rafmacalaba/data-use-mentions-extended huggingface dataset:
process. New technologies are not adopted en masse. Rather, adoption usually begins with a few early adopters, followed by a more rapid period of adoption, with the rate of adoption leveling off once most potential users have adopted the technology. This process generates the well-known S-shaped diffusion curve: the rate of adoption rises slowly at first, speeds up, and then levels off as market saturation approaches.

The role of information is important for diffusion in both developed and developing country settings. In one recent developing country example, Rebane and Barham (2011) survey households in Nicaragua about their knowledge and adoption of solar home systems for electricity. These systems are at an early stage of market penetration and are rarely seen in some poorer areas of the country. Rebane and Barham estimate a biprobit model where they first estimate determinants of knowledge about solar home systems and then estimate determinants of adoption of such systems. Not surprisingly, awareness of the technology is important for adoption. Among non-adopters, half were unaware of solar home systems. Of those aware of the systems, most learned about them from a family, friend, or neighbor. The importance of learning from others exemplifies how adoption provides a positive externality to others by increasing awareness of the technology. Rebane and Barham suggest that demonstration projects (e.g. on public buildings) or subsidies for early adopters can thus help spread technology within a market. Moreover, early adopters may also benefit future users by reducing uncertainty about the quality of new technologies.

Recent work also suggests that important differences can be found between adoption rates in developed and developing countries. World Bank (2008b) notes that in industrialized countries, once technologies reach the country, they almost always achieve mass-market scale. In contrast, there is more disparity in developing countries. Of 67 technologies studied by World 29
3

# **The credibility of pre-electoral promises and government performance**

The extreme heterogeneity of democratic performance is easily demonstrated. The rule of law in half of all countries exhibiting either checks and balances or competitive elections in the 1990s was the same or worse as in the median country lacking either one or the other.<sup>1</sup> Comparisons using corruption indicators yield similar results, as do comparisons using objective indicators of performance. More than 20 percent of countries exhibiting strong checks and balances and competitive elections exhibit worse gross secondary school enrollment than the median country that does not. Lindert (2003) documents this as well, showing that “full” democracies are more likely than “elite” democracies to provide primary education. Baum and Lake (2003) find no relationship between democracy and female secondary school enrollment. The sources of this varied performance remain a continuing puzzle of growing importance, since the number of countries holding competitive elections has doubled, from 53 to 101 between 1985 and 2000, and the number exhibiting some checks and balances has risen from 62 to 112.<sup>2</sup> What explains the heterogeneity of democratic performance? Keefer and Vlaicu (2004) point to the credibility of pre-electoral political promises. Most models assume either that all pre-electoral promises are credible to all voters, or none are (see Persson and Tabellini 2000 for a review). There are, however, scarcely any democracies in which these polar cases apply. Keefer and Vlaicu (2004) argue that when political competitors are less able to make credible promises to voters regarding public good provision and economic performance generally, they attempt either to build their credibility among smaller groups of

> 1 The rule of law measure is from Political Risk Services’ _International Country Risk Guide_ and the measures of checks and balances and competitive elections from the _Database on Political Institutions_ .  These are discussed below.

> 2 Based on World Development Indicators PPP adjusted income per capita, and using the Database on Political Institutions variables _Executive Index
Based on data from Ministry of Agriculture and Food Security and Bank of Sierra Leone

# _Rice price_

Government's policies with respect to the incentive framework have had a serious negative effect on agricultural production. The overvalued leone imposed low producer prices for rice, since imports at the low rate of exchange depressed the domestic urban market price. The price of rice in the retail market in 2016 was Le4,887.05 per kilogram.<sup>13</sup> This is equivalent US$769.76 per ton based on the exchange rate of Le6348.79 per US$ in 2016. The world price of US$396 per ton and the implicit average cif price of US$289.68 calculated from Table 8 would suggest that the mark-up on rice to the final consumer is substantial—more than a doubling. However, since the price of imported rice is variable over the months, reaching and average cif of US$440.34 between June and December 2016, it is assumed that the risk averse, profit-maximizing importer/distributer would base the final price on the US$440.4 cif price rather than the lower US$289.68 per ton. The average price of rice increased to Le12,831 per kilogram in 2018, in large part because of the substantial depreciation of the leone, as the world market price increased by only about 2 percent between 2016 and 2018. With the exchange rate of Le 7,393.57 per US dollar the 2018 retail price amounts to Le 4,877 per kilogram or US$660 per metric ton.

The reference or border price of imported rice used in the model is given algebraically as: Pb = (Pw + Tw) + Td - Cb; and the export border price is given as: Pb = Pw - Tw - Td - Cb Where: Pb is the reference price at the farm gate; Pw is the world price; Tw is the shipping and insurance charges; Pw +Tw represents the CIF price
11 We now move closer to the core of our empirical analysis and relate workers’ remittances to current account reversals. The source of the data on current account is again the IMF Balance of Payments Statistics database; we divide the current account balance, measured in dollars, by the GDP, again in current dollars, as provided by World Bank’s World Development Indicator database.

Our preferred definition of a current account reversal occurring at time _t_ is based on the following three requirements: i) the current account balance at time _t-1_ must be a deficit; ii) the current account balance must improve by at least 5 percentage points of GDP; iii) the size of the improvements must exceed one half of the current account balance at time _t-1_ . For every country-year observation, we thus create a dummy variable, denoted by BP5<sup>15</sup> , which takes on a value equal to 1 if a current account reversal occurs and 0 otherwise.

Few comments on BP5 are needed. Firstly, BP5 stands in a close relationship with some of the measures used by MFR and Edwards: criteria i) and ii) are common to all measures, while criterion iii) is a stricter version of MFR’s last requirement. This strict analogy with MFR and Edwards is crucial: our purpose is not to innovate on the criteria for identifying current account reversals, but to show that remittances can play a role while using “well-established” measures of current account reversals. Along these lines, we will show that our results hold also when using only criteria i) and ii) so as to resemble more closely Edwards’ first measure; in particular, we provide two versions of it: ED4 denotes an adjustment of the initial current account deficit equal to at least 4 percentage points of GDP, exactly as in Edwards (2004b), while ED5 raises the required adjustment to 5 percentage point<sup>16</sup> . Secondly, we purposely choose not to allow for the adjustment to
STC data set. Then, we expand the data set so that each firm-product that exported at least once is combined with every destination and year. There are 12,918 firms exporting 1,197 HS4-products to 115 destinations, and there are 102,295 firm-product combinations that are exported at least once.<sup>12</sup> This expanded panel data set allows us to include a rich set of fixed effects and exploit firms’ previous export experience to estimate the effect of RTAs on non-member country firms.

Using this data set, we construct two dependent variables: firm export participation and entry dummies. The export participation dummy is equal to 1 if firm _i_ exports to destination _j_ in year _t_ , and equal to 0 otherwise. The export entry dummy is equal to

> 10On average, 27% of exporting firms each year are new entrants. Appendix Table A.3 provides yearly statistics on exporting firms and the share of new entrants.

> 11The distribution across industries of products subject to FDA prior notice is reported in Appendix Table A.5. Products are listed at the HS10-level which we aggregate up to HS4-level to be consistent with the level of disaggregation in the WTO STC data set. The list of products that require prior notice to the FDA were downloaded, in June 2018, from https://wayback.archive-it.org/7993/20170721215512/https://www.fda.gov/Food/ GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/ImportsExports/ucm170031.htm.

> 12This means that we have 12 _,_ 918 _×_ 115 _×_ 15 = 22 _,_ 283 _,_ 550 observations in the expanded firm-level data set and 102 _,_ 295 _×_ 115 _×_ 15 = 176 _,_ 458 _,_ 875 observations in the expanded firm-product-level data set. Note that the vast majority of these observations are filled with zero trade flows, but are needed for the

5
Incorporating even a small number of satellite predictors, with moderate predictive performance, markedly improves the accuracy of the sample estimates. In particular, the correlation with the benchmark official estimates improves from 0.8 to 0.86 when using a household-level “unitcontext” model instead of estimates derived from the sample survey. Precision improves dramatically as well, as the coefficient of variation drops by roughly a factor of two, roughly equivalent to quadrupling the size of the survey. The gain in precision is moderately overestimated, but after adjusting for that to maintain the same coverage rate as the direct estimates, incorporating the small area estimates effectively increases the sample size by a factor of about 2.4.

When using satellite data, there is a large difference in accuracy between estimates for in-sample and out-of-sample municipalities. While the preferred household model generated a correlation of 0.86 with the intercensus benchmark in-sample, this fell to 0.7 for out-of-sample. This difference in performance comes for two reasons. First, empirical best predictor models improve accuracy by efficiently combining the survey data with model predictions for each municipality. Second, outof-sample municipalities are systematically smaller and poorer than sample municipalities, which reduces the accuracy of out-of-sample predictions in this context. These results suggest additional caution when relying on purely synthetic predictions generated from satellite data.<sup>23</sup> An important benefit of this type of evaluation exercise against a credible benchmark is the opportunity to compare different statistical methods for incorporating geospatial data into small area estimation. In the main set of results, using the 2014 survey data, a household-level model gives more accurate predictions than a sub-area model for sampled areas (correlation of 0.86 vs 0.83) and equally accurate predictions for non-sampled municipalities (correlation of 0.7). When using the 2016 survey, the predictions from the household-level model are more accurate than the sub-area model both in and out of sample. The area-level model is generally less accurate than the sub-area model, with in-sample correlations of 0.8
contained gender requirements and 28 percent included age requirements. Though there were usually no explicit gender requirements in vacancy announcements for more male-dominated occupations (including IT specialists, welders, and drivers), the wording in Uzbek and Russian commonly referred to applicants as “he” or “him.” Annex 1 provides a summary of requirements specified by employer in online job ads (Spot, 2019).

The following sections proceed as follows. Section I includes an overview of the relevant literature, section II describes data, experimental design, and empirical approach. Section III summarizes survey findings, and section IV closes with discussion and policy recommendations related to the results.

# I. Audit and Correspondence Studies

Discrimination occurs when there is differential treatment of otherwise identical individuals from different social groups, such as age or gender (Bohren et al, 2019). Audit and correspondence studies are two common and closely related methods used to explore discrimination in hiring. Audit studies were first introduced in the 1960s to identify discrimination in the labor market that is difficult to conclusively establish with observational data. Such studies make it possible to measure the source and strength of employer preferences across demographic attributes such as gender, age, and other characteristics (Gaddis, 2018). The approach typically relies on real testers or auditors who physically meet their potential employer.

However, there are several limitations to direct audit studies, discussed in detail in Heckman and Siegelman (1993) and Heckman (1998). The main weakness of the approach is that job applicants from different groups may not appear identical to employers for reasons other than the characteristic being studied. Another prominent critique of audit surveys is that they are not double-blinded. As Bertrand and Dufflo (2017) note, auditors are aware of the purpose of the study and may have a belief that employers treat minorities differently, which can result in measured differences in treatment.

Correspondence surveys largely address these weaknesses by using fake resumes or digital applicants for which the qualifications are by design exactly identical across groups. This method
collected disaggregated cost data via WFP’s accounting ledgers and interviews, including staff costs. To improve the reporting on cost-effectiveness, the World Bank Strategic Impact Evaluation Fund’s 2019 guidelines recommend relying on administrative data (e.g., budgets and spending reports) as much as possible to use costs information that is “disaggregated, intervention-specific, and captured in real-time over the course of an intervention.” The document also discusses the need to collect non-financial information that is not typically quantified in accounting ledgers. Beneficiaries’ travel time to obtain transfers (as in Aker et al. 2011) is an example of non-financial data that can be systematically different across transfer modalities.<sup>23</sup>

# **4. Comparative Performance of Transfer Modalities**

Decisions about the types of transfers (e.g., cash, voucher, vs. food) and the delivery mechanisms (e.g., mobile money vs. cash-in-envelope, or lump sum vs. multiple transfers) involve multiple factors. First, the condition of market functioning needs to be assessed. As is the case in many humanitarian settings, missing markets can undermine the effectiveness of cash transfers if there are limited opportunities to use the cash transfer. Second, the existing physical and financial infrastructure needs to be taken into account because different modalities require delivery systems. The system may vary from staff traveling to communities distributing manual cash or goods, to payment service providers such as post offices disbursing cash over the counter, to electronic mechanisms such as transfers by mobile phone or directly into bank accounts. In particular, conflict and rapid-onset natural disasters can disrupt payment systems, damage infrastructure, and displace people and businesses. Delivering cash digitally may not be the most effective delivery method if the cost of cashing it out is high for villagers in remote areas with poor road conditions. Third, the selection of food items matters. If the in-kind food items are _inframarginal_ (the consumption quantity even without transfer is greater than the quantity provided)
weakly) and is reinforced when entered with the other regulatory services linkage variables. Other coefficient outcomes remain entirely insignificant as part of this specification. In column (6), the regulatory services linkage index for FDI now receives a negative and significant sign as well when entered next to the other regulatory variables.<sup>21</sup> In sum, although different types of regulatory measures appear to affect downstream productivity depending which TFP specification one uses, one thing that clearly stands out in this regard is that conduct regulations remains the most robust indicator that explains services reform as having an effect on the wider economy in the EU when taking both downstream services and goods into account. These results suggest that removing conduct regulations to an achievable level of the three most deregulated EU economies would increase the TFP level performance of the firm in both services and goods sectors for an average EU country by a maximum of 3.38 percent.

# **4.1 Role of Institutions**

Our regression results for the role of institutions as having a differential effect on firm‐level TFP are reported in Tables 4 and 5, all using our preferred Ackerberg measure. Tables A14 and A15 present results for the remaining three TFP specifications. In similar order, the results are presented for the country‐specific input‐output coefficients (Table 4) as well as when using EU‐wide input‐output 21 Table A13 in the annex shows the estimation results for the three remaining TFP measures and show that both the Olley and Pakes as well as the Hsieh and Klenow TFP measures provide a negative and significant sign for conduct regulations. Moreover, in Table A13 columns (15), (17) and (18) in addition show that the significance of conduct regulations corroborates our initial findings as presented in Table 2 in that it remains significant when entered separately as well as when entered together with the other regulatory variables.

16
**Figure 1. Minimum-to-median wage ratio, Kosovo and selected OECD countries a. Kosovo, 2012-2018**

<!-- Start of picture text --> 0.6<br>0.52 0.51<br>0.49<br>0.5 0.45<br>0.44 0.42 0.40<br>0.45 0.44 0.43<br>0.4 0.42 0.40<br>0.42 0.38<br>0.3<br>0.2<br>0.1<br>0.0<br>2012 2013 2014 2015 2016 2017 2018<br>Age 15-34 Age 35-64<br>Minimum-to-median wage ratio<br><!-- End of picture text --> _Source: Own estimates based on 2012-2018 Kosovo Labor Force Survey._

# **b. Selected OECD countries, 2018**

<!-- Start of picture text --> Spain 0.41<br>Czech Republic 0.42<br>Estonia 0.43<br>Germany 0.46<br>Belgium 0.46<br>Netherlands 0.47<br>Ireland 0.48<br>Greece 0.48<br>Slovak Republic 0.49<br>Latvia 0.50<br>Lithuania 0.51<br>Canada 0.51<br>Hungary 0.52<br>Poland 0.53<br>Luxembourg 0.54<br>United Kingdom 0.54<br>Romania 0.58<br>Slovenia 0.59<br>Portugal 0.61<br>France 0.62<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>Minimum-to-median wage ratio<br><!-- End of picture
Capacity: Evidence from Biometric Smartcards in India." American Economic Review, 106 (10): 2895-2929.

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Reinikka, Ritva; and Svensson, Jakob. 2004. Loca Capture: Evidence From a Central Government Transfer Program in Uganda. The Quarterly Journal of Economics, vol. 119 (2).

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between entrepreneurship and job creation is detectable only when purely self-employment and informal companies are excluded from the analysis (see Ghani, Kerr and O’Connell, 2011a and 2011c).

Turning our attention from the macroeconomic to the sectoral level, the empirical evidence concerning industrial dynamics also casts much doubt on the progressive potentialities of business start-ups. Firstly, survival rates for new firms are strikingly low: according to Bartelsman, Scarpetta and Schivardi (2005), who worked on data for ten OECD countries, about 20-40% of entering firms fail within the first two years of life, while only 40 to 50% survive beyond the seventh year (see also OECD, 2003, p. 145). The econometric evidence at the sectoral and microeconomic levels is largely consistent with this outcome; studies on different countries and different sectors reveal that more than 50% of new firms exit the market within the first five years of activity (see Dunne, Roberts and Samuelson, 1988 and 1989; Reid 1991; Geroski, 1995; Mata, Portugal and Guimaraes, 1995; Audretsch and Mahmood, 1995; Audretsch, Santarelli and Vivarelli, 1999a; Johnson, 2005)<sup>5</sup> .

Secondly, entry and exit rates are significantly correlated; this is one of the uncontroversial ‘stylized facts’ of the entry process according to Geroski (1995, p. 424), who pointed out that the “mechanism of displacement, which seems to be the most palpable consequence of entry, affects young, new firms more severely” (see also Baldwin and Gorecki, 1987 and 1991). Indeed, entry and exit rates have been found to be positively correlated across industries in both OECD countries (see Bartelsman, Scarpetta and Schivardi, 2005) and in DCs (see Bartelsman, Haltinwanger and Scarpetta, 2004<sup>6</sup> ).

This evidence opens the way to some considerations regarding the alleged role of entry as a vehicle for technological upgrading, productivity growth and employment generation. Consistently,