rafmacalaba/data-use-mentions-extended huggingface dataset:
Such an outcome supports recent efforts to target investments directly to improving the livelihoods of the rural poor in remote and fragile environments (World Bank 2008). For example, in Ecuador, Madagascar and Cambodia poverty maps have been developed to target public investments to geographically defined sub-groups of the population according to their relative poverty status, which could substantially improve the performance of the programs in term of poverty alleviation (Elbers et al. 2007). A World Bank study that examined 122 targeted programs in 48 developing countries confirms their effectiveness in reducing poverty, if they are designed properly (Coady et al. 2004). A review of poverty alleviation programs in China, Indonesia, Mexico and Vietnam also finds evidence of “the value in specifically targeting spatially disadvantaged areas and households”, although the benefits are larger when programs, such as PROGRESA in Mexico, were successful in employing second-round targeting to identify households in poor locations and thus reducing leakages to non-poor households (Higgins et al. 2010, p. 20).

Appropriate targeting of research, extension and agricultural development has been shown to improve the livelihoods of the poor, increase employment opportunities and even reduce environmental degradation (Barbier 2005 and 2010; Carr 2009; Caviglia-Harris and Harris 2008; Coxhead et al. 2002; Dercon et al. 2009; Maertens et al. 2006). Empirical evidence of technical change, increased public investments and improved extension services in remote regions indicates that any resulting land improvements that do increase the value of homesteads can have a positive effect on both land rents and reducing agricultural expansion (Bellon et al. 2005; Coxhead et al. 2002; Dercon et al. 2009; Dillon et al. 2011; Maertens et al. 2006; Sills and Caviglia-Harris 2008).

Improving market integration for the rural poor may also depend on targeted investments in a range of public services and infrastructure in remote and ecologically fragile regions, such as extension services, roads, communications, protection of property, marketing services and other strategies to improve smallholder accessibility to larger
(7) years before the survey and between 16 and 27 (21 and 27) years before the 1984 census in the first (second) case. We do not find large differences by year of migration. The average migrant household migrated between 2000 and 2011 has a 78 percent higher consumption level relative to the average non-migrant one, and the estimated effect is slightly larger (+80 percent) among households who migrated between 2005 and 2011. Separating out migration from Coastal and Inland Ghana does not change the findings of the main sample: the estimated coefficient is statistically significant only among households originally from Inland Ghana and the effect is about 8 percentage points larger among most recent migrants (100.8 versus 108.5 percent).

Table 6 reports heterogeneous effects by gender of the household head. The effect of migration on current consumption levels is statistically significant only among migrant households headed by a male, both overall and among households moving from Inland Ghana. The point estimates, 0.612 on average and 0.714 among households from Inland Ghana, implies that consumption is about 84 and 104 percent higher than among non-migrant households, respectively. Finally, we also look at the existence of heterogeneity by educational attainment of the household head (Table 7). Because of the relatively small sample size and distribution of individuals by educational level, we use

> 16 We perform a robustness check to rule out the concern that lack of common support drives the results. The coefficients in the consumption equations estimated via OLS and with the correction for selection bias are identified from the sample of migrant and non-migrant households that, as described above, have different characteristics. Therefore, we construct a common support and re-run the analysis on the sample of households within the common support. Our estimates are not affected by such restriction. Moreover, we experiment with a number of matching estimators. It is well known that matching techniques allows comparison of observably similar (or identical) observations and come with less binding functional form assumptions. On the other
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# 3. **Field experiment**

# _3.1. Design_

The field experiment involved distributing the Mirt stove under different terms to a sample of households in 2013. In our study area and throughout much of Ethiopia, households with no refrigeration typically bake injera at least twice per week (Bizzarri, 2010; Tesfay, 2014; DFID, undated). This is the definition of regular usage.

We identify cooking events by measuring the surface temperature of the Mirt stoves using electronic Stove Use Monitors (SUMs).<sup>2</sup> The SUMs we used are approximately the size of a watch battery (see Figure 1). We set the SUM device to record surface temperature every 10 minutes and the memory can record for approximately 60 days and can tolerate temperatures up to 120 degrees Celsius. Temperature, time and date data are then downloaded after the monitoring period is completed. Respondents were informed that the purpose of the SUM device was to record the surface temperature of the stove and that enumerators would come back periodically to download the data and reinstall the SUM devices.

We have three levels of monetary incentives: (i) users receive the stove for free; (ii) users pay 25 birr for the Mirt stove (about 13% of the real cost),<sup>3</sup> and (iii) users pay nothing for their stoves and receive a 50 birr<sup>4</sup> incentive payment if the stove use monitors (SUM) that were affixed to all

> 2 The Mirt stoves are monitored over five periods of up to about 8 weeks per period over approximately three-and-a-half years. A subset of 108 households participated in three controlled cooking tests that are not part of this study but are discussed in Gebreegziabher et al. (2018).  In our regression models we control for participation in this activity.

> 3 This is a typical subsidized cost level if stoves are not provided free as in Rosa et al. (2014).
(12)|
|Forced Migrant|0.06***|0.06***|0.06***|0.05***|0.04**|0.05***|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|
|R-squared|0.01|0.03||0.00|0.02||
|Observations|2686|2686|2686|2686|2686|2686|
|Mean Dep. Variable(Colombian Sample)|0.04|0.04|0.04|0.20|0.20|0.20|
|Control Variables||✓|✓||✓|✓|
|Propensity Score Matching|||✓|||✓|

_Notes:_ The table presents the estimates of equation (1) using the percentile distribution of the Peabody Vocabulary Test Score as a dependent variable. The Peabody Vocabulary Test is an untimed test of receptive vocabulary for standard Spanish and is intended to provide a quick estimate of the test taker’s receptive vocabulary verbal ability. It includes 125 questions, each containing four picture options with only one correct answer. Items are organized in order of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. Control variables include children’s sex and age, the maximum level of education completed by the most educated parent and grandparent, and the wealth index is the pre-migration wealth index for the Venezuelan sample and the current wealth index for the Colombian sample. Standard errors are clustered at the household level *** * p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

naire, completed
**Figure 10. Kernel Density Functions of change in log employment under alternative scenarios**

<!-- Start of picture text -->
-4 -2 0 2 4<br>change in ln(employment)<br>(1) (2)<br>(3) (4)<br>.8<br>.6<br>.4<br>kernel density<br>.2<br>0<br><!-- End of picture text -->

Notes: This figure depicts the kernel density functions for four scenarios: what happens to firm employment in four different cases: (1) sales and share exported increased, (2) sales did not increase and export sales did, (3) sales increased while the share going to exports did not, (4) neither sales nor export shares increased. Dashed vertical lines depict the mean value for change in log employment. Note that the mean for the four groups are: (1) 0.86, (2) 0.17, (3) 0.16, and (4) -0.26. Calculated using 2016-2020 panel weights. Sample excludes the upper and lower one percentile of change in log employment.

Source: Authors’ elaboration using data from the Enterprise Survey for Egypt, 2013, 2016, and 2020.

42
Difference-in-Differences Results**

Before presenting our main results from the pooled SCM, as a benchmark we provide results from DiD regressions to estimate the effect of IHC adoption on the five firm-level outcomes discussed in Section 4.1.<sup>42</sup> The DiD estimator compares the differences in an outcome variable

> 40 The restriction that the w* weights all be positive and sum to one forces the synthetic control firm to be a convex combination of the firms that are used to construct it.  Smaller donor pools may not contain a sufficient variety of firms to allow the joint fitting of time series outcomes and a large number of firm characteristics.

> 41 In one of the robustness exercises (log import value with the average lag of log imports included in the characteristic variables), we required a larger set of placebo firms to insure the construction of synthetic control firms for two of the treated firms and their respective placebos.  Since 125 potential donors were not sufficient, we expanded the size of the donor pool to 200.  Because our inference relies on rank order of the treated firm, relative to its placebo firms, it is not directly affected by varying the donor pool size so long as we give the treated firm and its placebo access to the same donor pool.

> 42 While we use logarithms and moving averages to smooth the dependent variables, which as discussed in Section 4, is important for the SCM estimation, the qualitative DiD results we present here follow through if we use the log of monthly variables rather than the three-month moving average of that log.

29
**Figure 3. Labor force participation, % of population aged 15-64, 2012-2018 a. By age and gender b. By educational level**

<!-- Start of picture text --> 70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10<br>10<br>0<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent Percent<br><!-- End of picture text --> _Source: Own estimates based on 2012-2018 Kosovo LFS. Note: Secondary refers to upper secondary._

**Figure 4. Unemployment rate, % of labor force aged 15-64, 2012-2018 a. By age and gender b. By educational level**

<!-- Start of picture text --> 70 70<br>60 60<br>50 50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent<br>Percent<br><!-- End of picture text --> _Source: Own estimates using the 2012-2018 Kosovo LFS. Note: Secondary refers to upper secondary._

**As a result of low labor force participation and high unemployment, employment rates were significantly lower in Kosovo than in EU peer countries.** In 2018, the employment rate was only 29.4 percent compared with 52.5 percent across the Western Balkans,
9707|0.1575|0.1650|1.6027|
|Percentage of Good Soils|3299|0.7881|0.3192|0.0000|1.0002|
|Rainfall - First Principal Component|3299|-0.0130|2.1967|-4.2315|6.8953|
|Rainfall - Third Principal Component|3299|-0.1705|1.3696|-2.4997|5.4633|
|ln Wage in 1991|3300|7.9233|0.4684|6.6139|9.3168|
|Proportion of Natives in 1991|3300|0.6765|0.1694|0.0855|1.0134|
|Homicides per Capita in 1991|3299|0.0015|0.0010|0.0000|0.0090|
|Employment in Farming Activities in<br>1991|3300|0.1995|0.0752|0.0000|0.5437|
|Bank Dummy (1991)|3300|0.7197|0.4492|0.0000|1.0000|
|Average Temperature|3299|22.1518|2.7223|13.8325|27.7363|
|Fertility Rate in 1991|3300|3.6513|1.1051|2.0100|7.9600|
10 Although the reasons for dividing income into these five sources should be apparent, the rationale for distinguishing between agricultural and livestock income may need clarification. On the one hand, some observers may claim that within a rural economy it is artificial (and empirically difficult) to distinguish between agricultural and livestock income, since outputs from one - such as straw and crop residuals from agriculture, and draft power and manure from livestock - are used as inputs in the other. On the other hand, the goal of this study is to disaggregate the sources of income inequality as finely as possible. For this reason, it seems essential to distinguish between agricultural and livestock income, because these two income sources have very different effects on inequality. According to the data, the simple correlation between agricultural income and total income is the highest of all five income sources: 0.844. By contrast, the simple correlation between livestock income and total income is one of the lowest: 0.232.6 Table 1 presents summary data for the five sources of income. The table shows quite clearly the importance of rural income other than agricultural income. Agricultural income accounts for less than one-quarter - 24.6 percent - of total rural income. By contrast, nonfarm income is the single most important source, accounting for 42.2 percent of total rural income. Although definitions of nonfarm income vary widely,<sup>7</sup> these percentage figures for nonfarm income are comparable to those recorded in other studies. For example, a recent review of rural household budget surveys in 13 African, Asian and Latin American countries found that nonfarm income accounts for between 13 and 72 percent of total rural household income (Braun and Pandya-Lorch, 1991: Table 13).8 According to this review, the contribution of nonfarm income to total rural income is
# Appendix A: Source Data

## Population census data

This paper relies on data collected by eight Pacific Island governments’ National Statistics Offices (see Table A1) through their national population and housing censuses, and disseminated by the Statistics for Development Division (SDD) of the Pacific Community (SPC) through their PopGIS platform (https://prism.spc.int/regional-data-and-tools/popgis2).

### **Table A1: Pacific country census years and National Statistics Offices**

|**Country**|**Censusyear**|**National Statistics Office**|
|---|---|---|
|Fiji|2007|Fiji Bureau of Statistics|
|Solomon Islands|2009|Solomon Islands National Statistics Office|
|Vanuatu|2009|Vanuatu National Statistics Office|
|Kiribati|2010|Kiribati National Statistics Office|
|FSM|2010|FSM Division of Statistics|
|Tonga|2016|Tonga Statistics Department|
|RMI|2011|RMI Economic Policyand PlanningStatistics Office|
|Tuvalu|2012|Tuvalu Central Statistics Divison|

The data on the PopGIS site are encapsulated in maps for each PIC, illustrating variation in the results to select census questions across different parts of the country. Although the unit-record data for each individual and household are not available (and so it is not possible to consider cross-tabulations of responses across questions, for example), the geographical disaggregation is generally very detailed, and it is particularly helpful for this study focused on spatial aspects of development in the PICs.

Table A2 describes the geographical disaggregation of the data published on the PopGIS platform. Included in this table is a note on the nature of the overlap of geographical area boundaries with islands; with the exception of Vanuatu, which publishes a purposeful disaggregation of the census results by island on PopGIS, the data cannot be manipulated to present results at the island-level.

## Geospatial data

The geospatial data we used were from several different sources. The PopGIS platform provides
raw datasets that may justify the differences sometimes observed between the export totals obtained from the exporter-level raw datasets and the export totals available in COMTRADE. One of the potential reasons for these difficulties relates to the manual registration of export transactions that still takes place in some countries and may result in under-recording of export transactions for some countries and years.

As a result of this second filter, we keep in the raw dataset the information for Macedonia, Jordan, Mali, Mauritius, and several years of El Salvador’s data which would be left out due to an unfavorable match with COMTRADE data. We should also note that for countries such as Macedonia for which the match with export totals in COMTRADE in the first few sample years is relatively poor, the match improves over time and this is likely due to a shift from manual to digital registration of export transactions.

# **III. The Exporter Dynamics Database**

The Database includes a series of measures classified under different categories reflecting basic characteristics of the export base in each country (size of the exporting sector, exporter size and exporter growth rates), concentration/diversification (Herfindahl index, share of top exporters, number of products and destinations per exporter), firm, product and market dynamics (entry, exit and survival rates) and unit prices (per exporter, product, market). The measures are available at different disaggregation levels: a) country-year (file CY.dta with 98 measures), b) country-year–product (HS2, HS4, or HS6) (file CYH2.dta with 113 measures, file CYH4.dta with 113 measures, and file CYH6.dta with 89 measures) and c) country-year-destination (file CYD.dta with 74 measures). The list of measures under each category and for each type of 14
an estimated increase in forest loss associated with such provisions and no effect on RTAs without provisions.

# **4.1 Exploring Potential Mechanisms**

We explore potential mechanisms through which trade agreement provisions protecting biodiversity and forest might effectively mitigate forest loss arising from trade liberalization. In particular, we consider production and trade in forest products and agricultural commodities.

The international timber trade, especially in illegally harvested timber, is a clear threat to standing forest. If trade agreements include provisions protecting standing forest stock and requiring legal harvest and proper certification, this may support sustainable timber trade and harvest. We measure aggregate forest product activity using data from the UN Food and Agricultural Organization (UNFAO) including total forest-product exports and the total production of raw or minimally-processed forest-derived commodities including e.g. round logs, sawn lumber, etc. The trade data in particular corresponds to aggregate exports across all trading partners, not exclusively signatories to a particular trade agreement. We believe this is the appropriate measure to consider as trade liberalization may lead to substitution effects in bilateral trade flows that extend beyond the limited signatories of a trade agreement. As with our forest loss data, we aggregate this measures across all signatories to a trade agreement by year to construct an agreement-level panel of forest product activity.

Agricultural land expansion is one of the leading drivers of forest loss across the globe (Gibbs _et al._ , 2010; DeFries _et al._ , 2010; Busch and Ferretti-Gallon, 2017; Curtis _et al._ , 2018). At the same time, the literature has found that agricultural trade, in particular, responds strongly to RTAs (Grant and Lambert, 2008; Sun and Reed, 2010; Jean and Bureau, 2016). Trade liberalization may lead to increases in agricultural production at the extensive margin, resulting in deforestation as forestland is converted into agriculture. But trade liberalization might also facilitate agricultural intensification through increases in agricultural capital or technology which, in turn, may reduce pressures on agricultural land expansion and forest loss.
feed it to genetically-improved stall-fed cows, from which they produce manure. They apply the manure along with chemical fertilizers to fields planted with hybrid maize varieties and often intercropped with nitrogen-fixing legumes. This integrated maize-livestock farming system is more labor-intensive and land-saving than the traditional farming system, in which livestock is grazed without much interaction with crop farming.

It is worth noting that this highly complex farming system is indigenous and represents a novel combination of technologies that has not been researched as a system so that the best combination of inputs and the optimum timing of their application is unknown.

# **7.2 Rice**

A series of empirical studies of rice farming were conducted based on household data collected in Mozambique, Tanzania, Uganda, Ghana, and Senegal published in Otsuka and Larson (2016). As the studies reveal, rice is a promising crop in Africa and one well suited to technologies currently used in Asia (Otsuka and Larson, 2013; Estudillo and Otsuka, 2013; and Nakano et al., 2013).

In the Senegal River Valley, paddy yields average 4.5 tons per hectare, highest among the five countries studied. Rice farmers in the Senegal River Valley have access to an irrigation facility with ample supply of water, adopt Asian-type semi-dwarf modern varieties, apply chemical fertilizers amply, and practice improved management such as bunding, leveling, and straight-row planting (Sakurai, 2016). Yields are comparable with yields observed in Asia’s irrigated areas in the late 1980s (David and Otsuka, 1994). Irrigated yields in Tanzania were lower, averaging 3.7 tons per hectare, partly because of lower quality irrigation facilities and partly because of the incomplete adoption of improved management practices (Nakano et al., 2016). Irrigated yields in Mozambique ranged between 1.6 to 2.0 tons per hectare, well below the average of 2.2 tons in SSA (FAOSTAT, 2015). This lower yield can be attributed to low-quality irrigation facilities, inadequate chemical fertilizer application, the