rafmacalaba/datause-encoder-data huggingface dataset:
Table 12 Occupation composition of town in-migrants (15 year old or above) by province: 2000

|__________<br>province|__________________<br>by province|_______<br>1|______<br>2|______<br>3|______<br>4|______<br>5|______<br>6|______<br>7|
|---|---|---|---|---|---|---|---|---|
|__________|__________________|_______<br>|______<br>(%)|______|______|______|______|_______|
|Zhejiang|within province|2.56|13.28|6.54|28.62|5.70|43.24|0.05|
||outside province|0.22|2.24|1.11|10.72|2.37|83.31|0.04|
|Henan|within province|4.05|21.30|11.49|28.36|12.02|22.60|0.17|
||outside province|2.29|12.06|8.79|28.62|10.03|37.89|0.33|
|Sichuan|within province|3.70|19.84|8.47|34.02|10.57|23.13|0.25|
||outside province|2.77|15.06|9.88|36.02|9.44|26.53|0.30|

________________________________________________________________________ Source: calculated from Tabulation on 2000 Population Census of the People’s Republic of China, Table L7-1b and L7-2b.

note: occupation title: 1, manager and administrative personals, 2. professionals and technician, 3. staff and other auxiliary workers, 4. salesperson, 5. agriculture, animal husbandry, and fishery, 6. production workers, 7. unclassified.

# **7. Conclusion**

Our analysis reveals a diversified pattern of town migration in the three provinces. Zhejiang exhibits steady and significant growth in town in-migration over 1995-2000, whereas the other two provinces, Sichuan and Henan, remain quite staggering. Nevertheless, there is one thing in common that majority of town in-migrants were from rural areas.

The diversified pattern of town in-migration in the three provinces probably suggests that migration has been quite responsive to the economic opportunities and living conditions in towns. Many migrants went to towns in Zhejiang province for the sake of seeking business and job opportunities. Nearly half of migrants came from other provinces, especially from the western and central regions. In contrast, a lack of a strong manufacturing sector and good living conditions in Henan and to some extent in Sichuan did not attract outsiders. Most in-migrants came from the same province for non-business reasons.

Our study also suggests that town in-migrants generally possess a higher level of educational attainment than the general population in towns; this is particularly true in less-developed provinces such as Sichuan and Henan. Thus, migration could benefit the development of towns, especially in the western and central regions. Data further suggest that as a town develops, as in the case of the richer province of Zhejiang, better-educated people probably did not leave the countryside to migrate to the cities in other provinces and instead were more likely to shift their jobs from agriculture to the non-agricultural sectors in towns nearby within the province. Thus, the development of towns provides a buffer that could hold more migrants, reducing the migration pressure on large cities.

14
16 (35 percent), and the rest from risk-weight modeling changes and other mitigating measures (figure 15).

# **Figure 15. Capital Impact of Banks’ Planned Capitalization Efforts**

In percent of aggregate capital shortfall <!-- Start of picture text --> Disposal of assets, 7%<br>EU state aid<br>deleveraging (loan<br>reduction), 2%<br>New capital+Reserves (26%): Other deleveraging<br>Conversion of hybrids to equity measures (loan<br>(22%) reduction), 1%<br>Issue of CoCos (6%)<br>Retained earnings 2012 (16%)<br>Other mitigating measures (7%) New advanced models<br>for risk weights, 7%<br>Changes of existing<br>advanced models for<br>risk weights, 2%<br>Percentage of capital<br>shortfall, 0<br>Other mitigating<br>measures, 4%<br><!-- End of picture text --> Source: European Banking Authority (EBA), February 2012.

- However, while these plans are still being evaluated by the EBA and national supervisors and the implied magnitude of deleveraging as a direct result of the EBA recapitalization requirements was much less than initially feared by the markets, these plans do not eliminate the need for, and possibility of, banks meeting their capital shortfalls through asset reductions, particularly if funding difficulties and economic and regulatory uncertainty persists. The April 2012 _Global Financial Stability Report_ (GFSR) of the International Monetary Fund (IMF 2012a) states that its assessment of the potential European bank deleveraging is much larger than suggested by the EBA‘s under different policy assumptions, with the baseline forecast suggesting asset sales of $2.6 trillion over the next 18 months.

- The expansion of the Euro Area firewall capacity has been a necessary step to improve market confidence. However, many observers continue to believe that the expanded capacity is unlikely to cover the refinancing needs of the periphery and hence unlikely to provide an effective protection against contagion, given the limited amount of funds that would be available for fresh lending (box 2).
<!-- Start of picture text --> meoanenes|<br>Choose your language ® English © AT9C%"<br>PAL 7 > enterprisesWomen Entrepr own e neud o r shippartlyDevelopmen owned by t heProjectparticipating (WEDP)femaleis a projectentrepreneurs providingintrain Add i ngs Ababa,and credD i tre to Dawa, micro andMekele, small<br>"WOMEN ENTREPRENEURSHIPDrei y DEVELOPMENTN+ yewPROJECT Bahl Dar, Adama and Hawassa cities to increase the earnings and employment of the enterprises.<br>Total Number of Registered Clients: 25611 Total Number of Trained Clients: 14617 Total Number of loans: 9143<br>Internal News General Report News<br>2017-10- Dear TVET V.Deans and Other Training Read Manufacturing 2419 2017-10- Training on: WEDP-MIS Loan Read<br>07 Providers . 10 Management<br>Construction 593<br>Trade 13589<br>Service 8186<br>Agriculture 455<br>Other 365<br>© 2018 - WEDP-FeMSEDA Developed by EXCEED IT SYSTEMS<br><!-- End of picture text -->
63

# **Table D3**

# _Delivery – Descriptive Statistics_

||All Ev|aluated Progra<br>Standard|ms||At-scale Progra<br>Standard|ms|
|---|---|---|---|---|---|---|
|Delivery|Mean|Deviation|Obs|Mean|Deviation|Obs|
|Cascade training model|0.519|0.509|27|0.587|0.494|138|
|Trainers are primary or secondary teachers|0.152|0.364|33|0.416|0.495|137|
|Trainers are experts - university professors /<br>graduate degrees in education|0.212|0.415|33|0.560|0.499|84|
|Trainers are researchers|0.091|0.292|33|0.051|0.221|137|
|Trainers are local government officials|0.242|0.435|33|0.022|0.147|137|
|Trainers are education university students|0.030|0.174|33|0.000|0.000|139|
|Initial period of face-to-face training for several<br>days in a row|0.938|0.246|32|0.854|0.357|48|
|Total hours of face-to-face training|59.742|39.667|31|13.265|14.864|34|
|Proportion of face-to-face training spent in<br>lectures|0.534|0.290|17|0.481|0.296|35|
|Proportion of face-to-face training spent<br>practicing with students|0.071|0.107|19|0.082|0.154|36|
|Proportion of face-to-face training spent<br>practicing with teachers|0.376|0.341|19|0.156|0.183|34|
|Duration of program (weeks)|9.800|13.566|30|7.216|12.559|37|
|Training held at schools|0.030|0.174|33|0.108|0.315|37|
|Training held at central location including hotel<br>conference room etc.|0.576|0.502|33|0.730|0.450|37|
|Training held at university or training center|0.091|0.292|33|0.162|0.374|37|
|Number of teachers per training session|30.794|10.227|17|30.972|15.157|36|
|Includes follow-up visits|0.760|0.436|25|0.496|0.502|139|
|Follow-up visits for in-class pedagogical<br>support|0.333|0.479|33|0.375|0.489|48|
|Follow-up visits for monitoring|0.242|0.435|33|0.333|0.476|48|
|Follow-up visits to review material|0.091|0.292|33|0.104|0.309|48|
|Includes distance learning|0.167|0.381|24|NA|||
|Duration of distance learning (months)|8.556|8.763|27|NA|||

Obs refers to the number of PD programs in each sample (top performing, evaluated programs, and at-scale programs) that report whether or not they have a given characteristic.
substitutability between goods from different country groups. For example, we can let goods from high income countries be better substitutes for each other than for imports from low income countries in the same product category. This is accomplished by dividing countries into groups, and assigning each source country to a country group. Each country will thus be identified by a pair {l,m}, where 1 is an indicator of the country group to which it belongs and m is an indicator of the country within the country group.

The trade data on import values from a particular source country in a particular tariff line are denoted by Mijk,l at the bottom of Figure 1, where the {ijk} subscripts identify the tariff line and the {lm} subscripts identify the source country. The tariff rates imposed on imports from that product category and country can sirnilarly be identified by y-jk,,. The aggregation proceeds by first aggregating over imports within a particular country group, as indicated by node IV in Figure 1. The trade value Kw = S **Mikhn 1** represents the value of imports from country group 1 in tariff line m {i,j,k}, with Oijkl the price index for this group and Zijk the quantity index. The data on imports from country groups at node IV is then aggregated over the groups within the tariff line to yield a price index Oijk and quantity index Zijk for the tariff line (at node III). The tariff lines within each subindustry are then aggregated to yield indices for the sub-industry (node II), and the sub-industry indices are aggregated to obtain the industry aggregate at node I.

The aggregation structure is quite flexible, and can be extended to allow for any number of levels of industry and/or country groups. The application to Chile considered in this section provides an example of how the aggregation can be carried out. The trade volume data from Chile consist of 1011 tariff lines at the 4-digit (CCCN) level for the year 1986. This trade data had to be aggregated to 41 different product categories, which represented the industries for which import usage data were 4
# **1** **<u>Literature review and our approach</u>**

Our paper is related to a number of different strands of the literature. First, we summarise the literature on global inequality, and define what we mean by this term. Second, we explain the various problems associated with deriving PPP exchange rates and why we consider the rates used in this paper to be the most robust. Finally, we address previous work on (a) the underreporting of top incomes in household surveys, and (b) the discrepancy between national accounts measures of income or consumption and their household survey equivalents. We argue that the two issues are closely related and consider them jointly in our analysis.

## **1.1** **<u>Global inequality</u>**

Milanovic (2005) distinguishes between three concepts of global inequality. First, unweighted international inequality is the inequality in per capita incomes amongst the countries in the world. Second, population-weighted international inequality, or between-country inequality (Anand and Segal, 2008), measures inequality amongst persons by assigning everybody the per capita income of his place of residence. It thus ignores any within-country inequality. Third, global interpersonal inequality captures the inequality of individual incomes in the world by giving everybody his or her own income.<sup>8</sup> In this paper we focus on the last concept, global interpersonal income inequality, and whenever we use the term “global inequality” this is concept we refer to. Unweighted international inequality (Concept 1) might be appropriate in studies of income convergence across countries (e.g. Barro and Sala-i-Martin, 1992), but it is not a measure of interpersonal inequality, not least because the weight attached to individuals depends on their country of residence. Bourguignon (2011b) shows that between 1989 and 2006, unweighted international inequality (concept 1) has continued to increase, whereas global inequality (concept 3) has declined, as measured by the P90/P10 ratio. This can be explained by the fact that some populous Asian countries have grown very fast, whereas some smaller (mostly African) countries lagged behind or even declined. Population-weighted international inequality (Concept 2) ignores within-country inequality which seems inappropriate given the widespread concerns on precisely this topic. It might be best

> 8 Anand and Segal (2008) add concept zero, which is the inequality in total (rather than per capita) income among countries.

6
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 25. Affordability in Niger in comparison to other low-income countries**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Niger<br>LIC<br>Average cost of power is 4 $/month per 25 kwh or 8 USD/Month per 50 kwh subsistence consumption<br>Average cost of water is 3.28 $/month per 4 m3 or 8.2 USD/Month per 10 m3 subsistence consumption<br>Power cost-recovery tariff is 0.16 $/kwh<br>Water cost-recovery tariff is 0.82 $/m3<br>Source: Banerjee and others 2009.<br>Note: LIC = low-income country; kWh = kilowatt-hour.<br>household budget<br>Share of urban households whose utility bill would be less than 5 percent of the monthly<br><!-- End of picture text -->

**Figure 26. Niger’s power and water utilities burden of inefficiency**

<!-- Start of picture text --> Uncollected bills and unaccounted losses as percentage of GDP<br>a. Power sector b. Water sector<br>0.700 0.20<br>0.600<br>0.15<br>0.500<br>0.400<br>0.10<br>0.300<br>0.200<br>0.05<br>0.100<br>0.000 0.00<br>Niger LIC-Nonfragile<br>Collection inefficiencies Losses<br>Percenatage of GDP Percenatage of GDP<br><!-- End of picture text --> <!-- Start of picture text --> 0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Niger LIC-Nonfragile<br>Collection inefficiencies Losses<br>Percenatage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

# Annual funding gap

Niger’s infrastructure funding gap amounts to $460 million per year, or about 14 percent of its GDP, once efficiencies are captured. This overall gap is predominantly in water and power, but also in the ICT, irrigation, and transport sectors (table 20).

41
pre-requisite for participating in global value chains and exporting to advanced economies (Bastos et al., 2018; Cadestin et al, 2018; Kummritz et al., 2017). However, producing high-quality exports remains a particular challenge for developing economies, with economic development often involving a step-change in the quality of production (Hallak and Schott, 2004; Henn et al., 2020). We contribute to this literature by examining quality upgrading effects for a broad range of developed and developing economies.

Historically in the trade literature, unit prices (trade values per unit quantity) had been used as a proxy for quality (Schott, 2004; Hallak, 2010). However, the literature has recently recognized that a range of factors other than quality itself may influence prices, such as transport costs or tariffs. We use detailed HS 10-digit level US import data to estimate export quality by employing a micro-founded measure of quality introduced by Khandelwal et al (2013). This measure has been widely used in the recent literature (e.g. Breinlich et al., 2016; Bajgar and Javorcik, 2020). Intuitively, this measure assigns a higher quality for products - conditional on prices – that have a larger market share in a given destination country (in our case, the United States).

We also contribute to the literature examining ISO quality certification – a manufacturing standard commonly linked with production quality. For example, ISO 9000 certification in Slovenian firms is found to increase firm exports along with other performance outcomes including profitability, labor productivity and wages (Javorick and Sawada, 2018). Following exchange rate devaluation Mexican firms, particularly those initially larger and more productive, are more likely to gain ISO 9000 certification, increase exports and wages (Verhoogen, 2008). ISO 9000 certification can also be a pre-requisite to join as suppliers to multinationals, which can demand continuous quality improvements in their supply chains (Javorcik and Spatareanu, 2009; Iacovone et al, 2015).

The trade literature has commonly focused on the effect of trade liberalization on technology adoption, rather than examine the impact of technology on trade as we do here (see for instance, Bustos, 2011; Bas and Strauss-Kahn , 2015; Bloom et al., 2016). Some exceptions are Kneller and Timmis (2016) who find that UK broadband rollout led new firms to trade services and Fernandes et al. (2019) who show that the internet rollout led both to increased intensity of Chinese manufacturing exports and new firms to start exporting. Our paper most closely relates to Artuc et al. (2018), who find that greater robot adoption leads both to an increase in imports sourced from and exports to less developed countries in the same industry. Our paper focuses on the impact 6
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- Francis, B.B., I. Hasan, X. Sun. 2009. “Political Connections and the Process of Going Public: Evidence from China.” _Journal of International Money and Finance_ 28(4), 696-719.

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- Goldman, E., J. Rocholl, J. So. 2009. “Do Politically Connected Boards Affect Firm Value?” _Review of Financial Study_ 22(6), 2331-2360.

39
_access to wholesale funding evaporating in a matter of days, if not hours”._ Also in its recent review of the causes of the recent financial crisis, Brunnermeier (2009) indicates that banks’ increasing use of short-term maturity instruments left them particularly exposed to a dry-up in funding liquidity, and Huang and Ratnovski (2008) present a theoretical model that highlights the dark side of bank wholesale funding. However, despite the emphasis put on the use of wholesale funding as a source of propagation of the crisis, and the anecdotal evidence from cases like the one of Northern Rock, there is so far no systematic evidence of its role and its potential to facilitate broad international contagion independently of other real and financial transmission mechanisms.

This paper provides such systematic evidence of the importance of banks’ reliance on wholesale funding as a source of vulnerability, and of its contribution to the international transmission of the ongoing financial crisis. It constructs individual bank-level measures of vulnerability to different transmission mechanisms for 662 banks across 44 countries excluding the US, and uses a methodology that combines ingredients from a standard event study with those of differencein-difference estimation for identification. In particular, the paper focuses on the largest liquidity crunch episode of the crisis: the money market freeze following the bankruptcy of Lehman Brothers on September 15, 2008 (henceforth _“Lehman”_ ), studies its impact on the stock-price returns of individual banks, and tests whether differences in the abnormal returns observed around those episodes are related to the banks’ ex-ante reliance on wholesale funding, as it would be if this reliance contributed to the propagation of the crisis. In addition, the paper checks whether a similar pattern of differential returns is observed after the initial stages of the liquidity crunch that took place on August 9, 2007.

The results show that the banking sector across the world experienced a large and significant abnormal return decline following this event. Banks’ returns worldwide declined about 2.9 percent in the three days after Lehman. Furthermore, banks that before the crisis relied more heavily on wholesale funding experienced a larger abnormal return decline in response to US events than other banks. In the days following Lehman, within a country, the returns of banks with high wholesale dependence (75<sup>th</sup> percentile) declined 1.6 percent more than those with low wholesale dependence (25<sup>th</sup> percentile). This difference is not only large but also statistically significant. Quantitatively, the results show that the use of wholesale funds can explain an important fraction of the early global transmission of this event, confirming that the widespread use of these types of funds played a significant role in the propagation of the financial crisis. A similar but weaker pattern emerges during the early stages of the crisis (August 9, 2007) which 3