rafmacalaba/datause-encoder-data huggingface dataset:
Table A2). A methodological lesson is that multicollinearity is not an extremely serious issue in our case: the point estimates of the backward and forward measures do not change much when we include the 2‐digit variable.<sup>24</sup> Similarly, including the three variables sequentially yields consistent point estimates. Consequently, we include all three variables in the different specifications.

# **Table 4 Main regression results**

|**Dependent:**||**Productivity (3‐**|**year growth)**|
|---|---|---|---|
||**Laborpr**|**oductivity**|**TFP**|
|HGF Downstream share|0.044***|0.043***|0.053***<br>0.051***|
||(10.207)|(9.969)|(9.184)<br>(8.944)|
|HGF Upstream share|0.014***|0.014***|0.008<br>0.007|
||(3.398)|(3.360)|(1.334)<br>(1.281)|
|HGF intra‐industry share||0.010***|0.020***|
|||(2.927)|(5.028)|
|Region‐year FE|yes|yes|yes<br>yes|
|Sector‐year FE|yes|yes|yes<br>yes|
|Firm controls|yes|yes|yes<br>yes|
|Observations|140,594|140,594|140,594<br>140,594|
|R‐squared|0.216|0.216|0.170<br>0.170|
|**Dependent:**|**Employ **|**Size (3‐year**<br>**ment(ln)**|**growth)**<br>**Sales(ln)**|
|HGF Downstream share|0.002|0.001|0.028***<br>0.028***|
||(0.467)|(0.306)|(5.628)<br>(5.604)|
|HGF Upstream share|0.004|0.003|0.013**<br>0.013**|
||(0.850)|(0.793)|(2.555)<br>(2.546)|
|HGF intra‐industry share||0.012***|0.002|
|||(3.475)|(0.454)|
|Region‐Year FE|yes|yes|yes<br>yes|
|Sector‐Year FE|yes|yes|yes<br>yes|
|Firm controls|yes|yes|yes<br>yes|
|Observations|140,594|140,594|140,594<br>140,594|
|R‐squared|0.084|0.084|0.069<br>0.069|
|**Dependent:**||**Income (3‐ye**|**ar growth)**|
||**Average **|**wage (ln)**|**ROA**|
|HGF Downstream share|0.026***|0.027***|0.020***<br>0.019***|
||(5.338)|(5.616)|(4.131)<br>(3.920)|
|HGF Upstream share|0.011**|0.011**|0.004<br>0.003|
||(1.981)|(2.069)|(0.866)<br>(0.803)|
|HGF intra‐industry share||‐0.017***|0.015***|
|||(‐4.538)|(4.175)|
|Region‐Year FE|yes|yes|yes<br>yes|
|Sector‐Year FE|yes|yes|yes<br>yes|
|Firm controls|yes|yes|yes<br>yes|
|Observations|140,594|140,594|140,594<br>140,594|
|R‐squared|0.161|0.161|0.095<br>0.095|

> 24 A reason for this is that the same sectors are omitted from the uptream and downstream measures.

13
Table 7. Cumuladve **MIFA** Quota Growth **Rates** (%) Under the final **M[FA**

|**_Exporters_**|_TEX_<br>**_US/Canada_**|_TILES_<br>**_European Union_**|_WEARING_<br>**_US/Canada_**|_APPAREL_<br>**_European Union_**|
|---|---|---|---|---|
|KOR|70|60|10|33|
|TWVN|34|46|6|33|
|HKG|48|14|15|19|
|CHI|50|69|60|53|
|IND|113|71|114|100|
|MYS|113|65|110|66|
|PHL|116|0|103|0|
|THA|108|51|106|99|
|LTN|111|62|100|67|
|SAS|118|89|121|89|
|ROW|115|54|87|39|

_Source:_ S.Bagchi, International Textiles and Clothing Bureau, Geneva, Personal Communication.

26
Although ex-post adjustment can be made for such chance imbalances, this is less efficient than achieving ex-ante balance, and can not be used in cases where all individuals with a given characteristic are allocated to just the treatment group.

The standard approach to avoiding imbalance on a few key variables is stratification (or blocking), originally proposed by R.A. Fisher. Under this approach, units are randomly assigned to treatment and control within strata (or blocks) defined by usually one or two observed baseline characteristics. However, in practice it is unlikely that one or two variables will explain a large share of the variation in the outcome of interest, leading to attempts to balance on multiple variables. One such method when baseline data are available is pair-wise matching (Greevy et al, 2004, Imai et al. 2007).

The methods of implementing randomization have historically been poorly reported in medical journals, leading to the formulation of the CONSORT guidelines which set out standards for the reporting of clinical trials (Schulz, 1996). The recent explosion of field experiments in development economics has not yet met these same standards, with many papers omitting key details of the method in which randomization is implemented. For this reason, we conducted a survey of leading researchers carrying out randomized experiments in developing countries. This reveals common use of methods to improve baseline balance, including several re-randomization methods not discussed in print. These are (i) carrying out an allocation to treatment and control, and then using a statistical threshold or ad hoc procedure to decide whether or not to redraw the allocation; and (ii) drawing 100 or 1000 allocations to treatment and control, and choosing the one amongst them which shows best balance on a set of observable variables.

This paper discusses the pros and cons of these different methods for striving towards balance on observables. Proponents of methods such as stratification, matching, and minimization claim that such methods can improve efficiency, increase power, and protect against type I errors (Kernan et al., 1999) and do not seem to have significant disadvantages, except in small samples (Imai et al. 2008, King et al. 2007, Greevy et al.

> 38% in an experiment with 50 individuals, 27% in an experiment with 100 individuals, 9% for an experiment with 200 individuals, and 2% for an experiment with 400 individuals.

- 3 -
contrast, more than half of the $8.8 billion non-agricultural imports from GSP-LDCs did enter the US under the GSP program. Though apparel represented 13% of US imports from GSP-LDC countries, most of this was excluded from the GSP, leaving only 1% of apparel imports entering under the program.

_Are US non-agricultural preferences comprehensive?_ Whether or not countries underutilize US preferences may depend, in part, on how extensive those preferences are. We measure the scope of preferences by calculating coverage rates, which are defined as the ratio of eligible US imports to total dutiable US imports, where dutiable is defined as subject to duty if no preference program is claimed. Apparel coverage is problematic, since technically _no_ apparel product is eligible, _ex ante,_ for preferential tariff treatment. The regional programs all have product eligibility requirements—typically, rules of origin (RoOs) regarding the components of the garments—which may or may not be met. The AGOA program also requires country apparel eligibility, and grants nearly all AGOA-LDCs SRA eligibility. In this study, overall coverage rates for countries in these programs are calculated assuming that all apparel imports from eligible countries are “potentially eligible” for duty-free access. This yields apparel coverage rates of 100 percent.<sup>14</sup> In the case of CBERA-only and the GSP, apparel coverage rates are calculated based on actual product eligibility.

Table 1 shows coverage rates for all non-agricultural imports and for non-apparel and apparel imports separately. Data for regional programs are shown on the left, and for the alternative GSP program on the right. Nearly 100 percent of US non-agricultural imports from members of CBTPA and ATPA were eligible for preferences under these regional programs. With the exception of El Salvador, coverage rates for non-apparel imports were 90% or more under both these programs. For CBERA-only countries, the scope of regional preferences was more diverse. Six of the ten members had CBERA

> 13 Non-agricultural products are defined as all those not specified in Annex 1of the WTO Agreement on Agriculture. Detailed data are available from the authors upon request.  All values are based on imports in HS chapters 1-97.

8
Indeed, the importance of local linkages is determined by the size of its industrial base (e.g., employment in each industry) and the extent to which local industries can provide interme.diate goods for local firms (from the IO coefficient vector). In this case, our measure takes two important aspects of buyer-supplier linkages into account--fit and size. While computing the indicator, we noticed that the industry categories in the NIC system and in IO accounts do not have an exact match. Therefore, we first developed a concordance table between them before multiplying wf and elr. Data on input output transactions are from the Input Output Transactions Table 1993-94, Ministry of Statistics and Programme Implementation.

# _Economic Diversity_

In addition to buyer-supplier linkages, there are other sources of inter-industry externalities. Prominent among these is the classic Chinitz-Jacobs diversity. The diversity measure provides a summary measure of urbanization economies, which accrue across industry sectors and provide benefits to all **finns** in the agglomeration. Chinitz (1961) and Jacobs (1969) proposed that important knowledge transfers primarily occur across industries and the diversity of local industry mix is important for these extemality benefits. They argue that cities are breeding grounds for new ideas and innovations due to the diversity of knowledge sources concentrated and shared in cities. The diversity of cities facilitates innovative experiments with an array of processes, and therefore new products are more likely to be developed in diversified cities. Therefore, industries with Jacobs type externalities tend to cluster in more diverse and larger metro areas. (Recently, Duranton and Puga (1999) designed a model providing the microfoundations of a Jacobs-type model.) The benefits of locating in a large diverse area go beyond the technology spillovers argument. Firms in large cities have relatively better access to business 11
that consist of future pension obligations, the focus was by necessity on optimizing asset allocation policies and on a more intensive use of hedging instruments.

Even though asset allocation has been changed by, for example, the significant addition of foreign bonds with longer duration, the biggest impact was achieved by the growing use of derivatives to hedge against potential risks, which, as Figure 4 illustrates, has grown virtually exponentially since 2000.

_Figure 4: Insurance Companies and Pension Funds use of Derivative Contracts /1_ <!-- Start of picture text --> bn DKK<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 Q1 2000 Q2 2000 Q3 2000 Q4 2001 Q1 2001 Q2 2001 Q3 2001 Q4 2002 Q1 2002 Q2 2002 Q3 2002 Q4 2003 Q1 2003 Q2 2003 Q3 2003 Q4 2004 Q1 2004 Q2 2004 Q3 2004 Q4 2005 Q1 2005 Q2<br><!-- End of picture text --> Source and Notes: Danish FSA /1: The numbers do not include the substantial amount of written options the institutions carry on their balance sheets from investing in callable mortgage bonds.

The major areas of market risk exposure of the Danish pension sector are:

- **Interest rates** are without comparison the largest risk factor, because of the interest rate guarantees on the liability side and the lack of interest rate sensitivity on the asset side.

- **Equity prices** have also proven to be a considerable risk factor. Most Danish pension institutions seized the opportunities arising from deregulation to increase their holdings of equities, especially foreign, thus leaving them exposed to the more volatile equity market.

- **Currency exposure:** Investments in foreign assets (especially foreign equities) are usually not hedged back to Danish Kroner. In the long run the expected return on this embedded currency exposure is zero, implying any gains and losses will even out over time. However, in the short to medium term currency fluctuations can be a

14
**Pollcy Research Working Paper Serles**

||**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|WPS828|How the Macroeconomic Environme<br>Affects Human Resource<br>Development|nt Arvil Van Adams<br>Robert Goldfarb<br>Terence Kelly|January 1992|V. Charles<br>33651|
|WPS829|Regulation of Securities Markets:<br>Some Recant Trends and Their<br>Implications for Emerging Markets|Terry M. Chuppe<br>Michael Atkin|January 1992|F. Harbottle<br>39616|
|WPS830|Fixed Parity of the Exchange Rate<br>and Economic Performance in the<br>CFA Zone: A Comparative Study|Ibrahim Elbadawi<br>Nader Majd|January 1992|V. Barthelmes<br>39175|
|WPS831|Real Overvaluation, Terms of Trade<br>Shocks, and the Cost to Agriculture<br>in Sub-Saharan Africa|Ibrahim Elbadawi|January 1992|V. Barthelmes<br>39175|
|WPS832|Sustainability and the Economics<br>of Assuring Assets for Future Gener|Richard B. Norgaard<br>ations|January 1992|J. Shin Yang<br>81418|
|WPS833|Stabilization and Growth Recovery<br>in Mexico: Lessons and Dilemmas|Daniel F. Oks|January 1992|L. Franchini<br>38835|
|WPS834|Scenarios for Growth in the 1990s|Shahrokh Fardoust<br>Jian-Ping Zhou|January 1992|J. Queen<br>33740|
|WPS835|Commodity Stabilization Funds|Patricio Arrau<br>Stijn ClaAssens|January 1992|S. King-Watson<br>31047|
|WPS836|Sources of Income Inequality in Rural<br>Pakistan: A Decomposition Analysis|Richard H. Adams, Jr.<br>Harold Alderman|January 1992|C. Spooner<br>30464|
|WPS837|Manpower Planning in a Market<br>Economy wfRh Labor Market Signals|Arvil Van Adams<br>John Middleton<br>Adrian Ziderman|January 1992|S. Khan<br>33651|
|WPS838|Measuring Trade Policy Intervention<br>A Cross-Country Index of Relative<br>Price Dispersion|: Brian J. Aitken|January 1992|R. Martin<br>39065|
|WPS8?9|Regional Integration Under VERs:<br>When Trade Diversion is Unambiguo<br>Beneficial|David G. Tarr<br>usly|January 1992|D. Ballantyne<br>37947|
|WPS840|Public Sector Debt, Fiscal Deficits,<br>and Economic Adjustment: A<br>Comparative Study of Six EMENA<br>Countries|Alfredo E. Tho,ne<br>Azita Dastgheib|January 1992|L. Ly<br>37352|
|WPS841|How Access to Contraception Affects<br>Fertility and Contraceptive Use in<br>Tunisia|Susan Cochrane<br>David K. Guilkey|January 1992|0. Nadora<br>S6-065|
social ties and repayment discipline instilled by group monitoring remain intact even with a shift to individual liability. Further, since they focus on pre-formed groups, they are not able to measure or identify the presence or importance of screening in group liability contracts.

Identifying the impact of group liability on outcomes such as default rate is complicated by the standard problems of selection and omitted variables bias. Individuals with different financial habits might choose to take one form of contract but not the other. Alternatively, lenders with different levels of sophistication may attract different client mixes, and offer different contracts. One cannot simply compare clients across lending contracts, since self-selection or other aspects of the program may be the root cause of any observed differences.

In this paper, we use a natural experiment to compare loan repayment and savings discipline between group liability and individual liability contracts.<sup>2</sup> Our empirical strategy takes advantage of a change in lending policies of Saath, a non-government organization providing microfinance services in India. Saath switched from individual liability to group liability lending. This transition was governed by a strict policy rule: after a particular date, all borrowers completing an individual liability cycle were subsequently switched to group liability in their next loan. Individual liability loan completion dates were distributed relatively uniformly throughout the year, which offers a natural variation in switching of loan contracts. Thus, in July for example, individual liability borrowers finishing a loan cycle would switch to group liability in the following loan, while those whose loan cycle ended after July would remain under an individual contract setting until the end of the cycle. This plausibly exogenous change was phased in over time, generating natural control groups, and allows us to credibly identify the causal impact of group liability structure. Further, since we have time series data both before and after group liability is introduced, we can study the dynamics of group borrower formation.

Consistent with Ghatak (1999 and 2000), we find evidence of assortative matching. Specifically, we find that the formation of groups is endogenous to borrower risk types: safe borrowers are significantly more likely to form joint liability groups with other safe borrowers, even controlling for other factors such as physical proximity.

Our main analysis focuses on loan performance and estimates the joint effect of assortative peer matching and ex-post peer monitoring under group liability. We find that group liability structure

> 2Throughout this paper, we use the terms “group liability” and “joint liability” interchangeably.

3
<!-- Start of picture text --> Age profile for LFP - Nepal<br>1970 aL)1368 1sSe_io_<br>—<br>£ 4 9875 a=|. ieee aT 1965 1 360s<br>ce _ 10 ; ee<br>Ld 198) 1 13975id ——___ 196<br>3S o0-Ee Gages, 19001988 1385<br>4 _<br>a + {aed —— i<br>1 Sri.<br>ax<br>u zt Fig bs Bd a id<br>Age<br>* 1990-04 145 1980-84 — 150889<br>104 eso —Ci- nM —— BM<br>3-fa Eo 190- —— T<br>= 2004<br><!-- End of picture text -->

<!-- Start of picture text --> 25 years 30 years<br>100<br>90<br>80<br>Zz 7<br>Q<br>romSo 60<br>5 50<br>5<br>€ 35 years 40 years<br>$100<br>‘S 90<br>2<br>g *<br>So 70<br>ao 60<br>50 Sanne<br>1950 1955 1960 1965 1970nEann Rn1975 1980EEE1985 1990 1995 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Nepal<br><!-- End of picture text --> <!-- Start of picture text --> 25 years 30 years<br>60<br>DoO 50<br>Ss 40 ZY<br>‘32 3<br>G 2<br>co 10<br>5<br>= 35 40<br>6 60 years years<br>2<br>3%<br>é 40<br>so<br>20<br>10 OO<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Bangladesh —— India —— Maldives<br>—— Pakistan<br><!-- End of picture text -->
Table A.6: Number and composition of peers invited to visit the ADC

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**A: Number of peers **|**sought**||||
|Own Voucher<br>BAF Voucher|0.180***<br>[0.048]<br>0.509***<br>[0.081]|0.181***<br>[0.047]<br>0.510***<br>[0.080]|0.187***<br>[0.046]<br>0.531***<br>[0.082]|0.204***<br>[0.047]<br>0.561***<br>[0.081]|
|Observations|612|607|603|603|
|Endline control mean|0.050|0.050|0.050|0.050|
|p-value: Own = BAF|0.000|0.000|0.000|0.000|
|**B: Sought company **|**from sister-in**|**-law**|||
|Own Voucher|0.050**<br>[0.020]|0.051**<br>[0.020]|0.051**<br>[0.020]|0.053***<br>[0.020]|
|BAF Voucher|0.179***|0.180***|0.181***|0.186***|
||[0.032]|[0.032]|[0.033]|[0.032]|
|Observations|595|590|586|586|
|Endline control mean|0.003|0.003|0.003|0.003|
|p-value: Own = BAF|0.001|0.001|0.001|0.000|
|**C: Sought company **|**from a non-re**|**lative**|||
|Own Voucher|0.021|0.021|0.026|0.031*|
||[0.016]|[0.016]|[0.017]|[0.017]|
|BAF Voucher|0.089***|0.089***|0.099***|0.103***|
||[0.025]|[0.026]|[0.027]|[0.028]|
|Observations|595|590|586|586|
|Endline control mean|0.009|0.009|0.009|0.009|
|p-value: Own = BAF|0.020|0.020|0.015|0.011|
|Balancing controls|No|Yes|Yes|Yes|
|Other balancing covari|ates<br>No|No|Yes|Yes|
|Village FE|No|No|No|Yes|

_Notes:_ Balancing controls are the baseline variables used to balance randomization, i.e., whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module. Other baseline covariates comprise baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and asset index. Variable definitions are in Online appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

54
He arrived at three broad conclusions in the case when goods are substitutes in consumption and production, and there is no two-way trade in the good intended for exports. An increase in the duty drawback, when none has been in place in the initial equilibrium, is welfare improving up to a point. Increases in tariffs on inputs complemented by full duty drawbacks are welfare improving up to a point. Tariffs on inputs unaccompanied by duty drawbacks on exports have an ambiguous effect on welfare.

In more recent years economists have focused their attention on duty drawbacks and the speed of trade liberalization. In general there is no agreement on this issue. Gruen (1999) argues that ‘concessional’ trade liberalization instruments enable the trade liberalization path to be specified in a flexible way ensuring targeted and gradual trade reform, which may not be possible using traditional tariff dismantling.<sup>8</sup> Cadot et al. (2003) however show that in a political-economy setting, where tariffs and duty drawbacks are endogenously chosen through industry lobbying, full duty drawbacks granted to exporters slow down trade liberalization since they decrease exporters’ incentives to counter-lobby against high tariffs on their inputs.

There is also no consensus whether countries should embrace duty drawbacks or whether and when they should abandon them. In the past the World Bank recommended in their loans the creation or improvements of duty drawback and temporary admission regimes (Krueger and Rajapatirana, 1999). A World Bank study by Thomas et al. (1991) also made a strong case in favor of duty drawbacks as instruments of export promotion when there is import protection. But more recently the IMF (International Monetary Fund, 2002) warns against the use of duty drawbacks and other indirect tax incentives since they

> 8 The political economy climate in a country is an important reason for choosing a duty drawback scheme over liberalization. In China, for instance, the government could never have gotten a full free trade regime in the late seventies, but it could get an agreement to liberalize tariffs on imports used in the production of exports.

5
One potential concern with this baseline version of the Bartik instrument is that it uses the contemporary number of native individuals in a given European region. As a result, the instrument may capture spurious effects due to the potentially endogenous native population growth in a destination region – which could be due, for example, to internal labor mobility of native workers in response to local demand shocks. To address this concern, we construct a second version of the Bartik instrument in which we fix the native population at the initial year (2002, in our case) as suggested in <mark>Mitaritonna, Orefice, and Peri (2017). We f</mark> irst define: where _Mst_ is the number of migrants in time _t_ in destination NUTS2 region _s_ ; _Ms,2002_ is the number of migrants in destination region _s_ in 2002; _Meu,2002_ is the number of migrants in the EU15 in 2002; and _Meu,t_ is the number of migrants in the EU15 in year _t_ . Then the instrument for the share of migrants _Pct_migst_ based on <mark>Mitaritonna, Orefice, and Peri (2017) –</mark> denoted MOP instrument, hereafter – is: where _Ns,2002_ is the number of natives in destination region _s_ in 2002.

The Bartik instrument is valid under the condition that the past migration networks (which create differential exposure to common immigration shocks) do not have a direct effect on natives’ occupational choices – such as, in our case, the allocation between jobs that are economically exposed or not exposed to COVID-19 (Goldsmith-Pinkham, Sorkin and Swift, 2020). In other words, the distribution of immigrants across regions of Europe in 2002 should be orthogonal to subsequent labor market shocks in the region, conditional on region and year fixed effects. Therefore, our identification strategy posits that the reasons to co-locate where previous immigrants lived are driven by preference and information networks, and arguably not by regionspecific labor market shocks.

A standard concern in the literature is that the initial distribution of immigrants is correlated with persistent local factors that also influences natives’ allocation across occupations. As discussed 23