rafmacalaba/data-use-mentions-extended huggingface dataset:
dependence and availability of financial services). In particular, we want to study which complementary factors can facilitate the return of innovation on productivity, based on the Hendry and Krolzig’s (2004) -general to specific or GETS- method.

We present the data in Section 2, discuss the empirical strategy and results in Section 3, and conclude in Section 4.

# 2. Data

The analysis presented is based on the most recent Enterprise Surveys data available from East Asian countries. They are China (2012), Indonesia (2015), Malaysia (2015), Philippines (2015), Thailand (2016), and Vietnam (2015).<sup>4</sup> The surveys provide data on a representative sample of formal firms operating in non-agricultural sectors. We exclude from the analysis micro-enterprises, firms with fewer than 5 full-time employees, such as self-employed entrepreneurs and micro firms, and sectors with fewer than 4 firms for each country and survey year.

We report in Table 1 the definition of the variables used in the analysis. They can be broadly put in these categories: (i) innovation measures, (ii) firm performance, (iii) internal capabilities, (iv) access to external knowledge, and (v) demand-pull, and a final miscellanea category. We selected these categories based on evidence from the existing literature, the choice of the specific variable considered is then dictated by data availability.

_Table 1 Variables definition_

|**_Innovation_**||
|---|---|
|Innovation|(0/1) if a firm introduces a product or process innovation|
|Product innovation|(0/1) if a firm introduces a product innovation|
|Process innovation|(0/1) if a firm introduces a process innovation|
|**_Performance_**||
|Labor productivity|Log (constant US$) sales per full-time worker|
|R&D per worker|Log (constant US$)
25 adopts the value that appears under the heading 'scenarios'). The EU average country is assumed to behave as determined by the current EU Average growth determinant values (see Table 6).

**Table 9: Components of the Index Z.**

||Degree of<br>Openness|Fiscal<br>Policy &<br>Size of<br>Governmen|Monetary<br>Policy & other<br>Financial Sector<br>t<br>Policies|Price<br>System<br>Distortion|Rule<br>of<br>Law|Average|
|---|---|---|---|---|---|---|
|EU**Average**|4.52|2.45|4.11|4.11|3.93|3.82|
|Czech Republic|4.50|3.00|4.50|3.50|4.50|**4.00**|
|Hungary|3.00|2.50|3.00|3.50|3.50|**3.10**|
|Poland|3.00|2.75|2.00|3.50|3.50|2.95|
|Slovakia|4.00|2.25|3.00|3.00|3.00|3.05|
|Slovenia|2.00|2.75|3.50|2.50|2.50|2.65|
|**CEE**Average|3.30|2.65|3.20|3.20|3.40|3.15|

Note: The Index of Policy Z for CEE countries is based on the original IEF Index while the EU Average is based on the Modified IEF Index
One caveat with the estimation of equation 5.5 is that the change in CMA may capture other shocks in the economy that shifts the allocation of labor across sectors and locations. These shocks can change the decision of workers to operate in the formal or informal sector, thus, generating a correlation between the change in CMA and the error term. This generates a bias in the estimation of _κ_ . To deal with this problem, I estimate equation 5.5 by two-stage least squares using two instruments. The first instrument is the change in the CMA measures when the number of residents and workers is held fixed, and the second instrument is the treatment dummy variable. The idea is to capture changes in commuting costs and clean the estimation from other economic shocks. For example, the treatment dummy variable captures changes in residents, employment, and market access in the treated census tracts only because of line B and not other economic shocks in the city.

Table 5 reports the results for the labor supply elasticity across sectors. I obtained estimates of _κ_ between 1.1 and 2.4. These estimates are consistent with the model and the commuting elasticities. The first two columns show the results for the OLS and the other four columns for the IV using each instrument separately. In my preferred specifications, which are the ones in columns 4 and 6, I obtained a point estimate between 1.5 and 2.4. For the counterfactuals, I take an average between these two numbers. Comparing the estimates from the 2SLS and OLS, it suggests that there were other shocks in the economy that created a downward bias for _κ_ . For instance, these shocks reallocated workers from the informal to the formal sector generating a negative correlation between the change in the CMA measures and the error term.<sup>4950</sup>

# **5.4 Labor and Capital Wedges**

Labor and capital wedges are a crucial parameter for the quantitative analysis. I follow the popular approach from Hsieh and Klenow
Thomas Hertel and L. Alan Winters (eds.), _Putting Development Back into the Doha Agenda: Poverty Impacts of a WTO Agreement_ , New York: Palgrave McMillan and the World Bank. Reprinted in: L. Alan Winters (ed.), _The WTO and Poverty and Inequality_ , An Elgar Reference Collection, Cheltenham, UK: Edgar Elgar Publishing, 2007.

- Rutherford, Thomas F., David G. Tarr and Oleksandr Shepotylo (2005), ―Poverty Effects of Russia‘s WTO Accession: modeling ―real‖ households and endogenous productivity effects,‖ World Bank Policy and Research Working Paper No. 3473, February.

- Sachs, J. D. and A. Warner (1995), ―Economic Reform and the Process of Global Integration,‖ in: _Brookings Papers on Economic Activity_ , W. C. Brainard and G. L. Perry (eds.), (Washington, D.C.), 1-117.

- Schiff, Maurice and Yanling Wang (2006), ―North-South and South-South Trade Related Technology Diffusion: An Industry Level Analysis of Direct and Indirect Effects,‖ _Canadian Journal of Economics_ , Vol. 39 (3), August, 831-844.

- Schott, P. (2004), ―Across-Product versus Within-Product Specialization in International Trade,‖ _Quarterly Journal of Economics_ 119, 647-678.

- Shepotylo, Oleksandr and David G. Tarr (2008), ―Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001-2005,‖ _Eastern European Economics_ , Vol. 46, 49-58.

- Shepotylo, Oleksandr and Volodymyr Vakitov (2012), "Impact of services liberalization on productivity of manufacturing firms: evidence from the Ukrainian firm-level data," World Bank Policy and Research Working Paper Number 5944, January.

- Stibora, Joachim and Aldert de Vaal (1995), _Services and Services Trade: A Theoretical Inquiry_ , Amsterdam: Tinbergen Institute.

- Tarr, David G. (2010), ―The Economic Impact of Export Restraints
.

Fifth, we estimate IV regressions for each of the specifications discussed above. We construct the instrument, denoted as synthetic benefits per population, as illustrated in Section 2.2. For each of the specifications discussed above, we use the synthetic benefits per population as an IV for the actual benefits per population.

# **4 Main Results**

We first focus on results using the retail scanner data and then discuss our findings using the consumer panel data. All results use data from 2007-2010 only to focus on the period 17
010 -1.064* -1.026* -1.016 -0.983 -0.483* -1.020 -0.992 -1.003 -1.248* -1.099 -1.427** (0.621) (0.654) (0.640) (0.619) (0.611) (0.720) (0.620) (0.602) (0.604) (0.594) (0.617) (0.593) (0.252) (0.619) (0.618) (0.603) (0.655) (0.659) (0.688) Maximum inflation (1997-2007) -0.012* (0.006) Interest spread (2007) -0.057* (0.031) Lending rate (2007) -0.043 (0.026) GDP growth ( average 2002 -2007) -0.034 (0.086) Dom.Credit/GDP,growth (avg 2002-2007) 1.331 (1.700) Domestic credit/ GDP (2007) 0.016*** (0.005) Legal rights index (2007) 0.067 (0.081) Credit information index (2007) -0.021 (0.153) Closing business - days (2007) -0.202 (0.360) Contract enforcement - time (2007) -0.082 (0.361) Starting a business - time (2007) -0.465** (0.173) Fuels and mining/ GDP (2007) 0.504 (2.292) Foreign owned banks, share of assets -0.658 (0.951) Government owned banks , share of asse 0.376 (0.914) Banking concentration -0.302 (0.995) Branches per
, D., S.L. Parente, and R. Rogerson. 2007. “The food problem and the evolution of international income levels.” _Journal of Monetary Economics_ 54:1230–1255. 4

- Hamory, J., M. Kleemans, N. Li, and E. Miguel. 2021. “Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata.” _Journal of the European Economic Association_ 19:1522–1555. 3

- Harchaoui, T.M., and M. Üngör. 2016. “Sectoral sources of sub-Saharan Africa’s convergence.” _Applied Economics Letters_ 23:642–651. 6, A.2

- Herrendorf, B., R. Rogerson, and A. Valentinyi. 2013. “Two Perspectives on Preferences and Structural Transformation.” _American Economic Review_ 103:2752–89. 1

13
created a major bottleneck. This has been solved in part with the access program. Nonetheless, the program itself has a major limitation: the expansion of the network has not been accompanied by an equal effort to increase connectivity. As a result, while 55 percent of the population resides in areas served by the network, less than 25 percent is connected to electricity services. Connections have lagged behind for several reasons, including the absence of a robust program and dedicated resources to roll out connections; lack of affordability; and capacity constraints at the utility level in handling a growing customer base. Given the massive investments mobilized under the program, results have been disappointing. In 2015, against the US$245 million spent, only 65,000 new customers were added to service, resulting in a cost per connection of US$3,770.

As new generation plants come online and as the number of connections increases, total system sales will increase and the cost of service per kWh billed is therefore expected to reduce dramatically from an estimated US$0.18 per kWh billed to less than US$0.10 per kWh billed. Average tariff levels are expected to increase as exports increase in the share of total sales, helping to reduce the financial gap in the sector over the medium to long term.

# 5.2 Distribution of quasi-fiscal deficit between consumer categories

Sales data are available for a subset of 16 countries to analyze the distribution of QFD between consumer groups. For the purposes of this analysis, it was assumed that industry and export customers have an average cost of service US$0.035 less than other customers. This is because industrial and export customers are typically supplied at high voltage, thereby avoiding the use of medium and low 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
as the number of days without rain or the variance in temperature, may have a negative impact on yields but we maintain the assumption that this impact will be statistically significant. Our empirical results then allow us to update our prior based on where the weight of the evidence lays. If the vast majority of a set of obfuscation/source combinations show that the number of rainy days significantly increases yield, then we retain our prior belief. We can then claim that an obfuscation/source combination that does not result in a significant coefficient, or results in a significant negative coefficient, on the number of rainy days suffers from mismeasurement. Conversely, if the vast majority of a set of obfuscation/source combinations show that average maximum daily temperature in the growing season has no significant impact on yield, then we reject our prior belief. We can then claim that an obfuscation/source combination that shows a significant impact on yields suffers from mismeasurement.

Our lack of data on the objective facts of precipitation and temperature informed how we implemented our research design. First, we developed a pre-analysis plan and registered it at Open Science Framework (Michler et al., 2019). While pre-analysis plans have become common in experimental economics, they are still relatively uncommon for binding a researcher’s hands when using observational data (Janzen and Michler, 2021). The use of a pre-analysis plan allowed us to pre-define the sources of data for inclusion in the study, what metrics would be tested using what functional forms, and how we would compare results across models in the absence of formal statistical tests. Second, we adopted a blinding strategy to help ensure objectivity in the analysis. In this blinding strategy the authors were divided into two groups: the Data Generating Group and the Data Analysis Group. Kilic and Murray were in the Data Generating Group and had full responsibility for extracting the remote sensing data and matching it to the household records in the LSMS-ISA data to create a number of different paired weather-LSMS-ISA data sets.<sup>3
2 The second page adds results for every 25 years from 2000 to 2150 (as well as rates for the 1985-2000 period). This page provides fewer indices, including only the population by age and sex, the birth, death, net migration, and growth rates, total fertility, life expectancy, and infant mortality.

Most of these numbers are standard demographic indicators, but the infant mortality rate requires ome qualification. More properly labeled lqo, it is an index extracted .;:om a Coale-Demeny model life table chosen to provide an appropriate life expectancy. It need not, and often will not, correspond to an observed infant mortality rate.

# Methods and Data Sources

The key elements in chese projections are base-year (mid-1985) total population estimates and age-sex structures, base-period (1985-90) mortality, fertility, and migration rates, and assumed trends in the rates. These elements and additional assumptions that are made will all be described.

The basic methodology is identical to that used last year (see Zachariah and Vu 1988). Those familiar with the previous publication may therefore wish to skip to the summary of results. Data and sources have been updated; an Appendix indicates the sources for all base-year or baseperiod estimates.

# Total Donulation and age-sex comDosition

The estimates of total population in mid-1985 come from diverse sources. Where possible, estimates are Bank projections from reasonably complete censuses or official figures from governments with comprehensive population registration. Other official figures are also used, either from the U.N. Population and Vital Statistics Report or from government publications. When these figures do not pertain to 1985, 'hey are projected forward. Other sources of estimates include the U.N. 1988 revision of world population prospects (forthcoming), the U.N. 1984 assessment (published in 1986), Eurostat (1987), the U.S. Bureau of the Census (1985), and Celade (1987). The specific source for each country is listed in the Appendix.

The 1985 population of
of 2.8 percent relative to national nonfarm productivity under the actual distribution of regional output wedges) and national aggregate productivity to 169.1 thousand cedis per worker (an increase of 2.9 percent relative to national aggregate productivity under the actual distribution of regional output wedges). Whereas, reducing regional output wedges by 90 percent increases national nonfarm productivity to 241.2 thousand cedis per worker (an increase of 13.8 percent) and national aggregate productivity to 184 thousand cedis per worker (an increase of 11.9 percent).

- C. _Effect of the disparity in regional output wedges on the contribution of structural transformation to national aggregate labor productivity_

Based on Equation 1, we calculate the contribution of a constant pace of subnational structural transformation to national aggregate productivity,<sup>∑</sup> ∅ (L −L . For the regional pace of structural transformation ( ), we uniformly apply the national average rate of rr rr rr<sup>NN</sup> rr<sup>AA</sup> rr<sup>NN</sup> )∆θθ increase in the nonfarm employment share of 1 percentage point, obtained from GGDC data. rr<sup>NN</sup> ∆θθ Table 3 reports the simulated effects of the disparity in regional output wedges on the contribution of structural transformation to national aggregate productivity. Under the actual distribution of regional output wedges, the contribution of structural transformation to national aggregate productivity is 1.38 thousand cedis per worker. Moving from the actual to the efficient allocation of labor across sectors increases the contribution of structural transformation to national aggregate productivity to 3.74 thousand cedis per worker (a 172.1 percent increase from the level of contribution of structural transformation to national aggregate productivity under the actual distribution of regional output wedges).

The results presented in Table 3 are broadly in line with those presented in Tables 1 and 2. They suggest a strong negative association between the disparity in regional output wedges and the contribution
trust in the system and the value of what has been established at high cost. In fact, studies show that, as they failed to carefully identify private and social benefits arising from their operation, compare them to low-cost models of service provision, and on this basis develop appropriate modalities for funding, formalization programs—including ones that had positive overall impact—in Africa (Atwood 1990), East Asia (Maurer and Iyer 2008), and Latin America (Galiani 2011) were eventually unsustainable and failed to deliver on their potential.

To assess if ‘new generation’ land projects will need to pay greater attention to the extent of subsequent transactions being registered, this paper focuses on the experience of Rwanda. The fact that this is the only African country that completed nation-wide, participatory, low-cost way land regularization makes it of great interest to document achievements made possible through this effort as well as remaining challenges. Beyond focusing on an oft-neglected, we rely on administrative data that provide reliable information on key aspects including number (and amounts) of registered mortgages, transfers (and land prices in case of sales), land owners’ gender, and actual vs. potential tax receipts, at high levels of frequency and low cost.

2
_24_

**_The substitution of card payments for cash in Europe._** Electronic POS card-based transactions with real-time linkages to account data represent a new stage in the evolution of payments in Europe and combine the finality advantage associated with currency use (legal tender) with the efficiency advantages of electronic account transfers. This is the case wit) on-line POS terminals that offer immediate settlement of transactions at the moment of exchange . These POS payments are close substitutes to currency in the sense that the party accepting payment takes no risk since funds are effectively transferred to the payee when the terminal is activated. On-line card-based payments thus enhance efficiency by reducing transaction costs, reduce the time interval before a payment order is credited to a payee's account, and limit the amount of paper in the payment system (Padoa-Schioppa, 1986).

In Europe, both the number of POS terminals accepting debit and/or credit cards and the number of transactions handled are, on a per person basis, greatest in Finland and France. Denmark, Norway, Belgium and the U.K. are also relatively advanced in this regard. Finland and France in particular demonstrate the power of cooperation between banks as a way to bring about a rapid diffusion of electronic payments. Cooperation in the establishment of infrastructure, the wide acceptability and compatibility of cards issued by different institutions, and the seemingly effective competition among institutions for customers has permitted the rapid diffusion of on-line card-based POS payments in France (Kearney, 1993) and Finland during the 1980s.

**_Reasons for giro dominance in Europe: banking concentration, nationwide networks, and cooperation._** A concentrated banking industry and the nationwide branch networks of banks and postal savings institutions made it possible to establish giro payment systems in Europe based on credit transfers. Within the postal bank network, for example, it was only a relatively small step to bring in business accounts and offer first local, then regional, and finally nationwide bill payment services to savings account holders (Thomson, 1964).
estimation of poverty and inequality, _Econometrica,_ **71** (1) **,** 355-364.

- Elbers, C., Lanjouw, J. O. and Lanjouw, P. (2005), Imputed welfare estimates in regression analysis, _Journal of Economic Geography,_ **forthcoming** .

- Emwanu, T., Okwi, P., Kristjensen, P., Hoogeveen, H. and Kuppa, R. (2004, forthcoming), Regional poverty estimates in Uganda, 1992 and 1999, UBOS/ILRI/WB/DFID.

- Forbes, K. J. (2000), A reassessment of the relationship between inequality and growth, _American Economic Review,_ **90** (4) **,** 869-887.

- Galor, O. and Tsiddon, D. (1997), The distribution of human capital and economic growth, _Journal of Economic Growth,_ **2** (March) **,** 93-124.

- Hentschel, J., Lanjouw, J. O., Lanjouw, P. and Poggi, J. (2000), Combining census and survey data to trace the spatial dimensions of poverty: A case study of ecuador, _World Bank Economic Review,_ **14** (1) **,** 147-65.

- Hoogeveen, H., Emwanu, T. and Okiira Okwi, P. (2003) "Updating small area welfare indicators in the absence of a new census", mimeo, Washington / Kampala.

- Lundberg, M. and Squire, L. (2003), The simultaneous evolution of growth and inequality, _Economic Journal,_ **113** ((April)) **,** 326-344.

23
13 2020-March 2021) and the first Maha season after the introduction of the ban (September 2021March 2022). Rice yields fell in almost all regions, with some spatial heterogeneity in the degree of yield reduction. The fall was more pronounced in Sri Lanka’s northern and western regions, including major rice-producing districts such as Ampara, Anuradhapura, Batticaloa, and Polonnaruwa.

We also examine yields of Sri Lanka’s major export crop, tea. Tea yields suffered a large drop of 28.7% in 2022 (after controlling for weather fluctuations) relative to historical averages, as seen in Appendix C.D. This evidence shows that the fertilizer ban harmed Sri Lankan agriculture substantially, consistent with the importance of fertilizer as an import determinant of agricultural productivity across the world (Donovan, 2021; Gollin et al., 2014).

# **IV.C Fact 3: Agricultural Exports Declined After the Fertilizer Import Ban**

Facts 1 and 2 show that fertilizer imports and rice and tea production declined, and rice imports increased, with the fertilizer import ban. We now show the ban also had adverse downstream effects on agricultural exports.

To assess the ban’s effects on the export performance of agricultural firms, we consider the sample of firms who exported in 2017-2019 (i.e. pre-ban) at least one of the 23 crops with fertilizer intensity data. To understand which firms were more exposed to the ban, we define a firm-level exposure measure capturing how much fertilizer is embodied in the crops each firm exports. This exposure measure is the weighted average of the firm’s exported crops’ fertilizer intensities, where the weights are given by the share of each crop in the firm’s agricultural exports portfolio preban.<sup>33</sup> We rank firms by fertilizer exposure and compute the third quartile of that measure. We define exporting firms to be treated if their fertilizer exposure is above the third quartile. As in Fact 1, we use the De Chaisemartin and d