rafmacalaba/datause-encoder-data huggingface dataset:
# _<u>Selection of mangrove species</u>_

High-resolution maps of the distribution of mangrove species from the Department of Forests, Government of Bangladesh have identified 20 mangrove species in coastal Bangladesh.<sup>13</sup> However, tidal characteristics and salinity tolerance ranges of these mangrove species are widely different. Since survival of mangroves is critical for coastal protection, selection of mangrove species took into account the tidal characteristics as well as salinity tolerance of various mangrove species (Dasgupta, Sobhan and Wheeler 2017) and water salinity<sup>14</sup> in the study sites. Upon consultation with ecologists, seven mangrove species: _Avicennia officinalis, Bruguiera gymnorhiza, Ceriops decandra, Exoecaria agallocha, Heritiera fomes, Sonneratia apetala and Xylocarpus granatum_ - which are likely to survive in the current water salinity - were short-listed. It was noted that _Avicennia officinalis_ and _Sonneratia apetala_ are likely to colonize only mudflats in newly-emergent areas where sedimentation is still ongoing, and _Ceriops decandra, Exoecaria agallocha, Heritiera fomes_ are likely choices for the intertidal surfaces higher than newlyemergent areas. Subsequently, field visits were made to identify mangrove species common in the study areas. Finally, local inhabitants evaluated their familiarity with the various mangrove species, the direct use value of the mangrove species and their interest in preserving the species on a 1 to 5 scale. The final choice of location-specific mangrove species is provided below: Study area 1: _Sonneratia apetala, Avicennia officinalis_ Study area 2: _Sonneratia apetala, Avicennia officinalis_ Study area 3: _Sonneratia apetala, Avicennia officinalis, Heritiera fomes, Exoecaria agallocha, Ceriops decandra_

> 13 These include: _Acanthus ilicifolius, Acrostichum auereum, Aegialitis rotundifolia, Aegiceras corniculatum, Avicennia alba, Avicennia marina, Avicennia officinalis, Bruguiera gymnorhiza,  Bruguiera sexangula,, Ceriops decandra, Ceriops tagal, Exoecaria agallocha, Exoecaria indica, Heritiera fomes, Kandelia candel, Rhizophora apiculate, Rhizophora mucronata, Sonneratia apetala, Sonneratia caseolari and Xylocarpus granatum_ .

> 14 See the River Salinity Information System at

<u>http://sdwebx.worldbank.org/climateportal/index.cfm?page=websalinity_dynamics&ThisRegion=Asia& ThisCcode=BGD Accessed April 2017</u> .

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# **1 Introduction**

Welfare measures such as poverty and inequality are central to the study of economic development. The causes and consequences of inequality have always been a key concern for governments and scholars, but interest in the evolution of inequality has recently spiked. This paper focuses on how to optimally measure inequality.

Household surveys have traditionally been the main data source to estimate welfare measures like poverty and inequality. However, they are subject to measurement error, underreporting, and non-response, among other problems (Székely and Hilgert 1999, Cowell and Victoria-Feser 2003, Atkinson 2007, Burkhauser et al. 2009, Burkhauser et al. 2012). These issues represent a particularly severe problem when measuring inequality: they mostly affect the top of the income distribution, and this can lead to an incorrect estimation of the levels and trends in the Gini coefficient. If non-response rates were constant over time, the measurement of changes in inequality over time would not be greatly affected by these problems (Gasparini et al. 2000). However, it is likely that these rates do fluctuate over time.<sup>1</sup> The income underreporting level at the top of the distribution is not constant and – homogeneous it can vary by economic strata, by source of income, and over time. Yet there is no mechanism available to detect and model precisely the level of underreporting in household surveys. The most notable pattern is that richer households are more hesitant to disclose their income and assets, which would explain why underreporting tends to affect the top of the income distribution, ultimately resulting in an underestimation of the Gini coefficient (Székely and Hilgert 1999). Time variation in underreporting at the top of the income distribution will also affect the estimated time series of the coefficients (see Burkhauser et al. 2004, 2009, Piketty and Saez 2006b, and Atkinson 2007). Finally, household surveys may fail to represent all types of income sources (for example capital gains), which may result in further bias (Burkhauser et al. 2012).

An alternative data source used to study top incomes in a population is income tax

> 1Statistical institutes have partly succeeded in addressing the non-response issue in household surveys through the use of multivariate regressions with demographic and socioeconomic characteristics.

1
MINIMUM WAGES IN CAMBODIA 14 Table 1. Impact of minimum wage on participation rates

||LFP|Wage|Garment<br>wage|Self-emp|Agric<br>self-emp|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||_All worke_|_rs_<br>|||
|1 quarter before|0.006|-0.005|0.011**|0.001|0.020*|
||(0.005)|(0.011)|(0.006)|(0.010)|(0.011)|
|0-1 quarters after|0.000|-0.024***|-0.009**|0.030***|0.025***|
||(0.004)|(0.008)|(0.004)|(0.008)|(0.008)|
|2+ quarters after|0.001|0.001|0.002|-0.004|0.003|
||(0.002)|(0.004)|(0.002)|(0.003)|(0.003)|
|Average|0.822|0.373|0.073|0.502|0.310|
|||_Female wor_<br>|_kers_<br>|||
|1 quarter before|0.007|0.005|0.020**|-0.005|0.018|
||(0.008)|(0.012)|(0.009)|(0.011)|(0.012)|
|0-1 quarters after|-0.000|-0.017**|-0.014**|0.022***|0.022**|
||(0.006)|(0.008)|(0.006)|(0.008)|(0.010)|
|2+ quarters after|0.001|0.004|0.003|-0.006|0.004|
||(0.003)|(0.004)|(0.003)|(0.004)|(0.004)|
|Average|0.775|0.318|0.117|0.548|0.324|

_Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The table presents the estimate of _α_ 0 _, α_ 1 _,_ and _α_ 2 from Equation (3). The sample is restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. The sample is further restricted to women in the bottom panel. Each column in each panel is a separate regression estimate which controls for location (province _×_ rural/urban) fixed effects and allows linear time trend for each location, as well as individual characteristics (age, gender, education, marital status, household size, numbers of females, adult, and children in the household, community wealth index) and their interactions with gender, in addition to fixed effects for survey month and year. Each panel is a separate regression estimate. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.

This suggests that garment firms do some employment adjustments just around the time of minimum wage hike, hiring more workers in the months preceding the change, presumably to finish the orders committed to their clients, and take an employment cut in the first few months of the increased minimum wage regime. Within the third quarter, however, they are able to maintain the same level of employment as before.

The spike in garment sector employment is offset by opposing changes in self-employment, particularly in the agricultural sector. In the two quarters of the hike, self-employment goes up by about 3 percentage points (6 percent) for all workers, and 2.2 percentage points (4 percent) for female workers (column (4), Table 1). Self-employment also falls back to its pre-hike levels after the third quarter.
The World Bank’s ROad Costs Knowledge System (ROCKS) provides the starting point for our analysis.<sup>16</sup> ROCKS is a standardized database of costs associated with various types of road works. It classifies individual projects by country and type of work, and allows the user to obtain cost per km information in a common (real) currency. Most database entries also include extensive additional information as to the tasks performed, as well as geographical conditions that can be expected to affect costs.

Since we do not have information on the exact work that would need to be performed on each road arc in order to bring it up to the level of quality assumed in our counterfactuals, we simply assume that all arcs in countries undergoing an upgrade would require “development” or “reconstruction” work in terms of the ROCKS classification. This includes partial and full widening and/or reconstruction work, along with improvements to the road surface. The types of work that we are considering lie towards the high end of the full range of unit costs in ROCKS (excluding those relating to entirely new construction projects).

Table 13 provides US$ per km cost data from ROCKS based on the types of work we have identified. We only take account of actual, incurred costs (not estimates), and focus on those from Eastern Europe and the Former USSR; the Western Europe and World cost columns are provided for reference only.<sup>17</sup> Based on this data, the range of expected unit costs for the countries under consideration here runs from around $36,000 per km to $666,000 per km, with an average of approximately $269,000 per km. We use these baselines to provide low, average and high cost estimates for the road upgrades implied by our Simulations I and II. We take the length of road to be upgraded in each country as the total length of arcs passing through that

> 16 ROCKS can be downloaded from http://www.worldbank.org/transport/roads/rd_tools/rocks_main.htm.

> 17 We have also eliminated two outlying observations, with unit costs around double the next highest data point.

- 23 -
**1. Introduction**

Economists have long recognized the efficiency gains from technological change. However, there is also concern, in both developing and developed countries alike, that routine, manual tasks are increasingly being replaced by technology, displacing lower-skilled workers (Autor, Levy, and Murnane 2003). Policy makers must weigh the broad economic benefits from technology adoption against the potential costs to the domestic economy. The gains from a more productive and flexible economy in the long run may be accompanied by short-term costs for workers, in terms of unemployment, income volatility, and poverty risk. Labor market institutions, including employment protection regulations, exist to protect workers from shocks, but they also have the unintended consequence of increasing the cost of labor for businesses. We analyze the impact that digital technology adoption has on local employment opportunities and on the employment composition of different skill groups. We pay special attention to the inherent policy trade-off—between job security for workers, on the one hand, and economy-wide productivity and growth, on the other hand—arguably one of the most prominent public policy debates around the globe.

Our paper exploits administrative data for Brazil, a country that underwent a substantive, subnational internet rollout, as well as significant increases in the enforcement of labor market regulations between 1996 and 2006. We first assess the extent to which the adoption of digital technology affects the overall demand for workers and different types of skills. Our empirical strategy exploits the fact that industries vary in their degree of reliance on digital technologies. In particular, industries that intensively use technology located in cities with early provision of internet services are most likely to adopt digital technologies. In addition, given the fact that _de facto_ labor regulations are heterogeneous within the country<sup>1</sup> , we also explore the role of social protections in influencing the impact of digital technology on labor demand and on the demand for different skills.

> 1 The _de jure_ labor regulations in Brazil, established in the 1988 Federal Constitution, are effective throughout the country. However, as the Ministry of Labor is designated with enforcing compliance with regulations, there is significant heterogeneity both within the country and over time in terms of how binding is the labor law.

2
years of potential experience. The size of the establishment, conditional on working does not exhibit strong significant patterns in the national model (table A5). Working in agriculture (table A6) also does not exhibit strong correlations with either youth employment or GDP growth. However, it is negatively correlated with initially high oil and wheat prices for the first five years of potential experience and positively correlated thereafter.

The results on log-monthly wages (table 9), which are only defined for wage workers are also broadly consistent with the overall pattern found in the regional level model, in which good initial economic conditions are associated with positive outcomes early in the career, and negative outcomes down the road. This is reflected in the negative and significant coefficients attached to the interaction of potential experience levels above 5 and GDP growth and youth employment. Correspondingly, the coefficients for log-oil prices and log-wheat prices (which proxy bad labor market conditions) are negative.

Table 6: Results with national economic condition indicators as control variables

|Dependent Variable|Empl|oyment|||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|0.216**|-0.0465|0.0122|0.00199|
|1.pot<br>exp#c.indepvar|0.313***|0.0132|0.0206|0.00487|
|2.pot<br>exp#c.indepvar|0.123|-0.0269|-0.0387|0.0117**|
|3.pot<br>exp#c.indepvar|-0.376|-0.0663***|-0.105**|0.0131**|
|4.pot<br>exp#c.indepvar|-0.508***|-0.0219|-0.0734***|0.00482|
|5.pot<br>exp#c.indepvar|-0.479*|-0.0113|-0.0490**|0.00201|
|6.pot<br>exp#c.indepvar|-0.0636|0.0227|0.00276|0.000374|
|7.pot<br>exp#c.indepvar|-0.342|-0.00259|-0.0216|-0.00734|
|8.pot<br>exp#c.indepvar|0.0871|0.0219*|0.0119|-0.00887*|
|9.pot<br>exp#c.indepvar|0.0672|0.0135|-0.0411|-0.0220***|
|10.pot<br>~~e~~xp#c.indepvar|-0.0211|0.0352***|0.0119|-0.000145|
|indepvar|0.00349**|0.0335**|-0.00137|0.00570***|
|cohort|-0.00505***|-0.00943***|-0.00775***|-0.00774***|
|# obs|15,822|15,822|15,822|15,822|
|R-squared|0.499|0.499|0.497|0.497|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: The dummy variable of being employed or not is regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation. (ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included. (iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence). (v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

18
Horton, B., G. Colarullo, I. Bateman, and C. Peres (2003), Evaluating Non-User Willingness to Pay for a Large-Scale Conservation Programme in Amazonia: a UK/Italian Contingent Valuation Study, _Environmental Conservation 30 (2); 139-146._ Kramer, R.A. and D.E. Mercer (1997), Valuing a Global Environmental Good: US residents' Willingness to Pay to Protect Tropical Rain Forests, _Land Economics_ , **73** , 196–210.

Lindhjem, H. and S. Navrud (2011a), Using Internet in Stated Preference Surveys: A review and comparison of survey modes. _International Review of Environmental and Resource Economics_ , 5 (4); 309–351.

Lindhjem, H. and S. Navrud (2011b), Are Internet Surveys an Alternative to Face-to-Face Interviews in Contingent Valuation? _Ecological Economics_ 70 (9); 1628-1637.

Louviere, J., D. Hensher, and J. Swait (2000 _) Stated Choice Methods: Analysis and Applications_ . Cambridge University Press. Cambridge, UK.

May, P., B. Soares-Filho, and J. Strand (2013), How Much is the Amazon Worth? Upublished working paper, Development Research Group, World Bank.

Mitchell, R. C. and R. T. Carson (1989), _Using Surveys to Value Public Goods: The Contingent Valuation Method_ . Baltimore, MD: Johns Hopkins Press.

Navrud, S. (2004): Value transfer and environmental policy. Chapter 5 (pp. 189-217) in Tietenberg, T. and H. Folmer (eds.) 2004: _The International Yearbook of Environmental and Resource Economics 2004/2005. A survey of Current Issues_ . Edward Elgar Publishing, Cheltenham, UK and Northampton, MA, USA Navrud, S and R. Ready (eds.) (2007): _Environmental Value Transfer: Issues and Methods_ . Springer, Dordrect, The Netherlands.

Rolfe, J. and J. Bennett (eds.) (2006), _Choice Modeling and the Transfer of Environmental Values_ (Edward Elgar, 2006).

Sackman, H. (1975), _Delphi Critique: Expert Opinion, Forecasting and Group Process_ , Lexington, MA: D.C. Heath.

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**Figure 22. Services Revenue as Percent of Total Revenue, all Regional Firms**

<!-- Start of picture text --> North Caucasian<br>Far Eastern<br>Siberian<br>Ural<br>Volga<br>South<br>Central<br>Northwest<br>0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0<br><!-- End of picture text --> _Source_ : RUSLANA.

46
capital flows, when reserves increased from $76 billion in 2002-03 to $310 billion in 200708.<sup>15</sup> Reserves then declined to $250 billion in 2008-09, due to capital flow reversals after the collapse of Lehman Brothers. Reserves were rebuilt to some $300 billion during 2009-2011, but only to the level last seen before the collapse of Lehman Brothers, at which level they were then maintained in subsequent years. Basu et al (2015) indicate no attempts to increase the reserve coverage further during this period of heightened vulnerability to external shocks, while providing a narrower room to intervene in the foreign exchange market. A corollary being that exchange rate volatility increased during this period (as highlighted in Figure 9).

**Figure 11: The Stock of External Reserves increased in Tandem with Capital Flows until 2008, but remained Stagnant in 2009-2013**

<!-- Start of picture text --> 350,000<br>300,000 Phase 2: Rapid accumualtion of<br>reserves, keeping with the<br>250,000 surge in capital flows<br>Phase 3: Reserves<br>200,000 declined during<br>the sudden stop<br>150,000 Phase I: Steady accumulation in 2008-09 and 2013,<br>of reserves and were refurbished<br>100,000 2014 onwards<br>50,000<br>0<br>Mn US $<br>1990‐91 1991‐92 1992‐93 1993‐94 1994‐95 1995‐96 1996‐97 1997‐98 1998‐99 1999‐00 2000‐01 2001‐02 2002‐03 2003‐04 2004‐05 2005‐06 2006‐07 2007‐08 2008‐09 2009‐10 2010‐11 2011‐12 2012‐13 2013‐14<br><!-- End of picture text --> Note: Reserve assets comprise foreign exchange assets (currency, deposits, securities and financial derivatives), monetary gold, SDR holdings, reserve position in the IMF and other claims (loans and other financial instruments). Source is RBI’s Handbook of Statistics on the Indian Economy The inclination to use reserves to modulate exchange rate volatility seems to have evolved in India since the early 1990s and is mirrored in the volatility of the exchange rate, Figure 12. While the monthly percent change in reserves were larger in the 1990s, consistent with observed exchange rate stability in the 1990s, the use of reserves declined in the 2000s continuing until 2013. The policy towards building reserves or to use them to modulate excessive exchange rate volatility that prevailed during 2009-2013, seems to have reversed

> 15 The issue of optimal level of reserves for India, whether India was holding excess reserves, and whether they adversely impacted growth was vigorously debated in the mid-2000s, see Joshi and Sanyal (2004).

15
identified in our sample.<sup>18</sup> The specifications also include the same standard gravity model controls described in Section 2.<sup>19</sup> Table 2 presents the results aimed at evaluating whether EAP economies are more (or less) integrated than North and South economies. It reports separately the benchmarking exercise associated with intra-regional, outward, and inward cross-border investments. To simplify the reported table, instead of showing the estimated coefficients themselves, we present only the differences in the estimated coefficients and the associated significance of a two-tailed Wald test for these differences.

The results show that, even after controlling for source and recipient’s GDPs and the other controls at the bilateral level, intra-regional investments are the largest for economies within the EAP region when contrasted with South regions. Regarding inter-regional outward investments, EAP is also ahead of all South regions. However, EAP lags behind South regions as a destination of some types of financial investments from the rest of the world. As a recipient region, EAP is ranked after the LAC region and Europe and Central Asia (ECA) for syndicated loans and M&As, whereas it is comparable to the other South regions regarding the value of its portfolio and greenfield investments. Comparisons with North economies reveal more mixed patterns across all investment types. For example, EAP has a greater degree of intra-regional and inter-regional inward greenfield investments than the North, but it lags behind on inter-regional outward greenfield investments. Regarding portfolio investments, it has relatively smaller intra-regional investments than the North, though comparable inter-regional ones.

> 18 For example, the EAP-EAP dummy is equal to one whenever an observation includes an EAP economy as a sender and an EAP economy as a recipient of financial investments—a connection between China and Thailand is considered an EAP intra-regional connection. An inter-regional EAP dummy takes the value one whenever there is a connection between an EAP economy and an economy outside the EAP region, such as a connection between China and the United States. There are two types of EAP inter-regional dummies because EAP economies may be either senders or recipients of international investments to and from the rest of the world.

> 19 Although we do not include bilateral trade as a control variable in the reported regressions, the results are qualitatively similar when we include the logarithm of exports.

20
## **Table A6. Time trends in the incidence of inorganic fertilizer use**

# **<u>Used any inorganic fertilizer</u>**

||(1)<br>Burkina Faso|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Uganda|
|---|---|---|---|---|
|Survey round|-0.0360***|-0.0151|-0.0394***|0.0477***|
||(0.0114)|(0.0137)|(0.0147)|(0.00783)|
|Constant|0.596***|0.683***|0.851***|0.00152|
||(0.0305)|(0.0336)|(0.0302)|(0.0138)|
|Observations|3,757|2,801|3,472|4,255|
|R-squared|0.006|0.001|0.006|0.017|

Note: Results from OLS regression of the outcome on a variable indicating the n'th data point over time for this variable. The variable is 1 for the first round in which data on the outcome was collected, 2 for the second instance, and so on. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

27