rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> ° xchange — 1096<br>— 2000<br>— 2000<br>°<br>02 oa os os 10<br><!-- End of picture text --> <!-- Start of picture text --> 5 nPerimeter — 1996<br>— 2000<br>— 2010<br>3<br>1<br>°<br>00 02 04 os os 10<br><!-- End of picture text --> <!-- Start of picture text --> Fullness<br>—— 10062000<br>20 — 2000<br>18<br>10<br>os.<br>oo<br>02 oo 02 oa os os 10 12<br><!-- End of picture text --> <!-- Start of picture text --> Circuity_avg<br>bas —— 20001996<br>— 2010<br>»<br>8<br>10<br>5<br>°<br>10 14 12 13 14<br><!-- End of picture text --> <!-- Start of picture text --> Intersection_density<br>ors —— 20001906<br>gots — 210<br>ors,<br>0.0100<br>0.0075<br>0.0050<br>0.0028,<br>0.0000<br>° 50 100 180 200<br><!-- End of picture text --> <!-- Start of picture text --> Street_density<br>0.00012 —— 20001996<br>0.00010 — 210<br>0.00008<br>0.00008<br>0.00004<br>00002<br>0.00000<br>° 5000 +0000 18000 20000 25000<br><!-- End of picture text --> <!-- Start of picture text --> ‘Sprawl<br>2s —— 20001996<br>— a0<br>20<br>15<br>10<br>os<br>oo<br>oo 02 04 os os 10 12<br><!-- End of picture text -->
likelihood of PPI commitments even controlling for fundamental characteristics of the economies.

**Table 3: The Effect of Country Risk on the Probability of Undertaking PPI Commitment**

|**VARIABLES**<br>**Inv. Commitment**|**Inv. Commitment**|**Inv. Commitment**|
|---|---|---|
|**(1)**|**(2)**|**(3)**|
|Country Risk<br>0.0408***|0.0485***|0.0196***|
|(0.0040)|(0.0036)|(0.00598)|
|Rest of Region<br>4.59e-05***||3.62e-05***|
|(3.70e-06)||(4.87e-06)|
|Log GDP PPP||0.670***|
|||(0.0728)|
|Log GDP Growth||0.0154|
|||(0.0544)|
|Log Inflation||-0.161***|
|||(0.0412)|
|Log Openness||0.445***|
|||(0.168)|
|Constant<br>-1.685***|-1.378***|-17.87***|
|(0.1650)|(0.144)|(1.969)|
|Observations<br>2,566|2,566|1,753|
|Number of countries<br>131|131|121|
|***p<0.01,**p<0.05,*p<0.1|||

Source: Authors’ calculations.

Note: Running the equation (1) and (2) with the same observations of the column (3), the coefficient associated to the country risk are: 0.046 and 0.052 respectively.

The relation between the country risk and the intensity of the commitments is estimated through the model in equation (2).

The results presented in Table 4 are those resulting from the fixed effect model, since the Hausman test rejected the hypothesis there is no difference between estimators and therefore the coefficients of the random effect model are not consistent.<sup><u>9</u></sup> The importance of the fixed effect model is that it allows capturing unobservable variables that explain the likelihood to participate in a PPI. Therefore, there is no selection bias problem. As robustness check we provide estimation results of the Heckman two step procedures in Appendix 4.

> 9 The Haussmann test rejected the null hypothesis that there is no difference between the coefficients of the random effects model and the fixed effects model. In other words, there is no evidence to support the assumption that there is no correlation between regressors and the random effects. In this way we assume that the idiosyncrasy of each country is also playing an important role.

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<!-- Start of picture text --> are relatively small for the first three categories, we nonetheless expect there to be a noticeable<br>difference in magnitude between effects stemming from changes along those dimensions, and those<br>associated with domestic market entry costs.<br>4 Empirical Model and Results<br>Our baseline empirical strategy to test the above predictions is a straightforward one. Since<br>our diversification measure takes the form of count data—i.e., the number of 8-digit product lines<br>exported in every 2-digit sector—we postulate that it follows a Poisson distribution with mean and<br>variance equal to ju, (where e indexes exporters and s indexes sectors).'* Its density conditional<br>on a set of independent variables X_., is:<br>f (lines_cn2., | X-s) = ————sexp (—p lines ) _cn2,,!oo plines_cndess#@——_— (20)<br><!-- End of picture text -->

We specify the conditional mean function j,, in terms of the parameters of the theoretical model developed above anda set of sector fixed effects (6,) to control for unobserved heterogeneity affecting all exporters in a particular sector in the same way.!> Thus: 8, In (entrye) + By In (export.) + 83 ln (1+ t2") + 6, In (dist) Mes = 0s XP (21) +85 In (1+ t2”") + 6, In (gdp_hs2.) + 8, 1n (gdppc.) We use entry. to refer to restrictions on entry in a given exporting country (f//), which we proxy using the Doing Business entry costs data referred to above. Export-specific trade costs (r?# and f/) are proxied using MAcMap bilaterally disaggregated applied tariff data for the EU (t£”) and Doing Business data on the costs of exporting (export.). While ad valorem tariffs impose '4We estimate the model at the sectoral (2-digit) level, since three of the variables of interest-EU and own tariffs, and sectoral expenditures—vary at that level. However, we are conscious that the other variables of interest vary only at the country level, and so we adjust all standard errors for clustering by exporting country. 'SWe expect the sector fixed effects to account for influences such as trade-related measures applied on an MFN basis within our country sample (e.g., product standards), as well as the different numbers of 8-digit product lines included in each 2-digit sector within the CN classification. 19
Matthews (2007) see the production of biodiesel from jatropha as a means to industrialize poor countries and help them overcome poverty. Planted on a large scale, jatropha may even enable these countries to reduce their imports of petroleum products or even export to international markets. Renner (2007) summarizes the perception of many “Jatropha seems to offer the benefits of biofuels without the pitfalls.” However, others raise some doubts on the benefits of producing biofuels from agricultural products. They raise concerns over biodiversity loss (Raizon, 2009) or endangering the food sovereignty of countries that are already struggling (Luoma, 2009; Raizon, 2009; Campa and Valentin, 2009). Other concerns include the possible influence of pourghère plantations on soil fertility or the risks of diseases or parasites transmission that could annihilate not only jatropha but also other cultures (Campa et al., 2009).<sup>12</sup> This literature shows that two opposing views are at odds when it comes to jatropha production and promotion. Nevertheless, as Benge (2006) states, “there is no indication that any scientific studies have been conducted to prove or disprove any of the above concerns.” Based on the fact that the African continent still disposes significant surface of unexploited agricultural land (approximately 58% according to GEXSI, 2008), it appears relevant in our view to contribute to this debate with a focus in economic and distributional impact which is what we propose in this case of Mali.

# **2 The Malian model**

## **_2.1 The model_**

Since the late 1990s researchers have been using CGE models to analyze the impact of policy reforms on poverty and income distribution. Three main categories of these models have been used during this period: the representative household approach (RH), the integrated multi-household approach (IMH) and the multi-household sequential approach (MHS).

The CGE-RH approach divides households into groups, choosing a representative household for each group and using that representative household in the CGE model. The variations in income of the representative agents generated with the CGE model are applied to households within their respective group from a household survey. This assumption does not allow the analyst to take into account within-group changes in income distribution, even though studies (Huppi and Ravallion 1991 and Savard 2005, for example) have shown that such changes can be greater than between-group inequality changes. Savard (2005) demonstrated that the results of poverty and income distribution analysis can be completely reversed by taking into account within-group distributional effects.

> 12 The authors would like to recall the African coffee plant (arabica) introduced to the entire intertropical area by colonists and whose plantations were decimated in the early 20th century by orange rust.

6
data flows among different stakeholders. At advanced levels of data maturity, the goal is to optimize the system for the best possible outcomes.

Kitzmueller _et al._ (2021) offer a discussion of the data systems and efforts to improve them using three real-life country examples—Ghana, Mexico, and Estonia that correspond to the three levels of data development—and well illustrate each country’s current position regarding statistical capacity with their SPI score. Jolliffe _et al._ (2023) argue that data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Carpenter _et al._ (2022) and Reddy _et al._ (2023) describe various South-South and North-South initiatives to improve local statistical capacity (in medical statistics), ranging from setting up joint graduate study programs and research projects to consulting consortiums for rapid response to the COVID19 pandemic. Using volunteer data collected by other stakeholders, including citizen scientists, private sector companies, and other organizations, could encourage the public to further participate in building up statistical capacity, filling SDG data gaps, and enhancing government ability to monitor public service delivery and better protect the environment (Conrad and Hilchey, 2011; Meijer and Potjer, 2018; Fritz _et al._ , 2019).

Yet, while the amount of data produced by the public sector is increasing rapidly, the full potential of data to improve development outcomes has not been realized yet because of challenges of suboptimal data quality or lack of data on vulnerable groups. This limitation is most highlighted during times of crises. For example, recent studies on the COVID-19 pandemic suggest that vulnerable groups, including poorer households, women, children, and refugees, were most affected (Dang and Nguyen, 2021; Sumner et al., 2022; Li _et al._ , 2023). In this regard, the importance of measurement of statistical capacity has been recognized in the SDG indicator framework with the recent formal population of SDG indicator 17.18.1 with the SPI pillars 4 and 5 indicators and the ODIN data availability indicators is a step in the right direction.<sup>2</sup> Since better statistical capacity could help collect more data, such as on gender-disaggregated data and climate change, Tichenor (2022) argues that assessment tools such as the SPI can be regarded both as a tool for monitoring development and as a development goal itself.

> 2 SDG 17.18.1 is made up of one ODIN indicator (component a) and two SPI indicators (components b and c).

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The conflict in the Republic of Yemen involves a number of different actors and motivations. The northern parts of the country- including the pre-conflict capital and territories where at least two-thirds of the population live- have been under control of Houthi forces since late 2014 and early 2015, who have become known as the de facto authorities (DFA) in Sana’a (e.g., World Bank 2017); the southern parts of the country are under control of the internationally recognized government (IRG), that has been based abroad in Riyadh and in Aden at various points of the conflict (e.g., OCHA 2019); a coalition of nine foreign governments from the Middle East and North Africa intervened beginning in March 2015 in support of the IRG, targeting DFA positions with air strikes and supporting IRG ground troops along the border of DFA controlled territory (e.g., OCHA 2016); and both a violent secessionist movement and terrorist forces vie for control of different portions of the IRG-controlled territory (e.g., OCHA 2021). As a result of the complex and multifaceted war that continues to unfold in the Republic of Yemen, the country has experienced a large increase in violence following the initial escalation in March 2015 (e.g., OCHA 2017; Tandon and Vishwanath 2020; etc.).

However, the large number of parties to the conflict and the large amount of violence increases the sensitivity of data collection surrounding the conflict. For example, it might be difficult for individuals to openly or without qualification support certain groups in the conflict that are involved in a large amount of violence, particularly violence that affects civilians (e.g., BBC 2022). When discussing the conflict itself, traditional modalities of data collection might not fully and accurately describe the feelings of the entire population.

Furthermore, the conflict has been accompanied by both a humanitarian crisis and a forced displacement crisis. Immediately following the escalation of the conflict, the prevalence of the population with poor food access escalated from approximately 10 percent of the population prior to the conflict to nearly 60 percent in the first nationally representative assessment conducted at the end of 2016 (e.g., FAO 2017; Favari et al. 2022); and in the months following the escalation of the conflict in March 2015, approximately 10 percent of the population became forcibly displaced according to official figures, with roughly 8
Table 1.3 <!-- Start of picture text --> Levels of GNP<br>(in 1960 US dollars and prices)<br>Total (billio ns of dollars) Per cap ita (dollars)<br>Third Developed Third Developed<br>Year World' countries2 World' countries2<br>1750 112 35 188 182<br>1800 137 47 188 198<br>1830 150 67 183 237<br>1860 159 118 174 324<br>1900 184 297 175 540<br>1913 217 430 192 662<br>1928 252 568 194 782<br>1938 293 678 202 856<br>1950 338 889 214 1,180<br>Beiroch uses the term "Third World" and includes Asia and other countries currently<br>referred to as 'developing countries-.<br>2 Includes Europe, America, Japan and other industrialized countries of today.<br>Source: Compiled from Bairoch (1993: Table 8.2, p. 95)<br><!-- End of picture text --> According to the figures in the above table, from the mid- 1 8 th century through the mid- 19 th century total t5NP was considerably higher (roughly one and a half to three times higher) in "Thircl World" countries (including Asia) than in developed countries. A reversal of this pattern began to occur around 1900 and is attributed to the long-term effects of the Industrial Revolution, which after a century and a half resulted in a "multiplication by more than five of the average standard of living" in developed countries. 1<sup>8</sup> Bairoch's estimate of total GNP in 1750 was $147 billion (in 1960 U.S. dollars), of which 76 percent was in "Third World" countries while only 24 percent was in developed countries. By 1860, these proportions had dropped to 57 percent for "Third World" countries and 43 percent for developed countries out of a total $277 billion of GNP.

However, given the large population of Asia relative to that of Europe, per capita GNP follows a different pattern. While Bairoch estimates that per capita GNP in 1750 is also greater for "Third World" countries ($188) than for developed countries ($182), this does not continue and is reversed over the next 50 years so that by 1800 developed countries' GNP per capita exceeded (by $ 10) that of "Third World" countries, for which the level remained the same ($188). His estimate for China alone, however, is $210, which exceeds that of both developed and "Third World" countries. 19 By the end of the colonial period in 1950, the per capita income of "Third World" countries had reached only $214 while that of developed countries was **51/2** times greater. Braudel (1984) points to Bairoch's calculations as indicative that despite Europe's "dazzling triumphs all, over the globe," its level of wealth was far from superior to that of the rest of the world. He supports this statement by referring to Bairoch's total GNP figures in Table 1.3, which shows that it was not until the late 19th century that the developed countries overtook the rest of the world in total GNP.<sup>2 0</sup> _5_
We first run simple regressions to calculate the raw gender gaps. For the LFP regression, where the dependent variable is a dummy variable, we use a logistic regression as follows: Where the dependent variable equals 1 if the individual is in the labor force, and zero otherwise. The variable f f f = 1 if the person _i,_ working in sector _j=_ {wage employment, entrepreneurship or agricultural production }ii,jj,kk and company/business sector/plot _k_ is female, and 0 if the person is male. The coefficient gives us the extent of the differences in outcomes between men and women.<sup>9</sup> γγ For regressions of the gender gap in earnings, we run the following specification: f f (2) ii,jj,kk ii,jj,kk ii,jj,kk The variable is the dependent variable measuring earnings of person yy = αα+ γγf + εε _i_ of gender G = {man, woman} working in sector ii,jj,kk _j=_ {wage employment, entrepreneurship or agricultural production} and company _k_ . For yy , we use an inverse hyperbolic sine transformation.<sup>10</sup> We then estimate equation (1) and (2) separately for urban and rural areas, as well as by region and age group (individuals aged 15-29 vs. 30-64). The ii,jj,kk yy urban/rural classification was taken from the dataset: the survey coded whether the respondent lived in an urban or rural area. We use robust standard errors.<sup>11</sup> Next, we use Kitagawa-Oaxaca-Blinder (KOB) decompositions to understand the factors underlying the gender gaps in the 2019 data. The method calls for running two separate regressions, one for men and one for women:

> 9 In this and in all subsequent regressions, we use the analytic weights of Stata as weights, which allows us to run regressions on smaller subsamples and obtain the standard errors. Analytic weights scale the regression by the square root of the weight, and this method allows us to get an unbiased estimate of the variance of the residual (for more information, see “What is the Effect of Specifying aweights in regress?”). We did a test where we used the full sample and ran the regressions using survey weight command and analytic weights respectively. The coefficients were identical, and the standard errors were very similar in almost all the cases.

> 10 The one exception is wage earnings, where due to most of the data consisting of zeroes, we instead use untransformed values for wage earnings, winsorized at the 1% and 99% levels in both years.

> 11 Although not reported, we also ran the KOB decomposition using clustered standard errors at the enumeration area level. While the clustered standard errors were calculated in the regressions using the full sample, the clustered standard errors were not produced when regressions on subsamples were run (by age or by urban/rural classification). However, we saw that for the full sample, the clustered standard errors and the robust standard errors were virtually identical.

8
reports indicate that women’s response to the initiative was positive. For example, from July 2021 to March 2022, the percentage of women traveling by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). According to Goswami (2021), women made the of riders on Delhi buses March 2021 after the up majority Transport Corporation by program’s launch.

To identify the causal impact of the program on women’s labor outcomes, we collated data from several sources. Our main empirical analysis is based on the Consumer Pyramids Household Survey (CPHS) data maintained by the Centre for Monitoring the Indian Economy (CMIE). The rich CPHS data is a panel of about 160,000 households across all major Indian states after 2014. It includes comprehensive information not only on household expenditures, members’ demographic characteristics, and employment status but also on their time use patterns and allocation of time on various activities. We bolster the findings with a large primary survey of women from Delhi that was collected after the program’s launch in Delhi.

Our identification approach compares women in treated states (i.e., Punjab and Tamil Nadu) to their geographical neighbors, serving as our control group. The implementation of the in 2021 variation. To address con- policy provides temporal endogeneity cerns, we implement a synthetic differences-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) at the state level. This approach combines the synthetic control method (Abadie and Gardeazabal, 2003) with the difference-in-differences strategy.<sup>25</sup> We compare the pre-trends in our treated and the synthetic control group using event study models and do not discern differential trends.

We examine the effect of the treatment on on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category that includes buses, was reduced for households with women in treated states compared to control states. Findings from the Delhi survey corroborate these results at the individual level. New female users of buses report negligible transportation costs after the start of the policy as opposed to their substantial expenditures on other modes of transport before switching to buses.

We then examine if women in the treated areas change the time spent on traveling and, if they do, the potential reallocation of that time to other margins of time use, such as household chores and labor Diverse outcomes women supply. emerge among

> 25The weights from the SDID approach are used in all survey data-based analyses at the individual level.

2
- 1? -

higher in rural ares, **i IId tI it rural** _Ipoor_ :iccounit for t **hIe** va.st majori ty of **a II** tbos;e **iln** povertV. I **ni Lit** inl **Aiiltri- ca** t he situat ion i s l&Sf; extre me, **pr** inarily because of the higher degree of urbaniization. Howeve r, so far as **non1-** i **ncomie** achievements are concerned, even **in** latin America rural areas lag belhinid urban areas - -- as shown by table 6, reproduced from the WDR.

Since povertv is primarily a rural phenomenon, it is also primarily an agricultural phenomenoni. A related characteristic of the poor is that they lack assets. They either have very small amounts of unproductive land, or no land at all. They lack human capital and are therefore reduced to selling unskilled labor, largely in an agricultural setting. This means that the main sources of income for the poor are from agriculture and (in both rural and urban areas) sale of labor. Those who are income poor also turn out to be poor in otiher dimensions -- they do not have as great an access to public services such as health and education as do the non-poor. This in turn affect. their income earning capacity.

Based on these very general characterizations, which have of course been known for a while, the WDR proposes a two pronged strategy for alleviating -- poverty. Firstly, increasing the demand for the poor's most abundant asset their labor power -- through a policy of broad based, labor intensive growth. Secondly, a concerted program of social expenditures to increase access of the poor to basic health and education. This also seems to be the strategy advocated by liDR, on the grounds that economic growth is a necessary but not