rafmacalaba/datause-encoder-data huggingface dataset:
## **5.2 Economic impact of GLOF**

A few micro-level economic assessments related to GLOF were reported in Nepal. ICIMOD (2011) compares the potential economic impact of GLOFs in three major glacier lakes in Nepal (Imja, Tso-Rolpa and Thulagi) with the 1981 GLOF in Bhotekoshi-Sunkoshi. Considering the potential tangible damage due to modeled and maximum flood heights of past GLOF events (worst case scenario), they found net potential economic damage of 11.9-35.5, 1.98.8, and 407-415 Million USD, for the three lakes, respectively, at present value (ICIMOD, 2011). When proposals for potential hydropower projects at these sites are considered, the damage due to GLOF would be USD 8.98 billion for Imja Tsho, 2.4 billion for Tsho Rolpa, and 2.2 billion for Thulagi unless risks are mitigated (ICIMOD, 2011). Following a similar approach, Shrestha et al. (2010) estimated the total value of properties exposed to GLOF risk to be USD159 million at Limu Chimi Lake, Sunkoshi Basin, and USD197 for high flood scenarios. Thus, the extent of future economic damage due to GLOF will be largely determined by the socioeconomic development status. While such analysis was not available for other regions in the study area, economic damage potential would be high in most of the Himalayan states in view of the current and potential future hydropower development.

# **6. Concluding Remarks**

This study attempts to shed some light on climate change impacts on the Himalayas by presenting a review of the existing literature. Based on the review of several reported hydroclimatic trends and observed temperature and precipitation patterns in the region, our study finds strong evidence of warming over the whole Himalayan region. The rate of warming, however, varies across sub-regions and across seasons. High altitude regions such as Trans-Himalayas in Nepal and Tibetan Plateau in China are warming at a higher rate than lowland areas. Warming rates are generally higher in winter than other seasons. Although patterns in precipitation trends are missing both spatially and temporally, a general decline in monsoon precipitation is noted in the western Indian Himalaya, while precipitation increases in the eastern Indian Himalaya and China. Moreover, we found growing evidence of increasing frequency of climatic extremes (both precipitation and temperature) in the region, especially in the mountainous regions. Like precipitation, there is a general lack of spatial patterns in streamflow trends in the southern Himalayan region, but streamflow trends in China, north of Himalaya, are generally increasing, 25
pricing, estimating production costs along the production process, forecasting of agricultural production, and controlling optimization of processes. This variable markedly differs from other ICT measures used in the literature (e.g., <mark>computer use, internet access, or IT capital/investment) and we argue it likely impacts the firm’s production process in different ways. Complex software adoption is largely managed by highly skilled workers and can lead quite c</mark> omplex routinecognitive but also non-routine analytical tasks. For instance, today production software can already perform the planning of the level of production, which had in the past been carried out by professional workers (such as engineers).<sup>6</sup> <mark>The ELE survey also collects rich information about the firm’s labor force, including the</mark> firm’s total employment across four occupation categories - managers, administrative workers, skilled production workers, and unskilled production workers. Table B1 (Appendix B) defines these categories. In some of the empirical exercises, we will also consider as outcomes indicator variables for whether the firm is engaged in subcontracting activities, as well as for different types of training provided by the firm. Finally, we also use firm size, age, exporter status, foreign ownership, access to credit, as well as the degree of education, the number of years of experience, and the age of the manager as control variables.

<mark>S</mark> econd, we exploit the Chilean PIAAC survey for 2014, which is a survey of adult skills collected by OECD. It measures several cognitive and workplace skills (e.g., literacy, numeracy and problem-solving) across occupations which are used to compute the task content of occupations.<sup>7</sup> Drawing on Acemoglu and Autor (2011) and Autor and Handel (2013), we define the task content of occupations using the following categories: abstract, routine-cognitive, routinemanual, and non-routine-manual tasks. Abstract comprises abstract problem-solving and creative, organizational and managerial tasks while routine-cognitive and routine-manual involve codifiable tasks that follow explicit procedures, and non-routine-manual comprises tasks that require physical adaptability. To define the task content of a specific occupation we identify in the PIAAC survey the set of questions closer to those used by other adult skills surveys (e.g., DOT, PDII, O*NET or STEP surveys used by e.g., Autor et al. (2003), Autor and Handel (2013), Di Carlo et al. (2016), and Messina et al. (2016)). Appendix A discusses the methodology used to construct the task

> 6 Some authors even argue that the increased automation and streamlining of processes that accompanies the use of business and production software might significantly constrain workers in their creativity (Engelstätter and Sarbu, 2013).

> 7 For more details see http://www.oecd.org/skills/piaac/.

9
|_Postgraduate_|2.5|2.9<br>|2.6|2.9|2.9|3.3<br>|3.1|3.2|
|---|---|---|---|---|---|---|---|---|
|Experience|||||||||
|_No experience_|0.4|0.4<br>|0.3|0.3|0.7|0.6<br>|0.6|0.5|
|_+0-3 years_|7.2|7.1<br>|7.0|7.2|8.0|8.1<br>|8.0|8.1|
|_+3-6 years_|10.0|10.0<br>|9.8|10.2|9.8|10.0<br>|10.3|10.4|
|_+7-10 years_|11.7|12.3<br>|12.0|12.3|11.3|12.1<br>|12.3|12.1|
|_+10-15 years_|12.9|13.1<br>|13.5|13.4|12.7|12.9<br>|13.1|13.1|
|_+15 years_|57.8|57.2<br>|57.5|56.6|57.5|56.1<br>|55.6|55.8|
|Part-time job (<40 hours per week)|11.9|12.6<br>|13.3|12.1|34.1|33.2<br>|33.3|30.4|
|<br>Formal|39.9|41.3<br>|36.5|38.7|38.7|41.1<br>|40.1|41.4|
|Labor status|||||||||
|_Employed_|86.0|83.6<br>|80.3|80.4|60.8|58.1<br>|49.7|52.0|
|_Unemployed_|6.2|7.6<br>|9.6|9.7|8.5|9.7<br>|12.2|12.1|
|_Inactive_|7.8|8.8<br>|10.1|9.9|30.7|32.2<br>|38.1|35.9|
|Labor relation|||||||||
|_Private employee_|48.6|50.2<br>|47.7|47.8|51.5|52.6<br>|49.6|51.1|
|_Government employee_|3.8|3.6<br>|3.3|3.3|5.0|4.4<br>|4.8|4.8|
|_Self-employed_|47.7|46.2<br>|49.0|48.9|43.5|42.9<br>|45.6|44.1|
|Sector|||||||||
|_Agriculture_|19.8|20.1<br>|27.0|20.2|6.3|6.1<br>|9.0|6.1|
|_Electricity, gas, water and mining_|2.6|2.6<br>|2.9|2.9|0.8|0.8<br>|1.0|1.0|
|_Industry_|12.4|11.3<br>|11.0|11.3|12.2|11.4<br>|10.9|10.4|
|_Construction_|12.1|11.6<br>|10.6|11.7|1.1|1.0<br>|1.1|1.1|
|_Trade and vehicle repair_|16.9|17.3<br>|15.6|17.4|20.9|21.0<br>|21.2|22.1|
|_Transportation, storage and communications_|13.1|12.8<br>|11.8|13.2|3.7|3.1<br>|2.9|3.1|
|_Accommodation and food services_|3.8|4.1<br>|3.3|3.8|11.4|12.0<br>|11.9|12.5|
|_Financial and real estate activities_|2.7|2.7<br>|2.2|2.7|3.0|3.0<br>|2.8|3.0|
|_Professional and administrative activities_|4.3|4.7<br>|4.3|4.9|7.9|8.7<br>|8.3|9.6|
|_Public administration_|3.2|3.2<br>|2.8|3.0|3.0|3.1<br>|3.2|3.5|
|_Education and human health_|4.6|4.5<br>|3.8|4.3|14.5|14.9<br>|13.6|13.9|
|_Other activities_|4.4|4.9<br>|4.6|4.7|15.3|14.9<br>|14.0|13.7|
|Observations(thousands)|207,102|201,801|123,872|188,251|243,894|235,040|142,129|219,796|
rates exceed 80 percent of the population.<sup>1</sup> Since the success of the reforms and the outgrower scheme varied from the initial phase to the final phase, we find rich dynamics in cotton markets. During the initial phase, the failure of the outgrower scheme led to a decline in the participation of households in cotton production and a decline in farm productivity of 45 to 53 percent. In contrast, the later phase of success induced farmers to increase the fraction of land devoted to cotton, and caused yields per hectare to increase by 20 to 21 percent with respect to the initial phase.

The paper is organized as follows. In Section 2, we review the main reforms in cotton markets. In section 3, we discuss the theory behind crop choices and farm productivity in Zambia. In section 4, we describe an empirical model of crop choices and farm productivity using the Post Harvest Survey farm data; and derive guidelines for the estimation of the impacts of the cotton marketing reforms. We discuss the results and assess the effects of the reforms in Section 5. In Section 6, we conclude.

# 2 The Zambian Cotton Reforms

Zambia is a landlocked country located in Southern Central Africa. With a population of 10.7 million and a per capita GDP of only 302 US dollars, Zambia is one of the poorest countries in the world. In 1998, for instance, the national poverty rate was 69.6 percent, with rural poverty at 82.1 percent and urban poverty at 53.4 percent. Nationwide, only around 4 percent of the income of rural households comes from the sales of non-food crops. Given the characteristics of the soil, cotton can only be grown in three Zambian provinces (the Eastern, Central, and Southern provinces). Where it is grown, cotton is a major source of income. Using data from the Living Conditions Monitoring Survey of 1998, we find that the share of cotton in income was 8.4 percent in the Central province, 9.5 percent in the Eastern province, and 2.8 percent in the Southern province. This makes cotton an important sector in rural Zambia.

The process of reform began in 1991, when the Movement for Multi-Party Democracy

> 1Poverty is widespread and deep in Zambia. For a comprehensive description of poverty trends, see Balat and Porto (2005).

3
# **Figure 9a: Urban households facing affordability problems by region (current US$)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>LAC AFRICA EAST ASIA INDIA<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data Note: Data from India and Latin America are based on household income, while data on Africa and East Asia are based on household expenditure, as a result the two groups are not directly comparable.

# **Figure 9b: Urban households facing affordability problems by region (PPP)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>LAC AFRICA EAST ASIA INDIA<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data 20
<!-- Start of picture text --> 80<br>@ Electricity @ Fuel @FISP<br>60<br>3a==Es 40<br>ES<br>= ea<br>= 20 m §£ 7 /<br>Z Z =<br>Z Z Z wm Z Z<br>0 ZZ ZZ ZZ ZZ ZeZ aAZ i Z 2e 2E<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market Income + Pensions decile<br>Source: Authors’ estimates based on LCMS 2015.<br><!-- End of picture text -->
The Impact of the Syrian Refugee Crisis on Firm Entry and Performance in Turkey Yusuf Emre Akg ̈und ̈uz<sup>_∗_</sup> Marcel van den Berg<sup>_†_</sup> Wolter Hassink<sup>_‡_</sup>

**Keywords** : refugees, firm entry, firm performance **JEL-classification** : F22, J61, R23

> _∗_ Central Bank of the Republic of Turkey, Istiklal Caddesi 10, 06060, Ankara, Turkey, e-mail: yusufemre.akgunduz@tcmb.gov.tr

> _†_ Statistics Netherlands, CBS-weg 11, 6412 EX, Heerlen, The Netherlands, e-mail: m.r.vandenberg@cbs.nl

> _‡_ Utrecht University, Utrecht University School of Economics, Kriekenpitplein 21-22, 3584 EC, Utrecht, The Netherlands and IZA e-mail: w.h.j.hassink@uu.nl. The content of this publication does not reflect the official opinion of Statistics Netherlands or the Central Bank of the Republic of Turkey. Responsibility for the information and views expressed in the paper lies entirely with the authors.
|||(0.172)||(0.221)||(0.146)|
|---|---|---|---|---|---|---|
|Technicians and associate professionals||0.387**||0.150||0.240|
|||(0.167)||(0.227)||(0.147)|
|Clerical support workers||0.0361||-1.366***||0.423**|
|||(0.205)||(0.475)||(0.186)|
|Service and sales workers||-0.267||-0.0594||0.148|
|||(0.174)||(0.254)||(0.156)|
|Skilled agricultural forestry and fishery workers||-0.388||0||0.331|
|||(0.923)||(.)||(1.240)|
|Craft and related trades workers||0.232||0.933***||0.501***|
|||(0.186)||(0.257)||(0.168)|
|Plant and machine operators and assemblers||0.271||1.073***||0.354**|
|||(0.188)||(0.256)||(0.169)|
|Elementary occupations||-0.268||-0.800**||0.446**|
|||(0.203)||(0.375)||(0.186)|
|**_Establishment size_**|||||||
|11-100||0.446***||0.789***||0.0839|
|||(0.113)||(0.239)||(0.103)|
|100+||0.442***||1.178***||0.0825|
|||(0.112)||(0.239)||(0.103)|
|Constant|-0.0701|-0.944***|-2.188***|-5.362***|0.927***|0.670**|
||(0.156)|(0.280)|(0.215)|(0.645)|(0.139)|(0.266)|
|Observations<br>Source: Authors’ estimation using RLMS 2016.|4768|4678|4735|4645|5286|4671|

# <u>Source: Authors’ estimation using RLMS 2016.</u>

<u>Note: This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their work conditions, controlling for other factors.</u> Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Data source for dependent variable in columns (1)-(6) is RLMS. All dependent variables are defined as binary. All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

47 Informal Employment and Worker’s Wellbeing in Russia / June 2019
economies versus one that covers only developing economies. Our aggregation methodology discussed in Section 3 below addresses these differences.

Finally, we note that some of these data sources may revise their historical data with each new data update. In addition, we have in the past added new data sources with historical data extending to previous years in the WGI. In both cases, we update the historical data in the WGI database and recalculate the historical aggregate WGI measures. For these reasons, each annual update of the WGI supersedes previous vintages of the dataset.<sup>10</sup> Typically, the revisions to the historical data between WGI vintages are very small.

2.3 How the WGI Data Sources Are Selected and Assigned to the Six Aggregate Indicators The WGI data sources are selected based on several criteria. First, the data sources must provide subjective views or perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data. We restrict attention to subjective perceptions data in the WGI for four reasons: (1) perceptions matter, because households and firms make decisions based on their views and perceptions of the quality of governance; (2) for some dimensions of governance such as corruption that do not leave a “paper trail”, data measuring subjective perceptions of corruption or self-reported experiences with corruption can provide valuable insights; (3) data capturing respondents’ perceptions and views can provide essential information on the gap between _de jure_ rules and their _de facto_ implementation; and (4) unlike objective indicators that capture the existence of specific laws, rules and regulations, data capturing survey and expert respondents’ views are not susceptible to “gaming” where policy makers target reforms to narrowly change specific measures simply because they are included in an aggregate indicator that they wish to influence.

Of course, we do not claim that perceptions data are the _only_ relevant type of data for measuring governance. Objective data on the specific laws and regulations “on the books” that shape the regulatory environment also are useful, even though they may differ substantially from their implementation in practice. Similarly, indirect measures based on data patterns such as gaps between spending on infrastructure and infrastructure actually built, or the performance of politically connected firms relative to non-connected firms, or patterns in tax return and procurement data can provide

> 10 Previous vintages of the WGI aggregate indicators are available on the WGI website at www.govindicators.org. In addition, we occasionally need to revise the historical source data if we discover an error we have made in processing the data and assigning it to governance clusters.  This also contributes to revisions of the historical aggregate WGI measures.  All revisions to the historical dataset, as well as the addition or deletion of data sources, is separately described at www.govindicators.org for each annual update of the WGI.

9
total health care expenditures. Powell-Jackson et al. (2014) report a 27 percent decline in total health expenditures for the insured in Ghana, again using a four-week recall period. The insurance scheme in Ghana covers basic care (preventive care and medicines) as well as secondary care procedures, all of which adds to the free care already available in public facilities. King et al. (2009) study _Seguro Popular,_ Mexico’s universal coverage insurance scheme, which covers 266 health interventions, 312 medicines, and a federal fund for catastrophic health expenditures for certain diseases. Using a 10-month recall period, the authors find an 85 percent decline in out-of-pocket expenditures and a 75 percent lower probability of catastrophic expenditures, the latter defined as cases where out-of-pocket expenditures exceeded 30 percent of a subsistence income level.

Defining catastrophic expenditures as a fraction of household income rather than a standard subsistence income level yields similar results. Celhay et al. (2019) report a 15 percent decline in the likelihood of such events in the Philippines when catastrophic events are defined as those where expenditures exceeded 10 percent of income; Fink et al. (2013) find a 30 percent decreased likelihood when the cutoff is defined at 5 percent in Burkina Faso. Yet another metric, adopted by Levine, Polimeni, and Ramage (2016), focuses on absolute thresholds of annual expenditures above US$250 or more than US$100 paid for a single event as well as instances of indebtedness due to health care payment obligations. They find a 20 percent decline in the likelihood of catastrophic out-of-pocket health expenditures. The authors attribute this to the free health services and drugs at made available by the insurance scheme at public facilities, suggesting that the public sector is used as an alternative to the private sector for large expenditures, especially when these involve taking on debt.

# Utilization and Health Outcomes

Access to health insurance also seems to increase utilization for a variety of health services. Evidence of increased utilization has been documented for preventive care (in Colombia, Camacho and Conover 2013; in India, Malani et al. 2021; in Peru, Bernal, Carpio, and Klein 2017), outpatient visits for acute or chronic diseases (in Nigeria, Fitzpatrick and Thornton 2019), and inpatient visits including surgeries (in India in both Sood and Wagner 2018; Malani et al. 2021).<sup>8</sup> Consistent with the idea that health insurance can lead patients to choose higher quality facilities, Thornton et al.’s (2010) study in Nicaragua and Levine, Polimeni, and Ramage’s (2016) study in Cambodia do find that patients substituted away from public and

> 8 A few studies do not find that health insurance increases utilization: for example,  King et al. (2009) in a study of Mexico; Raza et al. (2016) in rural India; and  Bauhoff, Hotchkiss, and Smith (2011) in Georgia.  Of course, this finding would also explain why health insurance in these countries does not improve health outcomes.

10
Third, compliance costs (paperwork, red tape, documenting origin, etc.) can be significant. The average estimate in the empirical literature is that documentary requirements imply costs of some 3-5 percent of the value of goods (Brenton and Manchin, 2003; Brenton and Ikezuki, 2004; Anson et al. 2003; Candau et al. 2004; Carrère and de Melo, 2004). This substantially reduces the actual benefits of trade preferences for developing countries as it requires MFN tariffs to exceed 4 percent on average for preferential access to be meaningful. Given that the average MFN tariff in OECD is only 4 percent or so, preferences can only matter where there are tariff peaks.

Fourth, to the extent there is market power on the part of either importers/distributors (Francois and Wooton, 2005) or the transport and logistics sector (Francois and Wooton, 2001), the terms of trade benefits of preferential tariff reductions will be captured at least in part by those intermediaries with market power rather than the exporters (although any diversification benefits will remain). If preferences apply to highly protected sectors in donor countries, they will result in high rents for those able to export free of trade barriers. However, buyers will know the existence of these rents, and if they have the ability to set prices (have market power), the rents may predominantly be captured by distributors or other intermediaries (Tangermann, 2002). There is evidence, based on the AGOA preference scheme, that the pass through of preference margins is indeed partial at best. Olarreaga and Özden (2005) find that the average export price increase for products benefiting from preferences under AGOA was about 6 percent, whereas the average MFN tariff for these products was some 20 percent. Thus, on average exporters received around one-third of the tariff rent. Moreover, poorer and smaller countries tended to obtain lower shares—with estimates of the share of the loss ranging from a low of 13 percent in Malawi to a high of 53 percent in Mauritius.<sup>6</sup> In the case of market power, the result is a simple redistribution of the benefits of preferences: rents are transferred to importers. In the case of administration costs, however, the result is not redistribution but a deadweight loss (waste).

Finally, for preferences to have value, the beneficiary countries need to have an export capacity in the products for which preferential access is granted. In practice, GSP

> 6 See Ozden and Sharma (2004) for a similar analysis of the US CBI program. Francois and Wooton (2005) obtain similar size-dependent results in an analysis of the incidence of markups along the distribution chain.

6
46 Aln(Y/L) _nb 11nSk+b 2_<sup>_1nS4+b)1n(n+g+8)_</sup> _+b4 1n(Y/L)+bs_ [Aln(ALp)J -be (x. 1n(- p)] +et. (21) **_PC-P- 1_**

# The restrictions implied by the model are given by:

**_ba-(14 )lnzo (ln)_** (e++)Pro. (22).

**_>0._** M (23) b2 _(1-e^_ 1 )O 24) .3<0 b3 2- (1-e «CO (') (25)

**_b-4- {-e 1 )_** c0. (26)

**_b5u_** (il-e t)l >o (27) **_16=_** (le-kc)O( 2<0,

The model implies that the growth rate of per capita real output depends positively on investment rates for physical and human capital, negatively on population growth, positively on changes in the real exchange rate at past prices and negatively on the inflation resulting from nominal devaluations.

# **Estimation and the Results.**

We estimated equation (21) via 2SLS using pooled time-series and cross-section annual data for the period 1971-1991 for 22 SSA countries. We assumed that investment/GDP and human capital are endogenous