rafmacalaba/data-use-mentions-extended huggingface dataset:
increasing the range of households who remain in autarky, again likely because of more limited credit supply.

# **5. Conclusions and policy implication**

An emerging literature has identified high levels of land holding fragmentation as an important constraint to higher productivity in Eastern European countries that radically restructured their agricultural sector. While consistent with _prima facie_ evidence, detailed study to assess productivity impacts as well as the channels through which they materialize has been limited. This makes it difficult to derive more specific policy recommendations regarding conditions that need to be in place for consolidation to be costeffective and sustainable, as well as the magnitude of expected benefits which would in turn be relevant for justifiable levels of program costs and performance indicators.

Although it is not based on a special survey explicitly designed for this purpose, our analysis allows better understanding of constraints to higher rural productivity. We find little effect of fragmentation on productivity of currently cultivated land. At the same time, with some 10% of potentially productive land lying idle, patterns of land holding affect economic outcomes. One-off consolidation programs will be appropriate if a structure with many uneconomically small plots is an impediment to productive land use and land market operation. However, if other barriers prevent land markets from transferring land to more efficient producers, such programs may be ineffective and/or unsustainable. In the case of Albania, 99% of the area where land remains uncultivated can be attributed to ill-functioning land markets rather than reasons that would be amenable to systematic consolidation efforts. While the generic nature of the LSMS data limits the ability to explore obstacles to land market functioning in more detail, it allows formulation of hypotheses and identification of proxy indicators that could be used to test these. Having data on transactions in markets (land type, intensity of use, potential productivity, contract terms, level of formality, length, price, transaction cost incurred etc.) would allow for doing so easily and at modest cost.

Land fragmentation and abandonment in the context of apparent economic decline and ageing of
product or because capital is substituted for labor. The pressure to increase productivity in the nontraded goods sectors is much weaker because firms in these sectors will be more able to pass on their higher costs to domestic customers than are firms in the traded goods sectors, hence real unit labor costs in the nontraded goods sectors are very likely to rise.

The most comprehensive investigation of the labor market effects of remittances is by Chami et al (2018). Using panel data from a large sample of countries, they estimate a series of regressions in which a labor market outcome is the dependent variable and remittances as a share of GDP is one of the independent variables. They find that remittances reduce unemployment and labor force participation, increase the prevalence of informal employment and depress wage growth but raise the labor share of income (which corresponds to raising real unit labor costs). The last two findings imply that remittances depress labor productivity growth. To explain this finding, they also show that remittances induce a shift of labor from traded goods industries (agriculture and manufacturing) to non-traded goods industries (services and construction), as would be expected in economies incurring Dutch disease effects, which they argue indicates a shift from higher to lower labor productivity activities.

Most of the rest of the empirical literature on the labor market effects of migration and remittances falls into two strands. In the first strand are mostly single country studies which use household survey data to examine the effects of remittances on the labor supply decisions of households. Several of these studies pertain to countries in ECA. Justino and Shemyakina (2012), using data from the 2003 Tajikistan Living Standards Survey, find that the receipt of remittance income by a household has a negative and significant effect on both male and female household members’ participation in the labor force and also on the hours of work of household members who participate in the labor force.<sup>.</sup> Similar effects have been found in other countries with high remittance receipts. Grigorian and Melkonyan (2011
.41|
|State schools|-0.02|0.67|-0.15|0.00|
|High school|-0.08|0.12|-0.18|0.00|
|Share classes in school with fu|1.40|0.00|1.07|0.00|
|Total duration of school day (|-0.37|0.00|-0.18|0.00|
|Intercept|-5.80|0.00|-6.14|0.00|
|Number of observations||25374||20013|
|Pseudo-_R_ <sup>2</sup>||0.24||0.28|

_Sources:_ MEC/FNDE, _Prova Brasil,_ School Census, and INEP. _Note:_ Propensity score was estimated using a Probit model and 2009 data. Treated schools are the schools that joined _Mais Educação_ in 2010 and remained until 2011. Our control group is the schools that, by 2011, had not yet joined _Mais Educação._ Schools that joined the program in 2009, 2010, and 2011 were excluded from the sample to define the control group. Table reports the estimated coefficients (columns (1) and (3)) and _p_ values (columns (2) and (4)). Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
-  20  -

**Table 12:** _Exports of end-use products in terms of their imports in 1996-2003 (in percent)_

||||Export<br>|inperc<br>|ent of i<br>|mports<br>|||
|---|---|---|---|---|---|---|---|---|
||1996|1997|1998|1999|2000|2001|2002|2003|
|Agricultural Food & Feeds|199|147|167|180|143|127|149|<br>127|
|Industrial Raw Materials|210|187|122|143|166|132|154|<br>131|
|Machinery, excluding Automobiles|87|77|61|**35**|38|38|44|<br>42|
|Automobiles & Parts|24|17|8.8|9.3|4.3|3.7|5.0|<br>4.8|
|Other Consumer Goods|136|149|113|101|107|73|72|<br>73|
|Fuels|19|25|15|25|33|125|126|<br>101|
|All Goods|96|100|86|73|74|70|72|<br>69|

Source: Based on Bulgaria's reporting from UN COMTRADE Statistics.

# **B** **_._** _Degree of processing embodied in EU-oriented exports: end-use product categories_

The composition of exports in terms of end-use product categories sheds light on changes in the extent of processing embodied in exported products as well as to differences in drivers of growth in both 1996-99 and 2000-03 phases in terms of the level of processing embodied in exports. Table 13 presents the structure of
2) the Enterprise Survey indicator on Security Costs and the Global Competitiveness indicator score on Police System; (3) the Enterprise Survey indicator on Web intensity and the Global Competitiveness indicator on Broadband; (4) the Enterprise Survey indicator on Power Outage and the Global Competitiveness indicator on Electricity Quality (or the _Doing Business_ indicator on Getting Electricity); (5) the Enterprise Survey indicator on access to bank finance (Overdraft) and Global Competitiveness indicator on Financial Development.

In contrast, there is no significant correlation at the conventional level (i.e., 5 percent) between (1) the Enterprise Survey indicator on skill constraints (Skill Labor Obstacle) and the Global Competitiveness indicator on Quality of Education System (and the World Bank’s

> 8 The variables are time-varying, but when being merged into the WBES data, which are largely cross-sectional, the _Doing Business_ and _the Global Competitiveness_ indicators are in effect time-invariant. 9 In table 4, all BE indicators are standardized with their sample mean and standard deviation so that they have means of zero and a standard deviation of one.

11
making by democratizing the design, collection, and analysis of data (World Bank, 2021). Representatives of over 200 tribal villages engaged in several weeks of deliberations to think about what constituted the good life for them, turned those ideas into indicators measured with survey questions and then tested and adapted those questions in their villages so that they did not take more than 30 minutes to answer. The questionnaires were quite different from those generally used to track poverty with only a 17 percent overlap in questions with the Indian National Sample Survey. We incorporated the community designed questionnaire into a tablet-based electronic survey and used a system of video-based training to train community representatives to use the tablet to conduct surveys of their own villages. In our pilot, which covered one district, we were able to conduct a census of 32,000 households in the district in about six weeks. Once the survey was conducted the data were dispatched directly to a cloud server to prevent anyone from tampering with them. The same exercise was repeated the following year.

The goal was for villagers to use the data for two purposes, to track changes in the quality public services and in their living standards, and to use the data to make better decisions in village meetings ( _gram sabhas_ ). This was challenging because levels of literacy were low (at about 65 percent), and numerical literacy was much lower. Thus, we could not use conventional data analytics and visualizations, and had to develop ways of showing the data that would be intelligible to someone who could not read or write. So, we co-produced data visualizations with the community, iterating a few times till we were confident that they were widely understood. These data were then presented at village planning meetings, including the village meetings described in earlier sections, and we found that they substantially helped improve the quality of discussion by allowing citizens and officials to focus on the issues rather than debate the facts. - (More information is available here <u>https://socialobservatory.worldbank
where _Yids_ denotes the outcome of interest for woman _i_ in district _d_ in state _s_ . The key explanatory variable of interest in the second stage is _Fds_ , the fraction of constituencies in a district with female winners in the most recent state election. We instrument for _Fds_ with the variable _FCds_ , the fraction of constituencies in a district where a woman won in a close election against a man in the most recent state election. We control for the fraction of constituencies in the district that had a close male–female election, _TCds_ , and include a third-order polynomial, _G_ ( _Mjds_ ), in the victory margin, _Mjds_ , between the winner and the runner-up for every (close or nonclose) male–female election, _j_ . We also interact these polynomials with _Ijds_ , an indicator for whether there was a male– female election in the district. We include district-level controls, _Zds_ , comprising the shares of the district population that are female, urban, and low caste, as well as male and female literacy rates and individual-level controls, _Xids_ , including the woman’s age, years of schooling, and indicator variables for religion, caste, and rural location.<sup>11</sup> We use robust standard errors clustered at the district level for inference.

Lastly, we estimate the following specification using our retrospective woman–year panel to examine the effects on fertility behavior: where _Yidst_ is the outcome for a woman _i_ in district _d_ in state _s_ and year _t_ . We include a vector of individual-level controls, _Kids_ , comprising the number of children that the woman had at the time of the last election and the individual-level controls specified in equations (1) and (2). We also include year fixed effects, _τt_ , to account for year-specific shocks at the national level. We cluster standard errors at the district level. Similarly to our estimation strategy described in equations (1) and (2), we instrument _Fds_ with _FCds_ and include the same set of controls as
- Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. _Scientific Data_ , _7_ (1). 61

- Lizzeri, A., & Persico, N. (2004). Why did the Elites Extend the Suffrage? Democracy and the Scope of Government, with an Application to Britain’s “Age of Reform”. _The Quarterly Journal of Economics_ , _119_ (2), 707–765. Publisher: Oxford Academic. 4, 7

- Lowes, S., Nunn, N., Robinson, J. A., & Weigel, J. (2017). The Evolution of Culture and Institutions: Evidence from the Kuba Kingdom. _Econometrica_ , _85_ (4), 1065–1091. 5

- Martinez-Bravo, M., Mukherjee, P., & Stegmann, A. (2017). The Non-Democratic Roots of Elite Capture: Evidence From Soeharto Mayors in Indonesia. _Econometrica_ , _85_ (6), 1991–2010. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA14125. 6

- Michalopoulos, S., & Papaioannou, E. (2013). Pre-Colonial Ethnic Institutions and Contemporary African Development. _Econometrica_ , _81_ (1), 113–152. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA9613. 1, 5

- Migdal, J. S. (1988). _Strong societies and weak states: state-society relations and state capabilities in the Third World_ . Princeton, N.J: Princeton University Press. 1

- Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2016). Building State Capacity: Evidence from Biometric Smartcards in India. _American Economic Review_ , _106_ (10), 2895–2929. 4, 5, 7

- Myerson, R. B. (
|$11,277|$20,745|$680|$2,594|$3,447|$5,608|$10,161|
|Western Province|$6,448|$10,068|$16,279|$33,188|$77,519|$2,974|$4,663|$7,556|$15,513|$25,799|

Source: Sri Lanka Census of Industry, 2004. Notes: See Table 1.

35
|5.5|4.4|3.3||
|Passenger average<br>yield|US cent/pkm, average<br>2000–05|2|3.3|2.4|2.2||

_Source_ : AICD railways database.

# **Challenges**

Boosting freight and passenger traffic on OCBN’s tracks is an important challenge for Benin, as traffic levels are among the lowest in the region. On average, between 2001 and 2005 only 24 million net ton-km and 18 million passenger-km were registered, levels that put the Beninese railways behind other railways in West Africa (table 6). The situation has deteriorated since 2005. Passenger traffic stopped in 2007.

OCBN also needs to enhance its productivity, which lags most railways in West Africa. At 40,000 traffic units per employee, OCBN’s labor productivity is comparable with NRC’s but behind the labor productivity of SITARAIL and GRC, with 481,000 and 84,000 traffic units per employee, respectively. On average, OCBN locomotives transported 3 million traffic units, the lowest figure of concessions in the region. Similarly, wagon productivity, at 74 thousand net ton-km per wagon, was just a fraction of the figures for SITARAIL, GRC and TRANSRAIL. Only carriage productivity, at 900,000 km per carriage, was comparatively high (table 6).

OCBN’s freight tariffs are the highest in the region, with an average of $ 5.8 cents/ton-km. Only SITARAIL has freight tariffs comparable with OCBN’s. However, the passenger tariffs, at $2 cents/passenger-km, were the lowest—until passenger traffic was halted in 2007.

17
*|(0.133)***||(0.104)|(0.104)|(0.117)||(0.104)***|(0.104)***|(0.118)**|
|IDN exports per worker *||||-0.0573||||-0.206||||0.207|
|Share of hhlds with computer||||(0.189)||||(0.194)||||(0.190)|
|Observations|195|195|195|195|195|195|195|195|195|195|195|195|
|R-squared|0.846|0.847|0.853|0.853|0.935|0.934|0.935|0.936|0.854|0.859|0.859|0.859|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. High-educated workers: workers with at least complete secondary education. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

Table 13. Indonesian Exports, ICT use, and Aggregate Labor Market Indicators
as the sum of wages (number of workers) of all types of workers which have an average number of years of education strictly larger than the number of years needed to complete secondary school divided by the total wage bill (total number of employees).<sup>28</sup> For Brazil, we use direct information on the percentage of workers with some college education.<sup>29</sup> In Table 3 Panel A, we present summary statistics on wage bill shares and employment shares of skilled workers according to our three measures. In both countries, wage bill shares and employment shares of nonproduction workers are larger than those of workers in engineering, technical and managerial occupations (by de fi nition) and these are in turn larger than those of workers with college education. For any given de fi nition of skills, average wage bill shares are larger than average employment shares. According to all skills measures, Brazil has lower average shares of skilled workers than China.

# 3.1.2 Absorptive Capacity and International Economic Activities

As measures of plants’ learning and absorptive capacities, we use a dummy variable for R&D expenditures and a dummy variable for the introduction of new product lines.<sup>30</sup> To measure the presence of international economic activities, we use a dummy variable for plants that export, the plant’s share of sales that are exported, a dummy variable for plants that are foreign-owned, the plant’s foreign ownership share, a dummy variable for plants that use imported inputs and the plant’s imported inputs share. For Brazil, we use data on direct exports, i.e. those not done through a distributor, whereas for China we use data on total exports since the survey does not distinguish between direct and indirect exports.<sup>31</sup> In Table 3 Panel B, summary statistics are shown for the different measures of absorptive capacity and international economic activities.

> 28The implicit assumption
We fielded the survey experiment between March 1-13, 2021 to a sample of 2,508 Colombian adults who were at least 18 years old.<sup>10</sup> These respondents came from a larger online panel of approximately 140,500 respondents.<sup>11</sup> Our sampling aimed to be representative across different dimensions of the country’s population based on the 2018 Colombian census, including key demographic variables including age, gender, region, and socioeconomic _strata_ (a measure of income used in Colombia). Table A4, which displays the distributions of these variables in our sample against those from the 2018 census, indicates how our sample shows close similarity with the census estimates.<sup>12</sup> We also recorded additional socio-demographic and economic variables for the respondents. Description and descriptive of other additional features of our sample appear in the Appendix <u>(Table A5).</u> Since each respondent saw five pairs of policy packages, our analysis comprises 25,080 observations.

# **4.3 Estimation Strategy**

We analyse the data in three steps. First, we estimate both the average marginal component effects (AMCEs) and marginal means (MMs) to measure public preferences towards each policy option within each dimension. AMCEs report a series of linear probability estimations where the dependent variable is a binary (forced choice) variable indicating the preferred policy by the respondent. The dependent variable is regressed on the levels of each policy dimension (with a researcher-chosen reference category as the baseline level) to estimate the probability of respondents choosing that policy option in relation to the reference category. To test for the consistency of the results, we also use a scaled rating version as a dependent variable where the policy ratings are re-scaled to vary between 0-1 (see <u>Table A3</u> for a description). In all estimations, we report robust standard errors clustered at the respondent level since each respondent saw multiple treatments (
Modebadze and Eroyants (2010) and our construction of a balanced input-output table for Armenia is explained in appendix G.

# **II. Estimation of the Tariff Equivalence of the Regulatory Barriers in Services**

Estimates of the ad valorem equivalents of the regulatory barriers in services are crucial to the results. In order to make these estimates, we first need to assess the regulatory environment in the services sectors in our model. We commissioned a 112 page survey of the regulatory regimes in key Armenian business services sectors, namely, insurance, banking, fixed line and mobile telecommunications services and air transportation services. We supplemented that information with research by regional experts into the relevant sector.<sup>4</sup> This questionnaire and research provided us with data and descriptions and assessments of the regulatory environment in these sectors.

Modebadze (2010) then estimated the ad valorem equivalents of barriers to foreign direct investment in fixed line and mobile telecommunications, banking, insurance and maritime transportation services. The process involved converting the answers and data of the questionnaires into an index of restrictiveness in each industry. Modebadze followed the methodology of Kimura, Ando and Fujii (2004a, 2004b, 2004c) to generate these estimates. The methodology involves classification of the possible restrictions into separate categories with unique weights summing to one, where the weights are determined based on the significance of each category. Next, Modebadze assigned a score to each potential restriction, where the score reflects the level of restriction imposed by the economy. Modebadze estimated two indices: an index of ―regulatory barriers‖ (RB index) where the regulatory barriers impose costs on both domestic and multinational firms in a non-discriminatory manner; and an index of discriminatory barriers against multinational service providers, which we call the foreign discriminatory index (FDR index).<sup>5</sup> This methodology further involves building on the estimates and methodology explained in the volume by C. Findlay and T. Warren (2000), notably papers by Warren (2000), McGuire