rafmacalaba/data-use-mentions-extended huggingface dataset:
**|**2.0**|
|**CAPITAL EXPENDITURE**|**0**|**0**|**0**|**4.4**|
|**TOTAL EXPENDITURE**|**100.0**|**100.0**|**100.0**|**100.0**|

Source: Own, based on CMM (2005). *Soums are the lowest formal tier of government. Soum data is for 2003. ** Aimags are intermediate-level governments. Aimag data corresponds to the 2002-2004 average for the aimgs of Bulgan, Uvs, Khentii, Dundgobi With high levels of current spending, the power of citizens to influence local budget allocations depends largely on the size of the capital budget –and just marginally on those non-wage parts of current expenditures over which citizens could have some kind of power. In the participatory budgeting process in Brazil, for example, citizens’ voice over budget allocations is restricted to the decisions on infrastructure investment. According to this rationale, a high proportion of capital spending should be positively associated with the level of local participation. Although this association still needs to be tested, there is already evidence in its favor. Souza (2001) found that when municipalities do not have to devote large portions of their budgets to cover basic education expenditures, they free up resources to introduce innovative policies like participatory budgeting. Similarly, another quantitative study found a positive and significant relationship between high proportions of capital expenditure in municipalities’ total expenditure and the likelihood that participatory budgeting is implemented (Baiocchi et al. 2005). However, the causality of the association between budget size and availability of participatory mechanism and processes remains to be empirically tested. It is not yet clear if such mechanisms and process exist because budgets are large, or if the fact that budgets are large call for the creation of such mechanisms.

**_2.2.3
−N_0_,−_1_,_<br>_w_ _−_<br>_w−_1)|0.101|0.299|0.101|

Notes: The baseline column reproduces results from Table 2. The Cobb-Douglas column shows results under the Cobb-Douglas technology assumption in equation (23).

values of _b_<sup>0</sup> _jt_<sup>and</sup><sup>_pjt_,which we need to input separately into the prot function:log</sup><sup>_b_0</sup> _jt_<sup>= 0</sup><sup>_._5 log</sup><sup>_bjt_;</sup> log _b_<sup>0</sup> _jt_<sup>= 0</sup><sup>_._5 log</sup><sup>_bjt_.</sup> We calibrate the output elasticities _αL_ , _αK_ and _αM_ from the 1997 input-output table for Argentina. From the rm data we estimate the stochastic process for _bjt_ and _Aijt_ given by equations (18) and (19). We estimate the adjustment cost parameters Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3 _, Ce, Cu_ ) following the SMM objective function (20). We rst recompute the second empirical moment, which is the correlation between investment and idiosyncratic shocks _Corr_ ( _Iijt, Aijt_ ) , as the shocks _Aijt_ need to be recomputed under the new technology specication.

Table B1 reports the results, both for our baseline Leontief case and the alternative CobbDouglas specication. The estimates of capital and labor adjustment costs are comparable in order of magnitude under the two technology specications. The moments are also well-matched in the Cobb-Douglas case, although moments related to labor allocations are a better matched in the baseline Leontief case.

55
12 long term perspective presented by Kuznets and others on Europe and the U.S. cannot be replicated for the nations of Africa, Asia and Latin America. Even the contemporary picture is difficult to piece together.

Since 1984, the World bank's, World Development Report (WDR), lists both central Ge as a percentage of GNP (WDR. 1987, Table 23) and Gc as a share of GDP (WDR, 1987, Table 5). The first series, drawn from the GFSY, excludes state, provincial and local government expenditures. The notes to the various tables contained in these sources all urge caution in making cross-country comparisons based on the data. Nonetheless, some basic trends are suggested.

Of the 96 low and middle income economies listed in the 1987 WDR, 39 (74) provide government expenditure (consumption) data for both years considered: 1972 (1965) and 1985. In this sample, the developing economies, as a group, possess smaller government spending shares than do the industrialized market economies (Table 7). But it is also evident that the proportion of government spending in developing economies bears little resemblance to the historical experience of the now industrialized nations. Using a narrower definition of government spending, the share of Gc out of GDP in 1985 for developing nations, generally, is already in double digits (82% of cases). As noted above, this level was not reached by most advanced economies until the 1940's and 1950's (Table 2), when per capita income levels were already multiples of those of today's low and middle income nations. It, therefore, appears safe to conclude that if there is a "pattern of development" for government consumption, it is not particularly robust.13
voltage distribution networks. In most cases the source of data for breakdown of sales by consumer group (revenues and energy sold) were different to the sources used in the base-case QFD estimates. In these cases, it is assumed that the sources used in the base-case QFD estimates contain the correct data at an aggregated total level, and that the sources used for the breakdown provide the correct average tariff data by consumer group and the correct revenue profile i.e. percentage of LCU sales to each consumer category. These assumptions are used to estimate the breakdown of total reported revenue and energy sales by consumer group.

The results are summarized in Figure 8. Residential customers account for 42 percent of the QFD, and 38 percent of consumption in the sample of countries included in this analysis, suggesting other customer categories marginally cross-subsidize residential customers. In these 16 countries, tariff schedules indicate that only a small number of customer categories (e.g., commercial customers in Mauritius, government customers in Niger) pay above the cost of service.

38
|Lesotho|2009-2016|Sierra Leone|2009-2017|
|---|---|---|---|
|Liberia|2009-2017|Slovak Republic|2009-2013-2019|
|Lithuania|2009-2013-2019|Slovenia|2009-2013-2019|
|Luxembourg|2020|Solomon Island|2015|
|Madagascar|2009-2013|South Africa|2007-2020|
|Malawi|2009-2014|South Sudan|2014|
|Malaysia|2015-2019|Spain|2021|
|Mali|2007-2010-2016|Sri Lanka|2011|
|Malta|2019|St Kitts and Nevis|2010|
|Mauritania|2006-2014|St Lucia|2010|
|Mauritius|2009|St Vincent and Grenadines|2010|
|Mexico|2006-2010|Sudan|2014|
|Micronesia|2009|Suriname|2010-2018|
|Moldova|2009-2013-2019|Sweden|2014-2020|
|Mongolia|2009-2013-2019|Tajikistan|2008-2013-2019|
|Montenegro|2009-2013-2019|Tanzania|2006-2013|
|Morocco|2013-2019|Thailand|2016|
|Mozambique|2007-2018|Timor-Leste|2009-2015-2021|
|Myanmar|2014-2016|Togo|2009-2016|
|Namibia|2006-2014|Tonga|2009|
|Nepal|2009-2013|Trinidad and Tobago|2010|
|Netherlands|2020|Tunisia|2013-2020|
|Nicaragua|2006-2010-2016|Türkiye|2008-2013-2019|
|Niger|2009-2017|Uganda|2006-2013|
|Nigeria|2007-2014|Ukraine|2008-2013-2019|
|North Macedonia|2009-2013-2019|Uruguay|2006-2010-2017|
|Pakistan|2007-2013|Uzbekistan|2008-2013-2019|
|Panama|2006-2010|Vanuatu|2009|
|Papua New Guinea|2015|Venezuela, RB|2006-2010|
|Paraguay|2006-2010-2017|Vietnam|2009-2015|
|Peru|2006-2010-2017|West Bank and Gaza|2013-2019|
|Philippines|2009-2015|Yemen, Rep.|2010-2013|
|Poland|2009-2013-2019|Zambia|2007-2013-2019|
|Portugal|2019|Zimbabwe|2011-2016|

Source: Constructed by the authors using the WBES.

30
Consequently, multiple measures have been developed expressing the gradation in rurality (Brown, Cromartie, & Kulcsar, 2004; Li, Long, & Liu, 2015; Waldorf & Kim, 2015) or urbanicity (Cyril, Oldroyd, & Renzaho, 2013; Dahly & Adair, 2007) to analyze issues of economic and social development. Doing so is important both for conceptual and empirical reasons.

A newly developed global spatial data set<sup>1</sup> by Cattaneo, Nelson & McMenomy (2021) opens up new opportunities to more systematically account for these rural-urban interconnections. This paper explores its applicability and relevance for policy making across a series of fields with a focus on developing countries, where adequate and easily accessible data for a more integrated territorial approach to development have so far been largely absent. The paper proceeds as follows. Drawing on the literature, Section 2 first briefly reviews how a rural-urban lens falls far short of capturing important nuances in development dynamics across space and why a more continuous urban-rural perspective is needed. Section 3 then takes stock of the two major approaches that go beyond the urban–rural dichotomy in analyzing drivers of economic and social development and provides a conceptual framework merging the merits of each, i.e. the urban-rural catchment area approach differentiated along an urban hierarchy (from megacities to small towns). The global spatial data set constructed using this framework is then briefly compared to another recent development, by Moreno-Monroy and

> 1 Data can be downloaded and visualized with https://data.apps.fao.org/?share=g-90e451f8f10c9894243a4885d8d1be5f.

2
in the Sahel has been qualitative. This reflects data challenges found in Sub-Saharan Africa in general (limited granularity in available variables, sample size and time coverage etc.), and others more specific to the region – including important variation in population densities. Water data itself has long been known to be imperfect and requires triangulation to overcome poor in-situ data. Nonetheless, the comparative advantage of an econometric approach to this analysis is its systematic and reproducible approach combined with a search for causal inference that can help disentangle concomitant factors and events.

Third, an unprecedented mobilization of financial resources has been directed towards the region in recent years, prompted in large part by the 2012-13 Malian political crisis and the resulting militant Islamist occupation of the country’s northern territory. This crisis has led regional and global leaders to coordinate against the spread of political instability in the region and beyond. Over the next three years, the World Bank alone plans to support G5 Sahel countries with funding totaling US$7 billion. A substantial part of those resources can be expected to either directly or indirectly involve water resources. The evidence presented in this paper seeks to inform these future engagements, as well as additional post-COVID-19 recovery efforts, to prevent lock-in effects and unanticipated spillovers, with lessons that may apply well beyond the region.

# 4. Data Sources

This section presents an overview of the various data sources utilized in the empirical approach presented in section 5. The first empirical approach, discussed in section 5.1, builds on data compiled by Harari and Ferrara (2018). HF utilizes gridded spatial data for the period 1997 to 2011 to present a plausibly causal effect of climate on conflict occurrence for the continent of Africa. Specifically, to measure the impacts of

> 3 See, for instance Eizenga (2019) and Guillaumont et al. (2016).

> 4 Inter-annual variability is measured by the coefficient of variation (= σ_i/μ_i , where is the grid-cell). ii

6
specification and find our results to be unchanged. This is further discussed in section 5.2.1 of the paper.

# **_3.2 Data_**

We combine firm-level data from the World Business Environment Survey (WBES) and country-level variables from a number of different sources. The WBES surveyed firm owners and managers on their perception of the business environment in the country with questions on judiciary, corruption, regulation, taxation, competition and access to financing. The WBES was conducted in 1999-2000 and covers more than 10,000 firms in 80 countries.

The limited availability of firm-level data has prompted the increased use of surveys in corporate finance. Graham and Harvey (2001) argue that survey-based analysis offers a balance between large sample empirical analysis and clinical studies, by asking very specific and qualitative questions across a broad section of firms for a moderately large sample. The WBES, in particular, has a broad scope in examining more than 200 questions in total, covering topics from property rights protection to corruption, political instability, financing constraints, regulation, business environment and government-business relationships. The survey has a reliable sampling methodology by administering the survey on a parallel basis in all the countries using a uniform methodology and parallel parameters for sample composition. To ensure uniform representation, the sample of firms surveyed was stratified on the basis of several variables like sector, size, ownership, exporters and location with the constraint that at least 15% of the firms are in each of the sub categories of these variables.

While there is a great amount of general information available on the firms, the identity of the firms and the interviewees is confidential information to encourage correct responses from the firms. This prevents us from validating the financial information provided by the firms from public records. However, the WBES has been widely used and most recently for its firm-level variation by Beck, Demirguc-Kunt, and Maksimovic (2005). It has also been used by Transparency International in computing its Corruption 12
|

22 more closely that the constituencies of national legislators match those of sub-national governmental jurisdictions, the more likely that those sub-national interests will be reinforced in national-level decision making.

Voting rules are a third aspect of electoral rules. The DPI records whether legislators are elected using majority (first past the post) or proportional representation systems. These rules affect candidate incentives to adhere to party positions (as in proportional representation) and to build up personal constituencies at the expense of party positions. If at least some members of the legislature are elected by simple majority vote, the electoral system is coded as “pluralistic.” If at least some candidates are elected based on the percentage of votes received by their party, the system is coded as proportional representation (PR). Countries are allowed to (and do) exhibit both systems; in this case, the system that governs the majority of candidates is indicated for each legislative chamber.

Proportional representation systems differ widely according to whether they favor large or small parties or privilege party leaders or individual candidates (see Cox 1997 for a thorough discussion). The data set therefore records if there is a threshold that parties have to overcome in order to gain any seats in the legislature and whether the D’Hondt system, which is less favorable to small parties, is used to calculate the number of seats that each party receives. There is also a variable that records whether the electoral system is closed or open list. A system is classified as closed if voters cannot express preferences withina list — that is, if the position on a party’s candidate list combined with the number of votes received by the party is the sole determinant of whether a candidate is elected or not.

Some, albeit scanty information is available on the candidate selection process. Especially in majoritarian settings, this is a crucial dimension along which countries vary in
are not meant to be used for policy analysis, for example to assess the possible labor market effects of a policy promoting specific sectors’ exports. The data are a representation of equilibrium outcomes, where firms have made input choices, given technology, output prices, labor market institutions as well as other relevant policies, and input prices. Because of that, the data are ill suited to predict the effect of a policy that changed this equilibrium in one way or another.

The analysis using the new LACEX data sets documents a number of new findings. First, the global share of labor value added embodied in gross exports (LVAX share or the labor intensity of exports) has been declining since 1995, when the total value of wages was $46 for each $100 of gross exports while in 2011 the value was $40. This decline is driven by a drop in high income countries as the LVAX share has remained flat in middle income countries, and has increased in low income countries. Overall the number of jobs supported by gross exports (JobX) has declined more rapidly than the labor value added in exports, a result consistent with labor saving technological changes across countries. It has also declined more rapidly in middle income than in high income countries. This decline notwithstanding, our data suggest that exports supported over 20% of total employment for the sample of 66 countries with data available in 2011.

Second, the labor value added directly contained in exports has declined over the past two decades. This result is not a reflection of changes in the share of labor value added in output (LVAD), which in fact has remained stable over the period. This difference between the direct LVAX share and direct LVAD share is thus mainly due to the different changes in the composition of exports versus output. The direct LVAX share has declined in high income and to some extent in middle income countries and has been flat in low income countries. On the other hand the indirect LVAX share has remained flat through the period
We utilized two main sources of data: COPSA-C administrative data regarding storage and related loan information (take-up, loan amount, and repayment) and independent survey data collected at several stages of implementation (Census, Baseline, Stockage, Destockage, and Endline).

Table 1 provides summary information of the sample. To describe the profile of the villages in which the intervention took place, we show average demographics from the census in column 1. Most households are male-headed. About one-quarter of households are polygamous; thus, households tend to be relatively large, with an average of eight household members. Knowledge of warrantage was high at 81 percent; this is probably a result of the concurrent sensitization activities run by COPSA-C. Columns 2 and 3 present means of selected census and baseline variables for lottery non-participants and lottery participants respectively. “Non-participants” refers to households in the village that were not interested in warrantage and thus chose not to participate in the lottery; participants, on the other hand, are those households that did participate in the lottery (i.e. both treated and control households). Comparing columns 2 and 3 thus sheds light on selection into participation in the warrantage lottery. Column 4 shows the p-value for the pairwise tests of equality between non-participants and participants. Lottery participants tend to come from larger households with slightly younger household heads and are more likely to be familiar with warrantage. Participant households also reported higher hypothetical interest in storing key grains (sorghum and maize) using warrantage. Despite these differences, using an omnibus F-test, we find that the set of variables available at the census presented in Table 1 do not jointly predict participation in the lottery (p-value: 0.152).

> 6 Consent to take part in the lottery was then obtained. The village head held an introductory speech emphasizing the importance of complying with the protocol (including forbidding storing other household members’ bags). Household representatives were asked to declare the number of bags they would be willing to store if allocated storage space; this
28 We would like to follow up with some more questions about your general health over the last few weeks. Response options: 1. No never 2. Not more than usual 3. More than usual 4. Much more than usual Do you find it difficult to sleep because you are nervous or preoccupied? <mark>Do you feel under continuous pressure? Do you feel capable of overcoming</mark> <u><mark>your problems?</mark></u> <mark>Do you feel sad and that there is no way out? Have you lost your self-confidence?</mark> The question “Do you feel capable of overcoming your problems?” is negatively phrased in the PPCS (“Do you feel incapable of overcoming your problems?”). In analysis of the Facebook data, this item has been reverse coded so the higher end of the response scale indicates more severe outcomes, in alignment with the rest of the module.

<mark>Do you see yourself as a useless person?</mark>

# **<mark>Depression</mark>**

_<mark>English text of Facebook questionnaire</mark>_ 29 In the past two weeks, how often were the following statements true for you? Response options: 0. Never 1. A little of the time (rarely) 2. Slightly less than half the time 3. More than half of the time 4. More often 5. Always <mark>You were happy with a good mood You were feeling calm and relaxed You were feeling energetic You woke up active and relaxed Your days were full of things you liked</mark> _<mark>Notes on comparability</mark>_

|**C**<br>_English text of Facebookquestionnair_|**OVID-19 exp**<br>_e_|**osure**<br>_Notes on comparability_|
|---|---|
to benefit from formal social insurance, which on average covers only 10.9% of the working population in Africa (Guven et al., 2021). Some social protection expansions in North and West Africa attempted to target informal workers, including in Benin, Burkina Faso, Sierra Leone, Egypt, Morocco, Tunisia, Madagascar, and Mauritius. However, this was difficult in practice as there was less potential to build on existing databases.

# **_Alternative targeting approaches_**

In addition to targeted cash transfers, other forms of social protection responses especially in Central and West Africa included provision of subsidies or waiver for utility payments. Angola, Benin, Cabo Verde, Chad, the Democratic Republic of Congo, the Republic of Congo, Côte d’Ivoire, Gabon, Ghana, Guniea, Guyana, Liberia, Madagascar, Mali, Mauritania, Namibia, Togo, and Zambia all implemented some version of utility payment relief (Gentillini et al., 2020). While in theory, this type of social protection response allows targeting based on past usage levels, many countries skipped this step and simply reduced payments or implemented moratoriums on cutting off utilities across the board. As wealthier households consume more utilities, this even-handed approach is likely to end up disproportionately benefitting the better-off. Even when the utility subsidy was targeted based on past usage levels, in practice, utility subsidies may still be regressive. A household level panel survey in Accra, Ghana, showed limited impact of the utility subsidy on protecting food security or increasing consumption (Berkower, 2022). A major contributor to this lack of impact was that for poor households, it is common to share a meter for multiple households or for the landlord’s name to be on the electric bill, so the program ended up being poorly targeted.

A new targeting approach pioneered in Togo used mobile phone usage patterns to identify poor households. While this approach does not reach the level of targeting effectiveness of the ideal unified social registry approach, it did compare favorably