rafmacalaba/data-use-mentions-extended huggingface dataset:
where _xk,j_ are exports of a product _k_ to importer _j_ and _mj_ are imports by importer _j_ . (4) essentially captures the aggregate market penetration of exports, where markets are defined as all countries that import a given product. The primary advantage of this measure, relative to the geographic Herfindahl, is that it not only captures the distribution of exports across markets, but importantly normalizes this distribution by the potential markets that exist for these exports.

Table 4 reports the calculated export concentration measures for the period 2001-2007. We classify the indicators according to three dimensions: (a) diversification between different products across all exports; (b) diversification across exports within defined product groups; (c) diversification by geographic destination. The first column includes a count of the number of distinct products at the 4-digit HS level. The second and third columns contain the (normalized) Herfindahl-Hirschman and the Theil index. The fourth column reports the Theil (between) measure, in other words the between-section component of the Theil measure of overall diversification. The next three columns are analogous to the first three, but instead report median values for levels of diversification in the 21 HS sections for each measure. The eighth column reports the Theil (within) measure, in other words the within-section component of the Theil measure of overall diversification. The final three columns provide, in this order, a count of the number of distinct trading partners, the (normalized) geographic Herfindahl calculated by country share of total exports, and the export market penetration index.<sup>9</sup> Across all Syrian exports, there is a trend toward increasing diversification (columns 1–4). The Herfindahl index between all export lines has fallen significantly between 2001 and 2007, from a fairly concentrated 0.62 to 0.14, which is more consistent with moderate levels of diversification. This diversification trend is also broadly supported by the decomposed Theil (between) statistics, which illustrate a declining trend after 2003. By way of
*Ages 6 to 18 Years)**|(0_._496)|(0_._030)||||

_Notes._ This table reports Ordinary Least Squares estimates of treatment effects. All regressions include 133 randomization PRA controls. Outcomes are listed on the left and primary outcomes are described in detail in Online Appendix Text 1. (***) (**) (*) denotes significance at (1%) (5%) (10%) level. Column (1) reports the mean and the standard deviation (in parentheses) of each outcome for the control group. Column (2) reports the treatment estimates in levels with robust standard errors (in parentheses). Column (3) reports the naive p-values [in brackets]. Column (4) reports the treatment estimates as a percentage of the control mean, except for indices. Column (5) reports the total sample size. Psychological well-being measures include questions on life satisfaction and happiness from the WVS (averages of two data collection rounds); and scores on the CES-D seven-point scale (negatively coded). Indicators were collected across different data collection rounds and, due to attrition between rounds, the sample for psychological well-being outcomes is reduced to households surveyed in all rounds in order to have a complete set of indicators. Each psychological well-being measure is estimated as a score on the variable’s scale, normalized to the unit SD of control HHs, with mean equal to zero. Impacts on the normalized measures can be found in Table A9 of the Online Appendix. The psychological well-being index is an index of the three previous measures, normalized in the same way. School enrolment is estimated for school-age children (ages 6 to 18 years), with the unit of observation being the child, hence all SEs are clustered at the household level. PW = primary woman; SE = standard error; SD = standard deviation; PRA = participatory rural appraisal; WVS = World Values Survey; CES-D = Center
in LTR. Finally, LAIS is being integrated with data on land use and agro-ecological suitability maintained by the Ministry of Agriculture and Animal Resources to monitor land use and its changes and in doing so facilitate siting of investments higher up in the value chain, e.g., in agro-processing.

# **3.2 Measures undertaken to improve sustainability**

Registering a transfer of property in Rwanda requires submission of documentation to the district land office (DLO). Once authenticity and completeness of documents is verified, the responsible officer enters data and scanned documents into the computerized system, triggering notification of affected parties by SMS at this and subsequent steps in the process.<sup>8</sup> The registration request is then electronically forwarded to one of the five regional offices where, if satisfied, the Deputy Registrar approves the transaction. This will lead to titles being printed with one copy sealed and signed to be stored in the regional office and one transferred to the DLO for issuance to the applicant who can sign it in the presence of a notary. While the process is clear in principle, unaffordable fees, lack of awareness, and difficult access may create obstacles.

First, registering a transfer incurs a flat fee of RwF 27,000 or about USD 40.<sup>9</sup> As flat fees are regressive and may be unaffordable or difficult to justify for rural people with small plots, a review was conducted in 2015. It recommended to waive stamp duty and possibly reduce registration fees, for rural parcels below 5 ha and to set urban fees at levels that would allow the registration system to sustain itself. This is in line with evidence that, even in developed countries, transfer taxes raise little revenue compared to land taxes but encourage informality and threaten sustainability of land registries (Dachis _et al._ 2012). A revised fee structure is yet to be finalized and to be approved formally by the government.

Second, lack of awareness by concerned parties may be a key
Subsidies on Commercial Fertiliser Demand and Maize Production : Panel Data Evidence from Malawi. _Journal of Agricultural Economics_ , _68_ (1), 70–97. https://doi.org/10.1111/1477-9552.12161

- Shigute, Z., Strupat, C., Burchi, F., Alemu, G., & Bedi, A. S. (2019). Linking Social Protection Schemes: The Joint Effects of a Public Works and a Health Insurance Programme in Ethiopia. _Journal of Development Studies_ . https://doi.org/10.1080/00220388.2018.1563682

- Slater, S., Baker, P., & Lawrence, M. (2022). An analysis of the transformative potential of major food system report recommendations. _Global Food Security_ , _32_ (January), 100610. https://doi.org/10.1016/j.gfs.2022.100610

- Smale, M., Thériault, V., Mason, N. M., & Mason, N. M. (2020). Does subsidizing fertilizer contribute to the diet quality of farm women? Evidence from rural Mali. _Food Security_ . https://doi.org/10.1007/s12571-020-01097-w ORIGINAL PAPER Does

- Snapp, S. S., & Fisher, M. (2015). “Filling the maize basket” supports crop diversity and quality of household diet in Malawi. _Food Security_ , _7_ , 83–96. https://doi.org/10.1007/s12571-0140410-0

- Sulaiman, M. (2016). Making Sustainable Reductions in Extreme Poverty : A Comparative Meta-Analysis of Livelihood , Cash Transfer and Graduation Approaches .

- Thome, K., Filipski, M., Kagin, J., Taylor, J. E., & Davis, B. (2013). Agricultural Spillover Effects of Cash Transfers: What Does LEWIE Have to Say? _American Journal of Agricultural Economics_ , _95_
constraints to meeting the agricultural potential present in Rajasthan, the limitations of water stand out; with less than half of the cultivated area is irrigated, and the tutelary rains differ from 300mm in the western region to more 1000mm on the eastern border (Rathore et al., 2013). A study of the long-term rain data indicates that the districts face 15 percent positive rain shock probability and 19 percent negative shock probability. The crops are diversified, and at least seven crops characterize the agricultural system: wheat, millets, maize, chickpeas, pulses, mustard and soybean (Singh et al., 2016).The variability in rainfall and the consequent weather shocks not only affect crop yields, but also significantly hinder the potential for economic diversification in rural communities, exacerbating vulnerabilities across both agricultural and non-farm sectors.

The relationship between weather patterns and agricultural productivity has become increasingly critical to understand as climate variability intensifies. Research demonstrates that rainfall variations significantly influence agricultural output through multiple pathways. Auffhammer et al. (2012) found that changes in monsoon timing and intensity can reduce rice yields by up to 23 percent in major growing regions of India, while Zaveri et al. (2020) documented that rainfall variability explains approximately 20 percent of year-to-year crop yield fluctuations across India’s semi-arid regions. The impact operates through several mechanisms: rainfall directly affects soil moisture content, temperature variations influence crop phenology and growing season length, and extreme weather events can cause both immediate crop damage and longer-term effects on soil quality and water availability (Ray et al., 2015). Kumar et al. (2021) demonstrated that even in years with normal total rainfall, irregular distribution during critical growth stages can reduce crop yields by 15-30 percent. Birthal et al. (2015) showed that factors such as soil organic matter content, irrigation 2
are included (so we exclude provisions covering labor and capital flows). This definition is more in line with the spirit of the model that highlights the role of sales and market size in development. Nevertheless, our preferred measure is the one covering all provisions related, directly or indirectly, to trade, as typically, provisions in trade agreements are negotiated jointly. The results in column 4 are almost identical to those in column 1.

In column 5, we rerun the estimation on a select subsample of the data, in order to test whether our results are affected by the additional volatility introduced from the use of income surveys to measure poverty. Specification 5 includes only consumption surveys, reducing the sample to 238 observations. The results are similar as in specification 1, but the standard errors are higher.

In column 6, we consider alternative five-year windows over which the evolution of poverty is examined, in order to explore the sensitivity of our results to the windows selected. When calculating sustained poverty reduction instead of the windows 1981-85, 1986-90, etc. we shift the window one year back to be 1980-84, 1985-89, etc. Right hand side variables are again matched on the first year of the window. Most coefficients in this specification are qualitatively similar to the results in column 1, with the exception of the one on agricultural productivity growth, which now becomes positive, though it remains insignificant. The market size threshold also increases. Based on these results, we conclude that our qualitative results, suggesting large thresholds market size thresholds and an important role of market size for development, are robust to the windows selected, but the exact magnitude of the effects may be affected by the choice of the window.

In the Appendix we report additional robustness checks. Column 1 in Table A1 repeats the results of our baseline specification (column 1 in Table 4) to facilitate comparisons. Column 2 of Table A1 reports results of a specification that includes only geographic and institutional controls. We include variables that the literature has shown to
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Power

## **Achievements**

Mozambique’s energy supply is relatively reliable compared to its African peers. According to the Enterprise Survey for 2007, firms’ value lost due to power outages in Mozambique was 2.4 percent of sales, less than half the value lost in other LICs and close to the level of MICs. In Mozambique there were 37 days when power outages occurred, vis-à-vis 70 and 124 days in middle- and low-income countries respectively, but the duration of power outages in Mozambique (4.2 hours) was above the level of most of neighboring countries. Around 11 percent of the energy consumed by firms in Mozambique was generated in-house, a level comparable to that of MICs and half that of other LICs (table 13). The delay in obtaining an electric connection (13 days) was one-third of the regional average (42 days). Due to the relativity good quality of energy supply, the percentage of firms identifying energy as a major constraint in Mozambique was below the Sub-Saharan average (table 14).

**Table 13. Benchmarking Mozambique’s power indicators**

||**Unit**|**Low-income,**<br>**nonfragile**<br>**country**||**Mozambique**|**Middle-**<br>**income**<br>**country**|
|---|---|---|---|---|---|
||||**1997**|**2003**<br>**2006–07**||
|National access to electricity|% population|32.8|6.6|8.1<br>9.4|49.5|
|Urban access to electricity|% population|72.8|25.8|25
A second policy implication is the role of policies to absorb this sub-optimal smoothing. In Cambodia, for instance, this emphasizes the role of social safety nets. While the economy becomes increasingly open (and therefore vulnerable to global shocks) while moving away from subsistence agriculture to industry and services – the paper suggests that these emerging sectors won’t be able to provide full smoothing for temporary shocks. Therefore additional safety nets will be needed to protect workers during economic crises.

Finally, our findings have implications for macroeconomic policies, through the role of expectations. Indeed insufficient smoothing can be self-fulfilling through a demand effect: if all firms perceive the shock to be permanent, they do a minimal amount of smoothing, which further depresses demand, making the shock indeed more permanent. In fact, a negative demand feedback loop might also arise from limited financial development or poorly regulated financial development (one or the other would increase the likelihood of firms being credit-constrained, which would constrain smoothing, hence depressing demand further). This emphasizes the role of credible policies (at least nationally, but possibly also through international coordination) to send a credible signal that the shock is temporary.

# **References**

- Arne Bigsten, Paul Collier, Stefan Dercon, Marcel Fafchamps, Bernard Gauthier, Jan Willem Gunning, Abena Oduro, Remco Oostendorp, Cathy Pattillo, Mans Söderbom, Francis Teal, and Albert Zeufack (2003), “Risk Sharing in Labor Markets”, _World Bank Economic Review_ , 17: 349-366.

- Borensztein, E. and J.-W. Lee (2002): "Financial crisis and credit crunch in Korea: Evidence from firmlevel data", _Journal of Monetary Economics_ , vol. 49, pp.853-75.

- Campello, M., J. Graham and C. Harvey (2010): "The real effects of financial constraints: Evidence from a financial crisis", _Journal of Financial Economics_ , vol. 97, pp.470-87.

- Claessens, S., S. Djankov
missionaries might have targeted more populated areas or cities. Regressions therefore control for a fourth-order polynomial in average population density in the 18th century obtained from the History Database of the Global Environment (HYDE). Furthermore, the control set also includes a dummy for the presence of cities at any time before 1800.

According to Robinson (1915), competition with Islam was a deterring factor, because spreading the gospel in predominantly Muslim areas was complicated. Muslim populations may receive less development aid for political and religious reasons, so the (log) distance from the closest Arab medieval trade route (which Michalopoulos, Naghavi, and Prarolo [2018] showed had a strong impact on adherence to Islam) is controlled for.<sup>17</sup> Table 2 presents summary statistics of the controls for cells with and without missions. More details about the data sources are given in appendix A.

# **2.4 Results**

Equation (1) is estimated by least squares, and the results are reported in table 3. The dependent variable is an indicator that equals 1 if the cell ever received a World Bank project between 1995 and 2014.<sup>18</sup> All regressions include the full set of historical and geographical controls described above. The first set of regressions employs different data sources and definitions of the dummy Mission _ik_ . In the first four columns of table 3, the mission data are from Roome (1924). The regression includes only Catholic missions in column 1, only Protestant missions in column 2, and both kinds of mission in column 3; in column 4 the two types of mission are collapsed into a single

> 17Some missions were set up for the purpose of ending the slave trade (Johnson, 1967), a practice that was especially prevalent along the coast of west Africa and had long-lasting detrimental effects on development and social capital (Nunn, 2008; Nunn & Wantchekon, 2011). The authors are not aware of precisely georeferenced measures of slave trade
on 2020 data; boundaries may have been different at the time of fielding, but using a constant set of boundaries is important to consistently measure conflict. It is, however, important to note that second level administrative geographies vary in size and number across countries,<sup>8</sup> which can introduce some variation in proximity to conflict across countries when discussing “local” (i.e. within an individual’s second level administrative geography) conflict events.

# **_3.4 Matching conflicts to locations_**

We matched each of the UCDP conflict events, based on latitude and longitude, to a unique second-level administrative geography location in the geospatial boundaries data.<sup>9</sup> We likewise matched the location of each DHS cluster to the same geographical unit and thus were able to merge the data.<sup>10</sup> Our analysis sample includes only those second-level administrative geography locations that, during some year in our data, had conflict. We thus are able to identify the relationship of between conflict and child marriage based on variation over time among

> 6 At the time the research was conducted in mid-2021, all data except Colombia (2010) were also the most recent DHS for the country.

> 7 We then used the Stata package shp2dta (Crow, 2006) to convert the administrative boundary shapefiles into a Stata readable format.

> 8 Within our analysis sample, the number of second-level administrative units per country is: 53 (Bangladesh); 48 (Myanmar); 99 (Burundi); 12 (Cameroon); 30 (Chad); 230 (Colombia); 120 (DRC); 53 (Ethiopia); 15 (Guinea); 337 (India); 22 (Indonesia); 18 (Cote d’Ivoire); 78 (Kenya); 24 (Mali); 248 (Nigeria); 31 (Pakistan); 107 (Peru); 301 (Philippines); 59 (Egypt). The
defining cities and metropolitan areas tends to be rooted in a labor market perspective based on the use of commuting flow data, as with the definition of MSAs in the US, such data is hard to come by for many countries in the world, especially for many developing countries. As such, attempts to define globally consistent data sets of cities based on the “true” extents of these cities have instead relied on either the use of estimated travel times to approximate commuting sheds (World Bank, 2008; Uchida and Nelson, 2009; Ellis and Roberts, 2016); approaches that associate cities with dense clusters of population (Dijkstra and Poelman, 2014; Roberts, 2018b); or approaches that rely on global satellite imagery and which identify cities based on their built-up area or the amount of light they emit at night (Ellis and Roberts, 2016; CAF, 2017).

While, however, much effort has been expended in developing and applying algorithms and approaches for the better delineation of cities and metro areas, little effort has been made to systematically compare these approaches in terms of the results that they yield (e.g., in the number of metro areas identified, or in the populations and areas of those metros).<sup>6</sup> There has likewise been little effort to compare the results of “second best” approaches to defining metro areas – i.e. approaches which rely on global data sets and which proxy or otherwise forego the use of

> 5 The development of global data sets of cities based on their “true” extents has been greatly facilitated by the increased availability of global satellite imagery, both optical and nighttime, and the derivation from this imagery of global data sets of built-up area, including the Global Human Settlement Layer (https://ghsl.jrc.ec.europa.eu/about.php) and the Global Urban Footprint (https://www.dlr.de/eoc/en/desktopdefault.aspx/
survey rounds, implying that the weights need to be constructed considering attrition and nonresponses in both phases.<sup>14</sup> We use a rich set of household and location characteristics collected in the 2019 survey to characterize and predict the (joint) probability of response in the pre-war and post-war-onset phone surveys using a logit model (see Table A3). We then construct sampling weights as the inverse of the predicted probability of responses in both pre-war and post-war-onset phone surveys. We note that applying the weights markedly reduces the differences between the unweighted and weighted mean differences in the observable characteristics sample (Table A4). In particular, Table A4 shows that applying the sampling weights makes observable characteristics

> 14 Households appearing only either in the pre-war or post-war phone surveys were dropped from the analysis and assumed as nonresponses in constructing the sample weights.

10
positive results can be seen from those programs where trainees had to pay at least part of the training costs. In addition, a survey conducted among the unemployed in August 2001 by the Ministry of Labor and Social Security in 10 large cities showed that 70 percent of the people surveyed had participated in re-employment training, but only half thought that the training was useful in helping them find jobs.<sup>71</sup> In order to maximize the effects, retraining programs need to be tailored to the job opportunities and to the ages and backgrounds of the workers to be retrained. One good example can be drawn from Shanghai’s case. The economic restructuring and SOE reform led to the shedding of about 500,000 textile workers in Shanghai since the end of 1980s. Most of the workers made redundant were women in their 40s and 50s with little education and few marketable skills. The Textile Re-employment Service Center, sponsored by the municipal government, set up several dozen training bases focusing on computer techniques, accounting, cooking, sewing and other skills. After massive retraining, an estimated 70% of the redundant workers were reemployed, while the remaining 30% chose early retirement or self-employment. Only one year after the Textile Re-employment Center was opened, Shanghai’s textile sector had restructured 164 loss-making enterprises by either shutting them down, suspending production or shifting to manufacturing other goods. 72 This experience definitely has applications to the rest of China.

> 70 The World Bank: “Has Training Helped Employ the Xiagang in China? A Tale from Two Cities”, East Asia and Pacific Region, the World Bank, 2002.

> 71 “Urban Jobless Face Difficulty in Re-employment”, China Internet Information Center, China.org.cn, September, 2001.

> 72 Fruman, C., “Life-long Learning in China: A Case Study of Shanghai,” the World Bank, 2003.

30