rafmacalaba/datause-encoder-data huggingface dataset:
literature has paid less attention to the migration outcomes of slow-onset changes compared to sudden disasters (Kaczan and <mark>Orgill-Meyer</mark> , 2020). Part of the reason is that slow-onset changes are not regarded as sufficiently extreme to trigger migration, since they have less of an immediate impact on people (Koubi et al., 2022). The effect of events such as drought, desertification, and warming on migration is less sudden than floods, landslides, hurricanes and similar, because they tend to emerge gradually, and attribution is intrinsically more difficult for departures in response to gradual changes. Moreover, migratory flows can be staggered, more difficult to capture and more susceptible to measurement error. There is a body of works documenting that slow-onset changes and rising temperatures increase migration (Bohra-Mishra et al., 2017; Cai et al., 2016; Dallmann and Millock, 2017; Dillon, Mueller, and Salau, 2011; Feng, Krueger, and Oppenheimer, 2010; Gray and Mueller, 2012a, 2012b; Hunter, Murray, and Riosmena 2013; Jessoe, Manning, and Taylor 2018; Mastrorillo et al. 2016; Mueller et al., 2014). Many of these studies are discussed in other sections of the paper. However, there are also some notable exceptions in this literature showing that slow-onset changes may also have the opposite effect, resulting in a reduction in migration. For example, Cattaneo and Peri (2016) find that, consistent with the presence of severe liquidity constraints, higher temperatures reduce the probability of migration from poor countries. The results of Cottier and Salehyan (2021) and <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) on the role of droughts in influencing international migration support this conclusion. From a within-country perspective, Hirvonen (2016) shows that temperature anomalies decrease (male) migration in rural Tanzania because of tightened liquidity constraints. Liu et al. (2022) study responses to slow-onset temperature changes in Indian districts, and conclude that progressive warming and rising temperatures inhibit structural transformation and limit rural-urban migration for households living in isolated areas. Other authors emphasize that, in most cases. migration depends much more on political and economic factors and is only minimally associated with slow-onset changes. For example, Selby et al. (2017) find that environmental variables have a marginal role in explaining migration flows to the Syrian Arab Republic in the period before the outbreak of the civil war. Similarly, Niva et al. (2021) performed a geospatial analysis of a gridded global net migration data set for the decade 1990-2000, and found that slow-onset changes, such as droughts and water scarcity, were the dominant environmental events in explaining net-migration. Yet, they also emphasize that income levels and adaptive capacity crucially mediate environmental variables in determining migration outcomes.

In short, while there seems to be a prevailing consensus that slow-onset shocks do increase migratory flows, there are cases in which the opposite happens, and affected populations are instead trapped in 6
**_<u>________________________________________________________</u>_**

||**TRANSP**<br>|**ORT INFRAST**|**RUCTURE**<br>|
|---|---|---|---|
|International Road<br>Transport Union|178||Road and railway networks|
|UN Economic<br>Commission of Latin<br>America and Caribbean<br>(ECLAC)|11 LA<br>countries.|2000|Foreign trade and transport statistics|
|World Bank : Railways<br>database|90|1980-99|Scale, output and performance|
|International Road<br>Federation|189|1997-2001|Road and vehicle statistics|
|Dutz, Mark (2005).<br>Road freight logistics,<br>competition, and<br>innovation: downstream<br>benefits and policy<br>implications|165<br>logistics<br>service<br>providers<br>and 493<br>user<br>enterprises|2004|the impact of innovative road freight<br>services on downstream business users|
|International Air<br>Transport Association<br>||**PORT Efficien**|World Air Transport Statistics (data on<br>IATA member airlines), Air Cargo<br>Annual(market trendsinaircargo)<br>**cy**<br>|
|US Department of<br>Transportation|US and<br>trading<br>partners||Maritime Transport Costs|
|MaerskSealand||current|MaritimeTransport Costs|
|International Monetary<br>Fund (IMF) :<br>International Finance<br>Statistics|||CIF/FOB|
|UNCTAD Review of<br>MaritimeTransport|World|Since 1968|Freight Costs as a percentage of Import<br>Value|
|Clark, Micco, Dollar<br>(2001) World Bank<br>WorkingPaper|35||Container Handling Charges|
|Fink, Mattoo, Neagu<br>(2001) World Bank<br>Working Paper|37<br>developin<br>g and 22<br>developed<br>countries||Cargo handling restrictions index,<br>Mandatory port restrictions index.|
|Global Economic<br>Prospects 2004 database<br>(Chapter 5) : Based on<br>Wilson, Mann, Otsuki<br>(2003)|75|For year 2000|Port efficiency|

64 __________________________________________________________________________________________________________
of economic growth, led to crowding out of investment in services connected to final consumption (retail trade, hotels and restaurants, personal services). Organization of central planning and abolishing of private ownership of productive assets resulted in underdevelopment in wholesale trade, financial and business services. For instance, the state-owned banking system and central planning of investment decisions resulted in only 1 percent of employment allocated to banking and insurance (Bićanić and Škreb, 1991).

Transition from the centrally-planned to market-based economic system required a larger and better developed services sector, which has been growing quite impressively. Figure 1 reports the dynamics of the services sector and financial and business sub-sector as a share of GDP of Ukraine in 1991-2009. By 2007, the share of services in the Ukrainian GDP had reached 42 percent. Still, the share was well below the average for the middle income countries, which was equal to 60 percent in 2007 (Francois and Hoekman, 2010) and much lower than the average for the EU countries, which had reached 65.5 percent in 2007<sup>1</sup> .

The whole period could be split into two sub-periods. Ukraine had entered its independence in 1991 with a share of services to GDP of 20.5 percent, with only 5.5 percent attributed to financial and business services. In the first decade since independence, the services sector grew primarily due to expansion of telecommunication, retail and wholesale trade sub-sectors. Between 1991 and 2000, the share of financial and business services increased only marginally. Between 2001 and 2009, on the other hand, financial and business services expanded from 6.7 to 18.8 percent of GDP.

# **3.2 Liberalization of the Ukrainian services sector in 2001-2007**

Liberalization of the services sector in Ukraine, first and foremost is linked to the WTO accession negotiations. Ukraine applied for accession on 30 November, 1993. The major obstacle on the way to the WTO accession was to bring the national legislation in compliance with the WTO rules and regulations. However, not much had been done till 2001, when

> 1The WTO database on services reports the profile of the EU services sector in 2007.

6
and Klenow, 2009). At the (efficient) competitive equilibrium, the marginal product should be equalized across firms. Hence, the very dispersion across firms that we measure through TFPR is a measure of misallocation.

The second important estimation issue with production functions relates to the endogeneity of inputs. Any firm-specific demand or productivity shock will affect both the residual of the production function estimation and the demand for factors of production. This endogeneity of input choices has received much attention since Olley and Pakes’ (1996) seminal work. In our work below, we follow the approach subsequently developed by Levinsohn and Petrin (2003) (LP), who rely on the use of intermediate inputs, in particular energy consumption, to detect demand and productivity shocks. The key idea that underlies this approach is that demand and productivity shocks will affect energy consumption but not capital, which is decided before those shocks are known. The main implementation problem is that the LP estimation of productivity requires a panel of firms which is unavailable to us from the ASI and NSSO surveys in the version with district identifiers. Using information for firms in the same location and industry, we can nonetheless implement an approach in the same spirit as LP that only requires information from repeated cross-sections. This approach was initially developed by Sivadasan (2009). We follow Duranton et al. (2015) in appropriately tailoring this TFP measure to our specific needs, and Appendix Box 2 provides the details of this methodology.

# **5. Results**

# _Some Facts about Access to Finance in Indian Manufacturing_

Panel A of Table 1a reports the detailed observation counts in organized and unorganized sectors by urban and rural locations. Summing over surveys, a total of 169,754 plants in the organized sector and 651,808 plants in the unorganized sector report their urban or rural locations. In Panel B of Table 1a, we apply sampling weights to the plants and estimate that the organized sector has a combined population-level establishment count of 490,309, while the unorganized sector has a plant count of 76,694,603.

Panel B suggests that, on average over the 1989-2010 period, about 66% of plants in the organized sector are in urban areas versus 29% in the unorganized sector. Over the years, the 19
45

# **Table 1**

## **Dependent Variable: Growth Rate of Real GDP Educational Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 1.1**|**Reg 1.2**|**Reg 1.3**|**Reg 1.4**|**Reg 1.5**|**Reg 1.6**|
|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2128*****<br>**(0.0442)**|**0.2166*****<br>**(0.0467)**|**0.2511*****<br>**(0.0363)**|**0.2118*****<br>**(0.0456)**|**0.2194*****<br>**(0.0484)**|**0.2521*****<br>**(0.0386)**|
|**Growth of Labor**|**0.5694*****<br>**(0.1335)**|**0.6831*****<br>**(0.1690)**|**0.3312*****<br>**(0.1189)**|**0.5867*****<br>**(0.1320)**|**0.6799*****<br>**(0.1688)**|**0.3416*****<br>**(0.1207)**|
|**Initial GDP (1960)**|**-3.8747*****<br>**(0.8375)**|**-3.8616*****<br>**(0.7987)**|**-3.6792*****<br>**(0.8213)**|**-3.8384*****<br>**(0.8858)**|**-3.8537*****<br>**(0.8217)**|**-3.7460*****<br>**(0.8543)**|
|**HC15**|**4.3613****<br>**(1.9441)**||**3.7613****<br>**(1.6161)**||||
|**Sq of HC15**<br>**HC15 with extended**<br>**educational returns**|**-1.0208****<br>**(0.4654)**|**1.6161***<br>**(0.8253)**|**-0.7590***<br>**(0.3870)**||||
|**Sq (HC15 with**<br>**Extended**<br>**Educational Returns)**||**-0.2688****<br>**(0.1264)**|||||
|**HC25**||||**3.7916***<br>**(1.9726)**||**3.6325****<br>**(1.5986)**|
|**Sq of HC25**||||**-0.9286****<br>**(0.4662)**||**-0.7321***<br>**(0.3787)**|
|**HC25 with extended**<br>**educational returns**|||||**1.6068***<br>**(0.9558)**||
|**Sq (HC25 with**<br>**Extended**<br>**Educational Returns)**|||||**-0.2960***<br>**(0.1575)**||
|**Log (Educational**<br>**Quality)**|**0.2933**<br>**(0.5121)**|||**0.3671**<br>**(0.5149)**|||
|**Log (Educational**<br>**Quality Extended)**||**0.3256**<br>**(0.5488)**|||**0.3574**<br>**(0.5429)**||
|**Log (Educational**<br>**Quality) -**<br>**Bosworth & Collins**|||**0.2405***<br>**(0.1277)**|||**0.2528***<br>**(0.1305)**|
|**Log (Patents)**|**0.1909*****<br>**(0.0728)**|**0.1838****<br>**(0.0785)**|**0.1102****<br>**(0.0516)**|**0.1955*****<br>**(0.0738)**|**0.1867****<br>**(0.0774)**|**0.1085****<br>**(0.0524)**|
|**Phones**|**0.0033*****<br>**(0.0010)**|**0.0041*****<br>**(0.0011)**|**0.0024****<br>**(0.0010)**|**0.0035*****<br>**(0.0011)**|**0.0040*****<br>**(0.0011)**|**0.0023****<br>**(0.0010)**|
|**Constant**|**-3.6660***<br>**(2.1096)**|**-1.8984**<br>**(1.7103)**|**-2.5109**<br>**(1.7300)**|**-3.2783**<br>**(2.0957)**|**-1.8733**<br>**(1.6959)**|**-2.3360**<br>**(1.6934)**|
|**R squared**<br>|**0.6137**<br>|**0.5967**<br>|**0.7621**<br>|**0.6078**<br>|**0.5933**<br>|**0.7596**<br>|
|**Number of Countries**<br>*, **, *** denotes significance<br>Robust standard errors obtain|**85**<br>at the 90, 95 an<br>ed usingW hite|**92**<br>d 99 percent co<br>'s correction for|**73**<br>nfidence level re<br>heteroscedastic|**84**<br>spectively<br>ity.|**90**|**72**|
|re-||p-||ted|
|---|---|---|---|---|
|Emissions<br>p<br>mium, _Bp_|N/A|Varies,<br>see<br>A<br>pendix B|�<br>_i∈C_<br> ̄_ait_0_,i_|�<br>_i∈I_<br> ̄_ait_0_,i_<br> fnal costs are discoun|
|Parameters<br>impacted|N/A|_Ti_ for _i ∈C_|_t_0_,i_ for _i ∈C_|_t_0_,i_ for _i ∈I_<br>nstraint, and the|
|Problem<br>solved|(II.1)|(II.3)|(II.3)|(II.1)_a_<br> economy co|
|ion|best policy|n date delayed|politically chal-<br> delayed|l sectors delayed<br>ed by the political|
|ipt|st-|tio|n <br>rs|al<br>act|
|scr|fr|iza|n i<br>cto|in<br>mp|
|y de|mal,|rbon|ctio<br>d se|ction<br>are i|
|licy type<br>Polic|mogeneous<br>Opti|terogeneous<br>Deca|terogeneous<br>All a<br>lenge|mogeneous<br>All a<br>_→B −Bp_, as all sectors|
|e<br>Po|Ho|ctoral ac-<br>He|ctoral ac-<br>He|economy-<br>n<br>Ho<br>solved with _B _|
|suit|est|d se|d se|d<br><br>ctio<br>) is|
|y|b|e|e|e<br> a<br>.1|
|c|t-|ax<br>|ay<br>|ay<br>e <br>II|
|Poli|Firs|Rel<br>tion|Del<br>tion|Del<br>wid<br>ase, (|
|umber||||r this c<br> _e−rt_0.|
|N|1|2|3|4<br>_a_Fo<br>by|

12
sovereign risk score is associated with a 27 percent increase in the probability of having a PPI commitment. That difference is also associated with a 41 percent higher level of commitments in dollar terms. By sector, on average, energy investment levels exhibit a higher sensitivity to country risk, while water and sewerage are much less sensitive. This is due to the form of PPI investment in water and sanitation, which to date has overwhelmingly been undertaken through greenfield water and wastewater treatment plants, rather than in “utilities” or distribution assets that are exposed to retail risk.

Transport sector investments have much greater standard errors than the other sectors, suggesting that individual investment policies, such as the use of guarantees, off-take agreements and other credit enhancements, play a critical role, or perhaps the risk profiles of sub-sectors (e.g., ports and airports versus railroads and highways) may vary widely among them.

As for the form of PPI contract, concession investments are more sensitive than greenfield agreements to country risk. As noted in the water sector analysis, greenfield investments often shield investors from economic and commercial risks that are part and parcel of sovereign risk, such as fluctuating demand (drive by changes in economic activity) and currency depreciation. Still, both forms of investment have a good statistical adjustment, meaning country risk is a predictor of investment levels for both concessions and for greenfield investments.

Comparable regressions were run on FDI to see whether country risk ratings were as strongly correlated as they are for PPIs, showing interesting results. PPI investments are more sensitive to country risk than FDI. Moreover, the size of the economy (i.e. GDP) is a better predictor of FDI than it is for PPI. This suggests that FDI is more likely to flow into high risk countries— particularly those with large economies—than PPI. The role of extractive industries and the relative value of tradable investments may explain the lower sensitivity of FDI to sovereign risk than that of PPI.

For conflict-affected countries, data on numbers of PPI transactions successfully transacted within nine years of a conflict ending illustrate how difficult it is for these countries to attract private infrastructure investments of any form. Very few investments took place in the first five years after conflict ended, and nearly all of those investments were in the telecommunications sector—primarily in mobile telephony. Energy investments took six or seven years to mobilize and came primarily in electricity generation—investments that are often characterized by sovereign-backed power purchase agreements, dollar denominated transfers and an asset footprint that is much easier to protect from attack than a distribution network.

20
56 Figure 12

# Trade Balance, Real Labor Cost, and Unemployment

<!-- Start of picture text --> Trade Balance (Seaonally Adjuated) 1978-86 Real Labor Cost per Unit of output in ausiness Sector<br>(USS Bill., 1980 Prica.) (Averagse 1975-1985197.-75.100)<br>LSS Bill.<br>2.1 -<br>1.9 I.<br>1.8 1<br>1.7-<br>1.6 I<br>1.5<br>1.4<br>1.2 Export<br>1.1 842<br>O.: RIA - tn ako sre nulR Im port. *OU.:u ,<br>0.7 Trade Daficit<br>0.8<br>0.5<br>0.4<br>0.3-<br>0.2-<br>0.1<br>1978 1979 1980 1981 1982 1983 1984 1985 18<br>Source ;BIAR, 1986. p. 182. __________________<br>BIAR stand for Bank of Israel Annual Report. Source: SIM 1985, ""t 1S 93 I p. 76. * 38 e I<br><!-- End of picture text -->

# Rate of Unemployment<sup>1977-80</sup>

<!-- Start of picture text --> (Quarterly, Seasonally Adjusted)<br>6-<br>5 - ~~~Total Uemployment<br>4<br>3<br>1977 1978 1979 1980<br>Source BLAR, 1980.<br><!-- End of picture text --> <!-- Start of picture text --> Employment and Unemployment, 19 74-85<br><!-- End of picture text --> <!-- Start of picture text --> % Annual data quarterly data<br>a<br>7<br>4, - .OUnemployment 19; of<br>i9soO I82 83 'esse 1 2 3 4. 1 2 3 4. 1 2 3 4~<br>Source : BIAR, 1985. 19 49e<br><!-- End of picture text -->
**Table 7.** _<u>Pantawid</u>_ <u>program impact on household expenditure</u>

||Log per capita<br>expenditure|Log per capita<br>food<br>expenditure|Log per capita<br>medical<br>expenditure|Log per capita<br>education<br>expenditure|Log per capita<br>alcohol and<br>tobacco<br>expenditure|<br>Log per capita<br>savings|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|OLS only controlling for municipality|0.005|-0.042|0.178|0.221|-0.079|0.169|
||(0.043)|(0.044)|(0.191)|(0.153)|(0.081)|(0.184)|
|Additional information:|||||||
|Number of observations|833|833|830|830|833|822|
|Observations in control group|422|422|422|421|422|415|
|Observations in treatment group|411|411|408|409|411|407|
|Mean in control group|9.357|8.917|2.969|4.453|1.087|-0.666|
|Mean in treatment group|9.345|8.859|3.108|4.652|0.988|-0.551|

Note. Estimates of program impact on household expenditure or savings. Sample restricted to eligible households with children aged 1014. Standard errors are clustered at the village level. *** p<0.01, ** p<0.05, * p<0.1 55
of Goos and Manning (2007) for the United Kingdom. Other important labor market developments that took place during this period such as changes in wage returns to education, changing occupation wage premia, higher female labor force participation, a rapidly aging labor force and human capital upgrading account for a lesser fraction of the rise in inequality.

An interesting feature of the decomposition model is that it allows to generate synthetic‐panels and makes possible to follow displaced routine workers. Therefore, we also investigate the profiles of workers who are more likely to lose their routine jobs. We find that many of them move to unemployment or to low‐skilled jobs.<sup>5</sup> Only a minority of them move to a non‐routine cognitive occupation. This is consistent with the predictions of Autor (2010) for the US. Educational attainment is an important factor at explaining the direction of the transition, as more educated workers are more likely to move to non‐routine cognitive occupations, while the least ones tend to move to non‐routine manual occupations or unemployment. Workers in routine occupations who change status are, in general, younger than those who do not.

This paper focuses on Europe, and more specifically on Germany, Poland and Spain, for three reasons. First, given the long‐standing emphasis on wage and employment protection policies in European economies, this paper shows that inequality may still increase because of changes in the employment structure, even as wages and employment rates are not shifting. Second, the countries selected represent three broad EU regions, namely Northern, Eastern and Southern Europe. These regions have different levels of economic development, trajectories, labor market institutions and policies, thereby any common patterns among them would add robustness and external validity to our results. Third, they have reliable and comparable household surveys over a time span long enough to capture changes in inequality and job polarization. Appendix 4 extends the analysis to four non‐EU countries which have witnessed a different evolution of their labor market: Georgia, the Kyrgyz Republic, the Russian Federation and Turkey.

The rest of this paper is structured as follows. Section 2 describes the methodology. Section 3 describes the data sources and provides descriptive statistics. Section 4 presents the results, and Section 5 concludes.

> 5 Since the model does not distinguish between unemployment and inactivity, we use the term “unemployed” to group both.

6
The business composition of general insurance reflects the size of different companies. Large companies tend to engage more in fire, marine and general accident business where profit margins are higher and reinsurance plays a much bigger part. Small and medium companies rely more on motor and/or medical insurance (Table 7), where technical results have historically been poor, although a major turnaround was observed in 2002. 6 companies (5 small and 1 medium) engage preponderantly in motor insurance (over 80 percent of their business), while another 9 (4 large, 3 medium and 2 small) are mainly active in motor and medical insurance. Small companies rely less on reinsurance and report higher levels of reserves (relative to their net earned premiums). However, as discussed further below, the latter statistic masks large differences among companies, irrespective of size.

|**Table 7: Institutiona**|**l Structure an**<br>% of<br>Total|**d Busine**<br>% from<br>Motor|**ss Comp**<br>% from<br>Medical|**osition of G**<br>% from<br>Other|**eneral I**<br>%<br>Re-|**nsurance, 2002**<br>%<br>Reserves|
|---|---|---|---|---|---|---|
||Premiums<br>P|remiums<br>|Premiums|Premiums|Insured|/NEP|
|8 Large|50.8|43.0|23.9|33.1|42.7|76.4|
|9 Medium|31.6|53.6|21.7|24.7|33.4|71.3|
|8 Small|16.0|76.5|5.2|18.3|25.4|91.9|

Large: Over 6 M JD; Medium: Between 3 and 6 M JD; Small: Below 3 M JD Source: Insurance Commission Despite the presence of a large number of companies, the overall results of the insurance industry have not been very bad. The underwriting result was negative, except for 2002 when a major turnaround took place (Table 8). Between 1997 and 2001 the underwriting result fluctuated between –1.8 and –8.0 percent. But in 2002, the overall loss ratio was 76 percent, down from 82 percent in 2001. With net acquisition costs of 11 percent, this produced a traditional combined ratio of 87 percent and a positive underwriting result of 4.2 percent of NEP.

The technical loss of earlier years was more than made up by investment income earned on the assets representing the reserves and equity capital of insurance companies. Reported data do not show the total investment income of insurance companies. This is because in implementing IAS 39, local rules require companies to show any unrealized capital gains from using fair values for investments available for sale as changes in equity rather than posting them through the income statement. However, with reserves representing about 80 percent of NEP and equity capital amounting to a similar amount, total investment income would have covered even the large technical deficits of 2000 and 2001.

Insurance reserves appear to be consistent with a policy of expeditious settlement of claims. However, the adequacy and appropriateness of reserve levels cannot be ascertained without direct knowledge of the business of each insurer. A low level of reserves for outstanding claims may reflect short delays in settling claims or it may be caused by inadequate reserving in the face of protracted disputes. Similarly, a high level 11