rafmacalaba/data-use-mentions-extended huggingface dataset:
two working papers at most.<sup>9</sup> This resulted in a database of 107 research papers linked to on one of 8 blogs: Aid Watch, Baseline Scenario, Chris Blattman, Economix, Freakonomics, Marginal Revolution, Overcoming Bias, and Paul Krugman. Among the other blogs, the majority were either finance blogs or blogs based on discussion of macroeconomic issues typically without reference to working papers. We use this database to formally test for whether blogging about a paper increases its abstract views and downloads through event study analysis.

The RePEc statistics are available at a monthly frequency, and so for each paper _i_ which is blogged about, we define _t=0_ in the month in which the blog entry occurred, _t=-1_ in the month before, _t=+1_ in the month after, etc. Then we estimate the impact of blog _s_ blogging about a paper using the following regression: _Blogi,t_ is a dummy variable which takes value one if the paper is blogged about in time period _t,_ βs is our coefficient of interest, measuring the increase in abstract views in the month of blog _s_ blogging compared to the paper-specific average, and γs allows for a one-month lagged effect which may arise particularly for blog posts towards the end of the month. We include paper-specific fixed effects (αi) and cluster the standard errors at the paper level. The corresponding equation is likewise estimated for paper downloads.

Equation (1) is known as the constant mean model in event study analysis (Campbell et al. 1997). A first threat to this assumption is if abstract views or downloads are trending over time. Paper view statistics appear to trend downwards over the first couple of months of release of the typical paper, but

> 9 There also seem to be spikes for these other blogs. For example, the blog Angry Bear linked to a paper which had abstract views jump from 3 to 150, and downloads from 0 to 40 comparing the month before to month of
transformation and school enrollment. While there is a growing body of literature adopting this method (Edmonds et al. 2010; Topalova 2010; Dix-Carneiro and Kovak 2017; McCaig 2011; Costa et al. 2016; Erten and Leight 2021; Kis-Katos et al. 2018; Li et al. 2019; Erten et al. 2019), almost all these studies focus on trade shocks, very limited attention has been devoted to FDI. By combining China’s population censuses, city statistical yearbooks 1990-2005, and the industrial firm censuses, the present paper assesses broader developmental impacts of FDI in Chinese local economies. Third, previous FDI-related studies often use aggregate FDI measures and are thus unable to differentiate between different types of FDI. The rich micro-level datasets in this paper allows us to shed light on the heterogenous effects of FDI by their export intensity and skill intensity, which could provide novel insights and better inform policymakers’ FDI strategies.

China offers an ideal case for this analysis: First, China experienced two waves of dramatic FDI liberalization in 1992 and 2002, with FDI inflows to China surging more than 20-fold during the study period 1990-2005. Second, since China is among the largest countries by land area and the largest country 6
# **3.2 The Asian Crisis of 1997**

The Asian Crisis originated in Thailand in 1997 and quickly spread to the rest of East Asia and its trading partners. It was a currency crisis. In July 1997, the Thai government had to abandon its fixed exchange rate against the US dollar that it had maintained for so long, citing a lack of foreign currency resources. Among others, Indonesia, Korea, and Thailand were affected the most. Relatively less impacted were countries, including Brunei, China, Singapore, Taiwan, China, and Vietnam, however, also suffered from a sharp decline in demand in the region.

Infrastructure PPP investments and the number of projects in transport and energy sectors declined substantially soon after the Asian Crisis in the East Asia and Pacific (Figure 4). Currency conversion risk in infrastructure financing projects translated into reduced government willingness to provide explicit guarantees for projects in the period following the crisis. The investments in the region were also affected during the Global Recession but the impact was relatively smaller.

**Figure 4: Greenfield Transport and Energy Investment (by US$ and number) in East Asia Pacific**

<!-- Start of picture text --> 1990 2000 2010 2020 1990 2000 2010 2020<br>125<br>40000<br>100<br>30000<br>75<br>20000<br>50<br>Number of Projects<br>Real Investment (USDm)<br>10000 25<br>0 0<br><!-- End of picture text --> _Source:_ Authors’ calculation based on private investment in infrastructure database (https://ppi.worldbank.org/en/ppi). Vertical lines represent 1997-98 Asian Crises and 2008-09 Great Recession. This figure shows the total number of greenfield projects in transport and energy sectors for the period 1990-2019 in the East Asia and Pacific countries. There is a sharp decrease in the number of projects and associated
# **3. Data**

The above regressions require the use of geo-referenced panel data sets at a common spatial unit, which we present in this section.

For our main explained variable, cropland area, we use the HYDE 3.1 panel dataset (Goldewijk, Beusen, van Drecht and de Vos, 2011), which provides a measure of cultivated land (excluding urban areas and pasture land) in areas of 5 decimal degrees (approximately 10 by 10 km, or 10,000 hectares) for the whole world. We use these data for the subset of 290,416 cells in Sub-Saharan Africa and for the years 1970, 1980, 1990, 2000 and 2005 for which we have road data available. Cropland area in the region increased by 57 million hectares, reaching a total of about 211.5 million hectares in 2005 (see Table A1 in the Appendix). The annual growth rate of cropland area steadily increased over the period from 0.61 percent in the 1970-1980 decade to 1.68 percent in the 20002005 period. Map A1 in the Appendix shows the actual spatial distribution of cropland for 1970 and 2005. As can be seem from this map, the increase in cropland area over the period occurred throughout the Guinea Savannah zone that covers a large part of Sub-Saharan Africa (World Bank, 2009). The increase is particularly noticeable in West Africa (in particular for Nigeria, Niger, Ghana, Senegal and Burkina Faso), Central/East Africa (the countries surrounding Lake Victoria, as well as Sudan, Ethiopia and Kenya) and the Southern part of the continent (especially South Africa).

The other regressand (in Regression 7) is a measure of economic activity. For this, we used the local GDP measure constructed by UNEP (United Nations Environment Program) and the World Bank and aggregated it to our spatial unit for the above mentioned years.<sup>7</sup> 9
Policy Research Working Paper 5948

# **Abstract**

The growth of India’s manufacturing sector since 1991 has been attributed mostly to trade liberalization and more permissive industrial licensing. This paper demonstrates the significant impact of a neglected factor: India’s policy reforms in services. The authors examine the link between those reforms and the productivity of manufacturing firms using panel data for about 4,000 Indian firms from1993 to 2005. They find that banking, telecommunications, insurance and transport reforms all had significant, positive effects on the productivity of manufacturing firms. Services reforms benefited both foreign and locally-owned manufacturing firms, but the effects on foreign firms tended to be stronger. A onestandard-deviation increase in the aggregate index of services liberalization resulted in a productivity increase of 11.7 percent for domestic firms and 13.2 percent for foreign enterprises.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at iborchert@worldbank.org, bgootiiz@worldbank.org, agrover1@worlbank.org, and amattoo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
14 Finally, for deindustrialization, we use as a proxy the decline in the GDP share of MFG over time. Indeed, if MFG productivity decreases relative to the world, MFG employment should decrease. The GDP share combines information on productivity and employment.

**3. Resources, (De-)Industrialization, and Urbanization**

**Data.** We study 116 relatively large countries, with a developing status as of 1960, and for which data on urbanization, resource exports, and the GDP shares of manufacturing (MFG) and services (SERV) are available every 5 years between 1960 and 2020.

We obtain urban shares (%) from the United Nations (2018) and the share of fuel and mining exports in total exports for the period 1960-2010 from GJV16 who rely on data from World Bank (2021) and the _Mineral Industry Surveys_ of USGS (2020). We extend their data up to 2020, modifying them when needed. The USGS data are important due to the fact that the World Bank data are incomplete or incorrect in many cases.<sup>17</sup> Measurement error, if classical, would make us under-estimate the contribution of these resources to urbanization. The export shares of all agricultural products come from FAO (2020). Using these shares and knowing from World Bank (2021) the export-to-GDP ratio of each country in each year, we calculate NRX/GDP (%) as in Sachs and Warner (1995, 1997). Next, we obtain the time series of the GDP share of MFG+SERV by relying on World Bank (2021), Central Intelligence Agency (2021) and United Nations (1960-1980, 2020c) and the GDP share of FIRE c. 2020 from United Nations (2020a).<sup>18</sup>

**Country-level results.** For countries _c_ , we estimate the following cross-sectional model:

URB _c,_ 20 = _α_ + _β_ NRXGDP _c,_ 60 _−_ 20
Policy Research Working Paper 7657

# **Abstract**

Using a cross-country data set on e-government systems, this paper analyzes whether e-filing of taxes and e-procurement adoption improves the capacity of governments to raise and spend resources through the lowering of tax compliance costs, improvement of public procurement competitiveness, and reduction of corruption. The paper finds that information and communications technology can help improve government capacity, but the impact of e-government varies by type of government activity and is stronger in more developed countries. Implementation of e-filing systems reduces tax compliance costs as measured by the number of tax payments, time required to prepare and pay taxes, likelihood and frequency of firms being visited by a tax official, perception of tax administration as an obstacle, and incidence of bribery. The effects of e-procurement are weaker, with the number of firms securing or attempting to secure a government contract increasing with e-procurement implementation only in countries with higher levels of development and better quality institutions. The paper finds no systematic relationship between e-procurement and bureaucratic corruption.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2016: Digital Dividends_ —is a product of the 2016 World Development Report Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at zhasnain@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and
sensitive to localized shocks and traditional data are not available (Andrée, 2021a).

We computed basic statistical metrics and implemented dual-layered correlative analyses. The first layer focused on the relationship between daily crowdsourced prices and ground truth prices, and this cascaded to second layer assessment of relationship between monthly averages of crowdsourced prices and monthly AI-estimates. Although the AI-estimates include open, high, low, and close (OHLC) prices, we opted to utilize closing prices because they often represent the prevailing market sentiment within each period.

The raw intraday crowdsourced dataset (level 0 data) was downloaded and queried for the coinciding commodities between actual data submissions and AI-estimates (rice and maize). Both commodities are major staples that are commonly traded in the region, and nationally. The ground truth data collection was implemented in parallel to the submission of data by crowd volunteers during the final 8-months of FPCA initiative (EC-JRC, 2022). Twelve (12) enumerators who are resident at different locations, dispersed across the focal region (Figure 1), were duly trained with a data collection protocol. By visiting the nearest market daily, the enumerators submitted observed or transactional data through a preconfigured data collection survey instrument (on smartphone). Multiple enumerators were enlisted to improve coverage of multiple market locations, and minimize potential human bias and artefacts in the data. It should be noted that the different unit packages for the prices that were submitted by volunteer crowd and enumerators reflect the market reality that commodities are usually transacted in diverse unit packages, which are standardized across various market segments and geographies. The price per unit (₦/kg) for each submitted datapoints were eventually computed with reference to standard conversion units (Arbia, et al., 2023).

Prior to implementing the two-step analysis, the raw intraday crowdsourced and enumerator-submitted prices were checked for obvious outliers, based on expert 14
economy, leading to aggregate consequences on output and productivity. Fifth, the inclusion of the home production sector is indeed relevant for assessing the efficiency gains of a rise or fall in the premium paid in the public sector.

# **4 Calibration strategy**

We calibrate the parameters in our model to match key moments of the allocation of talent in Egypt. Our data were extracted from ILOSTAT databases.<sup>10</sup> We select _α_ such that _ηeα_ (the share of capital) equals a capital income share of 30%, as is standard in the literature (e.g., Gollin, 2002). We assume that the capital income share in the public firm is the same as in the private sector. Hence, we determine _κ_ such that _ηgκ_ equals 0.3. The fractions of women and men in the population, _φf_ and _φm_ , are directly taken from the data: _φf_ = _φm_ = 0 _._ 5. For the parameters of the Euler equation used to set the location of the infinitely elastic supply of capital ( _β_ and _δ_ ), we take the standard value of 0.95 for the discount factor _β_ , as reported in the literature, and we follow Cagetti and De Nardi (2006) and Buera and Shin (2013) to choose _δ_ = 0 _._ 06.<sup>11</sup> In line with Guner et al. (2008), we assume that log-managerial ability follows a (truncated) normal distribution with a mean of _m_ and a standard deviation of _sd_ . Additionally, we impose that this distribution represents the majority of production units, constituting a total mass of 1 − _p_ ˆ. To address the remaining portion of the establishment distribution, we set a top ˆ ˆ ˆ value for managerial skill, _s_ , along with its corresponding fraction, _p_ . We normalize _p_ = 0 _._ 1% and let _s_ ˆ to match the employment share in firms with 20 or more
surveys, as recommended by De Brauw and Carletto (2012). As of now, there is no best practice in the household surveys realm for collecting this data, whose quality is likely to be compromised by different types of bias (e.g., recall bias, respondent fatigue bias). Testing and validating alternative methods for collecting information on past migration episodes in the context of household surveys is another area of development for migration-related methodological research.<sup>33</sup>

# **_Assessing and overcoming sampling issues_**

Not having a sufficiently large sample of migrants remains the biggest limitation preventing the use of household surveys for studying any type of migration-related issues. As already discussed, this problem originates from the ‘rare event’ nature of the migration decision which is even more pronounced in the case of climate-induced migration. Given that internal migration is much less rare than international migration, LSMS-type surveys have often been used to study internal movements but – despite some exceptions such as Albania (e.g., Mendola and Carletto, 2012) – hardly ever to look at cross-border migration. An easy way to diagnose the magnitude of the ‘rare event’ problem in household surveys is to look at data on the prevalence of migrants (or households with migrants) in the national population relative to the size of the existing sample for the specific survey, as suggested by Bilsborrow (2016). The larger the sample size and the higher the prevalence, the less severe the ‘rare event’ problem.<sup>34</sup> However, should this type of preliminary diagnostics confirm the ‘rare event’ issue, there could still be options for overcoming it. Some of the main options, and related potential innovations going forward, are:

- The use of specialized sampling techniques as proposed by De Brauw and Carletto (2012) remains the most effective solution to the issue. This essentially consists in **oversampling** areas with a high prevalence of migrants which, however,
# **_Estimation_**

To identify the middle class, we use a regression-based approach which is organized in three stages (López-Calva and Ortiz-Juarez 2014).

In the first stage, we classify households in different groups depending on their transitions in and out of poverty. In this stage, we use the four-year intervals panel (the longest panel available) of the EU-SILC datasets. For each country and four-year interval and depending on their transitions, households are classified into four groups: poor-to-poor, poor-to-nonpoor, nonpoor-to-nonpoor, and nonpoor-to-poor (Tables A.1 and A.2 in the Appendix). Across all countries, the average share of households that become poor was around 4 percent, that means that 4 percent of households move from being non-poor to being poor.<sup>8</sup> We thus specify this 4 percent chance of falling into poverty in a four-year period as the “low” probability of falling into poverty.

In the second stage, using a logistic probability regression, we estimate the probability of being poor at the end of each panel (year _t2_ ) as a function of characteristics measured at the beginning of the panel (year _t1_ ).<sup>9</sup> Formally, this can be written as follows: where: _pic_ is the probability of household _i_ in country _c_ of being poor in year _t2_ ( _pooric,t2_ ) conditional on observable household characteristics in year _t1_ ; **X** is the vector of such household characteristics; is the associated vector of country-specific model parameters for the logistic probability function _F(.)_ . ββ The vector of household characteristics includes measures of human capital and occupational status of the household head, as well as demographic characteristics, the quality of housing, a subjective measure of welfare, and observed health and labor shocks at the household level. We refer to these characteristics, which include tangible and intangible variables, as _assets_ of households.

> 8 For middle-income countries, this average is almost 10 percent (Cruces
<!-- Start of picture text --> vt<br>° e<br>n e °° 8°<br>ee %e e e<br>e es e ee<br>° e e Hy e ec e e<br>i] H e e<br>i)y ee ° .3 e e<br>i e<br>Yt e<br>.<br>Zaha pie CUE), esau ase<br>®@ Local Concentration Anomaly (ppm) |———— Fitted values<br><!-- End of picture text -->

<!-- Start of picture text --> Te<br>e<br>e<br>e<br>nN<br>e e e e e<br>e e<br>e ee<br>wn<br>Y e<br>2017m1 2018m1 2019m1 2020m1 2021m1<br>mdate<br>®@ Local Concentration Anomaly (ppm) |———— Fitted values<br><!-- End of picture text -->

<!-- Start of picture text --> e e e<br>° °.$ e = *° 2 ° § % °° e<br>ee 2 . . e of e . ”° “o_oe ° 2 8<br>e 8 °<br>% e<br>+4°<br>e<br>©<br>2017m1 2018m7 2020m1 2021m7<br>mdate<br>© Local Concentration Anomaly (ppm) |——— Fitted values<br><!-- End of picture text -->

<!-- Start of picture text --> e<br>ne<br>ry<br>e<br>- e . e .<br>6 8<br>°
# _e. Seasonal rain S3 (Sep‐Dec)_

Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value with the points outside below or above that identifying outlier observations. Values are measured as the mean of rainfall levels and mean temperature in the respective months.

Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

14
worsen. In fact, according to the family distribution of income based on the per capita income of Costa Rica’s households, Gini’s coefficient has not changed much between the years 1987 (0.401) and 2003 (0.425), with an average of 0.395 for the period and a coefficient of variation of only 4.9%.<sup>7</sup>

> 5 According to the records of Costa Rica’s Foreign Trade Ministry, the country had 1744 exporting businesses in 2003.

> 6 In fact, according to figures from the last population census (2000), immigrants make up one fourth of Costa Rica’s population.

> 7 Due to a change in the methodology for conducting the household survey in 1987, figures from previous years are not comparable. For that reason, the income distribution and poverty figures used in this paper are from 1987. However, for the purposes of our analysis, this situation is not a serious restriction, since Costa Rica’s liberalization period began in 1986.

10
(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0245*|0.0027|0.1278***|0.0623**|0.2379**|0.0333|
|female=0)|(0.0129)|(0.0091)|(0.0374)|(0.0276)|(0.1026)|(0.0914)|
||-0.0129|0.0503**|-0.5654***|-0.2406***|-0.3353*|0.4499**|
|Ethnic minorities (yes=1, Kinh=0)|(0.0228)|(0.0198)|(0.0754)|(0.0356)|(0.1794)|(0.1958)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1996***|0.0740*|9.1621***|8.8840***|1.9772***|0.5824|
||(0.0506)|(0.0441)|(0.1536)|(0.1129)|(0.3941)|(0.4371)|
|Observations|21,737|28,920|8,215|8,085|28,920|28,920|
|R-squared|0.116|0.123|0.384|0.335|0.207|0.126|

Note: This table reports district fixed-effect RDD-DD regressions of loans of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year