rafmacalaba/data-use-mentions-extended huggingface dataset:
*

A similar procedure is adopted to build the model for village level poverty using variables derived from public remote sensing data as predictors which are grouped into: (i) population density and land size, (ii) night time lights, (iii) road connectivity and distance to cities, borders and rivers, (iv) climate and environment, and (v) agricultural suitability variables. Among these categories of variables, night time lights, market access (i.e. road connectivity), and local climate and environment report the highest correlations with village poverty with similar in-sample goodness-of-fit levels. The final model has 15 explanatory variables.

We estimate the model by maximum likelihood. The distance decay parameter _τ_ is set to _τ_ = 0 _._ 54, which maximizes the in-sample goodness-of-fit (measured by the correlation between predicted and observed poverty rates). It turns out that, except for _ρ_ , the parameters (as well as their conditional covariance matrix) are not very sensitive to the choice of _τ_ . The estimation results are reported in Table 4, where poverty is measured as a percentage. Details on working out the asympototic variance-covariance matrix of the parameter estimators are given in Appendix 1.

While also here the regression coefficients capture correlations and not causal effects, it will nevertheless be instructive to inspect the signs of the correlations. Lower poverty rates are associated with higher levels of population density, higher values of night time lights and higher shares of cultivated land that can be irrigated through rainfall. In contrast, higher poverty rates are associated with increased remoteness (i.e. larger distances from major cities), higher levels of ruggedness, and higher rates of forest loss.

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between importables and exportables is important, because of the changes terms of trade that occurred at the same time as the devaluation, and also because of differences in domestic taxes on importables and exportables.

As we discuss below, on the price side, the evidence shows that there was relatively little difference in the increase in the prices of tradable and nontradable consumer goods. Thus, one would not expect that the impact of the devaluation would vary significantly among socioeconomic groups. The relatively similar composition of their consumption baskets would also tend to reduce differences in the impact of the devaluation. The impact of the devaluation on real incomes earned from production of exportables and nontradable goods (or wages in these sectors) is likely to be a far more important determinant of how different households fared. This section looks at some of the theory and evidence for what we would expect to have happened on the consumer price and income side, and then compares the expected outcomes with the household survey results based on the 1993 and 1995 surveys.

# Devaluation, inflation, **and the** implications **for poverty**

CMte d'Ivoire was among the more successful of the CFA franc zone countries in containing inflation following the devaluation of the CFA franc by 100 percent in local currency terms. In 1994, inflation (based on the blue collar consumer price index) at the end of the first year following the devaluation was 32 percent. At end 1995, prices had risen a total of 44 percent since the devaluation. The relatively low rate of overall inflation was due to imported prices rising less than would be expected assuming a simple doubling of prices in local currency, to the small rise in wages, and also to the subsidies on rice consumption which accounts for 5 percent of the consumption basket.

As Leenhardt and Massuyeau (1995) point out, there was not, however, a large difference in the behavior of prices of consumer tradable and nontradable goods. 1<sup>3</sup> They found a difference
power, biomass, hydropower, and biofuels), electric motors and low emission transportation, energy efficient lighting and appliances, and green buildings. The energy sector, the dominant source of today’s emissions, is also central in the adoption of cleaner technology. Given the well-established fact that the private sector acting alone will tend to under invest in research and development (R&D), governments who are moving early towards abatement, such as Poland’s, need to consider whether active support to clean technology R&D is an</mark> important complementary policy measure (World Bank, 2011a).

Source: Jakubowski, et al. (2010); ECA Social Protection Databse; World Bank (2011a)

# **a.** Labor Market Flexibility

Labor market flexibility refers to the ease with which firms hire, maintain and fire workers as determined by regulation (employment protection legislation, minimum wage, and the maximum length of temporary contracts), interventions (the level of the tax wedge indicating the cost of hiring workers) and institutions (the bargaining power of workers, measured by union density).

Labor market flexibility would allow workers to move to other (greener) sectors more easily. McMillan and Rodrik (2011) find that countries with more flexible labor markets experience greater growthenhancing structural change. This also stands to reason, as rapid structural change is facilitated when labor can flow easily across firms and sectors. The rate at which structural change in the direction of modern activities and greener growth takes place can be facilitated by ease of entry and exit into industry, the flexibility of labor markets, a population with more general and non-cognitive skills, labor market policies that improve employability, social protection that provide income support for workers during transitions, and economic policies that support new industries while limiting distortions. Moreover, McNeill and Williams (2007) also show that combining labor market reforms – in the form of lower labor taxation – with climate change policies could actually lead to an increase in employment
, and does not include bonuses, allowances, and in‐kind payments, which can be significant in the public sector. The data set has information on the presence of some work benefits, such as health insurance and social security, but these are not monetized and cannot be added to wages to provide an estimate of total compensation.

As discussed above, we follow the bulk of the literature in taking a “workers approach” to estimating wage premia, which differs from the many benchmarking studies that use the “jobs approach” (Bales and Rama, 2001; Gittleman and Pierce, 2011). The jobs approach is potentially problematic, as it assumes that the relevant private sector comparators are workers in large, formal sector firms. Using nationally representative household surveys rather than administrative data on jobs offers more flexibility, by allowing results to be compared across a variety of control groups. The standard approach includes all private sector employees in the control group, irrespective of the jobs where they are employed. Because the data in this case also include information on formality and occupation, the standard workers approach can be supplemented by limiting the control group to similar formal 5 The two main empirical approaches in the literature are the Mincerian wage regression with a dummy variable indicating whether the worker is employed in the public sector or private sector; and the Oaxaca‐Blinder decomposition which does not assume that the returns to education, gender, age and other observable worker characteristics are the same in the public and private sectors. The latter method decomposes the wage differential into a part that can be explained as resulting from worker endowments, and an unexplained part presumably due to economic rents that the public sector enjoys. The two approaches in general give similar results (Gittleman and Pierce, 2011), so we use the dummy variable method as it is simpler to present. To allow the public sector earnings differential to vary between individuals we estimate Mincer‐style wage gaps by gender, age, occupation, skill level and other characteristics.

> 6 Standard
ways. It can produce its goods at home and directly export them to the other market. Alternatively, it can indirectly export its goods to the other market by assembling them in the low cost country, _China_ . Since _China_ is located in the vicinity of _East,_ the model provides an explanation for the negative correlation between export and import distance for _China_ ’s processing trade: the inputs that _China_ imports from nearby _East_ are processed into final goods and exported to the far-away _West_ ; conversely, the inputs that _China_ imports from the far-away _West_ are processed into final goods and exported to the nearby _East_ . Furthermore, the model allows us to develop a number of testable hypotheses relating trade costs to China’s processing trade patterns. First, China’s processing exports should be negatively affected by both an increase in import distance and an increase in export distance. Second, China’s processing exports to _East_ should be more sensitive to export distance and less sensitive to import distance than its processing exports to _West_ . The intuition underlying the model is the following. For _Eastern_ firms, the key distance factor that determines _China_ ’s attractiveness as a processing location is its vicinity to Eastern input suppliers, i.e. import distance. The larger is import distance, the less attractive _China_ becomes as a location for processing activities and therefore the less processed goods _China_ exports. Conversely, for _Western_ firms, the critical determinant of _China_ ’s attractiveness as a processing location is its proximity to the East Asian market, i.e. export distance. The larger is export distance, the less attractive _China_ becomes as a location for processing activities. Using China’s bilateral processing trade data, we find support for the theoretical predictions of the model. Specifically, our empirical analysis provides evidence that China’s processing exports are negatively affected by both import and export distance. Furthermore, it shows that processing exports to East Asian countries are more sensitive to export distance and less sensitive to import distance than processing exports to
> RR αα treatment measurement error. Similarly, 5 captures heterogenous information impacts based on a farmer's prior misperception (measurement error), holding constant plot size. Finally, 7 reflects αα the interactive effect of plot size and pre-treatment measurement error. Before we discuss αα estimation results, we note that farmers’ response and adjustment to new information may not represent “learning” if farmers just change reporting behavior without updating their beliefs. This seems unlikely; thus, we interpret these changes as learning.

Table 7 reports results using continuous and bidirectional indicator of measurement error in self-reported plot size, both in the pre-treatment and post-treatment surveys. The dependent variable in Table 7 is given as log-transformed differences between self-reported and GPS values in the post-treatment round. Table 8 reports censored estimation results. The dependent variable in the first three columns in Table 8 is the post-treatment overestimation percentage, while the dependent variable in the last three columns is the underestimation percentage. Our preferred

> 17 Because the post planting survey was only administered for treatment group plots, we use the (no treatment) postharvest measurement error for the control group plots.

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significant intake shortfalls that cannot be met and result in high levels of acute malnutrition, and at least 20 percent of households face a complete lack of food that results in starvation.

In the announcement for Yemen, two governorates were identified as IPC 2; 10 governorates were identified as IPC 3; three governorates were identified as being IPC 3, but would be classified as IPC 4 if it were not for humanitarian assistance (we refer to these regions as IPC 3+); and seven governorates were identified as IPC 4.<sup>4</sup> Throughout, we analyze how the distribution of food assistance changed following the 2017 IPC announcement, and how the distribution of assistance varied based on each governorate’s IPC classification.

The 2017 IPC announcement listed 16 separate quantitative and qualitative data sources that were available to be used to collect all the different types of indicators used in the classification, including the high-frequency mobile phone survey used in this analysis.<sup>5</sup> However, the data source listed most prominently in the announcement was the Emergency Food Security and Nutrition Assessment (EFSNA), which was conducted between November and December 2016. Furthermore, it is important to note that accessibility issues in Yemen since the beginning of the conflict in 2015 have made it difficult to collect data in the country that is necessary for the IPC classification. For example, the EFSNA avoided the two most conflict-affected governorates, was potentially plagued by a host of logistical difficulties given the security situation in the regions they were able to access, and was further made difficult by the structural break that has occurred since the last census. Thus, it is difficult to know how representative the survey is of the entire population (FAO 2017).

One example of how this difficulty in collecting data affected the 2017 IPC classification was the use of internal displacement, which was one of the statistics mentioned prominently by the official announcement. However, it is very difficult to accurately identify the size of the displaced population in the
Geographic overlays of salinity maps in Figure 3 with the mangrove species displayed in Figure 1 reveal a broad spatial correlation pattern in Shyamnagar, Koyra and Dacope: apparent domination for _Ceriops decandra_ (Goran) in the salinity range 23-29 ppt, _Excoecaria agallocha_ (Gewa) mixes with _Ceriops decandra_ (Goran) and _Heritiera fomes_ (Sundri) in 17-21, and _Heritiera fomes_ (Sundri) in 11-17.

Although these observations provide a useful overview, our high-resolution information permits more precise estimation of salinity tolerance ranges. Our mangrove database identifies 14 mangrove types (species and mixed species) in 8.44 million pixels for the region. We assign each mangrove pixel to the nearest salinity pixel, calculate total pixels for each integer salinity value and mangrove type, and use the totals to compute incidence probabilities for mangrove types across salinity values. We drop marginal occurrences beyond the 90th percentile to ensure robust projections Table 2 corroborates the previous observations about mangrove spatial distributions, while providing much more detail about the relative incidence of mangrove species across salinity intervals.<sup>8</sup> Among individual species, clustering is pronounced for _Avicennia alba, marina, officinalis_ (Baen) (in the salinity range 12-14 ppt), _Excoecaria agallocha_ (Gewa) (14-16 _), Ceriops decandra_ (Goran) (27-28) _and Heritiera fomes_ (Sundri) (14-17), while _Sonneratia apetala_ (Keora) is more evenly distributed across a broad salinity range. Salinity ranges for mixed groups reflect the ranges of their constituent species (e.g., _Excoecaria agallocha_ (Gewa) - _Ceriops decandra_

> 8 In Table 2, we use bold print to identify key focal species for our analysis.  The column for each species records the percent of its range that is observed at each salinity measure.

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# **6. Conclusions**

One-stop shops and making it easy for individuals to formally register firms have been mainstays of the policy recommendations pushed by Hernando de Soto and the Doing Business reports, and have been widely implemented. However, recent evidence has called into question the view that most informal firms would like to formalize, noting that there are often few benefits to them of doing so. This is particularly likely to be the case once one goes outside the main centers to more isolated municipalities.

We use government administrative data to evaluate the impact of a reform – the opening of Minas Fácil Expresso offices to ease registration in more remote municipalities in the state of Minas Gerais in Brazil. We find that opening these offices actually led to a small reduction in the number of firms registering in the month of implementation and following month, and zero increase or perhaps small decreases in registration rates over subsequent months. There was also no significant change in tax revenue. This reform therefore does not appear to have succeeded in its goal of increasing formality in these municipalities, suggesting the need for policymakers to consider further what the other constraints to firms formalizing are – and in particular, whether it is worth it on a cost-benefit basis to try and draw more of them into the formal sector.

More generally, this paper illustrates how the rich administrative data collected by a state in Brazil allow for rigorous evaluation of municipal level business reforms. Typically when reforms are not randomly introduced, one is concerned about the plausibility of non-experimental approaches to evaluate these reforms. However, in cases like this one, where a large time series of data are available pre-reform, one can test and have more confidence in the assumptions required for difference-in-differences to hold than is the case in evaluations with only one or two periods of pre-treatment data. Given that data of this sort should be collected as part of normal government functioning in most countries, the evaluation was very cheap (requiring only researcher and government time costs), and so this sort of analysis of
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Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1994 and source IlIl, Code 1: _Employment_ office _statistics_ and only include persons who are actually registered at the employment office at the end of each month.

Central, Non-central Govemment employment as well as Education and Health data are taken from the Republic of Mauritius' Central Statistical Office Digest of Labor Statistics, 1995 and relate to 1994.

GDP at market prices is from IMF Background report No. 96/1 of January 1996 and refers to 1994. Central Government Average salary is also taken from IMF background report and is for 1994. Data on that report was for monthly wages and therefore had to be multiplied by 12 to obtain yearly wages. Data on paid employment in nonagricutural activities is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1988.

# Mozambique

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1988. It reflects data gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in tum may result in an underestimation of employment.

Namibla Unemployment and employment in non agricultural activities data are taken from ILO sources.

Central Govemment employment data is taken from Public Expenditure Review of July 12, 1995. Education employment data are also taken from Public Expenditure Review of July 12, 1995 (Table 10.4) and relate to 1994. They includes Primary School Teachers, Secondary School teachers and Literacy promoters. Health employment estimate also are taken from the Public Expenditure Review of July 12, 1995 (Table 10.2) and relate to 1994. They include Doctors, Nurses, Community Health workers and Traditional Birth Attendants.

Niger Paid employment in non-agricultural activities is taken from the ILO's Yearbook of Labor Statistics 1995 and refers to 1991. The data reflect the working population.

Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1991
Uganda would bring itself 2200 km closer to its main trading partners—two-thirds the distance from Kampala to Cairo. If the Central African Republic reduced its factory-toship time from 116 days to 27, exports would nearly double. The same effect could be achieved if the Central African Republic cut 6200 km from its distance to the main markets—greater than the distance from Bangui to London.

The paper proceeds as follows. The next section discusses the data. Section III presents the estimation strategy. Section IV presents the results. Section V evaluates time sensitive products, and Section VI concludes.

# **II. Data**

Our data are based on answers to a detailed World Bank questionnaire completed by trade facilitators at freight-forwarding companies in 146 countries in 2005.<sup>5</sup> Freightforwarders are the most knowledgeable to provide information on the procedural requirements to trade since most businesses use their services to move their products in and out of the country. Globally, 43,000 freight-forwarding companies employ 11 million people and handle approximately 85% of foreign trade. Their services range from arranging the most appropriate route for a shipment, preparing documentation to meet customs and insurance requirements, arranging payments of fees and duties, and advising on legislative changes and political developments that could affect the movement of freight.<sup>6</sup>

> 5 The World Bank’s Doing Business dataset has 155 economies. However, Afghanistan, Angola, the Kyrgyz Republic, Lesotho, Marshall Islands, Micronesia, Niger, Solomon Islands, and Uzbekistan did not provide data on the time delays in exporting.

> 6 This information comes from the International Federation of Freight Forwarders Associations’ (FIATA) website.

5
the daily number of hires (or separations), S is dummy for the period after the onset of the pandemic (26 February 2020), R is a dummy switched on after the implementation of layoff restrictions (18 March 2020), c a linear daily time trend, and W is a vector of day-of-week fixed effects (i.e. d=1,...,7); u is an error term. Model (1) is estimated with negative binomial regressions and for two different sample sizes: (a) one for 2020 only, and (b) one covering the total period (2018-2020) for which ERGANI daily data are available. In the latter case, models additionally control for month and year fixed effects. Under the assumption that pre-pandemic labor market flows would have prevailed in the absence of the pandemic and the government responses to it, this method offer an approach for identifying COVID-19-related impacts on daily labor market activity.

However, changing trends before and after the pandemic onset and the related government interventions could be driven by unobserved factors. To account for such unobservables, we follow Powdthavee et al. (2019) and Metcalfe et al. (2011) in constructing a “control group” of observations based on trends from earlier years unaffected by COVID-19. More specifically, we compare the size of weekly labor market flows during weeks after Greece was exposed to the virus (“treated” group) with weekly flows for previous years (“control”) group.<sup>13</sup> The exposure period for hires

> 12 Due to data limitations in the ERGANI monthly reports, we extracted information on monthly labor market flows (January-April 2019 and January-April 2020) disaggregated by occupation (2-digit) and sector of economic activity (2-digit) from the National Institute of Labor and Human Resources (NILHR) website.

> 13 Averaging over weeks for years before 2020 also smooths out any seasonal effects.

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|**Table 1: Objective and subjectiv**<br>|**e indicators**<br>|**on land service**<br>|**delivery in Afri**<br>|**can regions**<br>|||
|---|---|---|---|---|---|---|
|**Variable**|**Central**<br>**Africa**|**East**<br>**Africa**|**North**<br>**Africa**|**South**<br>**Africa**|**West**<br>**Africa**|**Total**|
|**Panel A: Land registry inform**|**ation**||||||
|Registration cost (%)|9.3|5.3|4.7|6.9|7.2|7.02|
|# of ctries w. digital registry|1|1|0|1|0|3|
|# of ctries dig. Cadaster|1|1|1|0|1|4|
|Reg&Cad. digital & linked|1|1|0|0|0|2|
|Main city in register|1|0|0|4|1|6|
|Main city in cadaster|1|0|1|3|1|6|
|Court decision in < 1 year|1|1|0|1|1|4|
|No. of countries|12|7|6|12|16|53|
|**Panel B: Survey information**|||||||
|Can get land information|