rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7163

# **Abstract**

Previous studies have established a negative relationship between total government spending and entrepreneurship activity. However, the relationship between the composition of government spending and entrepreneurial activity has been woefully under-researched. This paper fills this gap in the literature by empirically exploring the relationship between government spending on social and public goods and entrepreneurial activity under the assumption of credit market imperfections. By combining macroeconomic government spending data with individual-level entrepreneurship data, the analysis finds a positive relationship between increasing the share of social and public goods at the cost of private subsidies and entrepreneurship while confirming a negative relationship between total government consumption and entrepreneurial activity. The implication may be that expansion of total government spending includes huge increases in private subsidies, at the cost of social and public goods, and is detrimental for entrepreneurship.

This paper is a product of the Global Indicators Group, Development Economics. 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://econ.worldbank.org. The author may be contacted at aislam@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
A further question is to what extent the poor are unemployed in each settlement (Figure 4.6). In each of the countries reviewed the unemployment rate of the poor is highest in urban areas (mainly the secondary cities) except for Bulgaria where the rural rate is slightly higher. In Georgia, the Kyrgyz Republic, and Tajikistan it appears highest in the capitals (although the sample sizes are very small). This indicates that urban poverty in income or consumption terms, especially in the secondary cities, reflects the failure of adequate new jobs to emerge to replace those lost since the demise of the Soviet Union. Several of the poverty assessments, such as that of Tajikistan, also refer to a new phenomenon of the “working poor,” that is, household heads who are employed but poor because of very low wages or nonpayment of wages.

**Figure 4.6.  Percentage of poor household heads who are unemployed, by settlement type**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>Capital city Other Urban Rural<br>%<br><!-- End of picture text --> Note: average among 20 ECA countries. Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz, Lithuania, Moldova, Poland, Romania, Russia, Serbia, Tajikistan, Turkmenistan, Uzbekistan Source: see Table 2.1 The population that is both poor and unemployed is overwhelmingly found in the secondary cities, with the main exception of three countries in Central Asia: Tajikistan, Turkmenistan and Uzbekistan.

**Figure 4.7.  Poor unemployed household heads – Distribution by country groups**

<!-- Start of picture text --> Rural Other Urban Capital city<br>100<br>80<br>60<br>40<br>20<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>%<br><!-- End of picture text --> Note: EU Accession- average among Hungary, Lithuania, Poland. Balkans-average among Albania, Bosnia, Bulgaria, Kosovo, Moldova, Romania, Serbia. Caucasus-average among Armenia, Azerbaijan, Georgia. Central Asia-average among Kazakhstan, Kyrgyz, Tajikistan, Turkmenistan, Uzbekistan. Slavic-average among Belarus, Russia.

Source: see Table 2.1 26
**Table 3: Average prices by** **<u>quarter</u>**

||**Quarter 1**<br>**(Fall)**|**Quarter 2**<br>**(Winter)**<br>_Full S_|**Quarter 3**<br>**(Spring)**<br>_ample_|<br>**Quarter 4**<br>**(Summer)**|
|---|---|---|---|---|
|Price of domestic wheat flour|18.09|23.51|34.19|36.51|
|Price of vegetable oil|64.81|76.93|88.90|91.70|
|Price of domestic rice|33.93|33.99|46.16|55.29|
|Price of lamb|182.34|186.20|189.28|180.27|
|Price of milk|23.44|25.66|27.23|30.75|
|Price of kerosene|43.15|45.78|46.83|55.48|

Note: Estimated population weighted means. Prices in Afghani per kilogram or liter (kerosene). _Source:_ NRVA 2007/08

**Table 4. Population statistics by** **<u>quarter</u>**

||**Quarter 1**<br>**(Fall)**|**Quarter 2**<br>**(Winter)**<br>_Full S_|**Quarter 3**<br>**(Spring)**<br>_ample_|<br>**Quarter 4**<br>**(Summer)**|
|---|---|---|---|---|
|Nominal value of monthlytotal consumption|2017.79|1902.86|1876.92|1914.64|
|Real value of monthlytotal consumption|2022.00|1718.27|1519.12|1477.56|
|Nominal value of monthlyfood consumption|1196.98|1123.25|1129.01|1182.97|
|Real value of monthlyfood consumption|1201.19|961.26|789.41|797.60|
|Dailycaloric intake|2884.92|2725.03|2445.83|2387.33|
|Food consumption score|67.88|61.28|57.86|57.69|

Note: Estimated population weighted means. All values in per capita terms, except the food consumption score that is calculated for the household. Real values in Afghani reflect adjustments for spatial and temporal price differences, covering 13 months of field work. Caloric intake in kilocalories. _Source:_ NRVA 2007/08 28
systems in Europe are currently explored by a working group of GSB and the National Horticultural Taskforce. Development, recognition and sector-wide establishment of such quality systems is, however, likely to take several years.

# **b. Kenya: Cut Flowers**

The cut flower industry has overtaken coffee and tourism as a source of foreign exchange for Kenya and now ranks second only to tea, according to the Kenya Flower Council.<sup>25</sup> Over the years, Kenya has become the European Union’s biggest source of fresh flower imports and has overtaken Israel as the market leader. It has a 25% market share, beating Colombia and Israel which have to be content with each 16%. Two-thirds of the Kenyan blooms go to the Netherlands, which dominates the trade in cut flowers worldwide through its auction halls where Dutch wholesalers buy flowers for re-export to markets as far away as the United States and Japan.

In Kenya, increased air travel for tourism reduced the cost of airfreight to Europe and provided new transport opportunities for small quantities of fresh products. Tourism also increased local demand for high quality fruit and vegetables and provided an outlet for produce not meeting export standards. A key success factor is the involvement of a variety of private institutions and marketing arrangements that allow for **fast, flexible and reliable delivery** of cut flowers to the foreign market. This success is even more remarkable given the relatively high air freight rate to Europe from Kenya ($1.60/kg), compared to direct competitor Israel ($0.75/kg).<sup>26</sup> One of the problems in the flower sector is that medium-scale exporters sell 100% of their consignment through the (mainly Dutch) auction system. As competition becomes increasingly stringent, support is required to identify and form linkages for medium-scale rose exporters to gain effective access into the _direct_ sales market. One clear trend is the sales of cut flowers through supermarkets. The successful entry of Kenyan cut flowers in the UK market is principally due to the fact that exporters are selling directly to supermarkets in pre-packs with ready labels and guaranteed vase life. In this new market segment, stringent quality control, packaging, labeling, and resources dedicated to marketing and client relations are a must.<sup>27</sup>

> 25 Kenya Flower Council, _Industry Information and Market Data_ . Online Available at: <u>http://www.kenyaflowers.co.ke/industryinfo/marketdata.php</u>

> 26 World Bank  (2005) “Kenya Growth and Competitiveness”.

> 27 Jaffee, Steven. 1995. “The Many Faces of Success: the Development of Kenyan Horticultural Exports”, in Steven Jaffee and John Morton, eds. Marketing Africa’s High-Value Foods. 319-374. Washington, D.C.: World Bank.

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NTMs that clearly address possible negative externalities of trade, such as measures protecting consumers’ health by establishing permissible maximum residues of harmful substances in food, and those which do not. As we do not observe the value of such externalities, our estimates cannot speak to the net economic effects of all NTMs. Hence, we also study the impact on exporters’ resilience to shocks of the most prevalent NTMs that do not clearly address a negative externality. In particular we identify 4 such measures and estimate their effects individually. The results suggest negative and significant impacts of each of these NTMs on exporters’ responses, thus identifying possible opportunities for welfare enhancing reforms.

We then investigate the mechanisms through which NTMs affect firms’ resilience to demand shocks. Consistently with the economic intuition discussed above, we find that these effects are driven by firms participating in global value chains—exporting firms that rely on imported inputs in their production processes.<sup>2</sup> We also find that firms that import inputs less frequently, are small in size, and produce high-quality goods suffer more from the presence of NTMs following a yuan depreciation. These findings are consistent with the intuition that NTMs make it harder to adjust inputs for firms that are less flexible. Note that a possible confounding factor is that a depreciation of the yuan increases the competitiveness of Indonesian firms relying on Chines inputs. In an extension, we use a more conservative specification in which we control for a firm-time level measure of exposure to Chinese input share for Indonesian firms. All NTMs continue to have a negative impact on exports of firms more exposed to NTMs.

This paper relates to three strands of recent literature. The first is the literature on the economic effects of trade policy (Goldberg and Pavcnik, 2016; De Loecker et al., 2016; Amiti and Konings, 2007; Handley et al., 2020; Fan et al., 2015; Amiti et al., 2019; Handley et al., 2020b; Fajgelbaum et al., 2020) and non-tariff measures in particular (Ederington and Ruta, 2016; Fontagn ́e et al., 2015; Grundke and Moser, 2019; Kruse et al., 2021). A key message

> 2A unique feature of the Indonesian customs data is that it explicitly keeps track of firms that are producers as opposed to pure trading firms.

4
**Table S3.S: Results for the baseline specification with interaction terms (similar firm size x network variables) added**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|Supbuy|0.0165|-0.203|-0.342**|0.448|-0.298|-0.0830|-0.122|0.369|
||(0.351)|(0.327)|(0.141)|(0.571)|(0.237)|(0.0875)|(0.263)|(0.340)|
|Samesup|-0.100|-0.177**|-0.175**|-0.277**|-0.327**|-0.00326|0.0235|-0.0235|
||(0.0773)|(0.0826)|(0.0720)|(0.118)|(0.136)|(0.0428)|(0.0845)|(0.0621)|
|Samecus|0.0168|-0.00395|0.127***|0.227***|0.172*|-0.0416|0.0793**|0.0194|
||(0.0228)|(0.0341)|(0.0370)|(0.0631)|(0.100)|(0.0295)|(0.0379)|(0.0729)|
|same_sec|0.00455|-0.00138|-0.0133|-0.0955***|-0.0299|0.00208|-0.0341*|-0.00870|
||(0.0101)|(0.0134)|(0.0185)|(0.0301)|(0.0309)|(0.00619)|(0.0189)|(0.0159)|
|Ldist|-2.85e-05|-0.00107|-0.0116***|-0.0213***|-0.0187***|0.00251|-0.00783***|-0.000565|
||(0.00176)|(0.00231)|(0.00291)|(0.00513)|(0.00602)|(0.00191)|(0.00299)|(0.00436)|
|ad_fmage|0.000119|0.000256|0.000364|-0.00318***|-0.00102|-0.000526*|-0.000422|0.000881|
||(0.000283)|(0.000368)|(0.00103)|(0.00120)|(0.00134)|(0.000298)|(0.000663)|(0.000991)|
|ad_meduc|0.00575|0.0410***|0.00742|0.0187|0.0666***|0.00394|-0.000371|-0.0144***|
||(0.00615)|(0.0143)|(0.00921)|(0.0167)|(0.0235)|(0.00338)|(0.00572)|(0.00472)|
|ad_mexper|-1.79e-06|-0.000297|-0.000414|-3.09e-05|-0.000118|0.000262|-0.00155***|0.000638|
||(0.000289)|(0.000267)|(0.00115)|(0.00218)|(0.00240)|(0.000591)|(0.000501)|(0.00108)|
|ad_mfemale|0.00116|0.0355*|0.0977**|0.435***|0.241***|0.0299*|0.0446|0.0847|
||(0.00633)|(0.0187)|(0.0396)|(0.0909)|(0.0750)|(0.0175)|(0.0375)|(0.0541)|
|ad_lemp|0.0252***|0.0549***|0.0364**|0.163***|0.203***|0.0216***|0.0558***|0.0788***|
||(0.00956)|(0.0141)|(0.0181)|(0.0449)|(0.0482)|(0.00575)|(0.0179)|(0.0218)|
|sizdist|0.00314|0.00374|-0.00467|-0.00608|0.00831|0.00545***|-0.00156|0.00135|
||(0.00234)|(0.00350)|(0.00441)|(0.00880)|(0.00907)|(0.00150)|(0.00396)|(0.00390)|
|sizlink|0.0812|0.0957|-0.0390|0.0123|0.0712|0.0212|0.0288|0.0180|
||(0.0713)|(0.0813)|(0.0815)|(0.142)|(0.138)|(0.0459)|(0.0966)|(0.0641)|
|Constant|0.457***|0.370***|0.334***|0.510***|0.802***|0.287***|0.379***|0.0946**|
||(0.0177)|(0.0296)|(0.0362)|(0.0689)|(0.0703)|(0.0162)|(0.0356)|(0.0391)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 33
# _3.2. Changes over time_

Almost all South Asian countries have a very large number of young people reaching working age every year. Between 2005 and 2015, the number of South Asians aged 15 and above grew by 1.8 million per month, a trend that will only moderate gradually over time. In relative terms, the share of the working-age group in the total population increased by over 1 percent per year in Bhutan, and by around 0.5 percent in Bangladesh, India, and Pakistan (Figure 6). At the same time, the employment rate decreased on average more than 1.5 percent per year in Bhutan and India and by more than 0.5 percent per year in Bangladesh. Since income per capita grew considerably in all these countries from 2005 to 2015, some decline in the employment rates could be anticipated. However, the declines in employment rates are much larger than can be explained by increasing incomes per capita alone (World Bank 2018a). This contrasts with the experience of many East Asian countries like the Philippines, Vietnam, Malaysia, or Indonesia, for which the employment rate even increased between 2005 and 2015.

**Figure 6.** Changes in the working-age share and the employment rate from 2005 to 2015.

<!-- Start of picture text --> Annual change in percent<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>Working age rate Employment rate Predicted on GDP per capita growth<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

**Figure 7.** Changes in employment rates from 2005 to 2015, by gender.

<!-- Start of picture text --> Annual change in percent<br>2<br>0<br>-2<br>-4<br>-6<br>Male Female<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

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||||||**Deviation from**|**Deviation from**|**Valuing**<br>**emissions a**<br>**Payments**<br>**or**|**'excess'**<br>**t $10 per ton**<br>**Payments**<br>**or**|**Valuing 'exces**<br>**$7.5 per ton**<br>**with a$1**|**s' emissions at**<br>**-- compatible**<br>**00 B fund**|
|---|---|---|---|---|---|---|---|---|---|---|
|**ECONOMY**|**CO2**<br>**production**<br>**(mil tons)**|**CO2**<br>**consumption**<br>**(mil tons)**|**Per Capita**<br>**CO2**<br>**(production)**|**Per Capita**<br>**CO2**<br>**(consumption)**|**global average**<br>**(per capita CO2**<br>**production -**<br>**4.61**<br>**tons/person)**|**global average**<br>**(per capita CO2**<br>**consumption**<br>**4.9**<br>**tons/person)**|**Receipts**<br>**(producti**<br>**on based)**<br>**USD**<br>**Billions**|**Receipts**<br>**(consumpt**<br>**ion based)**<br>**USD**<br>**Billions**|**Payments or**<br>**Receipts**<br>**(production**<br>**based)**|**Payments**<br>**or**<br>**Receipts**<br>**(consumpt**<br>**ion based)**|
|Anguilla|0.15||10.05||5.44||0.00||0.00||
|Turks and Caicos<br>Islands|0.22||6.01||1.39||0.00||0.00||
|Antigua and<br>Barbuda|0.49||5.13||0.51||0.00||0.00||
|British Virgin Islands|0.18||6.17||1.56||0.00||0.00||
|Saint Pierre and<br>Miquelon|0.06||10.65||6.04||0.00||0.00||
|Montserrat|0.04||7.02||2.41||0.00||0.00||
|Nauru|0.05||5.16||0.55||0.00||0.00||
|Niue|0.01||5.34||0.72||0.00||0.00||
|Saint Kitts and Nevis|0.24||4.61||-0.01||0.00||0.00||
|Cook Islands|0.07||4.25||-0.36||0.00||0.00||
|Saint Helena|0.01||1.97||-2.64||0.00||0.00||
|Liechtenstein|0.15||4.00||-0.62||0.00||0.00||
|Tuvalu|0.01||0.63||-3.98||0.00||0.00||
|Barbados|1.23||4.31||-0.31||0.00||0.00||
|Marshall Islands|0.15||2.52||-2.10||0.00||0.00||
|Suriname|2.50||4.38||-0.24||0.00||0.00||
|Dominica|0.17||2.34||-2.28||0.00||0.00||
|Grenada|0.29||2.61||-2.01||0.00||0.00||
|Saint Vincent and<br>the Grenadines|0.23||2.13||-2.49||0.00||0.00||
|Tonga|0.14||1.35||-3.27||0.00||0.00||
|Saint Lucia|0.50||2.74||-1.88||0.00||0.00||
|Malta|1.58|5.16|3.62|11.79|-1.00|6.88|0.00|0.03|0.00|0.02|
|Kiribati|0.06||0.54||-4.07||0.00||0.00||
|French Polynesia|0.79||2.85||-1.76||0.00||0.00||
|French Guiana|0.71||2.58||-2.04||-0.01||0.00||
|Montenegro|2.28||3.63||-0.98||-0.01||0.00||
|Samoa|0.24||1.22||-3.39||-0.01||0.00||
|Maldives|1.56||3.15||-1.46||-0.01||-0.01||
|São Tomé and<br>Príncipe|0.13||0.61||-4.01||-0.01||-0.01||
|<br>Mayotte|0.30||1.18||-3.44||-0.01|0.00|-0.01||
|Switzerland|37.94|118.28|4.49|14.00|-0.13|9.09|-0.01|0.77|-0.01|0.58|
|Chile|84.16|91.87|4.56|4.97|-0.06|0.07|-0.01|0.01|-0.01|0.01|
|Belize|0.63||1.67||-2.94||-0.01||-0.01||
|Vanuatu|0.16||0.54||-4.07||-0.01||-0.01||
|Guyana|2.33||3.00||-1.61||-0.01||-0.01||
|Croatia|18.02|21.30|4.31|5.09|-0.31|0.19|-0.01|0.01|-0.01|0.01|
|Mauritius|4.40|5.51|3.48|4.36|-1.14|-0.55|-0.01|-0.01|-0.01|-0.01|
|Latvia|7.44|12.52|3.81|6.41|-0.80|1.51|-0.02|0.03|-0.01|0.02|
|Cabo Verde|0.56||1.05||-3.57||-0.02||-0.01||

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Table 1. Summary Statistics Main Variables

||Obs.|Mean|S.D.|Min|Max|
|---|---|---|---|---|---|
|Share High Educated Workers|8,396|26.75|30.76|0|100|
|Share of Skilled Occupations|9,623|34.31|29.63|0|100|
|Total Employment|9,776|369.04|1665|1|67,598|
|Exporter|9,418|0.34|0.47|0|1|
|Foreign Ownership|9,662|0.19|0.40|0|1|
|Technological Innovation|9,650|0.48|0.50|0|1|
|Age Firm|9,693|14.94|13.27|0|215|
|Public Ownership|9,546|0.14|0.35|0|1|
|Managerial Education|9,776|0.96|0.19|0|1|
|Sales per employee|9,374|6.40|3.11|-4.6|19.9|
|City > 1mln individuals|9,059|0.37|0.48|0|1|
|City 250k-1mln individuals|9,059|0.17|0.38|0|1|
|City 50k-250k individuals|9,059|0.17|0.37|0|1|
|City > 50k individuals|9,059|0.13|0.34|0|1|
|Access to External Finance|7,540|0.60|0.49|0|1|
|ISO certification|9,085|0.30|0.46|0|1|
|R&D Activities|5,970|0.33|0.47|0|1|
|R&D/Sales|5,970|1.82|9.32|0|100|
|Computer Use|5,181|0.59|0.49|0|1|
|Email/Internet Use|6,334|0.58|0.49|0|1|
|High-Tech Manufacturing Sectors|7,580|0.42|0.49|0|1|
|Low Income Countries|9,776|0.22|0.41|0|1|
|Low-Middle Income Countries|9,776|0.63|0.48|0|1|
|Upper-Middle Income Countries|9,776|0.09|0.29|0|1|

Source: _Enterprise Suveys_ (World Bank). Note: All variables defined in table A1 in the appendix.
more innovation, and more complex transaction in several relatively advanced cities in the early 2000s. The results hold even with controls for city and industry fixed effects. The effects of court quality on firm outcomes are thus inferred from relating firm outcomes to the variations in court quality across cities within the same industry. Thus, law seems to play a positive role even in a quintessential “poor law yet high growth” country, and in its more mature stage of transition for selective advanced cities (e.g., the sample cities in Long’s study are the most advanced ones in China).

# **5. The Effects of Labor Regulations**

Referring to the rules and regulations for the government to control how firms manage labor, labor regulations are tighter when firms have less discretion in freely choosing and adjusting the quantity, quality, and prices of labor. Particular types of labor regulation include employment protection through severance payments, advance notice of dismissal, administrative authorization, and prior negotiation with trade unions (Cahuc and Zylberberg 2004). The Organisation for Economic Cooperation and Development (OECD) has an index of labor flexibility, according to which Canada, England, and the United States are more flexible than France, Germany, and southern European countries (Italy, Portugal, and Spain). The study of the effects of labor regulations at crosscountry level is often inconclusive, partly because the data on labor regulation are available only for a limited number of developed countries. The estimated effects of labor protection on the level of unemployment range from positive (Lazear 1990), to negative (Nickell 1997), to insignificant (Bertola 1990). Thus, the existing cross-country studies on labor regulations cannot offer much policy guidance for developing countries.

Recent firm-level studies of labor regulations in developing countries have substantially improved our understanding about how labor regulations work in developing countries. They suggest that labor flexibility facilitates better firm performance, faster factor adjustments, and a more efficient distribution in firm sizes.<sup>17</sup> China has been a good example. During the past two decades, China featured remarkable labor flexibility and economic growth (Dong and Xu 2008, 2009). Thus, it is not surprising that labor flexibility is found to be efficiency-enhancing in China. HallwardDriemeier et al. (2006) use the World Bank Investment Climate Survey in five Chinese cities to examine how the investment climate affects firm performance (e.g., sales growth, investment rate, TFP, and employment growth). After controlling for firm characteristics (age, size, and ownership),

> 17 Labor regulations have other important objectives, such as health, job safety, job security and reducing income inequality. Since the new empirical literature on labor regulations is mainly concerned about the income and growth effects, I do not have much to say about these additional objectives. The effects of labor regulation on these other objectives remain topics for future research.

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