rafmacalaba/datause-encoder-data huggingface dataset:
(Tokman (l9783).9' Such relations can be represented as trade flows. Cole and Sanders [1985], for example, built a model of migration in which labor flows from one sector to the other in response to trade balance disequilibriums. The scarce evidence available indicates that the informal sector is relatively "open to trade".<sup>2 0</sup> The question then becomes under which conditions will a change in whe distribution of income within the formal sector. induced by a reduction in the minimum wage. affect the inter-sectoral trade flows.

Unfortunately, very little is known about the nature of those trade flows. The available evidence appears to indicate that the informal sector exports mainly non-durable consumption goods and services. Tokman [1983], for example, reports that in the case of Chile and Mexico, 70% of such exports are consumption goods and services, the rest being mainly intermadiate goods. However, given the characteristics of the informal sector, it is safe to assume such intermediate goods are used in the production of consumption and not capital goods. Thus, it is not unreasonable to characterize informal sector exports as consumption goods.

> 1 9It had been observed that the informal sector both buys and sells consumption and inputs from and to the formal sector. In addition, formal sector firms provide credit to the informal sector. Chaudhuri (1989] offers a recent example of a model in which the formal and informal sectors are linked through the labor, goods and credit markets.

> 20 Tokman (19833, for example, suggests that in Chile and Mexico, the informal sector "exports" approximately half of its output.

17
It is notable that Georgia, among these four countries, incurred the smallest reduction in the undervaluation of the BSA RER between 2000-04 and 2015-18.

If we are to better understand the long-term macroeconomic consequences of large migration and remittances on the FSU countries of CASC, two directions for further research would be worth pursuing. First, we need better estimates of the Balassa-Samuelson adjusted real exchange rates which can more accurately incorporate cross-country productivity differences in the production of traded goods than is possible by using GDP per capita as a proxy. Better estimates of the BSA RERs would enable us to have a clearer understanding of whether the receipt of remittances has undermined the price competitiveness of traded goods industries and, in turn, contribute to understanding whether price competitiveness or some other factors are the binding constraints to improving trade performance in these countries.

Second, we need a better understanding of why relatively high levels of human capital in the FSU countries cannot be fully translated into GDP per worker. One approach to this issue would be to utilize the information in the annual Global Competitiveness Reports which provide crosscountry data which score and rank countries on 12 different pillars which affect their international competitiveness. In the 2017-18 report, all four of the remittance receiving FSU countries in CASC had scores for the primary education and health pillar which were much higher than their overall competitiveness score but scored poorly on pillars such as market size, technological readiness, business sophistication and innovation (World Economic Forum, 2017).

# **References**

Asian Development Bank (2019), _Good Jobs for Inclusive Growth in Central Asia and the South Caucasus: Regional Report_ (eds Giovanni Capannelli and Ravi Kapur, Asian Development Bank, Manilla.

Balassa, Bela (1964), “The Purchasing Power Parity Doctrine: A Reappraisal”, _Journal of Political economy_ , Vol 72, No 6, pp584-596.

Choudri, Ehsan U, and Moshin S. Khan (2005), “Real Exchange Rates in Developing Countries: Are Balassa Samuelson Effects Present?”, _IMF Staff Papers_ Vol 52, No 3, pp387409.

Eromenko, Igor (2016), “Do Remittances Cause Dutch Disease in Resource Poor Countries of Central Asia?”, Central Asia Program economic Papers Series no 18, George Washington University.

18
Although there is evidence that messaging using social norms can be effective, there is also evidence that these effects depend on the specific context of the experiment and the presentation of the message. According to some researchers, these effects have been overemphasized in recent years. Schultz et al. (1999) studied energy conservation among California residents and found that although normative social influence was rated very low by survey respondents as a motivation for conservation, experimental findings suggested that it actually had a greater effect on actions than many other influences. In contrast, Carlson (2001) found that in cases such as recycling, which is a large-number, small-payoff problem of collective action, researchers have overestimated the effects of social norm messaging in changing behaviors and suggested that governments should focus on financial incentives or reducing efforts to change behaviors.

In addition, these studies leave important questions about the characteristics of treated groups unanswered. Carlson suggests that the significant effects observed in social norm-oriented messaging might be positively related to, and dependent on, the long-term prorecycling views prevalent in the American population and that in a setting that lacks these views, treatment effects might be different. In Peru, although concerns about general environmental issues are as strong as in the United States, average views of recycling are far less reliably positive, and recycling itself is less well understood. Until recently, the separation of reusable refuse from garbage was performed by approximately 100,000 unofficial workers who reported that they were viewed and referred to as “scavengers” (Chauvin 2009). These baseline differences in the experimental population may cause different treatment effects than those found in a population in the United States or another developed country. Very little academic work has examined recycling in developing countries even though these are the places where insufficient waste 3
# Contents

|1<br>New**B1U**banana policy is highly protectionisL<br>.....<br>.**................**|**............. 1**|
|---|---|
|<br>Traditionally some EU countries have given preferential market access ...........|..............1|
|The new policy also gives preferences and is even more inefficient ..................<br>|...............I<br>|
|The opportunity for an efficient, virtual free trade outcome was missed|.<br>2|
|There are strong pressures to change the new policy .......................................|...........<br>2|
|Reform would bring big clhanges for some developing counties<br>.3||
|2<br>**h**<br>**U**<br>**a**<br>**dtewo**<br>**l**<br>**a**<br>**a**<br>**am**<br>**r**<br>**e**<br>**..............................**<br>**.....**<br>**..................**<br>**....**<br>**2-**<br>**The EU and thie 'world banana market.4S***<br>..|<br>................................................|
|Efficiency of export suppliers varies widely<br>.<br>.|.<br>_5_|
|EU policies have changed but preferences remain for now<br>.<br>.|.<br>7|
|With lower prices demand would increase<br>.<br>.<br>.|.|
|**3**<br>**How EU policy distorts world banana trade.....................................**|**. .. -12**|
|The previous policies were extremely inefficient<br>.<br>.<br>.|12|
|The new policy is mor costly tan the old<br>.<br>.|.<br>13|
|The new policy is a big step backwards<br>.<br>.<br>|.<br>16|
|4<br>Why**the**new EU policy is unsustainable**.......................**||
|Many groups are pressing for reform: Scenano 1 -liberlizatin<br>.18||
|Scenario 2: muddling along|19|
|5<br>Coherent policy responses for banana exporters**..... . ..**||
|Policy considerations for ACP countries ..|22|
|Policy considerations for Latin American exporters ..|29|
|6<br>**Where**to**next ...-**|30|
|References**..........**<br>**...........................**<br>**........... ...**<br>**4**||
|Box||
|1.1 Prefecrred and non-preferred suppliers of EU banana imports<br>.4||
|Charts||
|2-1 Latin America dominates banana exports<br>_.5_||
|2.2 The European Union is the main importer of bananas<br>.|.<br>6|
|2.3 Banana producers in Latin America are much more efficient th<br> <br>||
|preferred suppliers<br>.7||
|2.4 EUreail prices are generaRy well above those ofthe UnitedStates|..<br>8|
|_2.5_Most producer prices of preferred suppliers to the EU markets||
|are more than double the world price indicator<br>..|8|
|3.1 National policies of EU countries influence world banana trade .12||
|3.2 Nationalbanana policies of EU countries were costly<br>.<br>.14||
|3.3 The new EU policy is even more costly than the national policies||
|it replaces_.15_||
|3A4 The over-quota banana tariff is clearly prohibitive<br>.<br>.16||
|_5.1_The downward wend in world banana prices .23||
Coefficient (ICC). This is the basic measure for the degree of dependence between observations and the proportion of variance accounted for by the group level, or the covariance of error terms inside a cluster. This proportional variance is found for higher levels (2, 3...n) by dividing the variance explained on level x by the sum of all levels of variance in the model. A percentage of variance explained by each level is found by multiplying this result by 100. This percentage tells how much of the variance found in the dependent variable is due to the differences between higher units (Boardman, 2003). A high variance explained on the state level would imply that states differ significantly on their observations of the dependent variable; a low variance explained would imply that states are similar in how the causal variables affect the onset of civil war. A high variance explained on level three would imply that the effects of the independent variables upon the dependent differ over regions; therefore, heterogeneity exists among regions. A low variance explained implies that differences in regional variance based on independent variables are similar. With the addition of state or regional variables (e.g. a regional ethnic fragmentation score) we can note whether the corresponding level variance decreases. For example, an ICC of .10 would imply that 90% of variance across level 2 (ex. classes) observations of the dependent variable is due to level one (ex. students), while 10% is due to class differences.

# **Equation 4.2: Estimating Intraclass Correlation Coefficients**

_= Variance due to the clustering variable / (variance due to the clustering variable + variance for all other levels)._ 2 2 For rare events data (used for civil war onset models), σ _e_ = π / 6 is the equation that estimates the level one logistic distribution (representing the variance on level one). For a standard logistic distribution (used for Uppsala/PRIO civil war onset models) 2 2 σ _e_ = π / 3 is the equation to estimate the intra-class correlation coefficient and represents the variance on level one (Rabe-Hesketh et al, 2004).

17
# **Shortening Supply Chains: Experimental Evidence from Fruit and Vegetable Vendors in Bogotá**<sup>**#**</sup>

Leonardo Iacovone, _World Bank_ David McKenzie, _Development Research Group, World Bank_ Keywords: Retail supply chains; microenterprise competition; social entrepreneurship; randomized experiment.

JEL Classification codes: O12, C93, D43, L31

> # We gratefully acknowledge funding support from the Strategic Research Program (SRP); i2i Trust Fund; the IPA SME Initiative; and the Jobs Trust Fund. We thank Carolina Medina and Verena Liedgens of Agruppa for their collaboration and willingness to test their idea; Cristhian Pulido, Marta Carnelli, Pablo Villar, and the IPA Colombia team for excellent research assistance and survey work; and Lauren Falcao Bergquist, Alvaro Gonzalez, Indhira Santos, Tristan Reed and participants at various seminars for helpful comments. This experiment is registered in the AEA RCT registry as trial AEARCTR-0001162.
|_Income classification (base: No earners)_||||
|---|---|---|---|
|Remittance recipients only|0.058|-0.202***|-0.161***|
||(0.069)|(0.065)|(0.052)|
|Female single earner|-0.095*|-0.176***|-0.201***|
||(0.055)|(0.060)|(0.049)|
|Male single earner|-0.212***|-0.230***|-0.273***|
||(0.040)|(0.049)|(0.043)|
|Majority female earners|-0.403***|-0.237***|-0.328***|
||(0.054)|(0.065)|(0.055)|
|Equal contribution|-0.201***|-0.246***|-0.268***|
||(0.043)|(0.055)|(0.046)|
|Majority male earners|-0.360***|-0.308***|-0.400***|
||(0.065)|(0.065)|(0.053)|
|**Displacement status (base: settlement IDP)**||||
|Non-settlement IDP|-0.048*||-0.076***|
||(0.028)||(0.028)|
|Non-IDP|||-0.100***|
||||(0.023)|
|Constant|-0.256**|0.114|0.314|
||(0.116)|(0.205)|(0.196)|
|Observations|1,902|6,341|8,243|
|R-squared|0.583|0.299|0.368|

_Source:_ Estimated by authors based on Somali HFS 2017-18 _._ Note: Robust standard errors in parentheses. Estimations include regional dummies. *** p<0.01, ** p<0.05, * p<0.1. _Note:_ Following the Durable Solutions Analysis Guide (Joint IDP Profiling Service [JIPS], 2018), the variable mechanisms to obtain or replace lost documents refers to access to support systems that grant or restore personal or other documentation such as IDs, land and housing titles.

We find some association between gender of the household head and poverty emerges for IDPs, controlling for other individual and household characteristics. Male headship remains associated with higher poverty for IDPs but not for non-IDPs. Among non-IDPs widow-headed households emerge as a group that with high risk of poverty but this result does not hold for IDPs.

21
**Table 5: Comparison of Trends in ERR Reporting with the IEG Report**

||Percentage<br>point change<br>1985‐<br>|Contribution<br>by sector<br>(%)<br>1995<br>|
|---|---|---|
|(1) Contribution within High‐CBA sectors|‐2.5|15.1|
|(2) Contribution within Low‐CBA sectors|0.6|‐3.5|
|(3) Contribution from sector shifts (from High to<br>Low‐CBA)|‐14.3|88.3|
|(4)Residual|0.0|0.0|
|Allprojects|‐16.2|100.0|
||1995‐<br>|2015<br>|
|(1) Contribution within High‐CBA sectors|‐9.3|92.3|
|(2) Contribution within Low‐CBA sectors|3.3|‐32.6|
|(3) Contribution from sector shifts (from High to<br>Low‐CBA)|‐4.4|43.9|
|(4)Residual|0.4|‐3.7|
|Allprojects|‐10.1|100.0|

Sources: IEG, 2015, for both investment and budget support projects, and staff calculation Regression analysis (Table A.2 to A.4) shows that the above decline in the frequency of ERR reporting is strongly significant. The full effect of the time trend (linear and quadratic specification of the approval year), suggests that the overall trend is declining; however, an examination of the marginal effect indicates that there is a turning point around the years 2003/2004. South Asia is also benchmarked against other regions in the regression using region dummy variables. The regression results show that the frequency of ERR reported in PADs is not statistically different between South Asia and East Asia and also between South Asia and Middle East and North Africa. In contrast, South Asia exhibits significantly greater frequency in ERR reporting compared to Africa and slightly more compared to Latin America. The positive and strongly significant results for the sector dummies simply highlight the difference between high and low CBA projects.

The size of the project, duration, and project cost are positively associated with ERR reporting at entry (Tables A.2 and A.4). For the ICR regression (Table A.3), extension of the closing date is positively correlated with ERR reporting. These are all indicators that could be (to various degrees) proxies for the level of attention, or visibility of a project and therefore lend support to the hypothesis that the quality of economic analysis tends to be better for projects with higher visibility. Also the team effects are significant in almost all the regressions.<sup>4</sup>

> 4 The exceptions are regressions in Table A.2 and Table A.5.  Table A.2 uses ERR reporting only in the PAD as a proxy for quality.  In contrast, team effects are significant across regressions that use reporting in both the PAD and ICR as the proxy for quality.  Another exception is Table A.5 which attempts to explain the divergence between ERR reported in the PAD and ICR.

18
of respective sectoral value added obtained from the WDI. These variables reflect the dependence on imports and the lack of production capacities in domestic economies, countries’ competitiveness abroad, and the importance of each sector in the total value added. In addition, the study uses the capital and labor<sup>10</sup> from the PWT 9 in constant 2011 U.S. dollars but converts them to constant 2010 U.S. dollars using the U.S. price levels from the PWT 9 to ensure consistency between the PWT 9 and WDI data. The capital and labor help control for demand effects from both the industry and household sides.

Figure 1. Electricity Constraints, Self-generation, and Average Electricity Consumption.

<!-- Start of picture text --> Electricity Constraints and Self-generation Average Electricity Consumption (GWh)<br>0.8 80000<br>70000<br>0.6 60000<br>50000<br>0.4 40000<br>30000<br>0.2 20000<br>10000<br>0 0<br>High Income Upper Lower Low Income High Upper Lower Low Income<br>Middle Middle Income Middle Middle<br>Income Income Income Income<br>Average share of self-generated electricity across<br>firms<br>Agriculture Manufacturing Services<br>Average share of firms that say electricity is an<br>obstacle<br>% of Electricity Constrained Firms<br><!-- End of picture text --> Source: Authors’ computations using WB Enterprise Surveys Data.

Interesting stylized facts emerge when comparing electricity constraints, self-generation, and consumption across country income groups and sectors: low-income countries lag behind advanced economies in terms of electricity consumption across all sectors, they have a higher share of firms

> 10 Capital stock estimates are derived from past investments and deprecation by asset class using the perpetual inventory method (see online appendix C in (Feenstra, Inklaar, & Timmer, 2015)). Total employment in PW9 is sourced mainly from the ILO, WDI, and The Conference Board (TCB), as described in the PWT9 labor detail file: <u>https://www.rug.nl/ggdc/productivity/pwt/.</u>

8
regimes: fixed (pegs), intermediate (limited flexibility, crawls, bands, managed floating), and flexible (free-floating). **13** The rest of our data-exchange rates, inflation, and country indicators, such as population and income level on which the sample selection is based-come<sup>from the</sup> World Bank-IMF databases.

We focus on industrial economies and middle-income developing countries. Within this broad group, sample coverage is dictated **by** the availability of<sup>adequate</sup> interest rate data.'<sup>4</sup> The sample that results comprises 47 countries **(18** industrial and **29** developing) in addition to the **US,** whose interest rate is used as an explanatory variable, and a total number of monthly observations exceeding 9,400.

# **3.** Interest Rate Sensitivity: Pooled Estimates

We first assess empirically the sensitivity of domestic interest rates to **US** interest rates **by** estimating equation **(1)** using the entire sample, as well as distinguishing between industrial and developing countries and considering subsamples defined<sup>**by**time</sup> period.

Table 1 presents the full-sample fixed-effects estimation results, using a fiveregime classification of currency arrangements. To avoid cluttering the table,<sup>here andin</sup> the rest of the paper we only report the coefficients of interest-i.e., the slope parameter

> **13** As a robustness check, we compared the results obtained with this condensed classification with those obtained using the classification of Ghosh et al. **(1996).** On the whole, the results were very similar.

> 14 We dropped country/regime episodes possessing less than one year of consecutive monthly observations. We also excluded countries with population under one million, countries without availability of long exchange rate series (which automatically leads to the exclusion of Eastern European economies), low income countries (in which the incidence of interest rate controls is more widespread), and countries with prolonged internal or external war periods.

**10**
Table A.2: Logit analysis, 1995‐2015, Frequency of ERR in PADs as dependent variable (Model 1)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Pres/Nopres|Pres/Nopre|s Pres/Nopre|s Pres/Nopres|Pres/Nopre|s Pres/Nopre|s Pres/Nopres|
|CPIA rating|0.353***|0.238**|0.381***|0.370***|0.350***|0.303***|0.281***|
||(3.85)|(2.53)|(4.12)|(3.51)|(3.77)|(2.76)|(2.95)|
|CPIA scale dummy|‐0.190|‐0.0910|‐0.193|‐0.0752|‐0.0824|0.0861|‐0.0365|
||(‐1.13)|(‐0.54)|(‐1.14)|(‐0.39)|(‐0.48)|(0.44)|(‐0.21)|
|Approval fiscal year sequence|‐0.502***|‐0.460***|‐0.495***|‐0.519***|‐0.563***|‐0.529***|‐0.507***|
||(‐7.19)|(‐6.56)|(‐7.03)|(‐6.23)|(‐7.90)|(‐6.15)|(‐7.03)|
|Quadratic form of approval year sequence|0.00868***|0.00801***|0.00857***|0.00896***|0.00990***|0.00935***|0.00895***|
||(6.75)|(6.23)|(6.61)|(5.79)|(7.54)|(5.86)|(6.76)|
|East Asia and Pacific|0.0593|0.0877|0.0351|0.0726|0.0223|0.0335|0.0370|
||(0.40)|(0.58)|(0.23)|(0.42)|(0.15)|(0.19)|(0.24)|
|Africa|‐0.462***|‐0.244*|‐0.524***|‐0.429***|‐0.400***|‐0.235|‐0.293**|
||(‐3.28)|(‐1.69)|(‐3.67)|(‐2.58)|(‐2.81)|(‐1.36)|(‐2.00)|
|East Europe and Central Asia|‐0.181|0.0703|‐0.192|‐0.106|‐0.167|0.126|0.0212|
||(‐1.19)|(0.45)|(‐1.26)|(‐0.60)|(‐1.09)|(0.69)|(0.13)|
|Latin America & Caribbean|‐0.314**|‐0.168|‐0.315**|‐0.285*|‐0.176|‐0.0512|‐0.100|
||(‐2.12)|(‐1.12)|(‐2.13)|(‐1.67)|(‐1.18)|(‐0.29)|(‐0.66)|
|Middle East & North Africa|0.0516|0.182|‐0.0147|0.0181|0.0833|0.113|0.124|
||(0.28)|(0.98)|(‐0.08)|(0.08)|(0.45)|(0.52)|(0.66)|
|Agriculture|2.229***|2.251***|2.232***|2.230***|2.185***|2.227***|2.218***|
||(20.65)|(20.64)|(20.63)|(18.10)|(20.07)|(17.71)|(20.19)|
|Energy & mining|2.752***|2.654***|2.764***|2.743***|2.794***|2.725***|2.713***|
||(20.40)|(19.43)|(20.44)|(17.53)|(20.41)|(16.93)|(19.61)|
|Transport|3.364***|3.200***|3.363***|3.319***|3.433***|3.254***|3.284***|
||(24.60)|(23.13)|(24.53)|(21.44)|(24.72)|(20.32)|(23.27)|
|Water|2.507***|2.450***|2.490***|2.413***|2.465***|2.331***|2.415***|
||(16.35)|(15.87)|(16.22)|(13.91)|(15.95)|(13.22)|(15.55)|
|Urban Development|1.376***|1.317***|1.390***|1.438***|1.386***|1.425***|1.347***|
||(8.60)|(8.16)|(8.66)|(7.86)|(8.57)|(7.62)|(8.28)|
|Log net commit. (m$)||0.269***||||0.238***|0.217***|
|||(7.33)||||(5.32)|(5.63)|
|Project length|||0.0772***|||0.0773***|0.0655***|
||||(3.53)|||(3.01)|(2.95)|
|TTL performance||||0.0870||0.0749||
|||||(1.61)||(1.35)||
|Log (preparation cost, us$)|||||0.376***|0.293***|0.268***|
||||||(6.85)|(4.48)|(4.67)|
|Observations|4020|4020|4020|3065|4006|3055|4006|
|Pseudo R‐squared|0.274|0.284|0.276|0.274|0.283|0.294|0.291|

Note: t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01 28
# TABLE OF CONTENTS

|Problems with Commonly Used Trade Policy Measures ........................... 4|
|---|
|A Model of Relative Price Dispersion ...............................<br>.......... 8|
|Relative Price Estimation<br>...................<br>. 11|
|Computing Relative Price Dispersion ....................................... 15|
|Comparing Relative Price Dispersion and Measures of Outward Orientation ...<br>22|
|Comparisons of Relative Price Dispersion with other Intervention Measures .26|
|Conclusion ...................................................................<br>28|
|References .......................... ..................................... 30|
|Appendix .................................................................. 32|
|Trade Policy Intervention and Outward Orientation ....................... 39|

*Thii l';,i)tr wa, wvritteit iii auoll,lll uj lilt tl, \Macrol.olumi, c Adjustnlet **aid(** 'ruwthI Dision Of thie W\orld **Banik. I asit** gratefiul for comimillenlt' alid uget froml **Jolit** A.-dleriooi. Bill Ea,tPrle. **'itan FichIer.** Atni Harrisoin. **ati(l Lanti Pritcliett**