rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

Poverty maps provide granular estimates of poverty at the sub-national level in order to deepen the understanding of poverty in a given country, better inform the targeting of resources, and support the design of interventions tailored to local needs (Bedi et al. 2007; Elbers et al. 2007). Household surveys provide estimates that are sufficiently reliable for large areas in a given country but often lack the desired precision and coverage to be able to properly inform targeting interventions at a granular geographic level. Thus, it is necessary to rely on small area estimation.

Small area estimation is a branch of statistics focused on obtaining estimates of higher quality than those obtained directly from the household survey. These small area estimation techniques often combine data from household surveys and auxiliary information from censuses, registers, or others to produce estimates of higher quality than what is possible from survey data alone for areas or groups with small sample sizes. Since there is no such thing as a free lunch, to achieve these gains in quality it is necessary to rely on model assumptions which must be thoroughly checked.

The literature on small area estimation is rich and several variations on this basic procedure have been proposed. Unit-level models conduct estimation and prediction at the household level, assuming a linear relationship between the welfare measure and covariates (Hentschel et al., 1998; Elbers et al., 2003; Molina and Rao, 2010). Unit-level models are not well-suited to situations where the survey and census data correspond to different years, which is often the case in developing countries where censuses are conducted infrequently. Area-level models represent a feasible alternative that similarly relies on linear functional forms, but conduct estimation and prediction using only aggregate data for the geographical entities of interest (Fay and Herriot, 1979; Torabi and Rao, 2014). Unit-context models represent another alternative and are characterized by an estimation stage wherein household-level measures are modeled exclusively as a linear function of area-level characteristics (Nguyen, 2012; Lange et al., 2018;
continued farm work.

We bring the three functions together in Figure 2, capping the range of the wage ratio at 20. We are now in a position to trace the preferred contract. The sharecropping yields a higher profit when the wage ratio is low, reflecting the fact that twice as much labour is available for farming. However, as _m_ increases, the landlord starts taking up opportunities in urban areas, reducing their input into farming. At the ratio _m/s_ = 16, their preferred contract shifts from sharecropping to fixed rent recognizing that it has become too expense for them to contribute to farming directly.

# **6 Empirical strategy**

The theoretical model highlights the role of opportunity cost of labor for tenant and landlord as a driving force of contract choice. While the model represents this as wages, there are other factors which might impact the landlord’s choice. For example, absentee landlords cannot easily confirm yields and the cost of migration might be more easily born by wealthier landlords. Hence, changes in the wage ratios are only part of the driving force towards fixed rent. In the empirical analysis, we will use the migration flows themselves and not the wages. This also allows us to take advantage of our data on bilateral migration between districts.

We start with the following specification, where subscript _i_ denotes the district and subscript _t_ denotes the time period: The dependent variable _Y_ is a measure of land tenure arrangements in district _i_ and year _t_ , including: acreage under sharecropping, acreage under fixed rent (paid by money) and acreage under fixed rent (paid by produce), duration of the contract (above 2 years of duration) and degree of formality (whether the contract is written, and whether the land is leased from relatives), all as a percentage of the acreage leased in.

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21,545|74,369|0.031|0.395|0.285|0.290|1.000|
|Meats and|3,821|14,847|131|6,576|25,375|0.151|0.585|0.005|0.259|1.000|
|Grain millin|415|538|49|59|1,062|0.391|0.507|0.046|0.056|1.000|
|Sugar & ba|14,420|33,297|1,912|16,008|65,637|0.220|0.507|0.029|0.244|1.000|
|Textiles an|22,415|32,212|3,996|238,463|297,087|0.075|0.108|0.013|0.803|1.000|
|Leather and|14,512|28,989|15,930|31,441|90,872|0.160|0.319|0.175|0.346|1.000|
|Wood and|16,394|47,045|2,587|7,287|73,314|0.224|0.642|0.035|0.099|1.000|

Source: Based on UN COMTRADE Statistics.

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Fixed capital|0.374**|0.268**|
|Labor|0.686**|0.698**|
|Privatized|0.382**|0.295**|
|New private|0.382**|0.289**|
|Foreign-owned|0.433**|0.226**|
|Exporter|0.216**|0.089|
|Capital city|0.092|0.178**|
|Country-survey dummy variables included|Yes|Yes|
|Number of observations|11,364 firms from 51 country<br>surveys|3,314 firms from 35 country<br>surveys|

** significant at the 1% level; heteroskedastic- and cluster-robust standard errors

This analysis can be extended to other measures of firm performance. Appendix 2 to the Investment Climate Assessment<sup>14</sup> reports the results using whether or not the firm reinvested any profits, invested in new plant in the preceding three years, and the log growth rate in its level of employment. The results reinforce those for TFP: in the international sample, private ownership and export orientation are good for firm performance.

**_Serbian evidence_** _._ What are the characteristics of productive, growing firms in Serbia, and how do these compare to those in the other PICS-BEEPS countries? We answer the first question by estimating the same regressions using only the sample of Serbian firms

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nominal variables using detailed 2-digit NACE code producer price indices.

# 3.2 Identifying green public procurement

Precisely identifying GPP in all its diverse forms is a key goal of this paper, which is challenging on its own. The above GPP definition encompasses a range of products, processes and considerations. It can relate to climate and sustainability goals solely; or more broadly, focusing on energy efficiency improvements, sustainable waste management or emission control, and waste and pollution reduction. Many of these considerations became part of public procurement markets long before GPP goals rose to the fore. For example, energy efficiency improvements are framed as an environmental consideration as part of GPP, yet they were pursued on purely cost-saving grounds for a very long time. In this sense, some of the GPP products we identify are not necessarily new but reclassified as being part of GPP and hence receiving far larger budget

> 1 We create a dummy variable for SOEs if the state owns at least 10 percent of the firm through direct or indirect ownership resulting from the investments of SOEs in private firms.

9
# **References**

- Abay, K. A., Abate, G. T., Barrett, C. B., and Bernard, T. (2019). Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity relationship in agriculture. _Journal of Development Economics_ , 139:171 – 184.

- Athey, S. (2018). _The Impact of Machine Learning on Economics_ , pages 507–547. University of Chicago Press.

- Azzari, G., Jain, S., Jeffries, G., Kilic, T., and Murray, S. (2021). Understanding the requirements for surveys to support satellite-based crop type mapping: Evidence from sub-saharan africa. _Remote Sensing_ , 13(23).

- Bardasi, E., Beegle, K., Dillon, A., and Serneels, P. (2011). Do labor statistics depend on how and to whom the questions are asked? _World Bank Economic Review_ , 25(3):418–447.

- Carletto, C., Jolliffe, D., and Banerjee, R. (2015). From tragedy to renaissance: Improving agricultural data for better policies. _The Journal of Development Studies_ , 51(2):133–148.

- Desiere, S. and Jolliffe, D. (2018). Land productivity and plot size: Is measurement error driving the inverse relationship? _Journal of Development Economics_ , 130:84 – 98.

- Fermont, A. and Benson, T. (2011). Estimating yield of food crops grown by smallholder farmers: A review in the uganda context. _International Food Policy Research Institute discussion paper_ , 01097.

- Gourlay, S., Kilic, T., and Lobell, D. B. (2019). A new spin on an old debate: Errors in farmerreported production and their implications for inverse scale - productivity relationship in uganda. _Journal of Development Economics_ , 141:102376
</sup> grade exposure to heat are negative, the effect size is infinitesimal.

## **7.2 Does temperature affect grade progression?**

It could be that our population of students is a selected sample of students who are resilient to or more adapted to higher temperatures. This would occur if only the students who can adapt remain in school long enough to appear in our sample. If this was true, we would be underestimating the effects of high temperature exposure on test performance.

To address this concern, we consider data on grade progression within the schools in our sample and check grade progression of each school from 11<sup>_th_</sup> grade to 12<sup>_th_</sup> grade. (We do not have cohort information prior to the 11<sup>_th_</sup> grade.) We have this information for approximately 83% of school x exam year combinations between 2017-2019.

We do not find any effects of high temperature exposure in the 11<sup>_th_</sup> grade on drop out rates and grade progression rates in our sample (Table 11). This is true for both male and female students, suggesting that our estimates are valid.

14
out? Typologies of urban growth across 478 cities of 1 million+,” _Environmental Research Letters_ , 2019, _14_ (12), 124077.

- Marconcini, Mattia, Annekatrin Metz-Marconcini, Soner Ureyen, Daniela Palacios-Lopez, Wiebke Hanke, Felix Bachofer,<sup> ̈</sup> Julian Zeidler, Thomas Esch, Noel Gorelick, Ashwin Kakarla et al., “Outlining where humans live, the World Settlement Footprint 2015,” _Scientific Data_ , 2020, _7_ (1), 242.

- , , Thomas Esch, and Noel Gorelick, “Understanding current trends in global urbanisation-the world settlement footprint suite,” _GI_ _~~F~~ orum_ , 2021, _9_ (1), 33–38.

- McKinsey, “The Rise and Rise of the Global Balance Sheet: How Productively Are We Using Our Wealth?,” 2021.

- OSM, _Planet Open Street Map_ , OSM, 2017.

- Palacios-Lopez, Daniela, Felix Bachofer, Thomas Esch, Mattia Marconcini, Kytt MacManus, Alessandro Sorichetta, Julian Zeidler, Stefan Dech, Andrew J Tatem, and Peter Reinartz, “High-resolution gridded population datasets: Exploring the Capabilities of the world settlement footprint 2019 imperviousness layer for the African continent,” _Remote Sensing_ , 2021, _13_ (6), 1142.

- Pesaresi, Martino and Panagiotis Politis, “GHS-BUILT-H R2022A - GHS building height, derived from AW3D30, SRTM30, and Sentinel2 composite (2018),” Jun 2022.

- Rode, Philipp, Graham Floater, Nikolas Thomopoulos, James Docherty, Peter Schwinger, Anjali Mahendra, and Wanli Fang, “Accessibility in cities: transport and urban form,” _Disrupting mobility: Impacts of sharing economy and innovative transportation on cities_ , 2017, pp. 239–273.

- Sun, Changlong, Yongli Zhang, Wenwen Ma, Rong Wu, and Shaojian Wang, “The impacts of urban form on carbon emissions: a comprehensive review,” _Land_ , 2022, _11_ (9), 1430.

- The World Bank,
a mixed picture. For five of the nine common names, there is a positive and statistically significant relationship between La Nueva Viga prices and La Paz prices. For these, the R-squared ranges from 0.016 to 0.877, depending on the species. Two of the nine common names have coefficients that are not statistically distinguishable from zero. The remaining two common names have negative and statistically significant relationships. Moreover, these relationships are not simply an artifact of La Nueva Viga sourcing particular species from La Paz. Of the four species that are sometimes sourced from B.C.S. in the data ( _almeja_ , _calamar_ , _ostion_ , and _pulpo_ ), three have a positive coefficient while one has a negative and significant coefficient. Of the species not sourced from B.C.S., two have a positive coefficient, with _cazon_ having especially high representation in the logbook data. While this exercise is starkly limited by data challenges, it suggests that the prices for some species are not just locally determined in La Paz.

> 8. Based on daily transaction data, we do sometimes observe multiple prices paid for a given species on a particular day. It is difficult to know whether this is measurement error or variation in price due to differences in time of day (e.g., a higher price for a more inconvenient time).

1
and other types of violence.<sup>11</sup><sup>_,_12</sup> Over this time period, the entire survey includes 18,078 separate households that were interviewed at least once, with 45 percent ever displaced. In the baseline empirical specifications, we compare how violence is changing at and around the time of displacement for the 1,116 displaced households on which we focus in origin and migration districts to the violence occurring in the district of residence at and around the time of the latest survey of the 9,992 households that never became displaced in the WFP survey.<sup>13</sup> Importantly, RDD mobile phone surveys have limitations. Specifically, they are only potentially representative of the mobile phone-using population and might suffer from sample selection biases based on who has access to mobile phones and which households choose to respond to the survey. However, all available evidence suggests that access to mobile phones (85 percent prior to the conflict) has remained high, and the survey is capturing changes in the population that are independently corroborated by other sources, including the timing and general evolution of the humanitarian and forced displacement crises (e.g., WFP 2019; Almoayad et al. 2020; Tandon and Vishwanath 2020; Tandon and Vishwanath 2021; D’Souza et al. 2022; etc.).

Figure 1 reports the number of violent incidents by types of violence- ground battles, remote violence (e.g., airstrikes), and all other violence (e.g., terrorism, violence against civilians, etc.). During the analysis period, 60 percent of all fatalities were from ground battles, with 38 percent from remote violence, and the remaining 2 percent were from other types of violence. However, despite there being significantly more fatalities from ground battles, the most common type of violence in the conflict was remote violence. Approximately two-thirds of violent incidents were instances of remote violence, 11We are only able
br>(2000/1991)|3516|0.9745|0.1732|0.0535|2.7704|
|Percentage of Good Soils|3531|0.7816|0.3179|0.0000|1.0002|
|Rainfall - First Principal Component|3531|0.0033|2.2334|-4.2315|6.8953|
|Rainfall - Third Principal Component|3531|0.0262|1.6087|-2.4997|9.2440|
|ln Wage in 1991|3532|7.9349|0.4619|6.6139|9.3168|
|Proportion of Natives in 1991|3532|0.6778|0.1760|0.0855|1.0134|
|Homicides per Capita in 1991|3530|0.0015|0.0010|0.0000|0.0090|
|Employment in Farming Activities in<br>1991|3532|0.1975|0.0743|0.0000|0.5437|
|Bank Dummy (1991)|3532|0.7087|0.4544|0.0000|1.0000|
||3532|9.2629|0.8171|6.6214|12.9814|
|Average Temperature|3531|22.4151|2.8196|13.8325|27.7363|
|Fertility Rate in 1991|3532|3.7759|1.2169|2.0100|8.6800|
with positive and large assigned profits in the final sample.

# **C National Accounts Aggregates from WID dataset**

Here we discuss the national accounts aggregate we use in more detail and provide additional information on the adjustment of the microdata to macroeconomic aggregates.

We start by using World Inequality Database (WID) aggregates for nominal National Net Income (NNI) in local currency for the period 2003 - 2019. We provide all numbers used in the analysis in Table C1. For 2019, for example, the Net National Income of reference is L 541 billion - in real terms, NNI grew by 80% in the period 2003-2019.<sup>2</sup> We then decompose NNI in four main components – Compensation of Employees (L 260 billion in 2019); Capital Income (L148 billion); Mixed Income (L 87 billion) and Taxes on Production (L 47 billion). In Figure C2, we plot the share of main components in total NNI over time - compensation of employees represent approximately half of total income over the period; total capital income ranges from 20-30%; mixed income approximately 15% and total taxes on production 10%.<sup>3</sup> The main change in aggregates’ shares we observe is a decrease in compensation of employees from a peak of 54% in 2013 to 48% by 2019, accompanied by an equivalent 6 That increase is p.p. increase in the participation of capital income from 21% to 27%. largely explained by a substantial expansion of corporate profits in total income - the level of corporate profits increases nominally by 150% between 2013 and 2019, from L46 billion to L116 billion.

As discussed in the text, we match wages in the microdata to Compensation of Employees; we match Mixed Income in the microdata to its equivalent in national accounts; and distribute taxes on production proportionally to total income, so it has no bearing on inequality measures. For distribution of capital income, we use a more detailed
2017) and each of the social sustainability dimension indices. Additionally, we also plot poverty headcount, using national poverty lines of each country’s latest year with available information, against each of the indices. A third set of scatterplots looks into the relationship between each country’s social sustainability and income inequality.

Figure 3 below shows how richer countries present higher values for each of the social sustainability dimensions. This means that there is a <mark>positive correlation between</mark> per capita GDP and social inclusion, resilience, social cohesion, and process legitimacy. Although the relationship is quite strong for the process legitimacy and social inclusion indices (P4 and P1), this is not the case for the two other dimensions. When focusing on poverty levels, societies with stronger social inclusion, social cohesion, resilience, and process legitimacy are also less poor. See figure 4 (and online annex C). Thus, worldwide, societies with greater access to markets and services; more trusting communities; individuals, communities, regions and countries more capable of confronting different types of shocks; and societies where governments are fairer and more transparent are expected to be less poor. This is captured by the negative correlation between poverty headcounts and P1, P2, P3 and P4.

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or because a sibling was one of the 1,292 students already chosen (i.e., for each household, we included only one child in our study sample). Potential participants were also excluded if their school attendance rate was lower than 80% over the previous four months. After drawing up an initial list of 1,292 students, we interviewed each child at his or her home to make sure that none of our participants had access to electricity or solar lights. Of these 1,292 children, we found 911 who had no access to electricity or solar lights at home during the period of our survey.<sup>3</sup> Of those, 882 became effective sample households for this study; the rest of the children in the sample had dropped out of school to get married or for other practical reasons (e.g., land erosion, resulting in forced migration) before the detailed baseline household survey was conducted. Figure S.2 in the supplemental appendix illustrates the detailed timeline for our surveys and interventions, along with Table S.1, which reports on the number of students selected through the screening process described above.

During the baseline survey conducted in July and August of 2013, we collected detailed data at the household level to understand the socio-economic conditions of the sample children and their households. The data included household demographic characteristics, details of energy use and its sources, expenditure, various income generating

> 2This does not necessarily mean that the traditional Hurricane lantern provides more brightness than the S2 and S10, because the former cannot maintain effective brightness/lumen for long hours due to the emission of carbon smoke.

> 3It emerged that the remaining students had access to SHS services, through the support of NGOs.

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and 70% of calorie intake for the national sample. On average, urban households devote significantly fewer resources to grains and dairy and significantly more resources to the other food groups than rural households; household calorie shares follow a similar pattern. We also present the consumption and food security statistics for the other subpopulations examined. (Table 3) Given that agricultural households and wheat producing households are largely situated in rural areas, they have consumption and dietary patterns similar to rural households.

Finally we present select national statistics by quarter (Table 4). Over the survey year, there was a small, statistically insignificant decrease in nominal monthly per capita total and

> 39   All point estimates are weighted to reflect population characteristics; all standard errors are corrected for sample stratification and clustering.

> 40 World Bank (2008)

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MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This report presents the key AICD findings for Mozambique, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Mozambique a is poor but stable country, two sets of African benchmarks will be used to evaluate its situation: those for nonfragile lowincome countries (LICs) and those for middle-income countries (MICs). Detailed comparisons will also be made with immediate regional neighbors in the Economic Community of West African States (ECOWAS).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the past 15 years, Mozambique’s economic performance has been strong, at 7.7 percent annually. The country has also managed to make impressive strides in terms of poverty reduction. Between 1996– 97 and 2002–03, the poverty headcount index fell by 15 percentage points, the infant-and-under-five mortality rate decreased by 7 percentage points, and primary-school enrollment increased by 33 percentage points. These achievements have set Mozambique on track to attain 13 of the 21 MDG targets, including those linked to poverty, under-five mortality, maternal mortality, malaria, and an open trading and financial system (Government of Mozambique 2010).

Despite its impressive progress in both economic growth and poverty reduction, Mozambique remains one of the poorest countries in the world. Fifty-four percent of Mozambicans live below the poverty line, and access to basic infrastructure services
given the structure of the Indian marriage market), the dowry information for co-resident sons’ marriages may still be in our data.

> 13See Chiplunkar and Weaver (2017) for documentation of the prevalence and evolution of dowry in India using the 1999 round of REDS.

> 14However, our data do contain marriages with missing information on gifts. A detailed discussion of this issue and how we deal with it is in Appendix C.

> 15We also report the composition of dowries, i.e., the kind of things that are usually given as gifts at the time of a daughter’s marriage, in rural India using the 2004-05 IHDS in Table A.1. This information is not recorded in the REDS data.

5