rafmacalaba/datause-encoder-data huggingface dataset:
15 prices increased, especially prices of fixed-interest-rate bonds."<sup>3</sup> In the case of T,atin America, a significant (negative) corTelation is also registered between interest rates and country credit rating. Black market premiums are significantly related with the secondary market prices (available for Latin America only) and the industrial production variable.

Table 4: Panel Data Correlations among Explanatory Variables 1988.01 - 1992.09

||Credit<br>Rating|Second.<br>Market<br>Price|US<br>Industrial<br>Product.|Black<br>Market<br>Premium|USAT|USAG|PCi|
|---|---|---|---|---|---|---|---|
|||Latin|America|||||
|Credit|1.000|||||||
|Second.|0.703|1.000||||||
|US INP|-0.210|-0.357|1.000|||||
|Black|-0.060|-0.179|0.300|1.000||||
|USAT|-0.224|-0.407|0.873|0.237|1.000|||
|USAG|-0.180|-0.313|0.799|0.313|0.816|1.000||
|PCi|-0.219|-0.391|0.887|0.286|0.976|0.912|1.000|
|Credit|1.000||Asia|||||
|Second.|NA|1.000||||||
|US INP|0.003|N.A.|1.000|||||
|Black|0.048|N.A.|0.070|1.000||||
|USAT|0.012|N.A.|0.873|0.085|1.000|||
|USAG|-0.001|N.A.|0.799|0.049|0.816|1.000||
|PCi|0.007|N.A.|0.887|0.076|0.976|0.912|1.000|

Sources: See Table 3.

Note: See Table 3 for the acronyms used. The lowest correlation between pairs of interest rates is 0.816 (between USAG and USAT). N.A. denotes not available.

13When interest rates fall prces of collateralized bonds will increase because the present value of collateral increases. Prices of uncollateralized, variable interest rate bonds can also be expected to rise when interest rates fall when payments on the bonds arc to some degree independent of the payment obligation, and in general, as the solvency of the country (discounted present value of future trade and/or fiscal balances) rises as interest rates fall.
buses. In particular, if congestion and environmental costs are considered and these externalities are proportional to the number of buses on the network, then the socially optimal number of buses may have to be lower still. This implies that the possibility that a free market will result in excessive, rather than insufficient, entry is greater.

There is an interesting dichotomy here between developed and developing countries. Whereas in the former the modal split is usually heavily biased towards private cars, in the latter public transport accounts for the majority of trips. For example, a recent report by NERA and TIS.PT (2001) reviewed the public transport system in 9 cities of developed countries. They found that public transport usually accounts for less than 25% of trips (the highest share of public transport was found in Zurich, 37%) in these cities. Private car transport, on the other hand, accounts for well over 50% of trips<sup>27</sup> . This is in stark contrast to developing countries, where in a city such as Santiago, Chile, 61% of trips are undertaken on public transport according to the 2001 Origin and Destiny Survey.

The modal split will have an influence on the causes of congestion and pollution. In a developing country where buses are the main source of urban transport, these will have a greater responsibility for congestion and pollution.<sup>28</sup> This argument is reinforced by considering that in developing countries bus technology is often older and more polluting than in developed countries. It is possible then that in developing countries a free market may provide an excessive supply of services (due to these externalities) while in a developed country they are a solution to these same externalities. The policy recommendations must then be very different in each case. While in the latter subsidies to public transport may be justified in order to increase services, in developing countries efforts may be needed to rationalize the public transport system.

15
# APPENDIX A: DATABASE OF PLANNED BRI ROAD, RAIL AND PORT INVESTMENTS (NOT FOR PUBLICATION)

# TABLE A1: SILK ROAD ECONOMIC BELT (“BELT”)

|CORRIDO|NO||IMPROVEMENT|COUNTRI|TYPE<br>(SEE||||STATUS|STAT<br>US|
|---|---|---|---|---|---|---|---|---|---|---|
|R|.|PROJECT|SECTION|ES|TABLE 3)|REF.|STATUS|DETAILS|DATE|REF.|
|CHINA-|||Ulan-Ude–|Russian||||Already in use European|||
|MONGOLIA||Central Rail|Ulaanbaatar–|Federation,|Rail upgrade|(1)||. ,<br>Mongolian and Chinese|||
|-RUSSIA|1.|<br>Corridor|Erenhot|Mongolia|||Operational|<br>firms are usin the|30-Sep-18|(2)|
|ECONOMIC<br>CORRIDO|||Erenhot–Beijing–<br>Tianjin|China|Rail upgrade|(1)||g<br>corridor.|||
|R (CMREC)|2.|Northern Rail<br>Corridor|Kuragino – Kyzyl<br>Kyzyl–Arts Suur–<br>Ovoot|Russian<br>Federation<br>Russian<br>Federation,<br>Mongolia|New rail<br>New rail|(1)<br>(1)|Planning|Construction started on<br>section to Ovoot coal<br>mine, to finish in 2019.<br>Section beyond Ovoot is<br>only planned. Final|10-Apr-18|(2)<br>(3)|
||||Ovoot–Erdenet<br>Erdenet–Salkhit|Mongolia<br>Mongolia|New rail<br>Rail<br>reconstructio<br>n|(1)<br>(1)||<br>feasibility study approved<br>in April 2018. Delays due<br>to funding shortfalls.|||
|||||||||Proposed under Mong.|||
||3.|Western Rail<br>Corridor|Arts Suur–Urumqi|Mongolia,<br>China|New rail|(1)|Proposed|national rail policy and<br>joint China-Mong.-Rus.<br>declaration. No concrete<br>steps taken.|25-Nov-<br>2018|(1)<br>(4)|
||4.|Eastern Rail|Choibalsan–Bichigt|Mongolia|New rail|(1)|Proposed|Proposed and still being|23-Jan-2018|(5)|
|||Corridor|Bichigt–Chifeng|China|New rail|(1)||discussed. China, Russian|||

52
In the discussion below we refer mostly to the challenges of measuring the top tail of the income distribution. But most of the challenges we discuss apply to measuring the wealth distribution as well. We provide a specific section on how these challenges differ for wealth below.

# _Unit non-response_

One key challenge in measuring the top tail of the income distribution is unit non-response. This means that a household was included in the sample, but no information was collected on that household. The main reasons for unit non-response are refusals and non-contact, with refusals likely to be more common (Ravallion, 2022). Unit non-response is usually classified as being missing completely at random (MCAR), missing at random (MAR) or not missing at random (NMAR) (Lohr, 2009). These mean, respectively, that non-response is ignorable, that non-response is ignorable conditional on some covariates or that it is not ignorable, i.e., that it is correlated with the outcome of interest, here income or wealth.

We did not find a comprehensive overview of unit non-response levels and trends for the World Bank’s Living Standards Measurement Surveys (LSMS). Scott et al. (2005) did review response rates from 8 LSMSs in the late 1990s and early 2000s, finding a non-response rate of 11%. Vaessen et al. (2005) reported an average non-response rate of 2.5% to the Demographic and Health Surveys conducted in 44 developing countries between 1990 and 2000. This, plus the fact that questions about incomes are probably less than or similarly sensitive to questions about health, suggests that unit non-response rates to surveys that include questions on incomes are likely to be much lower in low-income countries than in rich countries. Hlasny (2020) uses surveys collated and harmonized by the Luxembourg Income Study for 38 middle- and high-income countries to show that non-response rates are correlated with GDP per capita. Countries with PPP GDP per capita of around $10,000 had an average non-response rate of around 10%, whereas this was around 25% for those with per capita incomes of around $35,000.

When measuring the top of the income distribution, the concern is the unit non-response rate at the top of the distribution. Ravallion (2022) notes that direct evidence that rich people are missing from surveys is scarce. As evidence that they are missing, he cites a paper by Székely and Hilgert (2007), who used household survey data on 18 South American countries which showed that the highest income individual in each survey had incomes that were similar to that of managers in medium-sized companies, meaning that the top of the distribution was missing. Ravallion (2022) notes that one reason that the rich are assumed to be missing is that rich people have a higher opportunity cost of time and may therefore be less like to respond. Choumert-Nkolo et al. (2023) focus on non-response and assert that high income households in low and middle-income countries (LMICs) often live in gated 5
**Table 7 Determinants of fiscal policy volatility: Cross-sectional regressions**

|||**Dependent v**|**ariable: Prim**|**ary expendit**|**ure volatility**|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|EMDE|4.930***|1.756**||-2.381*|-0.398|0.536|-1.255|
||(0.657)|(0.870)||(1.266)|(1.276)|(1.151)|(0.970)|
|Commodity exporters|3.295***|-1.053||0.661|3.356***|0.0398|1.082|
||(0.852)|(0.845)||(0.747)|(1.134)|(0.849)|(0.753)|
|Resource rents||0.376***||0.281***||0.543***|0.402***|
|||(0.0814)||(0.0688)||(0.180)|(0.0678)|
|GDP per capita||-1.240***||-0.548||1.023|0.282|
|||(0.423)||(0.464)||(1.091)|(0.651)|
|SWF||||||-0.324|-0.349|
|||||||(0.832)|(0.703)|
|Political constraints|||-11.27***|-3.596|||3.963|
||||(2.502)|(3.224)|||(4.676)|
|Control of corruption|||-1.997***|-1.836***|||-0.761*|
||||(0.421)|(0.570)|||(0.388)|
|Fiscal policy rules|||||-0.947***|-1.040**|-0.694**|
||||||(0.348)|(0.411)|(0.308)|
|Capital account openness|||||-7.312***|-4.256**|-4.202**|
||||||(2.574)|(1.729)|(1.666)|
|Exchange rate regime|||||-4.722***|-4.541***|-3.206***|
||||||(1.093)|(1.294)|(0.857)|
|Constant|3.930***|17.390***|17.92***|19.44***|14.04***|0.928|8.710|
||(0.460)|(4.495)|(1.305)|(5.025)|(2.836)|(10.191)|(5.579)|
|Observations|178|177|133|132|93|93|77|
|_R_-squared|0.219|0.451|0.394|0.595|0.371|0.636|0.713|

_Source_ : World Bank.

_Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘EMDE’ is a dummy variable that takes the value 1 if a country is classified as an EMDE, and 0 otherwise. ‘Commodity exporters’ is a dummy variable representing whether a country is a commodity exporter or not. ‘SWF’ is a dummy equal to 1 if a country has a sovereign wealth fund and 0 otherwise.

51
Table 12

|||89.3-|92.2|89.3-|91.3|91.4-|92.2|
|---|---|---|---|---|---|---|---|
|Firm||Estimate|t-stat|Estimate|t-stat|Estimate|t-sta||
|Type||||||||
|AAA|13,|-0.025|-2.04|-0.018|-1.55|0.025|1.99|
||-lt|0.007|1.88|0.006|1.76|-0.003-|-0.82|
|A|1i,|-0.051|-6-13|-0.037|-5.60|-0.007|-1.56|
||=-|-0.001|-i.63|-0(0004|-0.63|-0.00003|-0.22|

Splitting the sample between type "A" and type "AAA" firms provides further insights on what is going on (cf Table 12). The same break around 91.4 as we saw in the pooled sample shows up here too, but much more pronounced: for type "AAA" finms. the coefficient on Drofitability switches from significantly negative to significantly positive; this is a clear indication that banks _are starting to exercise governance._ After the governance reforms in the banks. profitabiliy of potential clients apparently began to be used as a signal of credirworthiness rather than as a signal of need. The break is less pronounced for type 'A" firms. the coefficient on profitability in the first half of the sample is highly negative there too and loses its significance and size after 91.4. However it does not switch to significantly positive as in the case of type "AAA" firms.

Similar evidence was obtained from direct survey questions. Managers were asked how difficult access to credit was for the years 1990 and 1991 and the first six months of 1992 (the tail-end of the survey period), with the choice of 6 different answer categories ranging from impossible to very easy. Table 13 lisrs the results of an ordered logit analysis'<sup>2</sup> of the results. The results strongly support the more indirect analysis based on actual loans extended that we just discussed.

12 A probit **based** analysis gives very similar results boti in terms of size and signis of coefficients and their significance.
Note: Central Statistics Office data for respective fiscal year; averages indicated are for Q1, 2014–Q2, 2017.

# **Figure 14: GDP growth recovered during Q2, 2017-18 to Q4, 2017-18**

14 A: GDP growth recovered <!-- Start of picture text --> GDP at Market Price<br>10<br>9 7.7<br>8<br>6.3<br>7<br>6<br>7.4 7.0<br>5<br>4 5.6<br>3<br>2<br>1<br>0<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text --> 14 C: Private consumption growth remains at levels seen in the past 14 B: Investment growth picked up after remaining subdued <!-- Start of picture text --> Investment<br>18<br>16 14.4<br>14<br>12<br>10<br>9.1<br>6.1<br>8<br>4.6<br>6<br>4<br>2<br>0<br>-2<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text --> 14 D: Government consumption growth is mostly at levels seen in the past

# **Private Consumption**

<!-- Start of picture text --> 12<br>10<br>7.2<br>8 6.8 6.7<br>6<br>4 5.9<br>2<br>0<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text -->

# **Government Consumption**

<!-- Start of picture text --> 40<br>30<br>16.8<br>20<br>6.0 3.8<br>10<br>6.8<br>0<br>-10<br>-20<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text -->

# 14 E: Exports growth shows modest pick up

14 F: Import growth has picked up 29
# 1 Introduction

Despite improvements in education and skills, gender disparities in labor market opportunities and outcomes remain widespread in the developing world. In many countries labor force participation rates among women remain indeed well below those for men and, when women do participate, they often face a higher likelihood of being employed in precarious jobs or of being openly unemployed.

Northern Africa provides a vivid illustration of these disparities. The labor market participation rate for women in that region remains very low by international standards; at just under 23 percent currently, it is less than half the world average of about 49 percent. It is also 51 percent lower than the participation rate for men. Working women in that part of the world are more likely than men to be in vulnerable forms of employment; they are over-represented among contributing family workers, who often perform a disproportionate amount of unpaid care and household work (Charmes (2015), International Labour Organization (2016a), and Rubiano and Viollaz (2018)). The gender gap in formal sector jobs is also large; in 2017, the shares of men and women in wage and salaried employment were 62.8 percent for men and 54 percent for women (International Labour Organization (2017, Table E5c)). In addition, women in the labor force are twice as likely as men to be unemployed, with an average female unemployment rate of about 20 percent compared to 9.5 percent for men–a gap of 10.5 percentage points.<sup>1</sup> At the same time, gender disparities in employment and unemployment only partially reflects the extent of the labor market challenges that women face, in North Africa and elsewhere: these disparities also relate to remuneration, across all levels of occupation and sectors. In many countries, the gender gap in hourly wages remains large, despite the introduction of equal pay laws (International Labour Organization (2016b)).

The persistence of these gender gaps in labor force participation rates, hours worked, and earnings is the result of a number of factors. Social and cultural norms, as well as entrenched stereotypes, are a key source of discrimination against women in the labor market. In turn, discrimination shapes women’s labor supply decisions; in effect, women

> 1See International Labour Organization (2017, Chapter 2). This gap is even larger for young females. The MENA gender paradox is an expression that has been used by some to describe the fact that despite having achieved higher educational levels, women in the Middle East and North Africa are also more likely to be unemployed or not economically active.

2
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE negligible one-time investment of $10 million, South Africa fulfills required power infrastructure backbone needs and benefits from the cheap hydropower generated in the Democratic Republic of Congo. Returns for other importers are much lower due to the high cost of interconnectors.

For power exporters, the decision to trade can be thought of as an investment in additional generation capacity and cross-border interconnection that yields an annual return. For example, the Democratic Republic of Congo could expect to earn annual export revenues of $623 million from a one-time investment of $7.4 billion, a rate of return of 8 percent. Mozambique could expect to generate $212 million in annual export revenues from a one-time investment of $2.1 billion.

**Table 3.7 Rate of return on power trade at the country level**

|Country|Price gain ($/kWh)|Net power trade<br>(TWh)|Annual benefits ($ millions per annum)|One-time<br>investment (US$ million)|Rate of return (%)|
|---|---|---|---|---|---|
|Exporters||||||
|Congo, Dem. Rep. of|0.01|49.6|622.8|7,480|8|
|Mozambique|0.03|3.1|212.4|2,156|10|
|Importers||||||
|Angola|0.05|6|395|870|45|
|Botswana|<.01|4.3|0|100|0|
|Lesotho|0.01|0.7|90|0|n.a.|
|Malawi|<.01|1.5|0|10|0|
|Namibia|0.01|3.8|43|300|14|
|South Africa|0.01|36.4|3,192|10|319|
|Zambia|<.01|1.8|0|1,420|0|
|Zimbabwe|0.01|3.5|187|370|51|

_Source:_ AICD calculations. n.a. = Not applicable. _Note:_ kWh = kilowatt-hour; TWh = terawatt-hour.

48
mation of the financial intermediation process from the _“traditional”_ banking model to the _“originate and distribute”_ model produced a lengthening of the chain linking the ultimate lender to the ultimate borrower (Adrian and Shin, 2011a). This lengthening may have had several consequences, including the erosion of lending standards (Keys et al, 2010), which likely played an important role in the crisis that started in 2007.<sup>10</sup> In what follows we describe the construction of the measure for each element of the U.S. financial system for the period 1952Q1-2009Q4.<sup>11</sup> We use data from the release Z1 of March 8, 2012. A Data Appendix contains details on the composition of the measure for different players within the financial system.

**Traditional financial institutions.** Traditional financial institutions include Commercial Banks, Savings Institutions and Credit Unions. For commercial banks, at the numerator of our measure we use the sum of Open Market Papers, Treasury Securities, Non-securitized GSE-backed securities, Municipal securities, Non-securitized Corporate Bonds, Bank Loans, Mortgages, Security Credit and Consumer Credit. At the denominator, we use the total amount of credit market instruments<sup>12</sup> . For Savings Institutions, we use a similar measure.<sup>13</sup> For Credit Unions, the Flows of Funds table does not distinguish between securitized and not-securitized bonds. By including all bonds in the numerator of our measure, once again we are conservative and we accept the risk of over-estimating the connectedness with the real economy.

Figure 2 reports the results obtained for the measure of connectedness of the traditional players. All the three indicators are falling over time, from values very close to 1 to values between 0.77 (for the Credit Unions) and 0.84 (Commercial Banks).

> 10Moreover, this lengthening of the credit chain might have increase the vulnerability of the system to sudden drop in trust, which arguably was an important element in explaining the freeze of credit markets in the fall of 2008. See Gennaioli, Shleifer, and Vishny (2012) for an example of a model where trust between actors play an important role in the intermediation process.

> 11A change in the accounting rule governing the GSE at the beginning of 2010 would introduce a spurious element in our calculations.

> 12Flow of Funds series FL.724.005.005, table L.110

> 13See appendix for details.

7
in China. Lardy (1995) argues that foreign investment flows increased because of the phenomenon of recycled capital of Chinese origin as foreign companies sought to take advantage of tax incentives for foreign enterprises.

In particular, investors have been attracted by growth prospects in China, strong especially in comparison to weaker prospects back home. In a study of the role of Chinese business networks abroad and their significant investment in mainland China contrasts compared to the smaller Indian overseas networks, Bhalla (2002) finds that the overseas Chinese are more in number, tend to be more entrepreneurial, enjoy family connections ( _guanxi_ ) in China and have the interest and financial capability to invest in China, and receive favorable treatment at home. Frequently, they eschew formal channels in favor of more informal systems. In the context of weak legal systems and opaque regulatory structures, Chinese entrepreneurs are using social networks to open up supply and distribution and operating licenses (Yueh, 2007). Furthermore, besides overseas remittances, Chinese send domestic remittances from rural areas back home. In a recent study using micro-level data from Hubei province, Zhu and Luo (2008) find that remittances actually led to an increase in rural income and a decline in rural poverty and inequality.

While the African diaspora is not as large as the Chinese one, there are close to 4 million Africans living overseas, mostly in Europe and North America. Moreover, a very high percentage of Africans have college or postgraduate degrees and have maintained significant cultural and economic ties with their homeland. While in theory, the African diaspora should be making strong contributions to developments back home, the evidence does not at present show a strong impact of capital, creativity, and knowhow from the diaspora. Thus, there exists a large untapped market for the contributions of the African diaspora for African development, especially in relation to using diaspora networks to support public projects such as schools and health facilities, and let professional networks share information with relatives and friends back in the homeland. While diaspora remittances have become a proxy for brain drain and a key foreign exchange 18
Table A11: IV regression: The effect of migration on land tenure arrangements by NREGA start date

||(1)<br>fxed rent|(2)<br>sharecropping|(3)<br>fxed rent|(4)<br>sharecropping|(5)<br>fxed rent|(6)<br>sharecropping|
|---|---|---|---|---|---|---|
|migration|1.469*|-1.138|1.508|0.450|5.466|-4.604|
||(0.801)|(1.091)|(1.201)|(1.109)|(3.526)|(3.162)|
|banks|-0.0514|0.0880|0.0821*|-0.114***|-0.0327|0.0154|
||(0.0484)|(0.0556)|(0.0445)|(0.0351)|(0.0441)|(0.0422)|
|road length (km)|-0.00106|0.000105|0.00151|-0.000294|-0.000671|0.00123|
||(0.000786)|(0.000764)|(0.00136)|(0.00108)|(0.00137)|(0.00113)|
|year 2012|0.0527|-7.608|-10.39|-5.021|-4.005|-4.432|
||(5.612)|(5.877)|(6.636)|(5.950)|(7.808)|(6.834)|
|observations|296|296|202|202|375|375|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). Columns (1) and (2) are districts with NREGA start = 1, Columns (3) and (4) are districts with NREGA start = 2, Columns (5) and (6) are districts with NREGA start = 3. The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.