rafmacalaba/datause-encoder-data huggingface dataset:
they should phase out within a decade, allowing the unsubsidized market to take over. (An exception is made for subsidies targeted to institutions serving the poorest and most costly customers.) Our analysis of global data shows that subsidies in fact continue to be important in microfinance, even for older institutions. Summing across the 1,335 institutions, the total subsidy – both implicit and implicit -- was $4.9 billion per year.<sup>4</sup> Of the total subsidy, 76% went to the 932 institutions that are older than ten years. Most (99.95%) of the subsidy takes the form of equity grants and cheap capital rather than direct donations. We conclude with reflections on next steps for a more transparent policy conversation around the optimal use of subsidy in the microfinance market.

# **1. Method and data**

The data are from the global database of microfinance institutions collected by the MIX Market. Within the microfinance sector, the MIX Market is responsible for collecting and disseminating financial data on microfinance institutions, and its database is the largest industry data source on the finances of microfinance institutions.<sup>5</sup> The raw data reflect local reporting standards, and the MIX Market adjusts the data to help ensure comparability across institutions when measuring financial performance. We begin with the MIX Market adjustments and then make further adjustments. MIX Market adjustments are made for inflation, the cost of subsidized funding, current-year cash donations to cover

> 4 The data use the most recent observation in the period.

> 5 Participation in the MIX database is voluntary, and the microfinance institutions in the sample tend to feature institutions that stress financial objectives and profitability (though the database has become more broadly representative as it has expanded over time). The skew is shown by Bauchet and Morduch (2010) who calculate that the average operational self-sufficiency ratio (a measure of organizational efficiency) of institutions reporting to the larger, socially-focused Microcredit Summit Campaign database is 95 percent, compared to 115 percent for institutions reporting to the MIX Market. Scores above 100 percent reflect “operational self-sufficiency.”

5
Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker. 2016. "The Great Plunge in Oil Prices: Causes, Consequences and Policy Implications." Policy Research Note 1, World Bank, Washington, DC.

Baker, S., N. Bloom, and S. Davis. 2015. "Measuring Economic Policy Uncertainty." NBER Working Paper 21633, National Bureau of Economic Research, Cambridge, MA.

Bayoumi, T., and A. Swiston. 2009. "Foreign Entanglements: Estimating the Source and Size of Spillovers Across Industrial Countries." _IMF Staff Papers_ 56: 353-383.

Bems, R., R. C. Johnson, and K. M. Yi. 2010. "Demand Spillovers and the Collapse of Trade in the Global Recession." _IMF Economic Review_ 58(2): 295-326.

Berkmen, S. P., Gelos, G., Rennhack, R., and Walsh, J. P. 2012. "The Global Financial Crisis: Explaining Cross-Country Differences in the Output Impact." _Journal of International Money and Finance_ 31(1): 42-59.

Bivens, J. 2014. "The Short- and Long-Term Impact of Infrastructure Investments on Employment and Economic Activity in the U.S. Economy." Economic Policy Institute. June 29.

Bloom, N. 2009. "The Impact of Uncertainty Shocks." _Econometrica_ 77 (3): 623-685.

Borio, C. and H. Zhu. 2012. "Capital Regulation, Risk-Taking and Monetary Policy: A Missing Link in the Transmission Mechanism?" _Journal of Financial Stability_ 8: 236-51.

Bowman, D., J. Londono, and H. Sapriza. 2015. "U.S. Unconventional Monetary Policy and Transmission to Emerging Market Economies." _Journal of International Money and Finance_ 55: 27-59.

Brayton, F., T. Laubach, and D. Reifschneider. 2014. "The FRB/U.S. Model: A Tool for Macroeconomic Policy Analysis," Board of Governors of the Federal Reserve System FEDS Notes.

Bruno, V., and H. Shin. 2015a. "Capital Flows and the Risk-Taking Channel of Monetary Policy," _Journal of Monetary Economics_ 71:119-32.

<u>.</u> 2015b. "Cross-Border Banking and Global Liquidity." _Review of Economic Studies 82_ (2), 535-564.

Cardarelli, R., and M. A. Kose. 2004. "The Economic Impact of U.S. Budget Policies." in Christopher Towe and Martin Muhleisen ed., U.S. Fiscal Policies and Priorities for Long-Run Sustainability, _IMF Occasional Paper_ No: 227. International Monetary Fund, Washington, DC.

Carriére-Swallow, Y.. and L. F. Céspedes. 2013. "The Impact of Uncertainty Shocks in Emerging Economies." _Journal of International Economics_ 90(2): 316-325.

27
|**COMPARATI**|**VE ANALYSI**<br>(1)|**TABLE 2: SUM**<br>**S of SOUTH AF**<br>Dependent varia<br>(2)|**MARY of COEF**<br>**RICA vs other AF**<br>ble: Automotive E<br>(3)|**FICIENTS**<br>**RICAN AUTOMO**<br>xports(log)<br>(4)|**TIVE PRODUCERS**<br>(5)|
|---|---|---|---|---|---|
||Base case|Base case with<br>main controls|Base case with<br>additional<br>controls|Multiple year and<br>interaction<br>dummies|Multiple year, country<br>and interaction dummies<br>(fixed effects)|
|**SA * >95**<br>**(effect MIDP)**|**0.769**<br>[0.244]*******|**0.785**<br>[0.186]*******|**0.649**<br>[0.234]*******|-||
|SA|**3.924**<br>[0.166]*******|**3.289**<br>[0.162]*******|**3.273**<br>[0.182]*******|**3.404**<br>[0.357]*******||
|>95|**0.453**<br>[0.260]*****|0.041<br>[0.153]|-0.023<br>[0.158]|||
|Economic||**0.017**|**0.017**|**0.016**|0.006|
|density||[0.001]*******|[0.002]*******|[0.002]*******|[0.006]|
|Terms of trade||**-0.042**<br>[0.012]*******|**-0.043**<br>[0.012]*******|**-0.035**<br>[0.012]*******|-0.003<br>[0.012]|
|Infrastructure|||0.0006<br>[0.0005]|||
|Inflation|||0.002<br>[0.0014]|||
|GDP per capita<br>growth|||0.0006<br>[0.019]|||
|SA 93||||-0.316<br>[0.368]|-0.209<br>[0.328]|
|SA 94||||-0.050<br>[0.380]|-0.137<br>[0.211]|
|SA 95||||2.332<br>[0.465]|0.266<br>[0.264]|
|SA 96||||0.514<br>[0.416]|**0.413**<br>[0.195]**|
|SA 97||||0.705<br>[0.536]|**0.682**<br>[0.375]*|
|SA 98||||0.777<br>[0.579]|**0.664**<br>[0.237]***|
|SA 99||||**1.256**<br>[0.470]*******|**1.088**<br>[0.218]***|
|SA 00||||**1.159**<br>[0.645]*****|**1.101**<br>[0.218]***|
|SA 01||||**1.586**<br>[0.747]******|**1.237**<br>[0.503]**|
|SA 02||||**0.948**<br>[0.539]******|**0.619**<br>[0.400]*|
|SA 03||||0.648<br>[0.531]|0.482<br>[0.337]|
|SA 04||||-0.061<br>[0.415]|0.022<br>[0.411]|
|SA 05||||-0.598<br>[0.491]|-0.438<br>[0.524]|
|||||0.195|-0.183|
|SA 06||||[0.504]|[0.434]|
|Year Dummies||||Yes|Yes|
|Country<br>Dummies||||_-_|Yes|
|Constant|**9.248**<br>[0.206]*******|**12.485**<br>[1.247]*******|**12.578**<br>[1.234]*******|**11.668**<br>[1.234]***|**9.689**<br>[1.266]***|
|Observations|90|86|86|86|86|
|R-squared|**0.67**|**0.87**|**0.88**|**0.90**|**-**|

Robust std errors in brackets * significant at 10%, ** significant at 5%, *** significant at 1% 23
concludes.

# **2 Export survival: What do we know?**

At the product level, the determinants of export survival have been explored by a small but growing literature. Besedes and Prusa (2006) used two panels of U.S. imports, one spanning 1972-88 with tariff-schedule data, the other spanning the 1989-2001 period with 10-digit data (the Feenstra-NBER dataset). In both cases, they found that half of all trade relationships lasted only one year and three quarters lasted three years or less. Once censoring was taken into account, median duration was two years. Most strikingly, this pattern of short duration was robust to aggregation at HS6, even though one would expect interruptions to be smoothed out by aggregation. They also found negative duration dependence, meaning that the hazard rate fell as export spells grew older. This finding, however, has been recently contested by Brenton, Saborowsky and von Uexhull (2010).<sup>1</sup> In terms of survival determinants, Besedes and Prusa (2006) found that industrial-country exports lasted longer, and so did exports of machinery, a finding confirmed by the analysis of Asian trade flows by Obashi (2010).

Besedes and Prusa (2006) explored the determinants of export survival further by testing the implications of a search model proposed by Rauch and Watson (2003) in which importers search for low-cost suppliers and exporters invest optimally in production capacity in the face of moral hazard (risk of non-payment). Such model implies that, in general, smaller initial transactions have a lower life expectancy; however, differentiated goods, where moral hazard is highest, involve both smaller initial transactions _and_ longer life expectancy. The model’s predictions are upheld by Cox regressions on U.S. import data using Rauch’s (1999) index of product differentiation as a regressor. That is, the hazard rate is 23% higher for homogenous products than for differentiated ones, although initial transactions are 40% to 350% larger. In related work, Besedes (2008) also finds supports for the Rauch-

> 1Brenton et al. argue that the assumption of proportional hazards, which is needed for Cox regressions to be valid, typically does not hold in export-duration samples (this can be verified using a Sch ̈ _o_ nfeld test). Using the alternative Prentice-Gloeckler (1978) estimator, they find no duration dependence. Brenton et al.’s critique applies to the quasi-entirety of the export-survival literature.

4
of other Commonwealth of Independent States (CIS) economies was US$ 40 per ton of crude oil in 1999.

2.25 Such a sizeable subsidy costs oil producers but also gives them leverage on the government, including in the area of tax reform: as long as they subsidize the national economy, how can the government expect to capture a greater share of the oil rent?

2.26 Unrealized rent also arises out of waste during extraction or production, e.g. if oil is left in the ground. Worse, rent is dissipated if the level of investments is insufficient to keep production levels constant. Russian oil wells have not been kept up, so their productive capacity has deteriorated and value has been wasted. This trend is hardly visible from one year to the other, as the volume extracted is relatively stable at 300-320 million tons, but over a period of 50 years, this appears more clearly, as the chart in Annex illustrates. Production peaked at 570 million tons in 1987 and declined steadily until 1996, after which it recovered slightly. Although it seems evident that waste exists, it is difficult to value it. Conservatively, this value will therefore be left at zero.

2.27 Next, Table 8 estimates the realized rent, or more exactly the recorded rent since it is assumed that there is little or no illegal extraction to speak of. Recorded rent comprises the rent captured through taxes and other levies, the entrepreneurial rent (normal profit), and the appropriated rent (excess payment).

Table 8. Average Current Appropriated Oil Rent (licensed oil scheme)

||19|99|200|0|
|---|---|---|---|---|
||No<br>transfer<br>pricing|Transfer<br>pricing|No<br>transfer<br>pricing|Transfer<br>pricing|
|Revenues (US$/ton)|69.2|69.2|110.3|110.3|
|Productioncosts(US$/ton)a|19.4|19A4|_26.6_|26.6|
|Depreciation(US$/ton)|2.5|2.5|2.4|2.4|
|Normalprofit(US$/ton)b|15.9|15.9|15.3|15.3|
|Taxtake(US$/ton)|27.9|16.6|48.0|28.5|
|Appropriatedrent(US$/ton)|3.5|14.7|18.0|37.5|
|Totalforegonerevenues(US$mn/yr)|1,071|4,492|5,760|12,001|

a) Including operating costs and net capital investments, without taxes and depreciation.

b) 20% return on equity defined as market capitalization Sources: IFEI (2000).

2.28 Revenues represent the weighted average of export and domestic oil sales prices. Export prices are DAF' Iprices. Domestic prices are the prices charged for oil delivered at the

> " Delivered at frontier, i.e. export gate.

17
using _T_ variables _x_<sup>_b_</sup> _mt_<sup>that take values -1 and 1 in ascending vs.descending MPL in choice</sup> _t_ and 0 otherwise. In a parallel manner, we may define _x_<sup>_s_</sup> _mt_<sup>to equal 1 when A is presented</sup> on the left and -1 when it is on the right (either across choices or choice specific).

In order to relax the symmetry assumption and pin down the relative size of the bias for each option without additional MPL data (see below), researchers may choose to conduct outside validation. For example, they could ask (some) subjects to review and potentially revise the MPL responses, as in Yu et al. (2021), or measure the rates at which subjects renege on their original decision, as in Maffioli et al. (2020).

# **3.4 Extensions**

**Multiple MPL.** We assumed above that we are measuring only one WTP parameter which expresses the relative willingness to pay between options A and B. In practice, researchers may want to estimate the WTP of the same subject for different options from different MPL. This can be achieved in one estimation by creating dummy variables that identify WTP for different option pairs. When estimating relative WTP between a set of different options, it is also possible to impose constraints on the WTP coefficients, such as transitivity. For example, the researcher may estimate _P_ ( _eit < α_<sup>1</sup> _zm_<sup>1+</sup><sup>_α_2</sup><sup>_z_</sup> _m_<sup>2+</sup> _α_<sup>3</sup> _zm_<sup>3+ ∆</sup><sup>_t_+</sup><sup>_βbxb_</sup> _mt_<sup>+</sup><sup>_βsxs_</sup> _mt_<sup>+</sup><sup>_ui_),where</sup><sup>_α_1denotestherelativeWTPforoption1over</sup> option 2, _α_<sup>2</sup> denotes the relative WTP for option 2 over option 3, _α_<sup>3</sup> denotes the WTP for 1 over 3, and the dummies _zm_<sup>_j_indicate the corresponding MPL. We would expect that</sup> _α_<sup>1</sup> + _α_<sup>2</sup> = _α_<sup>3</sup> .

**Covariates and WTP heterogeneity.** The researcher may also want to allow for heterogeneity in WTP for groups of subjects or examine treatment effects of an experimental intervention. This can be straightforwardly accommodated by including group dummies or covariates and treatment indicator variables.

**Other framing effects.** We have focused our arguments on order biases. However, our proposed approach offers a framework for investigating other sources of variation in measured WTP. For example, as in Andersen et al. (2007), the researcher might want to test the effect of varying the range of values covered – the range of ∆ _t_ – or the total number of binary decisions _T_ within a given range. Differences in elicited WTP can be measured using dummies, in the same way we estimate order effects above. Again, the researcher would have to impose assumptions about the relative size of bias in any two different ways

21
# Inequality in China: An Overview

______________________________________________________

## John Knight<sup>1</sup>

**________________________________________________________________________**

JEL Classification: D31, D63, O15.

Keywords: China, Gini coefficient, Household surveys, Income inequality, Social instability, Subjective well-being.

> 1 Emeritus Professor, Department of Economics, Manor Road Building, University of Oxford, OX1 3UQ, <u>john.knight@economics.ox.ac.uk</u> , and China Institute of Income Distribution, School of Economics and Business Administration, Beijing Normal University. I am grateful to Ravi Kanbur, Li Shi, Martin Ravallion, Terry Sicular, and the editor and three referees for their helpful comments.
# **5. Results**

The presentation of the results of the experiment is divided into two parts. In the first part, we examine differences across the survey assignments for key labor statistics on the individual’s main activity: labor force participation, weekly hours, daily earnings, the sector of work, and type of work (employment status). We compare the statistics produced for three comparisons: 1) the short module compared to the detailed module, 2) responses given by proxy compared to self-reported responses, and 3) the short module and proxy respondent interaction compared to the other three groups. The latter is of interest because the combined short-proxy assignment reflects the least expensive approach which is frequently used. We then estimate the survey assignment effects using standard models (OLS, probit, multinomial logit) controlling for observed household and individual characteristics. In principle, we expect the multivariate results to be similar to the first set of results, due to the randomized experimental design, and this is indeed what we find.

In the second part, we analyze whether the characteristics of proxy respondents and/or the difference in characteristics between the proxy respondent and the individual on whom the proxy reports affect the labor statistics. Again, we first examine the mean statistics, and then run multivariate regressions where we control for individual characteristics of the proxy and the interaction between individual characteristics of the proxy and the subject.

20
**Energy Prices and International Trade: Incorporating Input-Output Linkages**<sup>_∗_</sup>

H. Ron Chan<sup>_†_</sup> Edward Manderson<sup>_‡_</sup> Fan Zhang<sup>§</sup> University of Manchester University of Manchester World Bank Keywords: Energy cross-subsidization, carbon leakages, energy costs, exports, competitiveness, input-output linkages JEL Classifications: Q4, Q5, F1

> _∗_ This paper is part of a broader analytical effort to assess the cost of power sector distortions in South Asia being conducted by the Office of the Chief Economist of the World Bank South Asia Region. We thank Vivien Foster, Martin Rama, Yadviga Viktorivna Semikolenova, Govinda Timilsina, Maria Vagliasindi, and seminar participants at the University of Toulouse for helpful comments on earlier drafts. Jiarui Wang provided excellent research assistance. Financial support from the Trade and Competitiveness Multi-Donor Trust Fund and the Partnership for South Asia Trust Fund is greatly appreciated.

> _†_ School of Social Sciences, University of Manchester, Arthur Lewis Building-3.078, Oxford Road, Manchester, M13 9PL, United Kingdom. Contact: ron.chan@manchester.ac.uk

> _‡_ School of Social Sciences, University of Manchester, Arthur Lewis Building-3.078, Oxford Road, Manchester, M13 9PL, United Kingdom. Contact: edward.manderson@manchester.ac.uk

> §World Bank, 1818 H Street NW, Washington, DC, USA. Contact: fzhang1@worldbank.org
**Table 16: Do inflation expectations feed into actual inflation?**

||**Dependent V**<br>Households'<br>|**ariable: CPI Inflat**<br>Expectations<br>|**ion (%)**<br>Professional<br>Expec<br>|Forecasters'<br>tations<br>|
|---|---|---|---|---|
|Lagged Inflation|0.90<sup>***</sup>|0.90<sup>***</sup>|0.62<sup>***</sup>|0.61<sup>***</sup>|
||(8.36)|(8.46)|(4.46)|(4.38)|
|Inflation Expectations|-0.10|-0.11|0.39<sup>**</sup>|0.41<sup>**</sup>|
||(1.14)|(1.34)|(2.66)|(2.66)|
|Inflation Targeting|6.14<sup>*</sup>|5.85|2.77|2.75|
||(1.79)|(1.64)|(1.51)|(1.48)|
|Inflation Expectations|-0.75<sup>**</sup>|-0.71<sup>*</sup>|-0.73<sup>**</sup>|-0.70<sup>*</sup>|
|× Inflation Targeting|(2.09)|(1.90)|(2.09)|(1.97)|
|Output gap (% of||-0.12||-0.09|
|GDP)||(0.93)||(0.81)|
|Constant|1.72<sup>**</sup>|1.91<sup>**</sup>|0.13|0.03|
||(2.04)|(2.25)|(0.14)|(0.04)|
|Observations|53|53|44|44|
|Adjusted_R_<sup>2</sup>|0.84|0.84|0.86|0.86|

Note: Robust t statistics in parentheses; *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

# **7. COVID-19 and credibility**

The question of the day is how an inflation-targeting central bank should respond to an exceptional shock like the Covid-19 pandemic. In practice, we have seen central banks around the world, including in emerging markets and developing countries, cut interest rates sharply and engage in a wide range of credit-market operations. This response contrasts with the response to past external crises affecting EMDEs when central banks were reluctant to cut interest rates significantly for fear of fanning inflation expectations. That inflation expectations in a number of EMDEs are now better anchored than before, we would argue, is part of the explanation for why they have been able to do more.<sup>42</sup> This is the case of India. On March 27, 2020, the RBI cut the repo rate by 75 basis points, the reverse repo rate by 90 basis points, and the Cash Reserve Ratio by 1 percent.<sup>43</sup> It followed by another 25 basis point reduction in the reverse repo rate and lowered the liquidity coverage ratio required of banks to 80 percent of the previous requirement. The RBI then further reduced the repo rate by 40 bps on May 22, for a total reduction of 115 bps between March and May 2020. The reverse repo rate was also decreased by 40 bps on May 22, 2020.

> 42 We would not deny that there is also a role for Federal Reserve (and advanced country central bank policy in general) in the contrast.  That the Fed responded so aggressively to the crisis opened up space for central banks, including in EMDEs, to do more.

> 43 In addition, <mark>the requirement of minimum daily CRR balance maintenance was also reduced from 90 percent to 80 percent for 3 months, while th</mark> e borrowing limit for the Marginal Standing Facility (MSF) was increased from 2 to 3 percent of the SLR.

26
are presented in Table A4 of the Appendix. Both the direction and the magnitude of the estimated relation between employment in mining and per capita income growth are similar to those obtained using the (conditional) measure of employment in mining. For example, increasing the (unconditional) share of employment in mining by one standard deviation in a community located inside a homeland (0.037) is associated with a 4.8 percentage point higher growth rate, similar to that obtained using the conditional measure of employment in mining.

## **6.3 Complementary evidence from individual-level data**

To shed light on the underlying mechanisms driving the interaction between the dismantling of coercive institutions and rent-sharing we use individual-level data from the 10% sample of the 1996 South African population census. We restrict the sample to men between the ages of 15 and 65, working as an employee (i.e. not self-employed or working in a family business). In Table 5 we present results for regressing the log of individual wage on indicator variables for black, working in mining industry and its interaction.<sup>24</sup> In alternative specifications we account for individual attributes such as age, highest attained education indicators, occupation indicators, and province or district fixed-effects.

Consistent with the presence of resource rents in the mining industry and with the high degree of unionization relative to other industries, we find a large wage premium of around 40 percent associated to employment in this industry. At the same time, there is a large income gap against black workers of approximately 50 percent—in line with the lasting impacts of coercive institutions in place before the end of apartheid. We also observe that black mining workers initially benefited less from resource rents than non-blacks. This individual-level evidence is therefore fully consistent with the mechanisms emphasized in the main analysis of the paper.<sup>25</sup>

# **7 Alternative explanations**

Though our results point to the importance of the interaction between the dismantling of coercive institutions and rent-sharing in the mining industry, the heterogeneity in spatial income across former homeland communities could in part be reflecting alternative ex-

> 24Note that income is available in categories. We imputed the mid-point of each category and truncated the highest category at its initial value (that is, 30,001+ rand was assigned value of 30,001).

> 25We would have liked to use similar data from the 2011 census in order to examine how the wage premium for black mining workers evolved over time. Unfortunately, the 2011 individual-data released by Statistics South Africa do not contain information on industry of employment or occupation. While this information was part of the census questionnaire, it has been excluded by Stats SA from the released files due to incomplete coding.

17