rafmacalaba/datause-encoder-data huggingface dataset:
- 11 -

**Figure 5. Number of security-related incidents by district**

<!-- Start of picture text --> Security incidence per month<br>(2006-2010)<br>0.05 - 0.28<br>0.29 - 0.65<br>0.66 - 1.03<br>1.04 - 1.58<br>1.59 - 2.53<br>2.54 - 3.90<br>3.91 - 5.68<br>5.69 - 13.35<br>.<br>Source: UN Nepal Security Database.<br>Figure 6. Number of security-related incidents in Nepal<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Source: UN Nepal Security Database.<br>2006-01 2006-04 2006-07 2006-10 2007-01 2007-04 2007-07 2007-10 2008-01 2008-04 2008-07 2008-10 2009-01 2009-04 2009-07 2009-10 2010-01 2010-04 2010-07 2010-10<br><!-- End of picture text -->

**Figure 6. Number of security-related incidents in Nepal**

In general, governance matters to public procurement. In Nigeria, the majority of private firms surveyed on perceptions of public sector procurement reforms answered that they decided not to submit their expressions of interest in recent public tenders, because they did not trust the selection process. There is a general perception that contractors are predetermined or must pay a bribe. Particularly, integrity tends to be relatively weaker at the subnational levels (World Bank, 2008c).

In Nepal, ethnicity may be another issue that might affect the bidders’ entry strategy in the market. In Nepal there are 103 castes and ethnic groups.<sup>3</sup> In some areas, a dominant ethnic group accounts for nearly half the population in a district. There is a general perception that

> 3 In the 1991 census, there were 60 castes and ethnic groups in the country, some of which are re-categorized into new groups in the 2001 census.
Bank’s Doing Business Report in 2006 (WBDBR) shows Morocco and Tunisia to have rigid labor markets compared to other developing countries (fig 4).<sup>2</sup> <!-- Start of picture text --> Fig 4: Rigidity of Employment Index<br>70<br>60<br>60<br>54<br>50<br>44 45 46<br>40 37 37 39<br>30<br>24 24<br>20<br>10<br>10<br>0<br>Malaysia Chile Czech Hungary Poland Slovak Bulgaria Philippines Pakistan Tunisia Morocco<br>Republic Republic<br><!-- End of picture text --> Rigidities in other factor markets in Tunisia include: delays in securing finance, land and construction permits; difficulties in closing businesses due to lengthy bankruptcy procedures; restrictions on majority capital ownership by foreign investors in key sectors; and general restrictions to entry (foreign and national) in certain activities (World Bank 2004). The recent Morocco ICA also points to difficulties faced by firms in raising finances from banks as well as in accessing land. The high cost of finance and access to industrial land was considered to be a major constraint by some 80% and 43% of firms surveyed, respectively. The 2006 WBDBR shows that the minimum capital requirement (as a percent of income per capita) in Morocco was 700.3% compared to an average of 177.3% for all countries included in the survey. The figure for Tunisia declined from 327.8% in the 2004 WBDBR to 29.8% in the 2006 WBDBR, showing improvement in this area.

# **Model**

It is against this background of existing factor market rigidities that we consider how improved flexibilities in the factor markets could affect the impact of trade reforms. A

> 2 Available at: <u>http://www.doingbusiness.org/CustomQuery [1</u> September 2005].

7
**Chart** **_2.2_ 11 Eurnow Union Is the mdn irnporter ofbananas**

<!-- Start of picture text --> Datasoiwcdd Bar*C 99) FAO (1).<br><!-- End of picture text --> Competitive production and marketing arrangements also favor Latin Amencan counries over ACP countries and EU trritories in the productivity stakes. Competiive conditions have **;atacted** considerable investment in productivity and quality improving technology and inasructe in Latin America Lack of competiive production and marketing arrangements in the ACP countries and EU territories have reduced incentives for simiar investment and technology tansfer.

6
owners and 78 percent of wage workers being classified correctly. Since a coin flip would classify 50 percent correctly, we might think of this as close to a 60 percent improvement over random classification.

Do the characteristics of the own account workers lead them to be classified as SME owners or wage workers? In all cases, the majority of own account workers are classified as wage workers. The largest percentage—almost 77 percent—are classified as wage workers when we use the ability measures alone. The largest percentage are classified as SME owners—37 percent—when we use only family background. Using all of the variables, we classify 70 percent of the own account workers as wage workers and 30 percent as SME owners.<sup>10</sup> The species classification analysis suggests that ability and attitudes differentiate own account workers from SME owners more than family backgrounds do. While the majority of the own account workers have characteristics more like wage workers than SME owners, a significant minority are more like SME owners than wage workers. Given the large number of own account workers in low-income countries, this suggests the possibility of job creation from the sector should not be ignored.

# **Who Grows the Enterprise?**

One criticism of the analysis to this point is that the comparisons between wage worker, own account workers and SME owners are based on separate cross sectional samples. We note that this is the standard in the literature. But for variables measuring attitudes, we should be concerned that outcomes may have influenced attitudes. That is, successful entrepreneurs may be more likely to report that work is central to their life, even if they were no more likely to say that before they entered business. Ideally, we would like to measure attitudes prior to the decision to enter self employment and the realization of the success of the business.

We can use the panel aspect of the survey of own account workers to examine growth of enterprises across time. The panel covers a period of two and a half years, from the April 2005 survey through the October 2007 survey. One of the screening criteria for

> 10 If we limit the sample only to males, we classify 63 percent of the own account males as wage workers and 37 percent as SME owners using the full set of characteristics.
|**5**<br>**Rev**|**iew and future directions**|**34**|
|---|---|---|
|5.1|Consider the long term growth of local industry . . . . . . . . . . . . . . . . . . . . .|35|
||<br>5.1.1<br>SGR spotlight: Non-existent capacity building . . . . . . . . . . . . . . . . . .|35|
|5.2|Diversify FDI sources to avoid overreliance on China . . . . . . . . . . . . . . . . . .|36|
|5.3|<br>Monitor debt levels from China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|37|
||5.3.1<br>Debt to China is growing quickly . . . . . . . . . . . . . . . . . . . . . . . . . .|37|
|5.4|Supply-side Shortages: Reducing labor costs<br>. . . . . . . . . . . . . . . . . . . . . . .|38|
||5.4.1<br>Encourage technology transfer and capacity building with infrastructure||
||projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|39|
||5.4.2<br>Bring more transparency to loans and infrastructure projects . . . . . . . . . .|39|
||5.4.3<br>Special Economic Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|40|
||5.4.4<br>Conclusion<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|41|
|**Appen**|**dices**|**47**|
|**Appen**|**dix A Gravity model of trade**|**47**|
|A.1|Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|48|
|**Appen**|**dix B**<br>**Gravity Data**|**49**|
|**Appen**|**dix C Results**|**50**|
|**List o**<br>|**f Figures**<br>||
|1|Kenya’s overall trade balance is a bigger concern<br>Central Bank of Kenya 2015||
|2|China takes a sizeable share of total Kenyan imports (2012-2014)<br>IMF World Economic Outlook Database 2014||
|3|China and India are major sources of imports (2004-2013) . . . . . . . . . . . . . . . .|5|
|4|Kenya imports rubber manufactures from China (2010-2014)<br>UN Comtrade database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|6|
|5|Kenya imports rubber footwear, tires, and fabrics from China (2012-2014)<br>UN Comtrade database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|7|
|6|Kenyan exports to China small relative to total exports (2012-2014)<br>IMF World Economic Outlook Database 2014||
|7|Kenya exports metals and hides and skins to China (2010-2014)<br>UN Comtrade database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|8|
|8|Kenya exports titanium ores and copper to China (2012-2014)<br>UN Comtrade database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|9|
|9|Metals prices have been falling since January 2010 (1990-2015)<br>World Bank Commodity Price Data (The Pink Sheet) . . . . . . . . . . . . . . . . . . . . .|9|
|10|<br>Kenya’s imports of intermediate goods is rising quickly<br>UN Comtrade 2015||
|11|China’s share of Kenya’s intermediate goods imports is growing<br>UN Comtrade 2015||
**<u>Table 4 Two-sample t test of total cooking time, pooled data and by period</u>**

|**Group**|**Obs**|**Mean**|**Std. Err.**|t–value|**Ha:**|Pr(T <t)|
|---|---|---|---|---|---|---|
|**a) Pooled da**|**ta**||||||
|Traditional|207|60.270|1.582516||||
|_Mirt_|211|67.407|2.02901||||
|Combined|418|63.873|1.299901||||
|Difference||-7.137|2.579417|-2.7669|Difference < 0|0.003***|
|**b) October/**|**Novembe**|**r 2013**|||||
|Traditional|106|59.038|2.136868||||
|_Mirt_|108|61.176|2.219007||||
|Combined|214|60.117|1.539044||||
|Difference||-2.138|3.081977|-0.6938|Difference < 0|0.244|
|**c) May/June**|**2013**||||||
|Traditional|101|61.564|2.347082||||
|_Mirt_|103|73.942|3.336078||||
|Combined|204|67.81373|2.087024||||
|Difference||-12.3774|4.092944|-3.0241|Difference < 0|0.001***|

|**Table 5**<br>**Likert Scale for Stove Attributes**<br>Stove attribute|Ranking (i|n %) (n=3|60)|||
|---|---|---|---|---|---|
||Very poor|Poor|Average|Good|Very good|
|How do youfind thenew stove?|-|-|0.6|15.8|83.6|
|How did your family members<br>find thenew stove?|-|-|1.1|16.9|81.9|
|Convenience<br>of<br>new<br>stove<br>compared withthe traditionalone|-|-|0.8|16.4|82.8|
|Durability of new stove|0.3|1.9|6.4|33.5|57.8|
|Reducing in amount of time spent<br>forcooking|-|0.8|5.8|31.4|61.9|
|Reducing amount of fire wood<br>use|-|0.6|3.1|18.3|78.1|
|Reductionofsmoke|-|-|1.4|13.9|84.7|

28 | P a g e
Kling, Jeffrey, Jeffrey Liebman, and Lawrence Katz (2007) ‘‘Experimental Analysis of Neighborhood Effects,’’ _Econometrica_ 75: 83–119.

Klinger, Bailey and Matthias Schündeln (2011) “Can entrepreneurial activity be taught? Quasiexperimental evidence from Central America”, _World Development_ 39(9): 1592-1610.

Mano, Yukichi, AlHassan Iddrisu, Yutaka Yoshino and Tetsushi Sonobe (2012) “How Can Micro and Small Enterprises in Sub-Saharan Africa Become More Productive? The Impacts of Experimental Basic Managerial Training”, _World Development_ , 40(3): 458-68.

McKenzie, David (2012) “Beyond baseline and follow-up: the case for more T in experiments”, _Journal of Development Economics_ , forthcoming.

McKenzie, David (2011) “How can we learn whether firm policies are working in Africa? Challenges (and solutions?) for experiments and structural models”, _Journal of African Economics_ , 20(4): 600-25.

McKenzie, David (2010) “Impact Assessments in Finance and Private Sector Development: What have we learned and what should we learn?”, _World Bank Research Observer_ , 25(2): 209-33.

McKenzie, David and Michaela Weber (2009) “The results of a pilot financial literacy and business planning training program for women in Uganda”, _Finance & PSD Impact note no. 8_ _<u>http://siteresources.worldbank.org/INTFR/Resources/Note8.pdf</u>_ Premand, Patrick, Stefanie Brodmann, Rita Almeida, Rebekka Grun and Mahdi Barouni (2012) “Entrepreneurship training and self-employment among university graduates: Evidence from a randomized trial in Tunisia”, Mimeo. World Bank Schulz, Kenneth and David Grimes (2005) “Sample size calculations in randomised trials: mandatory and mystical”, _Lancet_ 365: 1348-53.

Sonobe, Tetsushi , Yukichi Mano, John Akoten, and Keijiro Otsuka (2010) “Assessing the Impacts of Management Skill Training in Ghana and Kenya”, paper presented at the CSAE conference, 2010.

Sonobe, Tetsushi , Aya Suzuki, and Keijiro Otsuka (2011) “Kaizen for Managerial Skills Improvement in Small and Medium Enterprises: An Impact Evaluation Study”, The World Bank http://siteresources.worldbank.org/DEC/Resources/FinalVolumeIV.pdf Valdivia, Martin (2012) “Training or technical assistance for female entrepreneurship? Evidence from a field experiment in Peru”, Mimeo. GRADE.

Woolcock, Michael (2009) “Toward a plurality of methods in project evaluation: a contextualised approach to understanding impact trajectories and efficacy”, _Journal of Development Effectiveness_ ,1(1),1-14.

26
presents a challenge due to the limited set of information collected in SAKERNAS. Yet this is an important task as exit and exclusion can lead to very different policy responses.

A number of studies have also alluded to the heterogeneous nature of informality in Indonesia. In other words, elements of exit and exclusion may apply to firms and workers to varying degrees. Empirically, very little is known regarding the nature of this heterogeneity. This may be partly due to difficulties in operationalizing this concept. While singular measures such as income or output provide a useful starting point, future studies will have to think more deeply about how this concept can be captured more effectively and holistically. Once heterogeneity has been established, the next empirical task would be to differentiate between firms and workers for whom informality is a voluntary choice versus those for whom it is a last resort. This would require us to go beyond comparisons based on workers’ employment status (i.e., self-employed informal workers vs. salaried informal workers) – an area that has already received considerable attention in the literature. Instead, more work will be needed to understand to what degree exit and exclusion apply to different socio-demographic groups (e.g., gender, age, skills or educational attainment); by industry or sector; by geographic location (e.g., urban vs. rural); or even by the level of economic development or poverty within a region.

More broadly, previous studies have relied heavily on binary categorizations of informality wherein firms and workers are either considered formal or informal. However, some scholars have suggested that informality is better represented as a continuum. This theme was also touched upon by Rothenburg and co-authors (2016), when they observed incomplete formalization among micro and small enterprises. Future work can thus examine whether this characterization of informality is accurate and identify where firms fall short in the process of formalization. In doing so, policies can be designed to address the specific barriers that prevent firms from completing the registration process.

Indeed, understanding what types of policy interventions are needed to address informality remains an ongoing task. From a firm perspective, Rothenburg et al.’s (2016) study was 24
to our model, this is likely to increase sovereignty demand and unmet demand should result in conflict in ethnically distinct regions.

# Table 1 – OLS Regression of Observed Sovereignty on Determinants of Sovereignty Demand

Coefficients with standard errors in parentheses; country fixed effects and regional weights

||1|2|3|4|
|---|---|---|---|---|
|Relative regional income|0.153**<br>(0.023)|0.154**<br>(0.024)|0.158**<br>(0.023)|0.385**<br>(0.079)|
|Regional population share|0.260**<br>(0.098)|0.281*<br>(0.125)|0.236*<br>(0.097)|0.284**<br>(0.097)|
|Regional ethnic distinctiveness<br>(binary)|-0.057<br>(0.139)|-0.121*<br>(0.052)|-0.038<br>(0.038)|-0.584**<br>(0.179)|
|Oil or natural gas in region|0.056**<br>(0.017)|0.051*<br>(0.021)|0.060**<br>(0.016)|0.061**<br>(0.016)|
|Regional Gini (within regional<br>inter-personal inequality)|0.003*<br>(0.001)|0.004**<br>(0.002)|0.001<br>(0.002)|0.005*<br>(0.002)|
|Conflict over sovereignty<br>(binary)|-0.003<br>(0.015)|0.001<br>(0.019)|-0.167**<br>(0.048)|-0.127**<br>(0.049)|
|Interaction: conflict and regional<br>Gini|--|--|0.004**<br>(0.001)|0.003**<br>(0.001)|
|Interaction: ethnic distinctiveness<br>and regional Gini|--|--|--|0.016**<br>(0.005)|
|Interaction: regional income and<br>regional Gini|--|--|--|-0.005**<br>(0.002)|
|Constant|-0.312**<br>(0.098)|-0.420**<br>(0.111)|-0.262**<br>(0.098)|-0.414**<br>(0.113)|
|CountryFixed Effects|Yes|Yes|Yes|Yes|
|Observations(Regions)|517|376|517|517|
|F-test for model(_p_-value)|0.000|0.000|0.000|0.000|
|Adjusted R squared|0.87|0.87|0.88|0.89|

Note: Relative income share and population share are fractions (all regions sum to a national total of 1). Relative regional income is mean regional income divided by mean national income (both calculated from household surveys). Regional Gini, calculated from household surveys, is expressed in percentages (ranges from 0 to 100). Significance levels of 5% and 1% are indicated by two and one asterisks respectively.

In regression 3, we add to the model an interaction term between conflict and regional inequality. The signs and significance of coefficients on relative income, population share, and region’s natural wealth are basically unaffected. The coefficient on conflict now becomes negative as posited in the model with very high statistical significance. An important new result is that the effect of inequality on sovereignty is indirect and a higher regional inequality, contingent 29
are likely to enhance welfare, since the gains from freeing intraregional trade are larger and the costs of reducing interregional trade are smaller.<sup>7</sup> To determine whether nature plays a role in RTA formation, Frankel et al. (1995) examine whether regional trade is greater than could be explained by natural determinants (proximity, sizes, GNPs/capita, common border, common language). They find in favor of “natural” trade bloc formation.

This view has however been challenged by Bhagwati and Panagariya (1996), who show that the volume of trade and transport cost criteria are not sufficient to ensure that an arrangement will raise welfare. They argue that volumes are not necessarily good predictors of diversion, and that comparative advantage can change over time. Furthermore, a country could be better off forming an RTA with a distant rather than a proximate country.

Krishna (2003) addresses this point by using detailed US trade data to estimate the welfare effects from 24 hypothetical bilateral trade agreements in a general equilibrium framework, then correlating the estimated welfare changes with geographical variables and trade volumes. Neither geography nor trade volume is found to be significantly correlated with welfare gains, suggesting that they are not good indicators of the gains from trade, as the natural trade blocs approach would suggest. Still, Krishna finds that 80 percent of the potential agreements he examines are welfare-improving. Given the predominance of trade creation, it is not clear that a correlation between distance or trade volume and welfare is necessary to indicate blocs are formed naturally. To determine which agreements are most natural, costs of forming an

> 7 Zissimos (2009) argues that forming an RTA with a nearby country can also facilitate rent-shifting because of lower (rent-destructing) transportation costs.

12
8 1974). In the case of agriculture which involves the use of farm land that is sectorspecific, the greater the variation of NRAs across industries within the sector, the higher will be the welfare cost of those market interventions. A simple index of that cost is the standard deviation of industry NRAs within agriculture. We therefore report not only the weighted mean NRA for the sector (using the values of production at unassisted prices as weights), but also the standard deviation around that mean each year.<sup>4</sup>

# **IV. Measuring the nominal rates of assistance (NRAs)**

We begin by describing the sources of agricultural NRAs, before turning to those for non-agricultural tradable sectors.

# **_Agricultural commodities_**

Australia’s Industries Assistance Commission began calculating estimates of the nominal rates of assistance for major agricultural commodities beginning with the year 1970-71. (All years are fiscal, beginning 1 July.) This series has been continued by its successors, 4 The effective rate of assistance (ERA) is, in principle, a better partial equilibrium measure of distortions to producer incentives than the nominal rate. This is because, in showing how value added rather than the gross value of production is affected, it takes into account differences in the value added share of output as well as distortions to intermediate input prices. Our simpler approach is dictated not only by insufficient Australian data prior to the 1970s but also by the comparable ERA estimates are not available for other countries. We do, however, attempt to capture input price distortions by estimating their equivalence in terms of a higher output price and including that in the NRA for agriculture.