rafmacalaba/data-use-mentions-extended huggingface dataset:
Overall, the analysis leads to five main conclusions. First, the literature on infrastructure has diversified hugely since the 1980s. From an almost exclusive initial focus on the output-elasticity and the use of public capital data, it has extended to specific sectors, many different outcomes, and a large variety of data sources and types. This has led to increasing heterogeneity in the literature, and has implications for the way we conduct this meta-analysis, leading us to focus mostly on specific sub-samples.

Second, as is generally the case for many other economic topics, it is subject to significant publication bias in favor of positive and significant results. Third, as the literature recognized the limitations and moved away from its initial focus on public capital, the estimates it has produced have become relatively smaller. We report average estimated elasticities that range between zero and 0.06 for most of the sector-outcome categories we consider. We are however careful in interpreting these magnitudes. As we discuss in the concluding section, given the differences in the nature of infrastructure indicators used, it is unclear whether lower elasticities necessarily entail smaller marginal product of capital.

Fourth, we look at the average elasticities specifically for developing countries samples, finding that they are generally larger than those for developed economies for the digital and transport sector, but not for energy. A few large estimates are found in very specific sector-outcome categories, such as digital-micro-output, and transport-labor. Finally, we discuss how these estimates could be leveraged to produce marginal rates of return for specific sector-outcomes, highlighting the lack of relevant data in this area, which appears to be the next priority in terms of research.

This study contributes to the literature on infrastructure by providing a systematic assessment of the body of research reporting elasticities of infrastructure for a large set of outcomes and across three sectors. In that sense, it aims at providing updated reference points regarding the quantitative effect of infrastructure, useful for both researchers and practitioners engaged in infrastructure projects. A number of review papers have been written on infrastructure, including
= ((ZDi,t m **V;,t n)/TVi,t** .). 100

where Vi,tn is the value of imports in tariff line item i in year (t-n) and Di,t is a dummy that takes a value of unity if an NTB is applied to the item and zero otherwise. If n and m are zero the index is based on current trade values, otherwise base year weights are used.

The UNCTAD records contain information about the following types of nontariff restrictions which are included in the frequency and coverage ratio tabulations: tariff quotas, anti-dumping duties, restrictive import authorizations, total prohibitions, suspended import authorization licenses, anti-dumping investigations, state monopolies, differential health and safety regulations, differential prohibitions based on non-commercial considerations. The records may understate the current importance of some restrictions like anti-dumping actions since they do not incorporate data on the surge in new cases that occurred in 1992-93.
simulated shocks in interest rates. The magnitude of the shock for the currency board countries is apparently deeper.

Though Estonian output exhibits a first period positive response to a one standard deviation shock in its domestic interest rate, it is immediately followed by the expected negative output response. This may be partially explained by a lagged reaction of output to a current change in the domestic interest rate. From Figure 4, we see that the Estonian adjustment process is more prolonged than that of the Mexican output response following the shock to interest rates. The most striking result in this chart is, however, the response functions of Hong-Kong and Argentina which show much more marked shock persistence relative to Mexico.<sup>9</sup> Moreover, the standard error bounds for Argentina and Hong Kong, do not approach zero for at least 24 months and 36 months respectively out in the forecast horizon, thus yielding a reasonable indication that the flatter slopes of the response functions are reflective of a relatively prolonged output adjustment to a shock in interest rates. Table 3 _roughly_ summarizes those empirical results by listing the countries in the sample and displaying the _approximate_ response time of output to a shock in interest rates, all else constant. 10 many periods it takes for a shock to completely dissipate, but rather, how the response functions compare with one another across countries.

> 9 The quarterly data for Hong-Kong is interpolated monthly in order to plot the response function on the same axes as the other countries. That is, one quarter for Hong-Kong is correspondingly plotted once every three months, and the interim monthly span is interpolated.

> 'O For standard error bands for the impulse response functions, see Figure 1.

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enrolment rate and poverty is conducted using the same baseline surveys that are used for the experimental evaluations of _PROGRESA_ in Mexico and _Bono de Desarrollo Humano_ (henceforth BDH) in Ecuador. The full set of _ex ante_ predictions are then compared with the highly credible and accepted _ex post_ results presented in Skoufias and Parker (2001) and Schultz (2000) for _PROGRESA_ and Schady and Araújo (2008) for BDH. The choice of these programs for the exercise is partly to do with the fact that they represent large and well-known CCT programs that contain many of the typical features of such programs. Equally important for our purposes is that for both these programs, the randomization of program implementation allowed researchers to conduct 4
couple of cases that point to innovations in public land valuation. Kuwait now requires two separate private appraisals when undertaking public-private partnerships (Peterson and Kaganova 2010: 6). Meanwhile, South Africa mandates that public land be taxed the same way as private land which means that public land undergoes the same valuation processes (Peterson and Kaganova 2010: Box 2). Yet many other developing countries are still struggling to value public land; in these places land auctions are often used as tool to reveal land value through bid pricing. Meanwhile, in Germany, land valuation is dictated by federal regulation.<sup>10</sup> Germany has local land valuation boards that are charged with collecting and maintaining land price data as well as disseminating land price information (Kertscher 2004 and Seidel 2006). The United States and Germany are two examples where land valuation techniques have been refined.

As an example, in the United States the legal system allows each state to define its own method for property valuation. In most cases, states delegate this power to local governments leading to a vast variety of systems used. While the most commonly used is the market value approach, in many cases all three methods are combined to estimate property values; New York City is an example. In New York, these methods are used selectively for different property categories. The sales comparison approach is used to value small residential properties and vacant land, using sales data of comparable properties for the previous three years. The income approach is used to value offices and businesses, taking an estimated income and dividing the net income by a capitalization rate. Finally, the cost approach is used for new construction and renovations, but also for special properties such as stadiums, museums, churches, etc. (Lafuente 2009). The following describe common land valuation techniques given access to relevant data on transactions and land attributes.

# <mark>2.1 SALES COMPARISON APPROACH</mark>

The sales comparison approach relies on market data to analyze information on
of firm and job characteristics, such as firms’ management practices, hiring policies, training and promotion opportunities, and local labor market conditions, which could also affect worker retention. Omitting such unobservable characteristics would thus bias the retention effects of coworker similarity. Focusing on changes in the workplace composition over time by controlling for workplace fixed effects in longitudinal data (Hirsch _et al._ , 2020; Leonard and Levine, 2006), may not be enough to mitigate these concerns, as these changes may also be driven by changes in confounding firm and job characteristics.

We exploit exogenous changes in the racial coworker composition that arise from unexpected deaths of workers. Specifically, we compare the responses of incumbent workers in peer groups that experience the death of a non-white worker with those that experience the death of a white

> 7Throughout the paper, we use the term ‘firm’ interchangeably to refer to establishments.

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4. Repeat steps 2 and 3 for each estimation method for all 500 sets of small area estimates corresponding to each sample.

The benchmark poverty alleviation from this scheme corresponds to the results of applying the above to the true municipal poverty rankings. The results for each method are compared to that result. In our implementation, we fix the poverty line at the 25th percentile of per capita income prior to transfers. The benchmark poverty rate, after delivering transfers to people in municipalities ranked by their true poverty rate, is 19.9 percent.

# **5 Results**

Recall from Section 4 that the _R_<sup>2</sup> measure often used to assess the Recall that the _R_<sup>2</sup> measure often used to assess the performance of machine-learning models is biased downward.<sup>27</sup> To understand the extent of this bias in practice, we have calculated the _R_<sup>2</sup> associated with gradient boosting when using both the direct estimates and true poverty rates as the basis for performance assessment. We conduct this comparison at two different levels of estimation, namely the PSU and municipality levels. All _R_<sup>2</sup> calculations are nevertheless made at the municipality level, meaning that the PSU-level model is estimated at that level and then the poverty estimates are aggregated to the municipality level. For each of our 500 samples, we thus calculate four _R_<sup>2</sup> measures: (1) PSUlevel estimates evaluated against the direct poverty estimates, (2) PSU-level estimates evaluated against the true poverty rates, (3) municipality-level estimates evaluated against the direct poverty estimates, and (4) municipality-level estimates evaluated against the true poverty rates. All models use only census-based covariates.<sup>28</sup> Panel (a) of Figure 1 presents these results and we find evidence of considerable downward bias for both the PSU- and municipality-level models. For example,
>gible.<br>ions.|

_Uneven Improvement across Sectors._ Figure 2 shows that improvements in the opportunity indices of basic housing services (BHS) and early childhood development (ECD) were impressive (18 and 12 points respectively). The rates of improvement of the opportunities in education and nutrition and hunger were modest or suffered a setback (5 points and -3 points, respectively). Table 2 (third column) shows the rates of annual progress for each opportunity separately. The telephone HOI recorded the largest annual rate of improvement at 6.3 points, followed by three opportunities: cooking energy, prenatal care, and assisted birth delivery (with similar annual rates, 2.8-2.5 points). Three opportunities – access to water, non-overcrowding, and post-natal care – showed rates of progress close to the average rate (between 1.1 and 1.3 points). These were followed by five opportunities with _modest_ improvements at rates close to half the average rate, namely the two education opportunities for graduation on time, access to sanitation, and nutrition weight-for-age. By contrast, the HOI index for nutrition non-stunting did not show any progress, and two other

> 19 In circa 2009 the percentage of children with access to four of six opportunities in ECD and nutrition was just 25% and inequality of access was the highest across all opportunities  (see Table A.1).

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, and stability might not fully capture all features of financial systems and it makes no attempt to construct a single, composite index of financial systems. Rather, the paper uses these four characteristics as a basis for describing, comparing, and analyzing financial systems around the world and their evolution over the last few decades.

The analyses presented in this paper, together with the underlying datasets, highlight the multidimensional nature of financial systems. Deep financial systems do not necessarily provide high degrees of financial access; highly efficient financial systems are not necessarily more stable than the less efficient ones, and so on. Each of these characteristics has an association with aspects of the broader socio-economic development, and each is in turn strongly associated with financial sector policies and other parts of the enabling environment for finance. The paper illustrates that financial systems come in different shapes and sizes, and they differ widely in terms of the 4x2 characteristics. It is therefore important to measure and evaluate each characteristic of financial systems.

The paper also documents that the global financial crisis meant not only increased financial instability: the crisis also translated into difficulties along the other dimensions, such as more people and firms finding it increasingly difficult to access financial services. Finance is not just about stability. The degree to which financial systems channel society‘s savings to those with the most promising investment opportunities is fundamentally important for economic growth, poverty alleviation, and the degree to which individuals have the opportunity to pursue their economic aspirations.
significantly more transactions with their accounts after receiving the transfers, even excluding initial withdrawals of the amount transferred. The increased usage began in the month following the transfers and persisted, with a cumulative average of 1.5 more transactions 12 months after the transfers. Thus, the transfer treatment provides random variation in subjects’ experience using their bank accounts.

> 2A related literature studies how experience can attenuate behavioral anomalies such as the endowment effect, another manifestation of the status quo bias where individuals are reluctant to part with their endowment (Thaler, 1980), and focus on experience acquired by transacting in the market rather than by gaining familiarity with the product (List, 2003, 2004; Feng and Seasholes, 2005; Dhar and Zhu, 2006; G ̈achter et al., 2009; Greenwood and Nagel, 2009; Seru et al., 2010; Engelmann and Hollard, 2010; List, 2011; Anagol et al., ming).

> 3Malawi is a country in eastern Africa with relatively low levels of financial inclusion. According to the 2017 Global Findex database (Demirgu ̧c-Kunt et al., 2018), 34 percent of adults had bank accounts or mobile money but only nine percent had formal savings.

> 4NBS Bank, formerly known as New Building Society is one of the 10 commercial banks operating in Malawi, with a network of 36 branches throughout the country.

> 5Similar to other basic savings accounts in Malawi, the account did not pay interest. Because the inflation rate was above 20 percent throughout the study, the real rate of return was negative.

5
the revenue of the participants has been constant in the period, whereas the revenue of the nonparticipants has declined, leading to a conclusion that PAFPA has had economic impacts, even though the level significance is rather high, namely 11%.

# **5. Conclusion**

In this paper the economic impact of the labor force training program in the informal sector of Côte d’Ivoire has been analyzed. The data collected are a subsample of the participants in three selected sectors, namely the agricultural sector, tailoring sector, and the electronics sector, and a comparable comparison group of nonparticipants. By the use of standard econometric tools developed for this kind of data, namely “difference-in-difference” estimators, the data have been analyzed in order to detect potential program impacts. The conclusions drawn are that positive economic impacts are found for some groups as a result of training received, namely women, the agricultural and electronics sectors, and for firms employing 1-3 individuals and firms with 10 or more employees.

There are six lessons from PAFPA during 1994-2002. First, an effective outreach program is key to reaching potential beneficiaries in the informal sector. Second, special attention should be given to the choice of training sponsors. To that end, institutional sponsors and enterprises should be given priority. Third, craftsmen do not respond very well to demands for training. It is, thus, necessary to work with their associations to sensitize craftsmen on the role they can play as trainers for the less-skilled labor force and to provide additional incentives. Fourth, allocation of public funds on a competitive basis (i.e., with public vocational training institutes competing with private institutions) can reduce costs and increase responsiveness of public spending on skills development. Fifth, lack of complementary inputs (water, credit, equipment, markets) tends to limit the impact on beneficiaries. This suggests a need for linking subprojects better with other projects and interventions. Sixth, existence of accountability mechanisms for a fund would enhance its efficiency (World Bank archives).

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Figure 15: Ratio of Incomes and Wages between Rural and Urban Households, 2009 and 2014/15

## (a) Income Ratio

<!-- Start of picture text --> 160%<br>2009 2014<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## (b) Wage Ratio

<!-- Start of picture text --> 120%<br>2009 2014<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> _Source:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

# Consumption

Both food and non-food consumption are significantly lower for the poor compared to the non-poor, in both rural and urban areas (Figure 16). Lower rates of food consumption by the poor in rural Sudan are of special concern, because lower food consumption can trap households in cycles of poverty. Insufficient energy needs can result in greater difficulties maintaining employment, especially in laborintensive agricultural work. Lower energy and nutrient intake can lead to greater incidence of disease and poor growth in children. Addressing this inequality, so that all Sudanese citizens have a healthy and sustainable diet of food, is vital for creating a prosperous Sudanese economy.

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percent of enterprises reported that informal payments or gifts were requested or expected when applying for a license. The Zambia survey also asked whether licenses were needed for each consignment or whether licenses covered all consignments over a set period. About 70 percent of enterprises reported that they needed licenses for each consignment.

Export procedures can also be burdensome. For example, exporters in Mozambique need to obtain a certificate of origin, a certificate of quality, a sanitary and phytosanitary certificate and an export license, which is needed for each transaction, before exporting (Nathan Associates, 2002). The certificate for quality and the sanitary and phytosanitary both require inspections. Only the survey for Zambia asked specific questions about export licenses and permits. About 52 percent of exporters in Zambia reported that they needed export licenses, with about 90 percent of these reporting that they needed a license for each consignment (as opposed to getting a license that covered all consignments over a set period of time). The average wait for an export license was 5 days. In addition to this, about 45 percent of enterprises reported that their exports were subject to special regulations.

Many countries offer exporters special incentives, such as duty drawback schemes. Although duty drawback schemes (schemes that refund custom duties for goods that are exported) can encourage exports, their application has often been difficult in developing countries. In particular, the schemes are often complex and poorly administered, resulting in high transaction costs for firms involved in these programs.<sup>11</sup> Weak administration and fiscal problems often result in long delays before they receive refunds. For the two countries where questions were asked about delays in receiving refunds, Ethiopia and Zambia, the average waits between filing and receiving refunds were 74 and 93 days respectively.<sup>12</sup> Similar problems are common for other refunds. For example, in almost all countries with value-added taxes, including all the countries covered in this study except Ethiopia where value-added taxes had not been imposed at the time
cleaning the house, washing/ironing clothes, cooking for the family, preparing tortillas for own consumption, taking care of children, and maintaining dwelling with adobe, among others (Table 5, row a)<sup>16</sup> These activities fall within the General production boundary but are not part of the System of National Accounts.

In terms of _employment_ , many of the women who reported “no” in the household survey do, in fact, engage in paid services or production of goods (Table 5, row b). These initially unrecognized paid activities include collecting and selling berries, raising animals (mainly chickens) to sell, washing clothes for others for payment, domestic work for others (both irregular and permanent) for payment, working in grocery store, running a grain mill (from home), and preparing and selling food one or several days per week (tortillas, _nacatamales_ , sweet breads).

Table 5. Women’s and men’s reported activities in accordance to ICLS 19th Forms of Work Framework

|Form of work|Activities – Women|Activities - Men|
|---|---|---|
|(a) Own-use production|i)Production of‘goods’:|i)Production of‘goods’:|
|work comprising<br>production of goods<br>and services for own<br>final use;|•<br>Collect fire wood<br>•<br>Livestock/poultry rearing (for own<br>consumption)<br>•<br>Agricultural production (planting, weeding,<br>harvesting, cleaning crops; collecting water|•<br>Agricultural production (planting,<br>weeding, harvesting, cleaning crops;<br>collecting water for crops; irrigation;<br>fumigation)<br>•<br>Collect/cut fire wood<br>•<br>Build house|

16 These
Table A1: Growing Seasons

||Growing Season|Span Calendar Years|Season Modality|
|---|---|---|---|
|Ethiopia|1 March - 30 November|no|unimodal|
|Malawi|1 October - 30 April|yes|unimodal|
|Niger|1 June - 30 November|no|unimodal|
|Nigeria|_North_: 1 May - 30 September|no|bimodal|
||_South_: 1 March - 31 August|||
|Tanzania|1 November - 30 April|yes|unimodal|
|Uganda|_North_: 1 April - 30 September<br>_South_: 1 February - 31 July|no|bimodal|

_Note_ : The table presents the growing season ranges, as defined by following FAO crop calendar for each country, respectively.

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