UNICEF. 2015. Levels & Trends in Child Mortality: Estimates Developed by the UN Interagency Group for Child Mortality Estimation. New York: United Nations Children’s Fund.

- Victora C.G., A.J. Barros, H. Axelson, Z.A. Bhutta, M. Chopra, G.V. França, K. Kerber, B.R. Kirkwood, H. Newby, C. Ronsmans, J.T.Boerma.  2012. “How changes in coverage affect equity in maternal and child health interventions in 35

40
General equilibrium simulation modeling can look at those broader effects, measuring not just the effect on regulated industries, but also the spillover effects throughout the economy. But nearly all of the existing general equilibrium models assume full employment (i.e., they assume that demand always equals supply in the labor market, implying that every worker who wants a job gets one). For many questions, that is a useful simplifying assumption, but it has clear limitations when the goal is to evaluate labor market effects. Specifically, such models generally do a good job of identifying which industries will be most affected by climate policy and the rough magnitude of those effects, but are much less accurate in assessing overall effects on the broad labor market or economy as a whole. Moreover, these models ignore the possibility that climate policies may result in persistent unemployment in some carbon-intensive sectors, due to labor market search frictions or more fundamental structural reasons, such as mismatch of skills.

As an illustrative example, we look at results from the well-known Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) computable general equilibrium model (van der Mensbrugghe 2010). These results suggest that job losses in carbon-intensive industries will be a substantial fraction of employment in those industries. But because jobs in such industries only represent a small fraction of total employment (less than 1 percent in most countries, and less than 5 percent even in countries that supply fossil fuels), these job losses are small relative to the overall economy. The model results also indicate that job gains in less-carbon-intensive industries exactly offset those job losses (i.e., the lost jobs entirely represent a shift from more-carbon-intensive to less-carbon-intensive industries, with no net change in employment overall). But one should be skeptical of the latter result, because the model’s assumptions necessarily imply that total employment will always remain fixed (and thus any job losses must be exactly offset by gains elsewhere). Moving away from that assumption is obviously crucial for looking at the aggregate effects on employment.

We go on to review a variety of alternative assumptions about the labor market that are used in general equilibrium simulation models, to move away from the fixed4
**Table 15. Fiscal Expenditures Due to Banking Crisis"' and Fiscal Deficit**

||**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|**Bulgaria**||||||||||
|FiscalExpenditures,%ofGDP|0.0|1.3|1.3|2.9|2.9|12.1|1.0|0.5|22.0|
|General Govt. Deficit (Surplus), %<br>of GDP|4.5|4.9|12.1|4.6|5.2|15.4|(2.1)|(0.9)||
|**Hungary**||||||||||
|Fiscal Expenditures, % of GDP|0.0|0.1|0.0|1.2|1.9|1.5|1.2|0.5|6.4|
|General Govt. Deficit (Surplus), %<br>of GDP|3.8|7.8|9.2|8.6|6.2|3.1|4.8|4.8||
|Macedonia||||||||||
|Fiscal Expenditures, % of GDP|0.0|0.0|0.0|0.0|0.4|0.4|0.4|0.0|1.2|
|General Govt. Deficit (Surplus), %<br>of GDP||8.7|12.1|2.9|0.7|0.3|0.4|1.6||
|**Poland**<sup>**2**</sup>**'**||||||||||
|Fiscal Expenditures, % of GDP|0.3|0.4|0.5|0.7|0.6|0.4|0.3|0.2|**3.4**|
|General Govt. Deficit (Surplus), %<br>of GDP|3.6|6.1|3.1|3.3|3.3|3.4|2.7|2.4||
|**Estonia**||||||||||
|Fiscal Expenditures, % of GDP|0.0|0.0|0.2|0.1|0.1|0.5|0.1|0.1|**0.8**|
|General Govt. Deficit (Surplus), %<br>of GDP|(5.0)|0.2|0.7|(1.4)|1.3|1.9|(2.2)|0.3||
|Latvia||||||||||
|Fiscal Expenditures, % of GDP|0.0|0.0|0.3|0.4|0.1|0.0|0.0|0.0|0.9|
|General Govt. Deficit (Surplus), %<br>of GDP<br>||0.8|(0.6)|4.0|3.5|1.8|(0.3)|0.8||
|**Lithuania**<br>**3'**||||||||||
|Fiscal Expenditures, % of GDP|0.0|0.0|0.0|0.0|0.0|0.4|0.2|0.1|0.7|
|General Govt. Deficit (Surplus), %<br>of GDP|(2.6)|(0.5)|3.3|5.5|4.5|4,5|1.8|5.8||
|**Kyrgyz Republic**||||||||||
|Fiscal Expenditures, % of GDP|0.0|0.0|0.0|0.0|0.0|0.2|0.6|0.3|1.1|
|General Govt. Deficit (Surplus), %<br>of GDP|(4.6)|17.4|14.4|11.6|17.3|9.5|9.0|8.1||

_Notes:_ The Czech Republic, Georgia and Kazakhstan have been omitted from this table because of the difficulty in ascertaining precisely which of the government costs listed in Table 9 were actually fiscal expenditures.

1/ Fiscal Expenditures include expenditures of the government resulting from bank recapitalization and deposit compensation, such as interest payment on bonds and other expenditures (see Tables 9 and 13 for details).

2/ Excludes compensation paid to depositors from Deposit Insurance Fund (this is only partly financed by the budget; from 1995 to 1998 DPF paid $51.4 Mln to depositors. This corresponds to less than 0.01 percent of GDP per year, see Table 13).

3/ Includes government deposits which were written off.

# **5. Bad Debt Recovery**

The above estimates for the costs of banking crises do not allow for possible cost recoveries on bad debt, which would mitigate some of these costs for the authorities. The choice of the bad debt recovery strategy by a country could have implications on its success in recovering bad loans and therefore on the fiscal costs it incurs from banking crises.

37
Mishra, S., R. Ghosh, and R. Kanungo (1990) “Measurement of Family Involvement: A cross-national study of managers”, _Journal of Cross-cultural Psychology_ 21(2): 232-48.

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<u>http://www.fao.org/sd/seaga/downloads/En/Microfinanceen.pdf</u> [accessed August 15, 2007].

Udry, Christopher (1996). “Gender, Agricultural Productio0n, and the Theory of the Household,” _Journal of Political Economy_ , Vol 104(5), pp. 1010-1046.

Ypeij, Annelou (2000) _Producing Against Poverty: Female and Male Microentrepreneurs in Lima, Peru_ , Amsterdam University Press.

Yunus, Muhammad (2004), "Grameen Bank, Microcredit and Millennium Development Goals", _Economic and Political Weekly_ , Vol. 39, No. 36, p.4077-4080.

31

-  -
# **III. Empirical investigation: Model and data**

We utilize a logit model for the incidence of civil wars: _P_ ( _PRIOCW it_  )1    _X_ 1 _it_  1  1  _X_ 2 _it_  1  2  _Int_ nhis_    _it_ , (8) in which the independent variables, _X_ 1 _it_  1and _X_ 2 _it_  1 , are the relevant distributional and control variables, respectively; and  _it_ is the error term. The distributional factors and some of the control variables are time invariant; the rest are set at their values in period _t-1_ . The binary explanatory variable, _Int_nhis,_ is 1 if there has been an external military intervention in at least one of the four years preceding period _t_ ( _t-1≤s<t)_ and 0 otherwise. We describe the data for each of these sets of variables next.

We study 137 countries over 1960-2005 and divide the sample into five-year periods so we have a total of 946 observations.<sup>25</sup> For comparison purposes, we first conduct the analysis for the period 1960-1999, considered by Montalvo and Reynal-Querol (2005), but then we estimate the model and test the robustness of the results over the full period up to 2005. We use the Peace Research Institute of Oslo (PRIO) data set for civil wars to construct the endogenous binary variable of civil war incidence, PRIOCW, which is set at 1 if a civil war occurred in a country _i_ in period _t_ and zero otherwise. In the baseline results we focus on intermediate armed conflict (PRIOCW, categories 3 and 4), defined as a contested incompatibility that concerns government and/or territory, where the use of armed force between two parties, of which at least one is the government of a state, results in a minimum of 1,000 deaths over the course of the civil war. In the robustness checks, we also consider low-level conflict (PRIO25) associated with at least 25 deaths,

> 25 The number of observations in a specific empirical model depends on the independent variables included in it, as different variables have different missing observations. In the baseline model, the maximum number of observations is 946.

17
considered. While the sale of emission allowances could help to some extent to increase the GDP, but other factors such as labor mobility, capital or investment mobility, and changes in consumption by households and provincial governments and changes in international and interprovincial trades due to emission trading are more dominant to affect the GDP. Changes in final consumption, investments and trade will be discussed later.

**Table 6. Impacts on GDP (by province) and gross output (by sector) due to ETS in 2030 (a) Impacts on provincial and national GDP in 2030 (% change from the baseline)**

|Province|Free Al|lowances|(Grandfath|ering)||Aucti|oning||
|---|---|---|---|---|---|---|---|---|
||Without|linkage|With li|nkage|Without|linkage|With l|inkage|
||Emissio<br>n based|Output<br>-based|Emissio<br>n based|Output-<br>based|Revenue<br>GOV|Revenue<br>HH|Revenue<br>GOV|Revenue<br>HH|
|Anhui|-0.7|-3.1|0.1|-1.1|-3.3|<br>-3.1|-1.2|<br>-1.1|
|Beijing|-7.3|0.8|-3.5|-0.3|-2.8|<br>-2.8|-1.9|<br>-1.9|
|<br>Chongqing|-0.9|1.2|-0.3|0.5|1.2|<br>1.9|0.5|<br>0.7|
|Fujian|0.8|1.4|0.3|0.6|1.9|<br>2.2|0.8|<br>0.9|
|Gansu|-0.1|-1.2|0.1|-0.4|-1.1|<br>-0.8|-0.3|<br>-0.2|
|Guangdong|0.9|-0.3|0.5|-0.1|0.9|<br>0.3|0.4|<br>0.2|
|Guangxi|1.0|1.6|0.5|0.7|1.9|<br>2.5|0.8|<br>1.0|
|Guizhou|-1.6|-7.1|-0.5|-3.0|-6.0|<br>-5.8|-2.5|<br>-2.4|
|Hainan|-3.4|1.0|-2.1|0.1|4.6|<br>8.1|1.1|<br>1.8|
|Hebei|1.0|-2.7|0.8|-0.9|-2.0|<br>-2.3|-0.6|<br>-0.7|
|Heilongjiang|-2.1|-2.9|-0.6|-1.2|-1.5|<br>-1.8|-0.5|<br>-0.6|
|Henan|-0.4|-1.1|-0.1|-0.5|-0.7|<br>-0.4|-0.3|<br>-0.1|
|Hubei|0.6|-0.5|0.4|-0.2|0.7|<br>0.8|0.3|<br>0.4|
|Hunan|-0.6|-3.4|-0.1|-1.3|-2.5|<br>-2.3|-0.9|<br>-0.8|
|Jiangsu|-1.1|0.2|-0.3|0.1|1.5|<br>0.5|0.6|<br>0.3|
|Jiangxi|-2.8|0.0|-0.9|0.0|-1.6|<br>-1.0|-0.6|<br>-0.3|
|<br>Jilin|-0.9|-0.9|-0.2|-0.3|-1.0|<br>-0.5|-0.3|<br>-0.1|
|Liaoning|1.0|2.4|0.5|1.0|1.9|<br>2.4|0.9|<br>1.0|
|<br>Inner Mongolia|-2.4|-5.9|-1.2|-2.8|-5.3|<br>-4.3|-2.5|<br>-2.1|
|Ningxia|-2.3|-6.4|-0.8|-2.6|-6.5|<br>-6.3|-2.6|<br>-2.5|
|Qinghai|-7.6|8.7|-3.3|5.6|-4.4|<br>-4.8|-1.3|<br>-1.6|
|<br>Shaanxi|-4.2|-4.2|-1.6|-1.6|-3.7|<br>-3.6|-1.4|<br>-1.3|
|Shandong|-2.3|-1.4|-0.6|-0.5|-2.3|<br>-2.7|-0.8|<br>-1.0|
|Shanghai|-0.6|-0.7|0.1|-0.2|-1.1|<br>-1.6|-0.3|<br>-0.6|
|Shanxi|-4.0|-5.3|-1.4|-4.6|-6.8|<br>-7.2|-3.4|<br>-4.0|
|Sichuan|-0.5|-1.4|-0.1|-0.5|-0.6|<br>-0.5|-0.2|<br>-0.1|
|Tianjin|-0.7|-0.5|-0.2|-0.3|-0.5|<br>-0.8|-0.2|<br>-0.3|
|Xinjiang|-2.1|-1.3|-0.6|-0.3|-1.0|<br>-0.8|-0.2|<br>-0.1|
|<br>Xizang|-0.1|1.5|-0.1|0.6|0.8|<br>1.8|0.3|<br>0.7|
|<br>Yunnan|-0.1|0.1|0.1|0.1|0.1|<br>0.6|0.1|<br>0.3|
|Zhejiang|-0.2|-0.4|0.1|-0.1|0.2|<br>-0.3|0.1|<br>-0.1|
|China|-1.5|-0.9|-0.5|-0.3|-1.2|<br>-1.3|-0.4|-0.5|

48
**Table A.10:** Bureaucratic Outcomes: Heterogeneity by Time of Appointment

||||**Bure**|**aucrat survey**|||
|---|---|---|---|---|---|---|
||Parent Head|Parent Served|Enthusiasm|Motivation|Interacts dailyw/ citizens|Freq. index|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||**Panel**|**A: Bureaucrats**|**appointed bef**|**ore last election**||
|New village head|-0.051|-0.104|0.252|21.931|0.347<sup>_∗∗∗_</sup>|0.544<sup>_∗∗∗_</sup>|
||(0.060)|(0.122)|(0.223)|(19.398)|(0.132)|(0.180)|
|Observations|531|531|529|527|529|529|
|Control mean|0.055|0.28|0.10|101.3|0.58|0.37|
|Robust p-value|0.237|0.427|0.175|0.218|0.00379|0.00106|
|MSE-opt. bandwidth|14.9|21.8|21.0|22.5|20.9|19.3|
|Effective obs.|214|279|269|287|269|256|
|New village head|-0.103|**Pane**<br>-0.389<sup>_∗∗∗_</sup>|**l B: Bureaucrat**<br>0.926<sup>_∗∗∗_</sup>|**s appointed aft**<br>37.045|**er last election**<br>-0.051|0.139|
||(0.090)|(0.162)|(0.291)|(26.698)|(0.200)|(0.425)|
|Observations|396|396|395|395|396|396|
|Control mean|0.045|0.25|-0.24|95.4|0.61|0.34|
|Robust p-value|0.177|0.00774|0.000777|0.115|0.860|0.599|
|MSE-opt. bandwidth|21.4|18.3|17.1|19.6|17.2|17.2|
|Effective obs.|186|172|164|179|165|165|
|Before vs After_p_|0.960|0.211|0.878|0.850|0.032|0.272|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in all columns. The dependent variables in columns 1–2 are identical to those in columns 2–3 of Table 4. The dependent variables in columns 3–6 are identical to those in columns 1–4 of Table 3. Panel A looks at bureaucrats appointed to their current position before the last village election while panel B looks at bureaucrats appointed since the last election. The bottom row reports the p-value from the test of Calonico et al. (2025) that robust RD estimates are significantly different across the two panels. See Section 4 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors clustered by village in brackets. Significance levels are based on the robust bias-corrected standard errors clustered by village.
**Table 3. Distribution framework for government securities before and after the Expanded Small** **<u>Investors Program</u>**

|**Before**|**After**|
|---|---|
|_Primary market_||
|•Only authorized securities dealers (banks) and large,<br>tax-exempt institutional investors (such as pension<br>funds) were allowed to participate in auctions of<br>securities|•Noncompetitive pricing; retail investors<br>acquire securities for the most recent<br>average auction price less 50 basis points<br>(0.5 percent)|
|•Dealers distributed securities through a subset of<br>their branch networks|•Nationwide|
|•Only large market participants (institutional<br>investors and dealers (GSEDs)) had accounts in the<br>Registry of Scripless Securities|•Retail investors open individual accounts<br>in the registry|
|•Minimum investment through dealers (GSEDs) is<br>100,000pesos, and not in theprimarymarkets.|•Minimum investment is 5,000 pesos|
||•Unlimited supply of securities|
|_Secondary market_||
|•Dealer and customer accounts were combined||
|•Minimum investment was 100,000 pesos through<br>dealers in secondarymarkets||
|•Supply of securities for distribution in secondary<br>markets depended on availability||
|•Prices varied according to the spreads charged by<br>dealers||

Although the new process does not eliminate dealers, they are no longer able to determine spreads or control access to government securities. Under the old arrangements dealers chose their technology and determined their workflows, then imposed these on the Treasury. But with the Expanded Small Investors Program, the Development Bank’s Data Center has been transformed into a shared services provider that connects investors, dealers, and the Treasury. Thus the power for determining efficiencies has been shifted to the Treasury.

# **What Incentives Does the Expanded Program Offer Stakeholders?**

The Expanded Small Investors Program uses an interesting business model that involves a variety of public-private partnerships. Explicit incentives for stakeholders encourage participation while supporting the program’s policy goals. Stakeholders include the Treasury (issuer of government securities), retail investors, the Data Center (applications service provider), participating banks 34
the intervals collected in the LFS and the distribution of wages across these intervals.<sup>12</sup> Only 3.3 percent of employees failed to report wages in the 2018 LFS. To convert wages into a continuous variable, in the baseline case, we obtain point estimates by assuming that wages are uniformly distributed on the reported interval and impute a random variable.<sup>13</sup> To address missing data, we use the standard practice of imputing mean wages conditional on observable characteristics, such as age, gender, occupation, marital status and urban status. In our analysis, we compare real wages net of taxes and deflated with the consumer price index (CPI), using 2015 as a base year. Specifically, we use the CPI estimates published by KAS (Harmonized Indices of Consumer Prices, 2018).

**Figure 2. Distribution of nominal wages reported in intervals (2018 euros)**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Euros<br>Percentage<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**We also analyze the sensitivity of the results to alternative imputation techniques** . In particular, we predict wages conditioning on observable worker characteristics using the 2017 HBS, in which the exact net wages are reported, and we use non-parametric matching techniques ( <mark>Ñopo</mark> , 2008) to impute these wages into the 2018 LFS. We also use the 2017 HBS to identify poor households because official poverty in Kosovo is measured based on a consumption aggregate, and this can only be constructed with HBS data.

# **Another empirical challenge is to establish causality since identifying sources of exogenous variation**

**is not possible with available data.** Only short pooled cross-section time series data are available, with no significant variation over time, and there is no variation by region in nominal minimum wages. Another option would be to exploit the fact that the minimum wage in Kosovo is set differently for different age groups, so that individuals experience a significant hourly wage increase at age 35.<sup>14</sup> In this case, changes in employment for individuals who are a few months younger and older than 35 years provide an estimate of the employment effect of the legislated wage increase. However, given that the wage data are in brackets, we cannot use a regression discontinuity approach that compares employment outcomes for individuals around this age

> 12 The current minimum wage falls in the third and fourth brackets, for the young and the old, respectively.

> 13 Note that this method does not account for worker characteristics.

> 14 This approach has been used in the United Kingdom, where the legal minimum wage varies by age (Dickens, Riley, Wilkinson, 2011).

10
**<u>Table 4: Estate characteristics</u>**

||**All**|||**Size categ**|**ory in ha**|||
|---|---|---|---|---|---|---|---|
|||**<=5**|**5-10**|**10-50**|**50-100**|**100-500**|**>500**|
|**Estate ownership**||||||||
|Years run by the current owner|19.00|13.14|12.54|15.28|21.13|19.84|30.77|
|Owner is Malawian|0.68|0.75|0.83|0.92|0.80|0.51|0.30|
|Owner is expatriate|0.10|0.13|0.00|0.01|0.04|0.29|0.21|
|<br>Owner is other|0.16|0.13|0.14|0.05|0.06|0.13|0.34|
|Owner is government|0.02|0.00|0.00|0.00|0.03|0.05|0.05|
|Owner is NGO|0.03|0.00|0.03|0.02|0.06|0.03|0.07|
|**Purchased inputs**||||||||
|<br>Share purchased fertilizer|0.87|0.75|0.83|0.96|0.90|0.94|0.93|
|Cost of fertilizer (US$/ha)|192.07|161.49|118.10|149.67|175.00|270.19|250.14|
|Share purchased pesticides|0.61|0.50|0.38|0.57|0.66|0.78|0.86|
|Cost of pesticides (US$/ha)|27.70|26.17|3.30|8.48|12.49|60.83|62.44|
|<br>Share purchased seed|0.61|0.50|0.62|0.68|0.71|0.61|0.60|
|Cost of seed (US$/ha)|9.71|1.79|10.00|5.27|10.32|13.18|19.92|
|Share purchased other inputs|0.19|0.25|0.28|0.14|0.16|0.10|0.07|
|<br>Cost of other inputs(US$/ha)|1.79|0.00|19.47|1.30|1.56|1.66|0.02|
|**Labor demand**||||||||
|Hired perm. male labor|0.60|0.38|0.59|0.52|0.52|0.82|0.92|
|No. of perm. male labor|28.8|1.5|4.9|3.7|7.2|49.4|108.2|
|No. of perm. male labor per ha|0.60|0.50|0.93|0.40|0.45|0.89|0.88|
|Hired perm. female labor|0.25|0.25|0.17|0.12|0.24|0.48|0.52|
|No. of perm. female labor|13.0|0.8|1.0|1.3|1.9|13.7|65.8|
|No. of perm. female labor per ha|0.19|0.28|0.19|0.11|0.05|0.19|0.58|
|<br>Hired temp. male labor|0.66|0.38|0.66|0.64|0.69|0.77|0.88|
|No. of temp. male labor|45.1|154.7|7.1|13.1|15.5|72.8|139.9|
|No. of temp. male labor per ha|1.51|31.28|1.96|1.48|0.96|1.14|1.03|
|Hired temp. female labor|0.52|0.13|0.45|0.47|0.54|0.65|0.79|
|<br>No. of temp. female labor|23.6|2.0|8.2|6.9|10.9|34.7|79.5|
|No. of temp. female labor per ha|0.71|0.50|1.65|0.86|0.50|0.54|0.45|
|<br>Total wage bill per ha (US$)|131.94|249.74|144.41|133.59|174.60|138.20|77.63|
|<br>**Tenancy**||||||||
|<br>Have tenants|0.31|0.13|0.10|0.44|0.45|0.24|0.10|
|Number of tenants|3.70|0.83|0.73|2.40|6.06|4.64|5.77|
|No. of obs.|868|8|29|422|96|192|121|

_Source:_ Own computation from 2006/07 NACAL.

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