rafmacalaba/datause-encoder-data huggingface dataset:
2 12 percent in the mid-1970s.2 Another important point is that some economists and policy makers seemingly view statistics on manufactures as a measure of a country's development; e.g., the higher the value or share of manufactures in total production or exports the higher the presumed level of industrialization.

As a results of these potential attributes, extensive statistics are compiled and numerous empirical studies have been undertaken on trends in, and the composition of, developing countries' exports and production of manufactures.3 However, in spite of this widespread interest, a key unresolved problem is that there is no general agreement on what constitutes a "manufactured" good. National and international organizations now use definitions of manufactures for compilation of production and trade statistics that contain major differences which may affect the findings and policy conclusions of empirical analyses. For example, statistical production (output) series published by GATT and the World Bank (IBRD) include goods like refined petroleum, processed animal and vegetable oils, tobacco products and consumable alcohols us manufactures while such items are excluded from these organization's definition of manufactures employed for compilation of trade data. This implies that some manufatud good Case to b a manufactu mrly 2 The World Bank (1987) provides a comprehensive analysis of the role of trade policy in stimulating the industrialization of developing countries. While much contemporary thinking on development policy continues to accent the importance of manufactures production, the World Bank (1986) argues that the reduction of domestic biases against agriculture have often been a key element affecting the success or failure of industrialization plans.

3 Appendix tables to the World Bank's annual Ldd nz1 provide data on individual country's trade i manufactures. UNCIAD (various issues) and GATI (various issues) also provide adetailed historical series on the manufactures trade of developed and developing countries. However, **these** three sources compile statistics on production of manufactures on a very different product base. UNIDO (1982) employed national trade statistics to compute indices of revealed comparative advantage for various countries manufactures exports. These tabulations are made using a product base that is consistent for both trade and output series.
commonly cited sources were the Fund for IndustriaL Development, the Fund for Improvement of SME Technology, and the Fund for Upgrading Industrial Technology, but even these were used by only 8, 13, and 11 sample SMEs, respectively. Frequency of use varies more across sectors in this area more than it does for other types of financial support. Under 50% of the sample woven textile finms, reported ever using technology development funds.

Credit Guarantee System: Table V.9 presents data on the use of four different sources of credit guarantees. On average, the sample SMEs have each made use of just over one such source. All four sources receive high average usefulness scores of nmore than 4.5, reflecting the extremely high evaluation of this form of financial support system by its users. V.4 Conclusions Despite the fmancial constraints and risks that induce many SMEs to minimize their need for external finance, access to financial support is of crucial importance to SMEs in Korea. The major conclusions that can be drawn from the survey data in this chapter are as follows: 1) Korean SMEs hav,: access to a large number of sources of financial support, and many are widely used. Although individual supports are often lightly used, overall use is high relative to both other support systems in Korea and to the use of financial support by SMEs in other countries.

2) With very few exceptions, both private and collective sources of financial support ranked high in terms of usefulness, i.e. they were rated 4.0 or higher on a 5-point scale by the SMEs. Private financial support received slightly better marks than collective financial support, however.

3) Despite the highly regulated and segmented financial market in Korea, SMEs do not appear to be starved for fnancial support. During the financially repressive 1970s, however, there was some evidence that financial constraints were a more serious problem. Much of Korea's success is attributable to the provision of strong incentives for loan repayment.

4) Korean SMEs are rarely dependent on a single source of financial support, and they often have a choice between collective and private forms of support, and among various collective fiums.

38
**Figure 11:  Cargo Dwell Time in Select African Countries, by the Percentage of Containers, 2011**

<!-- Start of picture text --> 0-5 6-10 11-20 21-40 41-70 >70<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Kenya Nigeria South Africa Uganda Zambia<br><!-- End of picture text --> _Source:_ Authors’, based on firm surveys.

# **Comparison with Shippers’ Perception of Dwell Time**

When importers’ perceptions of cargo dwell time are compared with actual dwell time in Sub-Saharan African countries, the latter is higher than expected in Kenya and Nigeria, which means that importers here are likely to want to reduce dwell time and might exert pressure to do so.

On the contrary, in South Africa, Uganda, and Zambia, the perception of normal cargo dwell time is higher than the estimated normal dwell time, meaning that there may not be strong pressure to lower it; importers might be satisfied with experiencing a shorter dwell time than expected. In both cases, average dwell time is lower in Durban than in the other ports studied, which may explain the relatively low expectations.

These differences between countries suggest that dwell time is also about perception and information (Table 1). Shippers may not understand the significance of dwell time and may not have accurate information on it (dwell time statistics are often unknown and incorrect).

**Table 1: Average Dwell Time and the Perception of Normal Dwell Time in Select African Countries, 2011** _dwell time (number of days)_

|_Indicator_|_Kenya_|_Nigeria_|_South_<br>_Africa_|_Uganda_|_Zambia_|
|---|---|---|---|---|---|
|Port|Mombasa|Lagos|Durban|Mombasa|Durban|
|Actual<br>average<br>cargo<br>dwell time|8.71|14.11|3.93|7.41|7.64|
|Perception of ―normal‖||||||
|average<br>cargo<br>dwell<br>time|7.7|11.2|6.5|8.7|14.1|

_Source:_ Authors based on firm surveys.

23
Table A16. Regions of the WTO model

|**Regions**|**Name of the macro-region**|
|---|---|
|msc|"Moscow"|
|stp|"Saint-Petersburg"|
|tmn|"Tumenskaya region"|
|vgd|"Northwest"|
|nor|"North"|
|cen|"Central"|
|sou|"South"|
|url|"Urals and near east"|
|sib|"Siberia"|
|far|"Far East"|

_Source:_ Authors’ mapping, see table 2 of the Tables section.

118
# **_Table A4.5a Relationship between staffing predicted performance and IEG outcome rating, ordered probit_**

|||**De**<br>|**pendent vari**<br>|**able: IEG 1-6**<br>|**outcome rat**<br>|**ing**<br>|||
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Preparation TTL|0.104***|||0.055*||0.069*||0.087**|
||(0.027)|||(0.030)||(0.040)||(0.044)|
|1<sup>st</sup>half supervision||0.168***||0.051||-0.002||0.005|
|TTL||(0.030)||(0.040)||(0.052)||(0.056)|
|2<sup>nd</sup>half supervision|||0.189***|0.150***||0.156***||0.161***|
|TTL|||(0.030)|(0.040)||(0.054)||(0.058)|
|Practice Manager<sup>1</sup>|||||0.0637|0.0373||0.096|
||||||(0.042)|(0.053)||(0.061)|
|Country Director<sup>1</sup>|||||||0.0117|-0.132**|
||||||||(0.044)|(0.067)|
|Observations|2,038|2,560|2,513|1,946|1,723|1,222|2,540|1,129|
|Pseudo R-squared|0.095|0.087|0.088|0.101|0.102|0.120|0.084|0.130|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using Ordered Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects. (1) Practice Manager and Country Director at project preparation.

# **_Table A4.5b Relationship between staffing predicted performance and binary IEG outcome rating,_** **_<u>probit</u>_**

||**Depend**<br>|**ent variable:**<br>|**Binary IEG out**<br>|**come rating (S**<br>|**atisfactory=**<br>|**1, Non-satisfact**<br>|**ory=0)**<br>||
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Preparation TTL|0.096***|||0.056||0.105**||0.140**|
||(0.034)|||(0.040)||(0.053)||(0.059)|
|1<sup>st</sup>half supervision||0.143***||0.025||-0.101||-0.010|
|TTL||(0.037)||(0.056)||(0.076)||(0.081)|
|2<sup>nd</sup>half supervision|||0.190***|0.173***||0.222***||0.220***|
|TTL|||(0.037)|(0.056)||(0.073)||(0.079)|
|Practice Manager<sup>1</sup>|||||0.063|0.039||0.101|
||||||(0.050)|(0.060)||(0.069)|
|Country Director<sup>1</sup>|||||||-0.004|-0.104|
||||||||(0.056)|(0.081)|
|Observations|1,851|2,366|2,316|1,767|1,543|1,069|2,355|985|
|Pseudo R-squared|0.130|0.109|0.111|0.138|0.125|0.174|0.105|0.195|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects. (1) Practice Manager and Country Director at project preparation.

51
|**Data Pro**|**perties**||||**M**|**odeling Approach**|
|---|---|---|---|---|---|---|
|**Sample**<br>**and**|**Freuenc**|||**Te**|||
|**Author (year)**<br> <br>**Source**|**qy**|**Variables**|**Integration**|**yp**|**Specification**|**Estimates**|
||beginning at<br>the break,<br>(_DT_= 9,<br>8,...,1) if<br>there is<br>a decrease in<br>the slope of<br>the trend.||||||
|Baliamoune-Lutz and Haughton 2004<br>1960 -<br>1999|Annual|GDP, M1,<br>M2|I (1)|VECM|Velocity of<br>money (v) is a<br>function of<br>anticipated<br>(sam) and<br>unanticipated<br>(sum) money<br>growth for<br>either M1 or<br>M2<br>Where M1(t)<br>or M2(t) is<br>regressed on<br>constant and<br>M1(t-i) and a<br>trend,<br>(sam) is<br>defined as the<br>estimated M1<br>or M2 and the<br>(sum) is the<br>difference<br>between|Long run co-integration relationship:<br>v M1 (t) =- 2.225 + Log v + 55 sam (t-1) –<br>0.004 sum (t-1)<br>v M2 Velocity (t) = 1.3417 + Log v - 33 sam<br>(t-1) – 0.110 sum (t-1)<br>Parsimonious short run model:<br>D Log (v)(M1) (t) = 0.008 – 0.047 ECM +<br>0.358 D Log v (t-1) - 0.064 D Log v (t-2) +<br>0.199 D Log v (t-3) – 0.296 D sam (t-1) +<br>4.807 D sam (t-2) + 2.043 D sam (t-3) +<br>0.0005 D sum (t-1) – 0.002 D sum (t-2) -<br>0.006 D sum (t-3)|

93
# **Table 3. Summary Statistics on Landline phone Availability**

|**Year**|**A. Landlin**|**e phone availabili**|**ty by year**||
|---|---|---|---|---|
|1993||37|||
|1995||41|||
|1996||44|||
|1997||50|||
|1998||53|||
|1999||56|||
|2000||57|||
||**B. Landline**|**phone availability**|**by province**|<br>|
|**Province**|**% villages with landline inst**|**alled, 1993 & 200**|**0**|**Number that add**<br>**landline**|
||Year 1993|Year 200|0||
|Liaoning|0.92|1.00||1|
|Shandong|0.60|1.00||4|
|Hubei|0.47|1.00||8|
|Guangdong|0.90|1.00||1|
|Yunnan|0.80|1.00||1|
|Gansu|0.00|0.67||6|
|**Year **|**C. Numbe **<br> <br> <br>|**rof villages with **<br>|**new access**<br>|<br>|
||Liaoning<br>Shandong<br>Hubei|Guangdong|Yunnan|Gansu<br>Total|
|1995|0<br>2<br>2|0|0|0<br>4|
|1996|0<br>0<br>2|0|0|2<br>4|
|1997|1<br>0<br>4|1|0|0<br>6|
|1998|0<br>1<br>0|0|1|1<br>3|
|1999|0<br>1<br>0|0|0|2<br>3|
|2000|0<br>0<br>0|0|0|1<br>1|

32
Policy Research Working Paper 10372

# **Abstract**

Using multiple waves of two public opinion surveys and a two-way fixed effect model, this paper analyzes how people’s perceptions and attitudes towards public institutions shifted with the business cycle in El Salvador during 2004–2018. It finds that individuals’ levels of trust toward both the president and the municipal government are positively associated with higher levels of income at the municipality level. Income is also a strong predictor of trust in mass media, confidence in the judicial system and, to a lesser extent, trust in the national legislature but income does not affect trust in the Catholic Church. The relationship between income and trust toward the president and municipalities masks a relevant heterogeneity from a rural-urban divide as well as from differences in municipal state capacity. Further, views of income distribution fairness as well as preferences for democracy are positively shaped by municipality-specific business cycles. In contrast, neither generalized trust nor satisfaction with democracy is empirically associated with income at the municipality level.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mrobayo@worldbank.org and brude@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **References** :

- Abbink, Klaus, Jordi Brandts, Benedikt Herrmann and Hendrik Orzen (2010). “Intergroup Conflict and Intra-Group Punishment in an Experimental Contest Game.” _American Economic Review_ , 100(1), 420-447.

- ACAPS (2021). https://www.acaps.org/country/jordan/crisis/syrian-refugees. Last retrieved: 19 September 2021

- Adida, Claire, Adeline Lo and Melina Platas (2018). “Perspective-Taking Can Promote ShortTerm Inclusionary Behavior Toward Syrian Refugees.” _Proceedings of the National Academy of Sciences_ 115(38):9521–9526.

- Agüero, Jorge and Eniola Fasola (2021). “Distributional Policies and Social Cohesion in a High-Unemployment Setting.” _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.

- Aksoy, Cevat and Thomas Ginn. (2021). “Host Country Policies and Attitudes Toward Refugees: A Global Evaluation.” _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.

- Alan, Sule, Ceren Baysan, Mert Gumren and Elif Kubilay (2020). “Building Social Cohesion in Ethnically Mixed Schools: An Intervention on Perspective Taking.” _The Quarterly Journal of Economics_ 136(4): 2147-2194.

- Albarosa, Emanuele and Benjamin Elsner. (2021). “Forced Migration, Social Cohesion and Conflict: The 2015 Refugee Inflow in Germany.” _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.

- Alrababa’h, Ala’, Andrea Dillon Scott Williamson, Jens Hainmueller, Dominik Hangartner and Jeremy Weinstein. (2020). “Attitudes Toward Migrants in a Highly Impacted Economy: Evidence From the Syrian Refugee Crisis in Jordan.” _Comparative Political Studies_ 54(1): 33-76.

- Alsharabati, Carole and Jihad Nammour (2015). “Survey on Perceptions of Syrian Refugees in Lebanon.” Institute des Science Politiques - USJ. Université Saint-Joseph. Beirut: Université Saint-Joseph.

- Barriga, Alicia, Neil Ferguson, Nathan Fiala and Martin Leroch (2020). “Ethnic Cooperation and Conflict in Kenya”. Ruhr Economic Papers No. 872.

- Becker, Gary (1968). “Crime and Punishment: An Economic Approach.” In _The Economic Dimensions of Crime_ (pp. 13-68). Palgrave Macmillan UK.

25
**Table 1**

# **Ethiopia: Total years needed to double the real GDP per capita**

||Average growth<br>rates of real GDP<br>per capita|Total years to<br>double real GDP<br>per capita|
|---|---|---|
|**Baseline**|0.80|86|
|**_EXPERIMENT 1_**|||
|Foreign interest rates are 50 % lower|||
|Experiment 1A (all allocated to CG)|0.97|72|
|Experiment 1B (all allocated to IG)|0.99|70|
|Experiment 1C (50% allocated to IG, 50% allocated to CG)|0.98|71|
|**_EXPERIMENT 2_**|||
|Foreign aid is 10 % of GDP|||
|Experiment 2A (all allocated to CG)|1.07|65|
|Experiment 2B (all allocated to IG)|1.23|57|
|Experiment 2C (50% allocated to IG, 50% allocated to CG)|1.14|61|
|Foreign aid is 12 % of GDP|||
|Experiment 2D (all allocated to CG)|1.19|59|
|Experiment 2E (all allocated to IG)|2.04|35|
|Experiment 2F (50% allocated to IG, 50% allocated to CG)|1.61|43|
|Foreign aid is 15 % of GDP|||
|Experiment 2G (all allocated to CG)|1.37|51|
|Experiment 2H (all allocated to IG)|3.15|23|
|Experiment 2I (50% allocated to IG, 50% allocated to CG)|2.26|31|

20
of civil liberties on the return to projects25. Taking the estimates from specification D, if the Freedom House civil liberties index were to improve from the worst (1) to the best (7, as in Costa Rica for all evaluated years), the ERR is predicted to increase by 7.5 percentage points. Similarly, with the estimates using the Humana index, improving from the worst civil liberties (13) to one of the best (91, as in Costa Rica) would improve the ERR by 22.5 percentage points. Since these civil liberties indices are on a different scale, a more standard method for comparison is to calculate how much the ERR is predicted to increase if each index were improved by one standard deviation (column 5). An increase of this magnitude in the Freedom House index would raise the predicted ERR by 1.6 points; a similar increase in the Humana index would raise the ERR by 5.2 points; a standard deviation increase in "media pluralism" would improve the predicted ERR by 3.1 points.

5 For the Freedom House and the Coppedge and Reinicke indicators, we have reversed the scales for comparability. Thus, for all indices, a higher value represents more liberties.

- 15 _-_
and reforms are considered one of the major factors explaining the productivity differences across countries or across industries in a country, their timing and magnitude can be critical for productivity gains, especially, in developing countries.

Many cross‐country or single country studies show that policies and reforms are significant sources of improving productivity through reallocation of inputs and outputs.<sup>4</sup> One of the commonly investigated policies is trade policies. In the literature, the link between trade policies and productivity gains are explained through different mechanisms such as higher competition introduced via more availability of imported products in domestic markets, lower cost of imported inputs, higher competition in international markets, technology spillover, or competitive elimination of less productive businesses. For example, Bernard and Jensen (1999) show that the overall contribution of exporters to U.S. manufacturing productivity growth. Following this study, Bernard, Eaton, Jensen, and Kortum (2000) create a model explaining plant‐level heterogeneity in exporting and productivity and test it for U.S plants. In a study where empirical studies are summarized, Tybout (2001) presents that exposure to foreign competition tends to improve intra‐plant efficiency, and firms that participate international activities tend to be more productive. Bernard and Jensen (2002) study the causes of U.S. manufacturing plant deaths within and across industries. The probability of shutdowns is higher in industries that face higher competition from low‐income countries. Hallward‐Driemeier, Iarossi, and Sokoloff (2002) use firm level data from five East Asian countries for the period of 1996 and 1998 to investigate the patterns of productivity in the region. Exporting firms and firms aiming for export markets have higher productivity.

One of the seminal papers in this area is by Melitz (2003). His model shows how trade may improve productivity at both micro and aggregate levels through reallocation of resources across firms. 1) Most productive ones export and improve more and 2) less productive ones exit the market. Both factors lead to higher productivity. Empirical papers have followed Malitz’s paper. Bernard, Jensen, and Schott (2006) show that U.S. firms located in the industries experiencing a larger decline in trade costs present stronger productivity gains and, in such industries, low productive ones die quicker. They also show that productivity growth within firms increases with decreasing trade costs. De Loecker (2007) for Slovenian firms, Fernandes (2007) and Eslava, Haltiwanger, Kugler, and Kugler (2005) for Colombian firms, Van Biesebroeck (2005) for Sub‐Saharan countries, Aw, Yan, Roberts, and Xu (2009) for Taiwanese firms, Pavcnik (2002) for Chilean firms, Muendler, Marc‐Andreas (2004) for Brazilian firms, Vadlamannati (2009), Pattnayak, and Thangavelu (2008), Topalova (2007) for Indian firms show that the global integration of countries through trade liberalization improves productivity and, as a result, growth performance.

> 4 See, for example, Bergoeing and Repetto (2006).

5