rafmacalaba/data-use-mentions-extended huggingface dataset:
country had a sufficient supply of raw materials, and producers of narcotics needed a special license/permit from the Minister of Health.

We obtain a combined indicator of investment restrictiveness, our measure of product

> 12 All Presidential Decrees are announced at the end of the calendar year, which implies that they become effective at the beginning of the next year. We follow this convention in constructing our dataset.

> 13 Only a few sectoral laws included investment restrictions which were not included in the Presidential Decree. These restrictions were still legally valid as laws have higher legal standings than decrees in Indonesia. As discussed below, we complement the investment restrictions data in the Presidential Decrees with these sectoral restrictions.

> 14 This differs from the definition in the previous Presidential Decision, which used only general industry descriptions, thus making it difficult to identify variation across product markets.

> 15 Further revisions were enacted since 2015, but these extend beyond the coverage of our manufacturing plants’ survey. It is also worth noting that the majority of the restrictions in the NIL was eventually eliminated through the so-called Omnibus Law for Job Creation in 2021.

11
, and among workers with high levels of education, in salaried employment and with access to social insurance. Low-income, self-employed, and informal workers are not only less likely to do their jobs when WFH is the only option, but also less likely to have access to income protection schemes if these are channeled through the existing social security infrastructure.

Our findings highlight the importance of social protection programs to safeguard the most vulnerable during the crisis. Efforts to reach informal workers are crucial since they are less likely to have access to existing social insurance benefits, and also to additional programs launched in response to the crisis through the social security or tax administration infrastructures. Our results also show the importance of fostering technology adoption to protect jobs while respecting social distancing measures. To a large extent, the between and within country divide in WFH amenability is driven by unequal access to ICT. These benefits of digital technologies should be considered by governments in developing countries when investing in broadband infrastructure.

> 3 In fact, the coefficient of correlation between the share of internet users by country in 2012 vs 2017 is 0.94 (own estimates based on data from World Development Indicators, WDI).

4
Out of the 1,200 households that form the experimental sample, a total of 1,197 were interviewed in July/August 2015, including 405 in the self-reported households and 792 in the proxy-reported households, respectively. As shown in Table 1, the average household head is male, 50 years of age, and has 4 years of formal schooling. On average, household size is 5.6 with 1.5 children aged 6 to 14 per household. These children are 10 years of age on average, with 2.5 years of formal schooling. The average monthly household income was around 1200 birr (about US$52), which was somewhat higher relative to the income of the average farmer in Ethiopia. Nonetheless, our sample participants live in houses with poor infrastructures such as mud-based floors (70 percent), no electricity (78 percent), and no sanitary services (80 percent). On average, the extent of the household plot is close to one hectare, of which 58 percent is used for coffee production while the remaining land is mainly used for cultivation of enset and maize. On average, households reported their plots as being located a 22-min walk from the closest primary school. The p-values of the t- test for the equality of means between the self-reported and proxy groups show statistical balance across all variables for the overall experimental sample and the sub-sample of households that have at least one child in the 6-14 age category. It is worth noting that around 90% of proxy respondents are males.

To address potential concerns about the representativeness of these data concerning nonFairtrade smallholder farmers, we compare key characteristics of our sample with that of a nationwide representative sample of rural households by using the 2015/2016 Ethiopian Socioeconomic Survey (ERSS) that was implemented by the Central Statistics Agency (CSA) as part of the Integrated Surveys on Agriculture program in close collaboration with the World Bank. The ERSS data is a representative survey of 4,954 households living in rural areas, small-town, and
tensions for water between agricultural and human needs and decrease yields, with particularly negative effects on soy and livestock farmers (World Bank, 2021).

Despite Paraguay’s relatively large rural population in comparison to other countries in Latin America, nearly 63% of its population was living in urban areas in 2021 (INE, 2021), which is projected to increase to 74% by 2050 (World Bank, 2021). Urban communities in Paraguay are increasingly exposed to flooding after intense rainfall events, and subject to property damage, loss of assets or negative health impacts due to inadequate infrastructure and draining systems (World Bank, n.d.-a). As urbanization rates grow, increasing pressure on dense urban structures and public infrastructure elevates urban populations’ vulnerability to weather shocks. In conclusion, Paraguay is highly vulnerable to the impacts of climate change due to a combination of the country’s economic structure, lack of resilient infrastructure and socio-economic activities that are particularly sensitive to climate risk.

# **4 Empirical Strategy**

## **4.1 Data**

**Household survey data.** We use household survey data from the Permanent Continuous Household Survey ( _Encuesta Permanente de Hogares Continua_ (EPHC)). The EPHC is an annual, cross-sectional survey that provides indicators related to labor markets, income, and other socio-economic characteristics that allow to analyze the evolution of well-being in the Paraguayan population over time (Instituto Nacional de Estadística, 2022). The EPHC also represents the main data source for official poverty estimations in Paraguay, with poverty being measured on an annual basis. For our purposes, we use data from 2004 to 2019, from which we generate a pooled cross-sectional data set of 379,170 individual observations (Table A.1 in Appendix A). Since 2017, the EPHC has been collected on a quarterly basis, while previously, the EPHC was collected only on the fourth quarter of each year. While data for the years 20202022 is available, the observed
to shrink small or weakly significant regressors to zero. Because of the shrinkage that the Lasso imposes in the penalized least squares estimation, parameter estimates are intentionally biased. For this reason, once the relevant set of final regressors has been determined with the Lasso procedure, we use the Ordinary Least Squares (OLS) estimator to obtain unbiased estimates of the regression parameters that are not shrunk to 0.

# **3.2. Data**

The source of our data set is the IMF’s International Financial Statistics (IFS) database. All data is on a quarterly basis. The maximum possible sample size in the time dimension is from 1980:Q1 to 2010:Q3. The cross sectional dimension of the panel data set, i.e., the number of countries that are included, is 49.<sup>19</sup> The credit variable that we use is defined as total bank credit to the private sector, expressed in local (national) currency units. Since the scale of private sector credit can be very different across the countries, we create a credit index, with the base of the index (where the value of the index is equal to 100) being 2001:Q1. The index version of the credit variable is then log transformed before used in the analysis.

The real GDP data (GDP for short henceforth) and GDP Deflator data are taken from volume measures, and are hence also index measures with different base years. Both, GDP and the GDP Deflator are also log transformed. The lending to deposit rate spread is computed as the lending rate minus the deposit rate. We use Consumer Price Inflation (CPI) data to construct an ex-post measure of the real interest rate. This is done by computing CPI inflation as 100 times the year-on-year inflation rate, ie., as 100 _×_ (ln( _CPIit_ ) _−_ ln( _CPIit−_ 4)).<sup>20</sup> The real interest rate
dropped out of the sample differ from those that were found and surveyed for the endline. Table 4 suggests that the attriting households and panel households differ in some respects. Attriting households were less likely to have owned their dwelling at the baseline (73 versus 83 percent, _p=_ 0.001). They were also more likely to have a female head (10.6 versus 3.9 percent, _p<_ 0.000), and have more adults ( _p=_ 0.004) and fewer children between ages six and 10 (but no difference in the number of children under age two). Attriting households were also slightly wealthier ( _p=_ 0.098) and used more liquid petroleum gas (LPG) as cooking fuel ( _p<_ 0.05) _._ As discussed earlier, we added households to the endline sample to attain a sample size of 2,500 households. We did this to increase the power of our endline tests. Table 4 further examines whether the added households differ from the panel households. Columns 3 – 5 report the results of tests of difference in means. Only a small number of differences are statistically significant at the 10 percent level. A lower percentage of the new endline households (20 percent) received

> 6 See Annex 1 in _Scaling Up Rural Sanitation: Findings from the Impact Evaluation Baseline Survey in Indonesia_ , Water and Sanitation Project Technical Paper, November 2010; http://water.worldbank.org/publications/scalingrural-sanitation-findings-impact-evaluation-baseline-survey-indonesia.

10
Finally, the role of users in ensuring data quality should not be underestimated. Users have a unique opportunity to detect errors in their daily work with the system, and it is important to create feedback loops from them to the system manager. Users had good suggestions for developing automatic aggregations for key figures and for how the data should be displayed, such as how it should appear in terms of tables, graphs, charts, and the like.

Although the MIS has generally been a success, there were many rounds of fixing problems until it was working in an acceptable manner. Some of the challenges encountered stemmed from lack of Internet reliability in Ethiopia. Frequent Internet interruptions can lead to duplication errors with automatic database updates and data not being automatically uploaded to the central server from satellite offices. Close monitoring and checking data transfer was therefore needed, even when the MIS was in regular operation.

# **6. Sustainability**

At the time of our fieldwork, the MIS was functioning well and was popular among users, although its sustainability depends on the sustainability of the WEDP. If the WEDP is discontinued, there will be no need for joint reporting even if training and finance continue to be provided to growth‐oriented female entrepreneurs. M&E of the services would then be integrated into the regular government system, and it would probably not be possible to continue to identify growth‐oriented female entrepreneurs registered in the WEDP (under the current government SME registry).

Our sustainability assessment is therefore split into two parts. First, we assess sustainability based on the assumption that the WEDP would continue as a dedicated program after donor support ends and assess the likelihood that the government will continue with the MIS, as opposed to integrating it into their regular M&E system. Then, we assess spillovers from the MIS and the benefits generated beyond its functions as an MIS project.

## **Continuation of the MIS beyond project life**

If the WEDP is maintained after donor financing ends, it is
Policy Research Working Paper 10865

# **Abstract**

Internal migration and structural transformation are strongly interrelated. This paper uses Indian data spanning 2001–13 to examine a little-known aspect of this relationship: how migration affects agricultural land rental contracts. Building on anecdotal evidence and theory, the paper hypothesizes that as landlords migrate away, their choice of contract for tenant-cultivators changes from sharecropping to fixed rent.

Using a shift-share instrument that exploits information on bilateral migration flows between districts, the paper shows that migration increased fixed rent tenancy and contract formalization. Given the continued importance of agricultural land rental markets, these findings have significant implications for rural efficiency and equity in developing countries.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at ssharma1@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
households using the CPI. Exploiting the variation in the timing of the survey we use the average rate of the monthly CPI increase in the three months prior to the survey interacted with a dummy for net-food consumers (defined as households in which nobody works in agriculture) to calculate the price shock experienced.<sup>6</sup> Previous analysis has shown that there is a five month delay in the response of nominal wages to sudden food price increases (Headey et al., 2012).

In Ethiopia crop yields are very dependent on rainfall and vary considerably by year, and across regions. We take advantage of the fact that in Ethiopia, much analysis has been done by policymakers on variability of rainfall, and crop production. The Livelihoods, Early Assessment and Protection project (LEAP) system, developed in 2006 by the Government of Ethiopia in collaboration with the World Food Programme (Hoefsloot and Calmanti, 2012), uses crop-modeling approaches to estimate the likely rainfall-induced crop loss in woredas throughout Ethiopia based on a water balance model and crop types.<sup>7</sup> The LEAP database contains estimates of the proportion of crop lost as a result of insufficient or poorly timed rainfall for each woreda from 1996 to 2012. These data are merged with the household survey data based on the woreda in which each household resides.<sup>8</sup> Table 1 shows how frequently different shocks are reported in the data, with the most prevalent shocks being adverse food prices for urban areas, and drought and crop damage for rural areas. Food price shocks are reported far more in 2011. The proportion in 2005 reporting the shock was only 2%, but almost 20% in 2011, reflecting much higher monthly increases in the CPI present during the survey period in 2010/11. The crop loss figures from LEAP show the proportion of crops lost in each community, weighted by crop type (where zero means no crop losses suffered). In 2010, few if any households experienced
two data sets by adapting the potential priority sectors of the WB-WAIPA survey to 28 NACE sectors, corresponding to either NACE1 (e.g. for agriculture, mining and services) or NACE2 level (for manufacturing sectors).

**Table 3: Study sample from WB-WAIPA Survey**

|**Study**|**sample**|**Excluded from**|**the study**|
|---|---|---|---|
|**Group 1: Sectoral FDI data**<br>**available, with sectoral targeting**<br>**(CORE SAMPLE)**|**Group 2: Sectoral FDI data**<br>**available, no sectoral targeting**<br>**(CONTROL ONLY)**|**Group 3: No sectoral**|**FDI data available**|
|Austria<br>Brazil<br>Bulgaria<br>Chile<br>Cyprus<br>Czech Republic<br>Denmark<br>Egypt, Arab Rep.<br>Estonia<br>Greece<br>Hong Kong SAR, China<br>Hungary<br>India<br>Ireland<br>Italy<br>Poland<br>Romania<br>Korea, Rep.<br>Slovak Republic<br>Slovenia<br>South Africa|Canada<br>Finland<br>France<br>Germany<br>Iceland<br>Nigeria<br>Portugal<br>Russian Federation<br>United States<br>Australia*|Albania<br>Andorra<br>Angola<br>Antigua and Barbuda<br>Azerbaijan<br>Bahrain<br>Bangladesh<br>Belarus<br>Bosnia<br>and<br>Herzegovina<br>Botswana<br>Cambodia<br>Cameroon<br>China<
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.5** . **Port performance compared across African regions**

||East Africa|Southern Africa|**Central/ West**<br>**Africa**|Global best<br>practices|
|---|---|---|---|---|
|**Performance**|||||
|Container dwell time (days)|5–28|4–8|**11–30**|<7|
|Truck-processing time (hours)|4–24|2–12|**6–24**|1|
|Crane productivity (containers per hour)|8–20|8–22|**7–20**|20–30|
|Crane productivity (tonnes per hour)|8–25|10–25|**7–15**|>30|
|**Charges**|||||
|Container handling (US$ per TEU)|135–275|110–243|**100–320**|80–150|
|General cargo handling (US$ per tonne)|6–15|11–15|**8–15**|7–9|

_Source:_ AICD Ports database. _Note:_ TEU = twenty-foot equivalent unit.

Individual port performance varies significantly across ECCAS countries (table 2.6). But even the best performers do not measure up to good global performance standards. Container dwell times are particularly high, around 25 days in Matadi in the Democratic Republic of Congo. Container dwell time is between 10 and 25 days, significantly worse than the global benchmark of 4 to 8 days. Truck-processing times are also relatively high in ECCAS’s ports. In Matadi it takes as many as 18 hours for truck processing. Douala
by which the vehicles travel on any route are used for the analysis (see 2.1. for details and maps about this data).

Third, we use the distribution of employment in each city. We base it on Barzin et al. (2022), which estimates the relative density of employment across the metropolitan area using a machine learning implementation and data on night lights and sites from open street map to create an estimation of concentration of employment opportunities across a metropolitan area

> 38 Out of all the jobs in the region.

42
|Country/Region|Authors|**Table A1**<br>Data|**:Experience of 2**<br>Time Coverage|**009 Stimulus Around the World**<br>Stimulus|<br>Findings|
|---|---|---|---|---|---|
|Australia|Li & Spencer (2016)|Output growth rate,<br>inflation, domestic interest<br>rate, growth rate of<br>government transfer<br>payments (quarterly)|March 1993-<br>December 2013|More active spending measures- transfers<br>to households and expenditures on public<br>works|Stimulus transfers were almost equally important as the<br>concurrent monetary easing actions in helping the economy to<br>avoid recession|
|Australia|Leight (2012)|<br>Household survey data|December 2008 -<br>May 2009|US$ 21 billion household payments<br>between December 2008 and May 2009|40% of households who received a payment spent it indicating<br>individuals are more likely to spend “bonuses” than “rebates”<br>as in the US|
|Australia|Buddelmeyer&Peyton<br>(2014)|Expenditure data for 62<br>local government area and<br>electronic gaming<br>machine data|July 2004 - June<br>2012|Lump-sum cheques sent to households as<br>compensation for the introduction of<br>Carbon Tax|Increased spending in electronic gaming machine in<br>December 2008 amounted to 1% of total stimulus for that<br>period.<br>Individuals are more likely to spend “bonuses”|
|China|Xiang_et al._(2019)|Sample of 143 banks<br>(including the big five,<br>joint stock, city, rural
data on occupational skills profiles is likely to proxy the other three Southeast Asian countries in the study more accurately than occupational skills profiles from the U.S. Second, the analysis identifies skills requirements from unstructured data – job postings – rather than from a predefined list of skills or tasks (as done in a firm survey, for example), allowing the data to “speak for itself” when creating skills categories.

The analysis has three conclusions. First, virtually the entire employed population in the four-country sample are in low- and medium-digital occupations. Around two-thirds of the employed population is in low-digital occupations, which means that their tasks require limited use of even the most basic digital technologies. Less than 3 percent work in highly digital occupations, which require frequent use of digital technologies.

Second, highly digital occupations require similar levels and kinds of cognitive and socioemotional skills as do low- and medium- digital occupations. Three exercises confirm this conclusion. Using data from Malaysia and regression analysis, we observe that low-, medium-, and highly digital occupations have different shares of digital skills but roughly the same shares of cognitive and socioemotional skills. The few exceptions are that medium- and high-digital occupations more frequently require thinking skills (one of the three cognitive skills subsets that we explore) and low-digital occupations more frequently require relationship skills (one of the three socioemotional skills subsets in our sample). Furthermore, pairwise correlations and an exploratory factor analysis confirm complementarities between levels of digital skills and subsets of cognitive and socioemotional skills. Similar analysis using occupational data from Cambodia, Thailand, and Vietnam show similar results.

Third, all occupations are digital to some degree. In Malaysia, the share of skills that are digital in very low-, low-, and medium-digital occupations are 5, 9, and 15 percent. At least 1 percent of skills in every occupation in Malaysia is digital, ranging from 1 percent for cooks to 58 percent for software and applications developers and analysts. Results emerging from Cambodia, Thailand