rafmacalaba/datause-encoder-data huggingface dataset:
# - 85 -

revenue in favour of the more uncertain returns signified by conservation. The research should look at the few examples that have been devised and assess their applicability.

# 8.2.4. Vernacular Economy

This term is borrowed from architectural history, in which the expression "vernacular architecture" signifies building that is specific to a place and which relies upon local materials. In this context, it refers to economies based directly upon local resources, used either for subsistence or as a source of revenue. Vernacular economy corresponds to the "third option" - proposed during the Bank seminar - for economic evolution which may emerge when indigenous and industrial economies come into contact. The mixed cash- subsistence economies that often result from such contact are interpreted by some as merely a transitional phase ir an inevitable process of assimilation. But others take this as evidence of an evolutionary process by which certain features of the indigenous economy are retained and combined with useful features of the industrial economy. The result is a changeable, but not necessarily. assimilative system - and one that eludes conventional economic analysis.

Vernacular economies may include agriculture but, to the extent that they rely upon wild resources, they may be compatible with wildland conservation. There will be thresholds of domestication or habitat manipulation beyond which this consonance begins to fade but the focus of this research would be upon the harvesting a wild resources. As such, it addresses the question of the economic potential of wild genetic material that was raised during the seminar.

Though indigenous economies may frequently adopt a mixed cashsubsistence aspect, and thus conform to this notion of vernacular economies, this is not intended as an exclusive category. Like the term 'area planning" this perspective short-circuits the problem of defining 'indigenous' by looking directly at a relatively neutral practice and its associated economic and social relationships.

The research would focus upon a wide range of case studies in different regicns. The object would be to isolate the common features and indicators which distinguish this form of remote community economy and to examine the mechanisms by which the economic potential of wild genetic resources may be realized so as to obtain the maximum local benefit. This would be useful to development planners when assessing the impacvs of development projects upon remote communities and to conservation planners when developing appropriate activities for conservation areas and for buffer zones surrounding protected areas. Four main research areas are listed below.

HARVESTING AND PRODUCTION: Methods for taking wild plant and animal resources. For increasing production in situ or for intensive production methods such as captive breeding and acquaculture. Contribution of new methbds in relation to traditional techniques.
<mark>Note: Each point represents the combination of employment to population ratio as (comparative data, Internet survey data). Average relative deviation is the average of relative values of absolute deviation of the Internet survey from the comparative data. Specifically, we calculate the average of abs(Onlinecomparative)/comparative.</mark> <mark>Data on other labor market indicators were collected by the survey, including the formal versus informal nature of employment, and type of employment. Respondents are classified to be working formally if they have a written contract, if they have any form of social insurance, and if they get annual paid or sick leave. Those who do not satisfy any of these three conditions are deemed to be working informally. The respondents are classified as self-employed if they own a business or business operated by the household or a family farm. They are instead classified as an employee if they work as an employee for another house, or another company, or as an apprentice, trainee, etc. The questions related to formality and self-employment are asked only to those who responded that they are</mark> _<mark>currently</mark>_ <mark>employed at work, and not to those employed but not</mark> _<mark>currently</mark>_ <mark>at work. Therefore, the survey sample size for these questions is lower than for the previous graph.</mark> <mark>The comparative data for formal and self-employment are available for fewer countries and fewer time periods. Figure 5.3 and figure 5.4 present a scatter plot of the formal share between online surveys and the comparative data, and a scatter plot of the self-employment share between online surveys and the comparative data, respectively. For the propensity score adjusted online estimates of formal employment, the deviations from the comparative data range from 12.7 percent to 55.04 precent, with an average of 39.1 percent. Without weight adjustments, the deviations ranged from 14 percent to 29 percent, with a lower average of 21 percent. The average of bias reductions due to propensity score weight-based adjustments across countries/waves is - 1.20 percent. Here again, the weight adjustment worsened the accuracy of Internet sample surveys. For the propensity score adjusted online estimates of self-employment, the deviations from comparative data range from 6 percent to 377 percent, with an average of 166.9 percent. Without weight adjustments, the deviations ranged from 38 percent to 402 percent, with a higher average of 210 percent. The average of bias reductions due to propensity score weight-based adjustments across countries/waves is 26 percent. In this case, the weight adjustment improved the accuracy of Internet sample surveys.</mark> 20
firms in the COV-ES database that received at least some government support. While there is no general rule about the minimum number of observations necessary, we restrict our focus to the countries with at least around 150 firms that have received government support by the second round of COV-ES.

To apply our first criterion, we used the Map of SME-Support Measures in Response to COVID-19 created and made publicly available by the World Bank, retrieved in early March of 2021, and covering information as of late October 2020.<sup>4</sup> Our first criterion ruled out the overwhelming majority of countries covered in the Map. Additional countries were ruled out after supplementing information given in the map through country-specific searches of information about policies that we conducted. The vast majority of governments tried to increase the reach of their fiscal capacity by concentrating resources on the firms that were hit the most, often using revenue as a measure of this hit and using drops in employment and other characteristics. Our second criterion, a minimum of 150 firms receiving support by Round-2 of COV-ES, also eliminated several countries, mostly in Africa, where government policy coverage by October/November was limited. To the best of our knowledge, these two conditions combined ruled out all the countries covered in COV-ES except for El Salvador and Georgia.

Governments in El Salvador and Georgia announced a set of support measures to businesses as well as to individuals and families to help cope with the pandemic and with the strict lockdowns that were introduced to limit the spread of the virus. Support measures for private businesses in El Salvador mainly targeted small, micro, and medium-sized companies (MSMEs) as well as informal businesses, and included access to new credit; wage subsidies; fiscal incentives for investment in new productive projects; deferrals of utility payments; and other supports for simplification of procedures for MSMEs. The Georgian government also implemented support policies, with some targeting of the tourism sector (loosely defined as hotels and restaurants) and small firms. The support measures in Georgia included fiscal relief; subsidies, guarantees, or other forms of assistance for loans; deferral of payments; and micro-grant programs for new business projects. Appendixes A.1. and A.2. provide details of government support policies during the

> 4 - The Map is available here: https://dataviz.worldbank.org/views/SME <u>COVID19/Overview?%3Aembed=y&%3AisGuestRedirectFromVizportal=y&%3Adisplay_count=n&%3AshowAp pBanner=false&%3Aorigin=viz_share_link&%3AshowVizHome=n.</u> See also the IMF policy tracker, <u>https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19.</u>

7
# **_Table_** _of Contents_

Summary Findings i

**1.** Introduction _I_

II. Exchange Rate Arrangements 3

- A. Exchange Rate Unification 10

- B. Fixed versus Flexible Rates

- under Currency Convertibility 19

## III. Exchange **Rate** Reform and Supply Response 22

- A. Overview of the Theory 22

- B. Empirical Studies on Contractionary Devaludtion in Developing Countries 25

- C. Empirical Evidence on Contractionary Devaluation in SSA Countries 30

## IV. Empirical Evidence from Sub-Saharan Arrica 38

- A. Real and Nominal Devaluations Dynamic 38.

- B. Real Depreciations and Real Economic Growth _42_

- C. Inflation, Money Growth, Output Growth, and Devaluations: A Causality Test 49

- D. Real Export Growth, Output Growth, and Devaluations: A Causality Test _51_

- E. Conclusions _55_

Bibliography 59

## Text Tables

1. Multiple Foreign Exchange Markets in Sub-Saharan Africa 4

2. Parallel Markets Exchange Rate Premium (percent) 5 3. The Evolution of Exchange Rate Arrangements in Sub-Saharan African Countries: 1976-89 7 4. Exchange Rate Arrangements in Individual Sub-Saharan African Countries, 1990 _11_ _5._ Empirical Studies on the Effects of a Devaluation 27 6. Long-run Estimates of Aggregate Agricultural Supply Elasticity in Developing Countries 35 7. Export Values and Shares of World Exports for Selected Agricultural Commodities Elasticity in SSA Countries 37 8. 2SLS Instrumental Variables Results for the Augmented Mankiw, Romer and Weil Model with Pooled Time Series and Cross-Section Data, 1971-1991 47 9. Summary of Causal Inferences for Twenty Countries-SSA (1971-90) 52
approximately 26 percent greater than the estimated poverty rate from the status quo approach. Section 4 concludes with some discussion of the policy implications for Jordan and some of the measurement implications for other countries that base their estimates on repeat visits.

# **II. Data and Country Context**

We use the most recent round of Jordan’s Household Expenditure and Income Survey (HEIS) in 2010, a nationally representative survey used to produce official poverty statistics. The HEIS has been carried out nine times since 1966, and every other year between 2006 and 2010. Besides household expenditures, it collects data on other household characteristics including demographics, employment, assets, and incomes. This survey’s sampling frame comes from the 2004 Population and Housing Census and is divided into 89 strata (or sub-districts). The survey is usually administered over a 12 month period and follows a two-stage sampling design where census enumeration areas serve as primary sampling units (PSUs). For the 2010 survey round, 1,736 PSUs were selected in the first stage out of a total of 13,027 PSUs for the whole country using a systematic probability proportionate to size (PPS) sampling method. Within each selected PSU, 8 households were randomly selected in the second stage. The 2010 round of the HEIS collected consumption data for 11,223 households.

Structured more like a household budget survey, a key part of the survey is the food consumption section, which asks respondents about consumption of a broad range of 243 food items consumed over the previous week. This includes consumption through purchases from the market, from own production, as gifts, and eating outside the household. Food consumption data are collected from every household on a quarterly basis. Non-food data on the other hand are 5
an updated international poverty line and test robustness to that choice. Recognizing that the new PPPs also change the $US value of national poverty lines in the poorest countries, our international poverty line of $1.25 per day in 2005 is deliberately lower than the 2005 value in the US of our old international line. The new line is the mean of the national poverty lines for the poorest 15 countries in terms of consumption per capita. To test robustness of our main qualitative results to the choice of poverty line we also give results for a range of lines spanning $1.00 to $2.50 per day in 2005 prices. The lower bound (not to be confused with the old “$1-aday” line in 1993 prices) is very close to the national poverty line used by India, while the upper bound is the median of the poverty lines for all countries except the poorest 15. The $2.00 line is the median poverty line found amongst developing countries as a whole.

The third data source is the large number of new household surveys now available. We draw on 675 surveys, spanning 1979-2006 and 115 countries. (By contrast, the original RDV estimates used 22 surveys, one per country; Chen and Ravallion, 2004, used 450 surveys.) Our methods of analyzing these data follow Chen and Ravallion (2001, 2004, 2007). The international poverty line is converted to local currencies in the ICP benchmark year and is then converted to the prices prevailing at the time of the relevant household survey using the best available Consumer Price Index (CPI) for that country. (Equivalently, the survey data on household consumption or income for the survey year are expressed in the prices of the ICP base year, and then converted to PPP $’s.) Then the poverty rate is calculated from that survey. All inter-temporal comparisons are real, as assessed using the country-specific CPI. We make estimates at three-year intervals over 1981-2005. Interpolation/extrapolation methods are used to line up the survey-based estimates with these reference years, including 2005. We also present a new method of mixing survey data with national accounts (NAS) data to try to reduce surveycomparability problems. For this purpose, we treat the national accounts data on consumption as a Bayesian prior for the survey mean and the actual survey as the new information. We show that, under log-normality with a common variance, the mixed posterior estimator is the geometric mean of the survey mean and its predicted value based on the NAS.

Based on these new data and methods we show that the incidence of poverty in the world is higher, or at least no lower, than past estimates have suggested. However, we also find that the poverty profile across regions of the developing world and the overall rate of progress against absolute poverty are fairly similar to past estimates.

4
6 resulting subsidy rates are shown in Appendix Table A3, along with information on the (relatively small) tariffs on cotton imports.<sup>3</sup> For the US it amounts to a production subsidy in 2001 of 40 percent. This may be conservative, as it compares with a recent projection for 2004-06 of 56 percent with the Step 2 program intact and 46 percent if it is repealed without re-instrumentation (Sumner 2005, Table 3).

The other modification to the GTAP protection database is to take account of the completion of the implementation of the Uruguay Round Agreement on Textiles and Clothing by end-2004, at which time all trade-restricting quotas were abolished and were replaced by a tariff-only regime. We then recalibrate the model’s baseline by implementing that protection change, and the change in US cotton subsidy rates, before running the simulations described below.<sup>4</sup> receive a consumption subsidy to compensate for the price-raising effect of the export subsidies on cotton, the two elements in the Step 2 program sum to the equivalent of just a cotton production subsidy. That program’s payments amounted to one-seventh of total dollars of cotton supports during 2000-02 (Baffes 2005, Table 4) and, if it were not repealed, would amount to one-sixth during 2004-06 (Sumner 2005, Table 3). In any case they were found to be illegal recently by the WTO’s Dispute Settlement Panel and Appellate Body (WTO 2004b, 2005a). As a result, on 1 February 2006 the US Congress agreed to reform both components of the Step 2 program by 1 August 2006. How this reform impacts the market depends on the extent of re-instrumentation of that portion of the overall support, as discussed on Section 5 below. 3 No cotton subsidies are shown for China in the GTAP protection database, even though there have been some in past years. According to Huang, Rozelle and Chang (2004), the degree of protection varied from positive to negative during 2001 and the key intervention was an export subsidy. Since then China has committed to zero export subsidies, as part of its WTO accession agreement. Those zero entries in Table A3 also are consistent with the OECD’s recent Producer Support Estimates for China (OECD 2006), which for 2001 show no direct subsidy payments and a slightly negative nominal rate of protection for cotton producers. For an up-to-date assessment of China’s cotton policies, see Shui (2005). 4 US and other cotton subsidy programs have been the subject of intense analysis in recent years, although mostly by partial rather than general equilibrium modelers. For reviews of that literature, see FAO (2004), Baffes (2005) and Sumner (2006).
**Figure 7: EU27 Goods Imports by Product Category, Ranked by August 2009 YTY Growth**

<!-- Start of picture text --> ‐0.2%<br>‐10.8%<br>‐42.8%‐34.0% Manufactured goods<br>‐44.6%<br>‐42.5%<br>54.6%<br>0.6%<br>‐43.9%‐35.3% Mineral fuels, lubricants and related materials<br>‐47.6%<br>‐42.4%<br>48.9%<br>40.2%<br>‐25.5%‐15.9% Animal and vegetable oils, fats and waxes<br>‐33.6%<br>‐41.3%<br>10.0%<br>0.7%<br>‐47.4%‐36.7% Crude materials, inedible, except fuels<br>‐46.7%<br>‐39.1%<br>14.6%<br>‐0.9%<br>‐20.6% Total<br>‐27.8%<br>‐30.9%<br>‐28.5%<br>‐6.7%<br>‐3.9%<br>‐23.0%‐22.7% Beverages and tobacco<br>‐16.8%<br>‐26.0%<br>‐0.3%<br>‐4.6%<br>‐20.6%‐17.6% Machinery and transport equipment<br>‐23.5%<br>‐20.7%<br>4.9%<br>0.5%<br>‐20.4%‐11.2% Chemicals and related products, n.e.s.<br>‐21.0%<br>‐19.2%<br>7.2%<br>‐1.6%<br>‐7.7% Food and live animals<br>‐9.9%<br>‐9.2%<br>‐10.3%<br>2.3%<br>3.9%<br>‐1.9% Miscellaneous manufactured articles<br>‐6.4%<br>‐9.8%<br>‐8.9%<br>11.9%<br>42.1%<br>63.2% Commodities and transactions not classified elsewhere<br>44.8%<br>42.7%<br>13.0%<br>Q3‐08 Q4‐08 Q1‐09 Q2‐09 2009 July 2009 Aug<br><!-- End of picture text --> Source: Own illustration. Data: Eurostat, External Trade by SITC.

# **4.2 Regional Analysis of Developing Country Exports**

While China’s exports to the US declined during the downturn by more than those in other countries (see _Figure 8,_ left-hand panel), it is only China among the top five exporters to the US that have seen a significant rebound in trade volume. This implies that China’s share of US imports has grown significantly, reaching 20% in September 2009, which represents the highest share since January 2007 besides January 2009. China has gained market share despite the decline in US imports. Canada’s import share has fallen between 1 to 2 percentage points since 16
- Nasr Esfahani, A. (2020, June 2). The attitute of Iranians toward Afghan migrants. _Shargh newspaper_ , p. 13. Retrieved from https://sharghdaily.com/fa/main/page/6168/13

- Nielsen, H. S. (2010). Estimating the effect of student aid on college enrollment: Evidence from a government grant policy reform. . _American Economic Journal: Economic Policy,_ , 2(2):185–215.

- Pham, P., O'Mealia, T., Wei, C., Bindu, K. K., Makoond, A., & Vinck, P. (2021). Hosting New Neighbors: Perspective of host communities on displacement and social cohesion. _Unpublished Working paper. Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group._

- Rajaee, B. (2000). The Politics of Refugee Policy in Post-Revolutionary Iran. _Middle East Journal, Vol. 54, No. 1_ , 44-63.

- Rodríguez-Planas, N. a. (2016). Labor market integration of new immigrants in Spain. _IZA Journal of Labor Policy 5.1_ , 1-15.

- Ruiz, I., & Vargas-Silva, C. (2012). The Economics of Forced Migration. _The Journal of Development Studies_ , 772-784.

- Salehi-Isfahani, D. (2020). _Iran Under Sanctions._ Washington, DC: The SAIS Initiative for Research on Contemporary Iran, Johns Hopkins University.

- Steiner, V. a. (2012). Financial student aid and enrollment in higher education: New evidence from grmany. _The Scandinavian Journal of Economics,_ , 114(1):124–147.

- Turner, T., & Cross, C. (2015). Do attitudes to immigrants change in hard times? Ireland in a European context. _European Societies 17.3_ , 372-395.

- Turton, D., & Marsden., P. (2002). Taking refugees for a ride. _The politics of refugee return to Afghanistan, Afghanistan Research and Evaluation Unit (AREU)_ .

UNHCR. (2020). _Global trends: Forced displacement in 2019. ._ Geneva: UNHCR.

UNHCR. (2021). _Iran at glance, Feburary 2021._ Geneva: UNHCR.

UNHCR. (February 2021). _IRAN AT A GLANCE._ Geneva: UNHCR.

Verme, P. (2017). _The economics of forced displacement: an introduction._ The World Bank.

- Verme, P., & Schuettler, K. (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. _Journal of Development Economics_ .

- Verme, P., & Schuettler, K. (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. _Journal of Development Economics_ .

17
# PPR Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS219|Rural Credit in Developing Countries|Avishay Braverman<br>J.LuisGuasch|June 1989|C. Spooner<br>37570|
|WPS220|Capacity Buildingfor PolicyAnalysis|Samuel Paul<br>David Steedman<br>Francis X. Sutton|July 1989|E. Madrona<br>61712|
|WPS221|How Does Uncertainty About the Real<br>Exchange Rate Affect Exports?|Ricardo J. Caballero<br>Vittorio Corbo|June 1989|A. Oropesa<br>61758|
|WPS222|Why Stabilisation Policies in Zambia<br>Did Not Succeed|Christopher Colclough|||
|WPS223|Overvalued and Undervalued Exchange<br>Rates in An Equilibrium Optimizing<br>Model|Jose Saul Lizondo|||
|WPS224|The Economics of the Govf rnment<br>Budget Constraint|Stanley Fischer|May 1989|S. Fischer<br>33774|
|WPS225|Targeting Assistance to theFnor<br>Using Household Survey Data|Paul Glewwe<br>Oussama Kanaan|June 1989|B. Rosa<br>33751|
|WPS226|Inflation and the Costs of<br>Stabilization: Historical Cases,<br>Recent Experiences and Policy Lessons|Andres Solimano|||
|WPS227|Institutional Reforms in Sector<br>Adjustment Operations|Samuel Paul|||
|WPS228|Economic Performance of Developing<br>Countries in the 1980s|Robert Lynn<br>F. Desmond McCarthy|||
|WPS229|The Demographic Transition and Saving<br>for Life-Cycle Motives in Developing<br>Countries|Steven B. Webb<br>Heidi Zia|||
|WPS230|Unemployment, Migration and Wages<br>in Turkey, 1962-1985|Bent Hansen|July 1989|J. Timmins<br>39248|