rafmacalaba/datause-encoder-data huggingface dataset:
transmitting them along the supply-chain (deep pockets firms) weakens the creditchain propagation mechanism (Kiyotaki and Moore, 1997).

This theory has received some empirical support. Raddatz (2008) provides evidence of the presence and relevance of credit-chains for the transmission and amplification of shocks. Boissay and Gropp (2006) find evidence in favor of the existence of trade credit default chains. In particular, firms that face default are themselves more likely to default. Liquidity shocks are transmitted along the trade credit chain until they reach deep pockets firms, which ultimately absorb the shock. Thus, by extending the maturity period of trade credit to their defaulting customers, deep pocket firms not only stop the propagation of the liquidity shocks, by relaxing the financial constraints faced by their direct customers, but also “invert” it, relaxing their customers’ customers financial constraints, and so on going back upwards along the supply chain.

This suggests that there may be external effects associated to supply chain productions which might amplify the downsides of a credit crunch. However, if it is true that inter-firm trade finance is a mechanism of propagation of shocks especially for firms operating along supply chains, it is also true that the repeated business interactions among these firms may provide relevant benefits, especially during a financial crisis. The typical fear that lack of trust in times of extreme uncertainty may squeeze intermediated trade finance, exacerbating the effects of the crisis, may be less of a problem for firms operating along supply chains. These are often involved into long-term relationships and thus less likely to experience an “uncertainty driven” contraction in financing. Anecdotal evidence reported by practitioners<sup>11</sup> shows that the increase in the perception of risk induced by the crisis has promoted supply chain solutions and that supply chain finance is being increasingly used to mitigate risk and increase firm’s capital needs.

This might also explain why trade credit is often countercyclical.<sup>12</sup> In times of recession banks are more concerned about credit risk and less willing to extend credit. Firms that rely more on relational contracts can increase their reliance on trade credit, thus making up for the higher credit risk with the relationship of trust with the supplier. Conversely, firms that rely on intermediated finance (formal contracts) are likely to be squeezed by the credit contraction, since the higher credit risk and the lack of a credit history, will discourage suppliers from extending them credit.

> 11 Reference in Euromoney Seminars (2009).

> 12 Calomiris (1995) and Love _et al._ (2007) for example show that in the U.S. and emerging markets respectively the extension of trade credit increases during financial crisis.

12
# CONTENTS

# ACKNOWLEDGEMENTS

|||Pane|
|---|---|---|
|PREFACE<br>the C|by Shelton H. Davis, Latin America and<br>aribbean Environment Division,||
|The W|orld Bank<br>..........|i|
|SU.MARY|<br>............................................<br>*|1|
|1INTR|ODUCTION......................................<br>.|6|
|1.1.|STUDY OBJECTIVE .................................|<br>6|
|1.2.|THE CONVERGENCE PROSPECTUS ............*..|7|
|1.3.|CONSERVATION<br>AS AN INDIGENOUS STRATEGY .........|. 9|
|1.4.|REPORT STRUCTURE..<br>.<br>.................<br>..<br>PART ONE: WORLD VIEW|12|
|2.THE|INTERNATIONAL CONSERVATION MOVEMENT..............|13|
|2.1.|INTRODUCTION: INEVITABLE POLARITIES.............|13|
|k.2.|THE WORLD CONSERVATION STRATEGY PROPONENTS......|13|
|2.3.|THE INDIGENOUS PERSPECTIVE<br>......................<br>2.3.1. Indigenous Survival International:|15|
||<br>World Conservation Strategy Amendment<br>....|16|
|2.4.|UNESCO MAN AND THE BIOSPHERE PROGRAM............|17|
|2.5.|NON-GOVERNMENTAL ORGANISATIONS<br>..................|19|
||2.5.1.<br>International<br>Conservation<br>Organisations.|. 19|
||2.5.2. Indigenous Support Organisations.... ......|20|
||2.5.3. Conservation<br>NGO's in Developing Countries|<br>20|
||2.5.4. The Animal Welfare/Protection Movement...|21|
first quarter of 2009 (Bank of Thailand 2011), with exports falling about 45% in the last half of 2008. Domestic demand could not pick up the export shortfall because of rising food and fuel prices that suppressed consumption. The Thai government responded by boosting its spending and making credit cheaper. In particular, it increased spending to boost wage and salary payments to the lowestpaid public sector employees, raised payments to pensioners, subsidized utilities for the poor, capped some food prices, lowered interest rates, provided credit guarantees, and spent on temporary job creation. The question of interest to us is whether the government measures were effective in helping households weather the storm of the crisis.

There is a large literature on how households cope in times of economic shocks and crisis. Within the developing-country literature, many studies have examined the varying impacts of aggregate shocks (that affect entire communities) compared to idiosyncratic shocks (such as illness or loss of employment that affects a specific household) (Paxson, 1993; Townsend, 1995; Storesletten et. al. 2001). Households can cope with sudden risks through a number of mechanisms, including drawing on savings, reducing consumption, relying on transfers/remittances, and seeking alternative sources of credit and employment through help from the government and other sources (Besley, 1995; Dercon and Krishnan, 2000; Mukherjee and Nayyar, 2011). However, coping strategies also depend on the nature of the shock - Alderman (1996), for example, uses a household panel data set from rural Pakistan that contains detailed income and credit data, and finds that households have more difficulty in smoothing consumption after successive shocks than with a single shock, thereby capturing the limitations of self-insurance.

A number of recent papers (focused mostly on industrialized-country contexts) have examined the role of government spending in mitigating shocks, particularly in the context of the 2008 financial crisis. One confounding issue is that the public anticipates that the government will step in to solve a crisis, and this expectation affects how they respond to government policy. The government‘s ability to commit to a policy response affects these expectations as well (Ennis and Keister, 2010). Endogenous spending dynamics can also center around the government‘s own 3
_Journal of Development Economics_ 106 (2014): 199–210.

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19
# **7   Appendix**

## **Appendix Table 1**

|||**Wording of decision-making**<br>**questions**|**Decisions covered in**<br>**[X]**|**Additional variables**<br>**collected: Individual-**<br>**level proximate**<br>**determinants/**<br>**outcomes of agency**|
|---|---|---|---|---|
||(1)|“Would you say that [X] is:<br>_(a) mainly your decision, (b)_<br>_mainly your_<br>_(husband's/partner's) decision, or_<br>_(c) did you both decide_|- Using contraception|- Preferences for fertility<br>and family planning<br>- Marital status<br>- Education<br>- Age at first marriage,|
|**Demographic**<br>||_together?”_||sexual intercourse, when<br>had first child|
|**and Health**<br>**Surveys (DHS)**<br>– sample:<br>women 15-49<br>and, in some<br>cases, their<br>husbands/<br>partners; 89<br>countries|(2)|“Who usually makes decisions<br>about [X]:<br>_(a) respondent, (b)_<br>_husband/partner, (c) respondent_<br>_and husband/partner jointly, (d)_<br>_someone else, (e) other?”_|- How respondent’s<br>earnings will be used<br>- How<br>husband/partner’s<br>earnings will be used<br>_(also asked of spouse)_<br>- Health care for<br>respondent<br>- Large household<br>purchases (_also asked_<br>_of spouse)_<br>- Visits to<br>family/relatives|<br>- Employment and type<br>of earnings<br>- Ownership of land<br>- Health outcomes<br>(anemia, illness/disease)<br>- Experienced<br>physical/sexual violence<br>(including whether<br>sought treatment)<br>- Constraints to seeking<br>health care<br>- Use of mass media and<br>technology (including<br>owning a mobile phone)|
|**Women’s**<br>**Emrmnt**|(1)|Did you participate in [X] in the<br>last 12 months (that is, during the<br>last one/two cropping seasons)?<br>_(Y/N)_|- Food crop farming<br>- Cash crop farming<br>- Livestock<br>- Nonfarm economic<br>activities<br>- Wage and salary<br>employment<br>- Fishing|- Marital status*<br>- Education*<br>- Employment and type<br>of earnings*<br>- Participation in<br>community organizations<br>(agricultural producer’s<br>groups, local|
|**powee**<br>**in Agriculture**<br>**Index (WEAI)**<br>– sample: adult<br>women and their<br>husbands/<br>partners|<br>(2)|How much input did you have for<br>[X, and separately, income<br>generated from X]:<br>_(a) no input or input in few_<br>_decisions, (b) input into some_<br>_decisions, (c) input into most_<br>_decisions, (d) input into all_<br>_decisions, (e) no decision made_|- Food crop farming<br>- Cash crop farming<br>- Livestock<br>- Nonfarm economic<br>activities<br>- Wage and salary<br>employment<br>- Fishing|government, credit or<br>microfinance, etc.)<br>- Confidence in voicing<br>concerns in public forums<br>(five-category response)<br>- Individual time diary on<br>activities in the last 24<br>hours, and individual<br>satisfaction with leisure<br>i l f 110|
||(3)|For different types of productive<br>capital [X], who would you say:<br>|- Agricultural land<br>- Large livestock<br>-Small livestock|tme (scae o -)|

54
Table 1. Summary Statistics

|Panel A: Tax Variables|Mean|SD|P10|P50|P90|
|---|---|---|---|---|---|
|Rate Diferential|0.043|0.108|-0.091|0.030|0.183|
|Rate Diferential, EU Hosts|0.015|0.087|-0.091|0.000|0.124|
|Rate Diferential, Non-EU Hosts|0.000|0.014|0.000|0.000|0.000|
|Non-Host Component|0.289|0.168|0.000|0.337|0.474|
|Non-Host Component, EU|0.228|0.153|0.000|0.282|0.391|
|Non-Host Component, Non-EU|0.004|0.039|0.000|0.000|0.000|
|Host Component|0.246|0.157|0.000|0.249|0.435|
|Statutory CIT Rate|24.587|7.301|16.000|25.000|35.000|
|Host KF-METR|13.535|8.838|2.833|13.080|24.661|
|Panel B: Investment Variables||||||
|I/K (FAI)|0.416|0.862|0.000|0.191|0.954|
|I/K (FDI)|0.421|1.592|0.000|0.054|0.790|
|FAI (mil. USD)|469.338|2730.990|0.000|4.385|666.202|
|Lag K (mil. USD)|1835.101|17882.450|0.000|13.134|2054.840|
|Log(FAI)|2.355|2.797|0.000|1.478|6.502|
|Panel C: Weights||||||
|Sales Share (Lagged)|0.027|0.127|0.000|0.000|0.023|
|Capital Share (Lagged)|0.036|0.159|0.000|0.000|0.024|
|Export Share (lagged)|0.060|0.176|0.000|0.001|0.142|
|Panel D: Zeroes||Share||||
|I/K (FAI)||33.79 %||||
|I/K (FDI)||44.98 %||||

_Notes_ : There are 12,630 observations in our main sample. Panel A shows summary statistics for taxrelated variables. The (statutory corporate tax) rate differential term is defined as the weighted difference scaled by (100-host country statutory rate) and multiplied by _α_ , as in equation (4). The statutory rate and KF-METR are shown in percentages. Panel B shows summary statistics for investment variables. _I/K_ ratios are investment flow divided by lagged capital stock, for the type of investment indicated (foreign affiliate investment and foreign direct investment, respectively). The summary statistics for _I/K_ in the FDI dataset are associated with a different sample (N=17,442). Panel C summarizes the data used to construct the rate differential: weights by sales, capital and exports. Panel D shows the proportions of observations for FAI and FDI in our main sample that are zero.

50
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25
15 States have a secondary education or higher, fully 88 percent of all immigrants to the OECD have this level of education. Moreover, of these secondary-educated immigrants to the OECD, over two-thirds -- 69 percent -- have a tertiary education.

However, it is important to emphasize that these estimates of the number of educated migrants probably overestimate the true extent of the brain drain to the OECD for two reasons. First, levels of educational attainment for migrants to the OECD must be estimated, rather than observed as in the United States, because the OECD does not collect data on the educational characteristics of immigrants. For example, since the educational distribution of Turkish migrants to the OECD must be estimated on the basis of U.S. data, and the educational distribution of these OECD migrants might be quite different from that of their United States-bound compatriots, the figures in Table 7 might over-estimate the extent of the brain drain. Second, as in the United States, published data on the number of immnigrants to the OECD exclude the large (and unknown) number of illegal immigrants to these countries, who are likely to have lower levels of education. For instance, it is likely that a large number of illegal immigrants from North Africa to the OECD have low levels of education, simply because rates of educational attainment in these North African countries are low.1<sup>5</sup> Table 7 presents migration rates by educational category for each labor-exporting country. Focusing on the tertiary level of education, the available data suggest that migration to the OECD causes more of a brain drain than it does in the United States. For 5 of the 13 countries for which data exist, more than 10 percent of those with a tertiary education have migrated to the OECD. For these five countries, the rates of

> 15 For example, in Morocco and Tunisia about 76 percent of the population over age 25 has a primary school education or less.
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27
34

**Figure 2. Sectoral Aid Composition of the Median Post-Conflict Country after Peace Start**

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>Pre-peace Peace-onset Post-conflict 1 Post-conflict 2<br>Humanitarian Government & Institutions<br>Budget Support Productive<br>Social Infrastructure & Communications<br>% of total<br><!-- End of picture text --> Note: The productive sector is made up of agriculture, industry, banking, and business. The social sector is made up of education, health, population, and water. The infrastructure and communications sector is made up of transport, communications, and energy. The figure is based on all the conflict countries that have RER misalignment data. See Appendix A for details.
relationsimp between the interacton term ot financial development or depth ot the financial system and external dependence and real growth in value added.

# **VI. Data and Measurement Caveats**

We note a number of caveats and qualifications with respect to our analysis. First, there is the qus^;no **fuh.** velibiiyo **d,~4ata+** dA..g crse.iMny **_._** leve.v aable **,,.L** rec **+...;** a **lag t.ad.** oe 'J%Aw~.W. **lwax'** "&W **IWI1A"WA"tjP** **_MJL~_** _%s"LL"r,_ %ILL% **IVJ.UIIY 1LILLUI-IVV%L VCGLIGUII..O** I **Va'L WI UI aG IrLU OuVF.I1'** shocks. Firm performance variables such as growth in value added tend to respond to financial crises with a lag. Perhaps growth in value added is not a good measure of firm performance, in particular during periods of crisis. In addition, financial development indicators such as credit to GDP tend to increase during periods of crises as GDP decreases to a larger extent than credit.

Second, determining the precise timing of crises is difficult, both in terms of identifying the **_4_** ~~ **'ninA** beE,=n..g **IJ~'&W1I** **_fka_ II''** **_p"A_ '1I1 6 41A4** r.dfheer. **_nfr_** of **.J±~11GW** a c.sis **_nr.co_** A **V A ..** **_-,_** .si **'IOIa11(13UULIAUj** **_cvo_** .. **-,n** ay **k ...** ld **A** **_-t_** p **_ni-nul-_ OLU** lol **V IY 13UIIU** a.drla **_.- A_ 111(a7 -nn. nn** not **LU** be **_U~_** r-esoleson **I** **_I-.a_** N **_.A_ CU U 0'JUh1.** Especially in the case of a typical V-shape pattern of recovery from a crisis it is crucial to get the timing right in order not to under- or overestimate the impact on firm performance. We use the data in Caprio and Klingebiel (2002) to define the beginning of a crisis and allow for a certain degree of mistiming by using a three year period around the Caprio and Klingebiel (2002) year as crisis period. Third, measures for the size of the financial system relative to GDP may not be good proxies for financial dev,elopmren.t. Durg periods of credit boomns, ofen. pr-eceding financial crises (see Kaminsky and Reinhart, 1999), for example, (private) credit over GDP may overstate the level of financial development or depth of the financial system. In addition, the political-economy of the policy responses to a financial crisis could affect the availability of credit in crises (see Kroszner (1998) and Klingebiel, Kroszner, Laeven and Van Oijen (2001)).

15