rafmacalaba/datause-encoder-data huggingface dataset:
experiences a reduction of 1.4 percentage points (Figure 11, Panel d). This outcome underscores the intricate nature of addressing poverty. It highlights the importance of considering various factors, such as the extent of change in transfer coverage, the generosity of assistance, and the diverse needs of different income groups, when formulating effective policies to reduce poverty and enhance economic well-being.

**Figure 11. Simulated performance and poverty and inequality impacts of the MSA and heating allowance schemes under policy scenario 1**

- **a. Coverage (% of households in each decile)**

<!-- Start of picture text --> b. Benefit incidence (% benefits allocated to each decile)<br><!-- End of picture text --> <!-- Start of picture text --> 4.2 0.2<br>83.4 1<br>2<br>52.3<br>3<br>40.4<br>54.8 4<br>4.1 0.2 0.4<br>5<br> 1 2 3 4 5 6 7 8 9 10 6<br>c. Generosity (benefits as % of MYPP) d.Inequality, poverty, and poverty gap impact<br>43.0<br>Marginal Contributions to<br>0.01<br>Inequality<br>Change in Poverty Gap 1.38<br>Marginal Contributions to<br>0.85<br>3.8 Poverty<br>0.3 0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br>Source: Own estimates using CEQ and Bulgaria SILC 2021<br><!-- End of picture text --> _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by comparing the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented. Notice that the coverage and benefit incidence are the same as in Figure 10, but the impacts of generosity, poverty, and inequality differ._

# **Finally, the fiscal cost analysis reveals moderate changes between the baseline and Policy Scenario 1.**

In the baseline scenario, administrative accounts show that the MSA program incurred a cost of 32.3 million BGN and a heating allowance cost of 140.2 million BGN, resulting in 172.5 million BGN. Under Policy Scenario 1, the simulations show a moderate cost increase, with the MSA program cost rising to 230.2 million BGN, heating allowance to 174.5 million BGN, and the total fiscal cost reaching 404.6 million BGN. This reflects a moderate change in fiscal spending, with an increase of 197.9 million BGN for social assistance, 30.3 million BGN for the heating allowance, and a cumulative rise of 34.2 million BGN in the total fiscal cost (table 4). These changes underscore the financial implications of the policy simulation, highlighting a moderate expansion in government expenditure in these support programs.

30
|**Panel A: Main Estimates (All outcomes in zscores)**<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>**Altruism**<br>**Policy Altruism**<br>**Opinion on Effort**<br>**Opinion on Economy**<br>**Opinion on Taxes**<br>Treatment (Priming = 1)<br>-0.036<br>-0.021<br>-0.071**<br>-0.067**<br>-0.188***<br>(0.036)<br>(0.035)<br>(0.035)<br>(0.034)<br>(0.034)<br>R-squared<br>0.000<br>0.000<br>0.001<br>0.001<br>0.009<br>Observations<br>3131<br>3384<br>3346<br>3360<br>3413<br>**Panel B: Heterogeneous Effect - Gender**<br>Priming = 1_×_Male<br>-0.057<br>0.038<br>0.087<br>-0.097<br>-0.028<br>(0.072)<br>(0.070)<br>(0.070)<br>(0.068)<br>(0.068)<br>Treatment (Priming = 1)<br>-0.006<br>-0.040<br>-0.119**<br>-0.013<br>-0.172***<br>(0.053)<br>(0.051)<br>(0.051)<br>(0.050)<br>(0.049)<br>Male<br>-0.051<br>0.217***<br>0.045<br>0.373***<br>0.041<br>(0.051)<br>(0.049)<br>(0.049)<br>(0.048)<br>(0.048)<br>Marginal Effects [Male = 1]<br>-0.063<br>-0.002<br>-0.032<br>-0.110_∗∗_<br>-0.200_∗∗∗_<br>( 0.049)<br>( 0.048)<br>( 0.048)<br>( 0.046)<br>( 0.046)<br>R-squared<br>0.002<br>0.014<br>0.004<br>0.028<br>0.009<br>Observations<br>3,117<br>3,370<br>3,332<br>3,346<br>3,399<br>**Panel C: Heterogeneous Effect - Age (Impressionable Years)**<br>Priming = 1_×_Pop. Aged 18-25 = 1<br>0.237***<br>0.048<br>-0.065<br>-0.051<br>0.140*<br>(0.089)<br>(0.087)<br>(0.087)<br>(0.086)<br>(0.083)<br>Treatment (Priming = 1)<br>-0.084**<br>-0.028<br>-0.057<br>-0.055<br>-0.210***<br>(0.040)<br>(0.039)<br>(0.039)<br>(0.038)<br>(0.037)<br>Pop. Aged 18-25 = 1<br>0.011<br>0.163***<br>0.160***<br>0.214***<br>0.335***<br>(0.061)<br>(0.061)<br>(0.060)<br>(0.059)<br>(0.058)<br>Marginal Effects [Pop. Aged 18-25 = 1]<br>0.153_∗_<br>0.020<br>-0.121<br>-0.105<br>-0.070<br>( 0.079)<br>( 0.078)<br>( 0.078)<br>( 0.077)<br>( 0.075)<br>R-squared<br>0.005<br>0.006<br>0.004<br>0.007<br>0.036<br>Observations<br>3,131<br>3,384<br>3,346<br>3,360<br>3,413<br>_Notes:_ The dependent variable represents the outcome for individual as measured in the survey. It was standardized using the mean and standard deviation of the control group. The<br>variable _Treatment (Priming=1)_ is the assigned treatment status to the COVID-19 priming. The table presents the estimates of a simple OLS regression of the dependent variable<br>on the treatment status. For the heterogeneous effects, we interact the variable on _Treatment (Priming=1)_ with an indicator variable for being male and ages 18 to 25 years old,<br>respectively. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%. Robust standard errors are reported in parentheses.|
|---|

20
24 Table 7: Expenditure share of monthly consumption of 30 kWh by location, quintile, poverty status, and gender of household head, based on total household cash expenditures

|||All<br>|househo<br>|lds||Poo<br>|r househ<br>|olds<br>|Househo<br>|ld head<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Q1|Q5|Urban|Rural|Total|Female|Male|
|Botswana|2.4|6.8|4.2|16.7|1.4|6.9|10.7|9.0|4.8|3.8|
|Burkina Faso|7.8|15.0|13.0|22.3|7.8|13.1|18.9|18.1|19.1|12.0|
|Côte d'Ivoire|1.6|3.2|2.4|6.5|1.1|3.2|5.1|4.5|3.1|2.2|
|Ethiopia|1.3|3.7|3.3|7.2|1.4|3.7|6.2|6.0|4.7|2.9|
|Ghana|1.2|2.4|1.8|4.4|0.9|2.9|4.3|4.0|1.9|1.7|
|Malawi|1.6|4.0|3.6|6.3|1.9|3.0|5.8|5.5|4.8|3.2|
|Mali|1.0|2.2|1.8|3.5|0.9|1.9|3.1|3.0|1.7|1.8|
|Niger|2.6|5.3|4.8|7.7|3.0|5.9|6.4|6.3|7.9|4.4|
|Nigeria|0.4|0.7|0.6|1.2|0.3|0.7|0.9|0.8|0.8|0.5|
|Rwanda|6.8|15.3|14.1|26.9|5.1|16.1|21.7|21.3|18.4|12.4|
|São Tomé and Príncipe|1.7|1.9|1.8|2.8|1.2|2.1|2.2|2.2|1.9|1.7|
|Senegal|2.2|4.4|3.3|7.8|2.0|3.8|6.1|5.4|2.8|3.5|
|Sierra Leone|3.9|7.6|6.2|10.6|3.7|5.8|8.8|8.2|6.2|6.2|
|Swaziland|1.8|2.8|2.4|5.3|1.1|3.4|3.6|3.5|2.5|2.3|
|Tanzania|1.6|5.8|4.5|10.0|1.5|5.3|10.1|9.7|6.0|3.9|
|Togo|4.3|12.3|8.8|22.2|3.9|7.0|15.4|13.4|11.0|8.2|
|Uganda|4.3|9.2|7.9|15.9|3.3|15.0|16.3|16.2|10.6|6.7|
|Zambia|2.4|11.1|8.0|17.4|1.9|4.9|13.3|12.0|10.4|7.3|
|Median|2.0|4.9|3.9|7.8|1.7|4.4|6.3|6.2|4.8|3.6|

_Source_ : World Bank staff calculations based on utility information and household survey data.

Predictably, electricity becomes markedly less affordable. The 5‐percent threshold is exceeded for the poor in two‐thirds of the countries (against one‐third previously), and for four‐fifths of the bottom quintile (against half previously). These results are consistent with much greater reliance on home‐grown food by the poor than the rich.

Table 8 reproduces Table 6 based on total household cash expenditures. In Burkina Faso and Rwanda, only about one out of every eight households not connected to the grid would find 30 kWh of electricity a month affordable, compared to one out of every four in Table 6. Out of the 15 countries for which full information was available, more than half of non‐connected households would not find electricity affordable in six countries.

Table 8: Expenditure share of monthly electricity consumption of 30 kWh by grid connection status and current spending, based on total household cash expenditures

|Country|Sh<br>All|are of hou<br>Expendi<br>electr<br>Yes|sehold ex<br>ture on<br>icity<br>No|penditur<br>Gr<br>conne<br>Yes|e<br>id<br>ction<br>No|of<br>All|househol<br>Expendi<br>elect<br>Yes|ds for whi<br>ture on<br>ricity<br>No|ch share<br>Gr<br>conne<br>Yes|5<br>id<br>ction<br>No|
|---|---|---|---|---|---|---|---|---|---|---|
|Botswana|4.2|1.4|5.6|1.8|6.3|83|98|76|96|72|
|Burkina Faso|13.0|5.0|14.2|5.2|14.2|21|64|14|63|14|
|Côte d'Ivoire|2.4|1.3|3.4|—|—|92|98|85|—|—|
|Ethiopia|3.3|1.2|3.7|1.4|3.9|86|98|83|96|83|
|Ghana|1.8|1.2|2.5|1.4|2.7|95|99|91|98|90|
|Malawi|3.6|0.7|3.8|0.9|3.8|80|100|78|100|78|
|Mali|1.8|1.3|2.2|0.8|2.2|96|98|94|100|94|
Figure 3: Trend in total export values over time across countries by sector <!-- Start of picture text --> Malawi Malawi<br>Burkina Faso Burkina Faso<br>Senegal Senegal<br>2000 2003 2006 2009 2012 2015 2018 2000 2003 2006 2009 2012 2015 2018<br>Years Years<br>(a) Agriculture (b) Manufacturing<br>1500<br>2000<br>1000 1500<br>1000<br>500<br>Total exports in million USD Total exports in million USD<br>500<br>0 0<br><!-- End of picture text -->

## **3.2.2** **_Margins of export adjustment_**

Following Fernandes et al. (2016), we consider both the extensive and intensive margins of exports to capture the export decision of firms. This is relevant given that various margins of trade adjustment may respond differently to trade costs. To define variables encompassing the firm’s extensive margin, we adhere to existing literature (Fernandes et al., 2016), and expand our initial dataset, which solely contains data on positive trade flows, by inserting zero export values for the years when exports within the firm-product-destination grouping do not take place. We then consider three alternate measures that capture the extensive margin of trade: (i) an export participation dummy variable which takes the value of 1 if firm _i_ in country _c_ exports a positive value of product _p_ to destination _d_ in year _t_ , and zero otherwise, (ii) a firm entry dummy variable which takes the value of 1 if firm _i_ in country _c_ exports product _p_ to destination _d_ in year _t_ but did not do so in year _t −_ 1, and zero otherwise<sup>14</sup> , and (iii) a firm exit dummy variable which takes the value of 1 if firm _i_ in the country _c_ did not export product _p_ to destination _d_ in year _t_ but did so in year _t −_ 1, and zero otherwise.<sup>15</sup> Finally, we define the intensive margin as the FOB export value in USD of firm _i_ in country _c_ of product _p_ to destination _d_ in year _t_ .

# **3.3** **_Auxiliary data: Bilateral tariffs_**

While NTMs are proliferating in scope and stringency, tariffs remain the classical market access tool. We access applied bilateral tariff data from the World Integrated Trade Solution (WITS). In cases where applied tariffs are preferential, we use the latter. Where both applied or preferential rates are missing we use the most-favored-nation or the bound rates.

> 14We omit firms that have exported to a product-destination market every year from the entry probability analysis. Also, we exclude the initial year of the sample from this analysis because we cannot ascertain whether this is the first time the firms are entering the market or not.

> 15We do not include firms that exported solely to a product-destination market in the last year of the sample in our market exit probability analysis.

10
recycled through PRISMA previously. These individuals were randomly assigned to receive either a recycling bin with a sticker with information on recyclables, a recycling bin without a sticker, or no bin (control). The bin component is designed to lower economic barriers, as the bins reduce the cost of recycling by not requiring the purchase of recycling bags and by requiring less labor. Stickers test whether salient information on recycling increases the frequency and quality of participation.

As a subcomponent to the participation intensity study, individuals who provided their cell phone numbers to PRISMA were randomly assigned to receive a personalized weekly text message reminder, a generic weekly text message reminder, or no reminder (control). The text message reminder component aimed to test whether limited attention is an important factor in participation in a recycling program.

Although the use of cell phones has grown recently in Peru, phones remain relatively expensive and are not affordable to all households. The full impact of the campaign was only experienced by households that both owned a cell phone and were willing to share their phone number because only these households received the reinforcement text messages prior to the marketing agent visit. As shown in supplemental table S1.1, cell phone owners in the participation study were slightly richer, more educated, and more interested in local affairs (especially recycling matters) than non-cell phone owners. This factor is important to note when considering the external validity of the impact of the SMS message treatments.

# **<<A>>** PARTICIPATION STUDY

**<<B>>** Experimental Design<sup>8</sup>

8
# **Appendix: For Online Publication**

## **A Dataset Construction**

This appendix describes the construction of the dataset used in this paper, as well as the relevant background information for capital flow data generally and the underlying data sources specifically. The purpose of this dataset is to split capital inflows and outflows by capital flow type and by sector of the domestic economy, focusing primarily on debt flows. We base our dataset on the Balance of Payments (BOP) dataset, which includes capital flow data with breakdowns by flow type and sector, but also has some missing data. We fill in gaps in the data using some external datasets, such as the Quarterly External Debt Statistics (QEDS) and banking and bond data from the Bank for International Settlements (BIS).

We describe first the basics of capital flow data, the structure and coverage of the BOP data. We then explain the filling exercise and the external datasets that are used. We present comparisons to illustrate the quality of the fit of our external data and the contribution of our filling exercise. Lastly, we summarize the samples and coverage of our completed dataset. In Appendix B, we give more detail on the BIS datasets and how those series are constructed.

### **A.1 Capital Flow Data**

Some of the presentations and definitions of international capital flow data can be ambiguous or inconsistent across data sources. In order to be clear about what we are we doing, briefly highlight some basic concepts regarding capital flow data generally.

46
Ethnic polarization and the duration of civil wars Ethnic polarization has been shown to be an important determinant of the incidence of civil wars (Montalvo and Reynal-Querol 2005a). For this reason, in principle it is also a natural candidate to explain its duration. The most important aspect in the measurement of ethnic polarization is the kind of indicator we should use, and be aware of the theoretical implications of using one index or another.

The number of papers dealing with the effects of ethnic diversity on issues of economic interest is growing at a fast rate, mostly with an empirical content<sup>5</sup> . By now it is common place to include, in growth regressions, an index of ethnic diversity. All these papers use the index of ethnolinguistic fractionalization (ELF) as the indicator of ethnic diversity. The raw data come from the Atlas Narodov Mira (1964), compiled in the former Soviet Union, which refers to the situation in 1960. The criteria for group formation were based on the historical linguistic origin. The measure ELF was calculated by Taylor and Hudson (1972), which summarizes the data of the Atlas using the Herfindahl index. In particular the index takes the form, where if we consider religious (or ethnic) diversity, π i is the proportion of people who belongs to ethnic group i. The broad popularity of the ELF index is based on its intuitive appeal. Hence, the index captures the probability that two randomly selected individuals from a given country will not belong to the same ethnolinguistic group.

However, many authors have found that, even though ethnic fractionalization seems to be a powerful explanatory variable for economic growth, it is not significant in the explanation of civil wars and other kinds of conflicts. These results have led many authors to disregard ethnicity as a source of conflict and civil wars. Fearon and Laitin (2003), and Collier and Hoeffler (2004), find that neither ethnic fractionalization nor religious fractionalization has any statistically significant effect on the probability of civil wars. Nevertheless, it is not clear to what extent an index of diversity could capture potential ethnic conflict. In principle, claiming a positive relationship between an index of fractionalization and conflicts implies that the more ethnic

> 5 For instance Bluedorn (2001), Vigdor (2002) or Caselli and Coleman (2002).

6
Table 1. Recursive correlations between a country's per capita GDP Growth and average LAC GDP growth (blue type) and the same total consumption (black type). Sample for 1992 is 1971-1992, for 2003 is 1971-2003.

||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|Argentina|0.53|0.54|0.55|0.56|0.56|0.58|0.58|0.59|0.58|0.60|
||0.59|0.59|0.60|0.61|0.62|0.63|0.63|0.64|0.63|0.64|
|Belize|0.48|0.48|0.41|0.44|0.41|0.37|0.37|0.34|0.35|0.36|
||-0.02|-0.01|-0,06|-0.06|-0.06|-0.06|-0.05|-0.06|-0.04|-0.05<br>+|
|Bolivia|0.44|0.44|0.46|0.41|0.42|0.44|0.42|0.44|0.44|0.43|
||0.51<br>|0.51<br>|0.51<br>|0.47<br>|0.48<br>|0.50<br>|0.49<br>|0.48<br>|0.48<br>|0.48<br>|
|Brazil|0.78|0.79|0.79|0.77|0.76|0.74|0.74|0.74|0.74|0.74|
||0.69|0.69|0.70|0.63|0.63|0.62|0.62|0.62|0.61|0.62|
|Chile|0.26|0.27|0.27|0.22|0.23|0.24|0.24|0.26|0.26|0.26|
||0.33|0.33|0.34|0.30|0.31|0.32|0.32|0.32|0.32|0.32|
|Colombia|0.56|0.53|0.55|0.51|0.48|0.46|0.44|0.45|0.45|0.46|
||0.47|0.44|0.45|0.38|0.39|0.41|0.38|0.38|0.35|0.36|
|Costa Rica|0.57|0.57|0.57|0.56|0.54|0.54|0.52|0.46|0.45|0.46|
||0.27|0.27|0.28|0.28|0.28|0.27|0.27|0.27|0,28|0.28|
|Dominican Republic|0.48|0.48|0.48|0.45|0.46|0.48|0.47|0.41|0.42|0.42|
||0.18|0.15|0.18|0.23|0.25|0.27|0.27|0.26|0.28|0.27|
|Ecuador|0.58|0.56|0.56|0.56|0.56|0.56|0.56|0.57|0.57|0.53|
||0.20|0.21|0.21|0.19|O17|0.19|0.19|0.23|0.20|0.14|
|Guatemala|0.72|0.72|0.72|0.68|0.07|0.67|0.66|0.65|0.64|0.65|
||0.41|0.41|0.42|0.39|0.39|0.40|0.40|0.39|0.39|0.38|
|Guyana|0.51|0.50|0.52|0.47|0.47|0.49|0.49|0.47|0.46|0.44|
||0.27|0.27|0.31|0.31|0.32|0.34|0.34|0.33|0.34|0.32|
|Honduras|0.42|0.43|0.35|0.34|0.34|0.35|0.35|0.38|0.39|0.39|
||0.34|0.34|0.29|0.31|0.32|0.30|0.30|0.31|0.33|0.31|
|Haiti|0.39|0.37|0.28|0.31|0.31|0.33|0.32|0.30|0.30|0.30|
||0.28|0.27|0,05|0.09|0.09|0.12|0.12|0.11|0.11|0.10|
|Jamaica|-0.25|-0.25|-0,.24|-0.25|-0.25|-0.26|-0.26|-0.26|-0,26|-0.26<br>+|
||-0.37|-0,28|-0.27|-0.30|-0,30|-0.30|-0.30|-0.29|-0,29|-0.30<br>+|
|Mexico|0.55|0.54|0.54|0.55|0.56|0.58|0.57|0.55|0.56|0.57|
||0.57|0.56|0.56|0.57|0.56|0.58|0.58|0.56|0.56|0.56|
|Nicaragua|-0.05|-0.04|-0.03|-0.06|-0.04|-0.02|-0,02|-0.05|-0.05|-0.06<br>+|
||0.02|0.01|-0.02|-0.04|-0,02|0.00|0.00|-0,02|0,00|-0.01<br>+|
|Panama|0.23|0.23|0.23|0.24|0.23|0.26|0.25|0.24|0.23|0.25|
||0.23|0.22|0.16|0.18|0.22|0.20|0.21|0.21|0.18|0.18|
|Peru|0.63|0.63|0.64|0.57|0.57|0.58|0.58|0.57|0.57|0.57|
||0.22|0.23|0.25|0.20|0.21|0.22|0.22|0.22|0.23|0.23|
|Paraguay|0.62|0.62|0.59|0.58|0.56|0.53|0.52|0.53|0,50|0.50|
||0.49|0.49|0.51|0.50|0.49|0.47|0.46|0.47|0.47|0.47|
|El Salvador|0.13|0.14|0.16|0.12|0.12|0.13|0.13|0.12|0.12|0.12|
||-0.21|-0.19|-0.17|-0.20|-0.20|-0.19|-0.19|-0.20|-0.19|-0.19<br>+|
|Trinidad|0.46|0.45|0.46|0.42|0.43|0.43|0.41|0.38|0.39|0.36|
||0.33|0.31|0.22|0.22|0.23|0.18|0.18|0.12|0.13|0.13|
|Uruguay|0.65|0.65|0.66|0.67|0.67|0.68|0.67|0.68|0.66|0.67|
||0.56|0.56|0.57|0.59|0.60|0.60|0.60|0.60|0.58|0.59|
|Venezuela|0.34|0.34|0.30|0.26|0.26|0.29|0.29|0.33|0,34|0.31|
||0.23|0.21|0.15|0.16|0.13|0.17|0.17|0.19|0.19|0.18|
- 25 -

# VAT: Simplification or Unification?

Difficulties with tax administration and opportunities for wasteful rent-seeking imply that considerable simplification is desirable in the structure of VAT rates in China. However, since the existence of price controls has a very significant effect on profitability,17 unification of the VAT rate structure without price reform would be premature.

A tax policy model based on Chinese data was used to explore the losses that would arise from unifying VAT rates in the presence of controlled prices for plan transactions and free market prices for other transactions.18 The model distinguishes twenty-four production sectors and assumes that inequality arises from differences in workers bonuses caused by backward shifting of taxes on that portion of sales occurring at controlled prices.19

# 17/ Some estimates are provided in Mitra et. al (1989!.

- 18/ For a fuller exposition and other details, see Heady and Mitra (1990a). It should be noted that the input output table used in this exercise is for 1981 ana is therefore somewhat out of date. While this affects the numbers in Table 7, it does not affect the basic argument of this section.

- 19/ This assumption is made to provide income inequality as a simple representation of the harm caused by unequal profitability. If, instead, unequal profits produced unequal investment, long-run inefficiency might result. However, given the increasing liberalization of capital markets in China, debt financing might be used as an alternative to retail profits. In this case, unequal profitability will not produce unequal investment, but the allocation of current profits to investment will reduce future interest payments on debt and lead to larger future bonuses. Thus, all profits eventually benefit workers through bonuses or the size of the workers welfare funds.
total number of crops produced and a higher likelihood of using purchased seed and farming export crops.<sup>2</sup> All descriptive statistics and regressions are weighted by the sample weight, which is the inverse probability of a household being included in the sample. The sample design exploited a two-stage clustered sampling method. At the first stage, a certain number of enumeration areas (EAs) were randomly selected with equal probability, and with implicit stratification by urban/rural and region, while in the second stage 20 households were randomly selected within each EA.

**Table 1: Descriptive statistics for the pooled sample and by gender of the household head (2008)**

||Pooled<br>sample|Female-<br>headed<br>households|Male-headed<br>households|
|---|---|---|---|
|Self-reported productivity (XOF/ha)**|213,993|177,852|219,486|
|Productivity (XOF/ha)- Export crops|234,350|165,920|239,725|
|Productivity (XOF/ha)- Food crops**|207,123|161,433|214,661|
|Total land area cultivated (ha)***|8.19|4.86|8.69|
|Number of Fields Managed by the household***|4.59|4.47|4.61|
|Household head age (years)***|44.89|49.54|44.18|
|Married***|0.81|0.33|0.88|
|Divorced/Separated***|0.05|0.18|0.03|
|Widowed***|0.08|0.45|0.02|
|Single/Never married**|0.06|0.05|0.06|
|Household head has Primary education***|0.24|0.13|0.26|
|Household head has Secondary education***|0.13|0.05|0.15|
|Household head has Tertiary education***|0.01|0.00|0.01|
|Household head disability**|0.04|0.05|0.03|
|Dependency ratio***|0.96|1.21|0.92|
|Household Male Labor Use (Number)***|1.20|0.32|1.33|
|Household Female Labor Use (Number)***|0.88|1.33|0.81|
|Household Child Labor Use (Number)|0.11|0.09|0.11|
|Hired Labor Use (Number)|1.58|1.42|1.60|
|Total number of crops produced|4.30|4.15|4.32|
|Household used any purchased seed|0.13|0.12|0.14|
|Plots that use Chemical Fertilizer (% of total) ***|0.09|0.04|0.10|
|Plots that use Pesticide (% of total) ***|0.17|0.05|0.19|
|Export crops dummy (1 if Household produced any<br>export crop)***|0.74|0.43|0.79|
|**Observations**|**5428**|**752**|**4676**|

Dependency ratio: number of individuals in the household under 15 and over 65 over the total number of household members. ***/**/* indicate statistical significance of the difference between male-headed and female-headed households at the 1/5/10 percent level respectively.

Table 1 shows simple averages for the overall sample, and then separately by gender of the household head. We also display significance levels in the table, obtained from t-tests.

> 2 These same patterns are observed in 2016, reassuring us as to the national representativeness of our longitudinal analysis.  Results are available upon request.

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