rafmacalaba/datause-encoder-data huggingface dataset:
implementation phase were important in changing management practices. The control firms typically did not adopt the more complex practices like daily quality meetings, formalizing the yarn monitoring process or defining roles and responsibilities.

Fifth, the non-experimental plants in the treatment firms also saw a substantial increase in the adoption of management practices. In these 5 plants the adoption rates increased by 17.5%. This increase occurred because the owners of the treatment firms copied the new practices from their experimental plants over to their other plants.

# **V. THE IMPACT OF MANAGEMENT ON PERFORMANCE**

Previous work has shown a strong correlation between management practices and firm performance in the cross-section, with a few papers (e.g. Ichniowski et al. 1998) also showing this in the panel.<sup>16</sup> Our unique panel data on management practices and plant level performance, coupled with the experiment, enables us to examine the extent to which these relations are causal. We begin with a panel fixed-effects specification: OUTCOMEi,t = αi + βt + θMANAGEMENTi,t+νi,t (2) where OUTCOME will be one of the key performance metrics of quality, inventory and output. The concern is that management practices are not exogenous to the outcomes that are being assessed, even in changes. For example, a firm may start monitoring quality only when it starts to experience a larger than usual number of defects, which would bias the fixed-effect estimate towards finding a negative effect of better management on quality. Or firms may start monitoring product quality as part of a major upgrade of workers and equipment, in which case we would misattribute quality improvements from better capital and labor to better management.

To overcome this endogeneity problem, we instrument the management practice score with log(1+weeks since the implementation phase began)<sup>17</sup> . We use this logarithmic form because of the concave adoption path of management practices shown in Figure 2, with the results robust to alternative functional form specifications such as linear or quadratic. The exclusion restriction is that the intervention affected the outcome of interest only through its impact on management practices, and not through any other channel. A justification for this assumption is that the consulting firm focused entirely on the 38 management practices in their

> 16 Note that most papers using repeated surveys have found no significant panel linkage between management practices and performance (Cappelli and Neumark (2001) and Black and Lynch (2004)). 17 Note that this is defined as zero for control plants and for treatment plants pre-implementation.

14
**Table 4. Trade and Terms of Trade (TOT) Changes, 1988-98**

||Low-Income<br>Countries|Middle-Income<br>Countries|High-Income<br>Countries|
|---|---|---|---|
|**Trade as a Share of GDP (percent)**|32.63|34.20|29.65|
|**Annual Change in Trade Share (percent)**|0.45|3.00|3.39|
|**_Standard Deviation_**|13.83|8.22|2.65|
|**Annual Change in TOT (percent)**|-1.00|-0.55|0.03|
|**_Standard Deviation_**|15.57|8.40|2.40|
|**Annual Change in TOT as Ratio to GDP**<br>**(percent)**|**-0.41**|-0.38|0.02|
|**_Standard Deviation_**|4.91|3.47|0.74|

Source. Bank Staff calculations.

America and Southeast Asia experienced TOT gains. In Paraguay, Guatemala, Panama and Argentina real income grew on average 0.35 percentage points faster than real output, and in Indonesia, Malaysia, Thailand and the Philippines the average gain was around 0.5 percentage points. The difference in standard deviation between the country groupings is remarkable. The TOT and the equivalent gains and losses in terms of GDP are much more volatile in MICs than in high-income countries.

# **VI.** A **CLASSIFICATION OF MICS**

_50._ The MICs vary from very small countries to huge federal systems, and from countries that are still poor to much better-off transition economies and relatively sophisticated emerging market economies. As the sections above show, the problems and development challenges faced by the MICs vary enormously, and a 'one-size-fits-all' approach is unworkable. It is nevertheless possible to classify the MICs into sub-groups that broadly share common characteristics. These sub-groups could be useful as a basis for further research and for guiding the policies of international institutions. The diversity among the MICs calls for an appropriately differentiated approach on the part of these institutions in defining their roles in these countries and formulating responsive support strategies.

_51._ Based on the foregoing discussion of the profile of the MICs in terms of their development challenges and integration into the international financial system, a threeway classification of these countries is proposed here (Table 5). While the classification is defined in terms of the different levels of international capital market access, it takes into account the other dimensions of the MICs discussed in this paper, including the extent of poverty and the quality of the policy and institutional framework. In general, these three dimensions are correlated: countries with higher capital market access tend to have lower poverty and a higher level of achievement in policy and institutional reform. As elsewhere in this paper, MICs are defined in the classification below to include all IBRD-eligible (including blend) countries: 18
Direct Democracy and Resource Allocation: Experimental Evidence from Afghanistan<sup>1</sup>

**Andrew Beath**<sup></sup> **Fotini Christia**<sup>†</sup> **Ruben Enikolopov**<sup>‡</sup>

1 The authors would like to thank Claudio Ferraz, Phil Keefer, Ben Olken, Ekaterina Zhuravskaya, and seminar participants at Columbia University, NES, MIT, European Economic Association Congress, NBER Political Economy Meeting, IIES, and Paris School of Economics for helpful comments. The authors are indebted to Kirill Borusyak, Hamid Gharibzada, Chad Hazlett, Vera Mironova, and Maiwand Siddiqi for excellent research assistance and gratefully acknowledge the generous cooperation and assistance provided by Tariq Ismati and Abdul Rahman Ayubi of the National Solidarity Programme; H. E. Wais Barmak, Minister or Rural Rehabilitation and Development; Ehsan Zia, Former Minister of Rural Rehabilitation and Development; staff of AfghanAid, C.H.A., InterCooperation, IRC, NPO/RRRAA, Oxfam UK, and People-in-Need; and Kamran Akbar, Josephine Bassinette, Ladisy Chengula, Philippe Dongier, Susanne Holste, Nicholas Kraft, Qazi Azmat Isa, Dean Jolliffe, Zishan Karim, Elliot Mghenyi, Norman Piccioni, Mariam Sherman, and Mio Takada of the World Bank. The study was financially supported by FAO, the World Bank, and the National Solidarity Programme. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors, or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use.

**_JEL Classification_** : H41; H43; D71; D72; D73. **_Keywords_** : Political Processes; Social Choice; Elections; Direct Democracy; Referenda; Local Governance; Public Goods; Resource Allocation; Decision Rules; Afghanistan. **_Sector Boards_** : Social Development (SDV); Agriculture and Rural Development (ARD); Public Sector Governance (PSM)

>  Office of the Chief Economist for East Asia and the Pacific, World Bank (abeath@worldbank.org)

† Department of Political Science, Massachusetts Institute of Technology (cfotini@mit.edu) ‡ New Economic School (REnikolopov@nes.ru)
<!-- Start of picture text --> Mean regression Median regression Average<br>o OVNM ° OVNM o OVNM<br>a a n<br>Se se oe<br>a" @" a"<br>a OBRA a OBRA a OBRA<br>Lo to Fol} >e<br>go gy . gy ~<br>fo} ‘ fe} a ° OROU bg<br>238‘ OcoL O45SY cri 238‘ Ocol~SOZAO“> cri 238‘ ONSHEXoOcol ~~zr ocr<br>Bo Oecu \ Bo Oecu ~ Bo Oecu ~~<br>al OPHL ~ =v OPHL ~ =v OPHL<br>OPER OPER OPER<br>2”29 Q9<br>-10 0 10 20 30 -10 0 10 20 30 10 0 10 20<br>% diff. with actual data % diff. with actual data % diff. with actual data<br>Avg. without outliers Including total GDP Including GDP*Informality<br>o Ovnm o OvNM o Ovnm<br>a no o<br>se 82 ge<br>o' oC o'<br>a OBRA a OBRA a OBRA<br>Qo” oy ‘ oY ><br>= R = R = ON “><br>2?fe)oo Oco.x S°U6 cr 2°fo2 Oca “SyO2Agcr! 2°fo° OmkDOcoL ~ ~ .“~ 4<br>oO OECU N OO OECU ‘ oO<br>sv OPHL \ sy OPHL ~ =v<br>OPER OPER OER<br>co]” lo}9 Qo9<br>-10 o 10 20 30 -10 o 10 20 30 56 0 5 10 15 20<br>% diff. with actual data % diff. with actual data % diff. with actual data<br><!-- End of picture text -->
45 -

-

# GRAPH 5a: ACTUAL COMMODITY PRICE CHANGES ALICPNF, FORECASTS AND ERROR BARS

<!-- Start of picture text --> &LICPNF _ FORECAST=<br>log<br>-. lOt199' X<br>-. 300.<br>i979 1980 198i 1980 i903 9ge4 1985 190C 1987 1900 1989<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988
network and = 0 otherwise. Following Thompson (1990), the population and unbiased, estimated variance of kk μ�H are shown in Appendix A.2. kk II

# **4. ACS and Simple Random Sampling**

ACS generally provides gains reducing what will be called here ‘fieldwork effort’, the number of blocks to be enumerated, particularly, when the underlying population of interest is rare and/or clustered (Borkowski and Turk, 2013).<sup>21</sup> ACS exploits data where concentrations of units of interest are in adjacent BAs (clustering) and where networks of these concentrations occur rarely: as such ACS can identify areas of notably high concentrations of these populations (Thompson and Seber, 1994). The definitions of relative clustering and rarity are specific to particular populations of interest; ACS has been shown to reduce fieldwork effort across a variety of fields, including rare populations of interest for the natural sciences.<sup>22</sup> The application of ACS to informal businesses presented in this paper is novel, however, and so the following sections discuss practical details for implementers interested in this application.

ACS implementers face a variety of design decisions that include the choice of expansion threshold , the delineation of the total geographic area , the size of BAs, the pre-set rule defining neighborhoods, and if stratification will be utilized. What follows is a simulation of different CC NN design choices and parameters to see how these choices perform against baselines based on SRS of BAs. The simulations use a unique data set from Eswatini (at the time in 2014, Swaziland) that provides a full census of all businesses operating in the country, including informal businesses. Uniquely, the data set includes GPS coordinates, and so allows for a real-life estimate of the

> 21 Fieldwork effort has also been referred to as ‘sampling effort’.

> 22 ACS has been used in a variety of fields, including ecology, ethology, and environmental studies. More specifically, ACS techniques have been used to estimate the density of freshwater mussels (Smith et al., 2003) sea urchins (Skibo et al., 2008) black sea bass (Cullen et al. 2017), yellow perch (Yu et al., 2011) and thresher sharks landings along the California coast (Hariharan et al., 2013); to monitor the populations of endangered species (Davis et al, 2011, Salehi et al., 2015); to determine infestation extent and spread rates of forest insect pest (Coggins et al., 2010); to identify disease levels in plants (Gattone et al., 2013) as well as their restoration levels (Bried, 2013), or simply the abundance levels of specific plant species (Philippi, 2005, Bowering et al., 2017). The conclusions of most of the studies above suggest that ACS tends to overperform other sampling techniques when populations are rare and clustered.

12
# **Figures**

Figure 1: Digitizing the archives of the Regional Court of Dakar Figure 2: Decree introduction and chamber dynamics over the study period (timeline)

|0<br>5<br>10<br>15<br>20<br>25<br>30<br>35<br>40<br>45<br>50<br>55<br>60<br>65<br>70<br>75<br>hearing period→|1st Commercial (early Feb 2014)<br>2nd Commercial (Dec 2013)<br>3rd Commercial (early Feb 2014)<br>4th Commercial (late Feb 2014)<br>1st Civil (Nov 2013)<br>2nd Civil (N/A)<br>3rd Civil (March 2014)|
|---|---|

Notes: The x-axis presents hearing periods, 0 being the first hearing in January 2012. Vertical lines indicate the decree application threshold in the different chambers (cf. legend). Horizontal lines show the operating timeline for the different chambers (cf. legend).

35
Gordon (2012), in an influential recent paper, argues that growth in the technological frontier country, the United States since 1906, has been slowing and that even if innovations continue at a steady rate, the U.S. economy faces headwinds from several directions, including demography, education, inequality, globalization, energy, unemployment, and debt overhang. He believes that the strongest headwind results from the interplay between globalization and modern technology, which has led to an acceleration of the process of catching up by the emerging economies and downward pressure on wages and real incomes in advanced economies.

Using recently compiled income inequality data for a large number of advanced and developing countries, Jaumotte, Lal, and Papageorgiou (2013) find evidence that globalization of trade reduced within-country income inequality and that financial globalization and foreign direct investment (FDI) increased it. They conclude that the main factor driving the rise in income inequality in the last two decades was technological progress, particularly in developing countries. Financial globalization and technological progress widened inequality because they increased the relative demand for skills and education.

There are also plenty of upside risks to the world economy. Technological developments related to green investments could lead to marked improvements in mitigating the risks of climate change. Revolutions in biotechnology and genomics could lead to vastly improved delivery of health services. And educational advancements could lead to social and political changes that in turn reduce income, gender, ethnic, and racial inequality (Franklin 2012).

This paper is concerned with the problem of long-term economic forecasting in the face of increased complexity and uncertainty. Its main objective is to identify lessons learned from past attempts at making long-term economic forecasts by focusing on selected exercises undertaken at the World Bank and other institutions. The paper uses “A Long-Term Outlook for the World Economy: Issues and Projections for the 1990s” (Fardoust and Dhareshwar 1990), which presented forecasts for the global economy in the 1990s, as the case study. We compare actual economic outcomes with the forecasts we made in 1990 in order to draw lessons about and seek guidance on conducting long-term forecasting.

A key novelty of the 1990s (and 2000s) was the very important interaction between the forces of globalization and modern technology. Many researchers believe this interaction brought an end 5
**Table 4.  The Export Response to Antidumping Imposed by High Income versus Emerging Markets: Change in Export Market Share**

||_F_<br>(total<br>High|_ull Sample_<br>AD cases=746)|_E_<br>_Exp_<br>(total<br>High|≡<br>_hijk_<br>_xports_<br>**_Chang_**<br>_orter j = China_<br>AD cases=126)|**_e in export m_**<br>_Exporte_<br>(total<br>High|**_arket share_**<br>_r j = High Income_<br>AD cases=352)|_Exporter_<br>(tota<br>High|_j = Other Eme_<br>l AD cases=26|_rging_<br>8)|
|---|---|---|---|---|---|---|---|---|---|
|Year|income<br>import<br>market_i_|Emerging<br>import<br>market_i_<br>_Test_<br>(1)|income<br>import<br>market_i_|Emerging<br>import<br>market_i_<br>_Tes_<br>(2)|_t_<br>income<br>import<br>market_i_|Emerging<br>import<br>market_i_<br>_Test_<br>(3)|income<br>import<br>market_i_|Emerging<br>import<br>market_i_<br>(4)|_Test_|
|**_t – 1_**|1.6<sup>a</sup>|1.9<sup>b</sup>|4.5<sup>a</sup>|4.3<sup>b</sup><br>|0.7|1.7|1.6<sup>a</sup>|0.8||
|**_t = year AD measure_** **_imposed_**<br>|-2.6<sup>a</sup>|-3.8<sup>a</sup>|-4.2<sup>a</sup>|-4.5<sup>b</sup>|-2.8<sup>a</sup>|-4.0<sup>a</sup>|-1.6<sup>a</sup>|-3.1<sup>a</sup>||
|**_t + 1_**|-4.0<sup>a</sup>|-5.2<sup>a</sup>|-7.3<sup>a</sup>|-6.0<sup>a</sup>|-4.1<sup>a</sup>|-6.0<sup>a</sup>|-2.4<sup>a</sup>|-3.8<sup>a</sup>||
|**_t + 2_**|0.1|0.0|1.6|1.7|-0.1|-0.5|-0.3|-0.1||
|**_T – 1_**|-0.1<br>|-0.8|1.9|0.6|-0.3|-0.7|-0.8|-1.5||
|**_T = year AD measureremoved_**|1.0<sup>b</sup><br>|1.3<sup>c</sup>|6.0<sup>a</sup>|5.3<sup>a</sup><br>|0.1|-0.1|0.1|1.4||
|**_T+ 1_**|0.9<sup>b</sup>|0.3|4.1<sup>a</sup><br>|4.3<sup>b</sup><br>|-0.7|-0.1|1.5<sup>a</sup>|-1.0|b|
|**_T + 2_**|0.6|0.1|2.9<sup>b</sup>|4.1<sup>b</sup>|-0.2|-1.1|0.6|-0.1||
|Total observations<br>||5,968||1,008||2,816||2,144||
|R<sup>2</sup>||0.03||0.11||0.03||0.03||
|AD cases of importers|399|347|65|61|182|170|152|116||
|Share of AD cases with full<br>export resumption*|45.4|48.7|72.3|77.0|33.0|35.3|48.7|53.4||
|Mean years between AD<br>imposed and removed|6.5|5.9|6.8|7.8|6.3|5.6|6.6|5.5||

Notes: The annual change in country _j_ ’s share of total world exports of product _h_ to policy-imposing country _i_ at year _k._ AD=antidumping. *The definition of an AD case resulting in export resumption is max{ _ExphijT_ +1 , _ExphijT_ +2} ≥ min{ _Exphijt_ 1 , _Exphijt_ 2} _,_ where _Exphijk_ here is the _level_ of export market share. Superscripts a, b, and c indicate statistical significance at the 1 percent, 5 percent, and 10 percent levels, respectively. “ _Test_ ” is the statistical test for whether the year’s estimated coefficient on the high income import market is statistically different from the coefficient on the emerging import market.

29
**Table 4: Contributions of High and Low‐CBA GPs to the declining ERR reporting** **<u>(1985‐2015)</u>**

||Percentage point<br>change*<br>**South A**<br>|Contribution by sector<br>(%)<br>**sia Region**<br>|
|---|---|---|
|(1) Contribution within High‐CBA sectors|‐23.9|85.9|
|(2) Contribution within Low‐CBA sectors|4.8|‐17.1|
|(3) Contribution from sector shifts (from High<br>to Low‐CBA)|‐7.9|28.4|
|(4)Residual|‐0.8|2.8|
|SAR‐Total Reduction in Reporting|‐27.8|100.0|
||**Wor**|**ld Bank**|
|(1) Contribution within High‐CBA sectors|‐25.7|73.1|
|(2) Contribution within Low‐CBA sectors|2.5|‐7.1|
|(3) Contribution from sector shifts (from High<br>to Low‐CBA)|‐11.9|33.9|
|(4)Residual|0.0|0.1|
|World Bank –Total Reduction in Reporting|‐35.2|100.0|

Sources: IEG, 2015, investment project only, and staff calculation Percentage point changes are calculated as: (1) = ΔERR_High_CBA*share of High_CBA_1975; (2) = ΔERR_low_CBA*share of Low_CBA_1975; (3) = ΔERR between High and Low CBA in 2014*Δshare of High_CBA; where ERR is the percent of projects with ERR calculations, and all changes are changes between 1975~79 and 2010~14 unless noted otherwise, where ΔERR is the difference in percent of projects reporting ERR at appraisal.

Table 4 uses the same methodology presented in the IEG 2010 study to examine which sectors have contributed most to the above trends for the South Asia Region and also for the whole Bank. There has been a moderate increase in ERR reporting among low‐CBA GP projects. However, this is not large enough to compensate for the reduction in reporting among high‐CBA sectors and the shift in the portfolio toward low‐CBA projects. For South Asia, of the total reduction of 28 percent in projects reporting ERRs at entry (or in the Project Appraisal Document or PAD), 24 percentage points arise from a reduction in reporting within high‐CBA sectors. This accounts for 86 percent of the total difference. Across all regions (including SAR), the total reduction is 35 percent and 25.7 percentage points arise from a reduction in reporting within high‐CBA sectors, which accounts for 73 percent of the total reduction. The increase in low‐CBA projects in the portfolio slightly offsets the reduction in the frequency of ERRs reported in the PAD, more so in SAR compared to the whole Bank.

Table 5 shows that over time, the reduction in ERR reporting within high‐CBA sectors appears to be contributing increasingly to the total reduction in ERR reporting. Specifically, between 1985 and 1995, 15 percent of the overall reduction in ERR reporting was due to a reduction in reporting within high‐CBA sectors; 88 percent of the reduced reporting of ERRs was due to shifts from high‐ to low‐CBA projects, reflecting the Bank’s shift towards more projects in the social sectors and more programmatic approaches. By 1995‐2015, 92 percent of the reduction in reporting was due to a reduction in reporting within the high‐CBA sectors. Also, over time, increased reporting within the low‐CBA sectors has played a greater role in offsetting the lower reporting within low‐ and high‐CBA sectors.

17
is also overstated to the extent to which provisioning falls short of expected losses. This is why under the Basel II internal ratings based (IRB) approach, the amount by which provisioning is short of expected losses is required to be deducted from regulatory capital.

At the center of the current debate are the proposals on impairment by accounting standard setters. After the initial divergence in their separate proposals, the International Accounting Standards Board (IASB) and the U.S. Financial Accounting Standard Board (FASB) reached a compromise and issued a joint proposal on an expected loss model in January 2011. It focuses on the timing of the recognition of expected losses in open portfolios that are measured at amortized cost.<sup>25</sup> The proposal answered some of the questions raised during the deliberation (see table 2) and would better address prudential regulators‘ concerns about provisioning and bring accounting rules more in line with the expected loss proposal put forward by the Basel Committee on Banking Supervision (BCBS).<sup>26</sup> However, deliberations are still ongoing concerning the comments received on the proposal and issues that were not addressed in the joint proposal.

**Table 2. Deliberation Considerations Concerning Expected Loss Provisioning**

|**Issue**||**To be c**|**onsidered**||
|---|---|---|---|---|
|Which expected<br>losses(EL)|Over the life or<br>shorter?|All EL or only ―mo<br>occur(for single in|re-likely-than-not‖ to<br>struments)|Through-the-cycle<br>or not|
|When are_initial_||Allocation over life||Upfront (likely be|
|loss expectations<br>recognized?|Integrated into<br>interest rates|Separately as an<br>annuity|Separately<br>straight-line|same treatment for<br>changes in<br>estimates|
|How are_changes_<br>in loss estimates<br>treated?|Full catch-up to<br>P&L in period of<br>change|Time<br>proportionate<br>catch-upto P&L|No catch-up in<br>P&L (adjust<br>prospectively)|Or combination<br>based on good/bad<br>book|
|Allowance<br>account floor|No floor||Floor (for example,|―incurred‖ losses)|

_Source_ : IASB.

The latest discussion results in two important points, although many details remain to be sorted out.<sup>27</sup> First, the expected losses are defined as estimated credit losses over the remaining life of a financial asset. This would allow all relevant information, including forward-looking information, in estimating expected losses. But the timeframe is for the life of the portfolio, not through the cycle, which means a certain degree of procyclicality may still remain. Also, the measurement of

> 25 See IASB (2009) and FSAB (2010) for the initial exposure drafts on their respective proposals on impairment and IASB (2011a) for the joint proposal on an expected loss model for impairment.

> 26 See BCBS (2010c) for comments on IASB‘s Exposure Draft, _Financial Instruments: Amortised Cost and Impairment_ (IASB 2009).

> 27 The discussion that follows is based on ongoing deliberation of the IASB and FSAB at the time of this writing and may be subject to further changes. See IASB (2011b).

13