rafmacalaba/datause-encoder-data huggingface dataset:
effects of reform episodes related to the oversight of international goods trade. Martincus _et al._ (2015) show that inspections impede exporting in a study of the risk management system that governs Uruguayan exports. Fernandes _et al._ (2021) conduct an evaluation of the effects of reduced inspections of import shipments by Albanian customs during a period of substantial reform. Fernandes _et al._ (2017) evaluate preliminary steps towards a reform undertaken by the Food and Veterinary Agency of North Macedonia. Cariolle _et al._ (2019) and Chalendard _et al._ (2019) show how external information on trade flows can be used to forecast tariff evasion in Gabon and Madagascar, respectively.

In this paper we isolate a channel through which risk management reforms contribute to better border management. We argue that the rarity of many types of import shipments makes untenable a presumption that inspecting agencies are fully informed about the conditional probability that a shipment does not comply with import regulations. It is likely that modern IT systems substantially reduce ambiguity by improving the agency’s conditional forecast of the probability that a shipment complies with import regulations. To motivate our focus on ambiguity, we introduce a new stylized fact - shipments that were less frequently observed in prior years are conditionally more likely to be inspected. We apply tools from decision theory to understand inspecting agencies’ likely response to ambiguity, and the manner in which ambiguity-reducing investments in IT are likely to shift inspection resources. Finally, we calibrate the model to illustrate quantitative relationships between ambiguity, search intensities and firms’ compliance with import regulations.

The rest of the paper is organized as follows. Section 2 briefly describes the operation of modern risk management systems as they apply to oversight of imported goods shipments. Section 3 motivates our focus on ambiguity with stylized facts from the Serbian customs data. Section 4 develops the theoretical framework incorporating ambiguity, describes the equilibrium, and derives comparative static predictions of the model. Section 5 calibrates a numerical version of the model and conducts counterfactual analysis. Section 6 concludes. The general framework describing the neo-additive capacity and the Choquet integral is relegated to the appendix as are additional details about the model calibration and conventional 6
||Share<br>(%)<br>31.7<br>10.7<br>8.0<br>6.5<br>4.7<br>3.2<br>3.1|3.0|3.0|2.2|re common|or the EU,||
|---|---|---|---|---|---|---|---|
||2007<br>Product<br>Refned petroleum<br>**Iron and steel**<br>**Chemicals**<br>**Motor vehicles**<br>**Crops**<br>Other food<br>**General machinery**|Metals|**Other chemicals**|**Special machinery**|untries or products in bold a|re not directly comparable f||
|**ducts**|ISIC<br>232<br>271<br>241<br>341<br>11<br>154<br>291|272|242|292|abase. Co|nd 2009 a||
|**Pro**|Share<br>(%)<br>18.1<br>13.9<br>10.2<br>6.1<br>5.6<br>4.7<br>4.0|3.1|3.0|2.9|cudadat|or 2007 a|.|
||2009<br>Product<br>**Crops**<br>**Chemicals**<br>**Motor vehicles**<br>**Iron and steel**<br>**Other chemicals**<br>**Special machinery**<br>Processed food|Grain mill products|Non-metallic minerals|**General machinery**|are from Syrian Customs’ asy|ts. Note that trade partners f|treat the EU as a single entity|
||ISIC<br>11<br>241<br>341<br>271<br>242<br>292<br>151|153|269|291|9 data|l impor|9 data|
||Share<br>(%)<br>9.8<br>8.0<br>6.9<br>5.8<br>5.7<br>5.5<br>5.0|4.6|4.4|3.9|while 200|es of tota|s but 200|
|**tners**|2007<br>Country<br>**Russia**<br>**China**<br>Italy<br>**Ukraine**<br>**Saudi Arabia**<br>Malta<br>**South Korea**|**Egypt**|**Turkey**|India|comtradedatabase,|are calculated as shar|egate the EU countrie|
|**Par**|Share<br>(%)<br>9.6<br>8.4<br>8.1<br>6.4<br>6.3<br>6.2<br>6.1|3.3|3.1|2.5|m the UN|s. Shares|ta disaggr|
||2009<br>untry<br>**ina**<br>**ypt**<br>**uth Korea**<br>**udi Arabia**<br>**rkey**<br><br>**raine**|**ssia**|rmany|dan|007 data are fro|cross both year|ecause 2007 da|
||Co<br>**Ch**<br>**Eg**<br>**So**<br>**Sa**<br>**Tu**<br>EU<br>**Uk**|**Ru**|Ge|Jor|_†_ 2|a|b|

12
# **Introduction**

Financial development has been shown to be associated with faster growth and improved allocative efficiency.<sup>1</sup> While the research focus has been on formal financial institutions, the literature has recognized the existence and role played by informal financial systems, especially in developing economies.<sup>2</sup> The dominant view is that informal financial institutions play a complementary role to the formal financial system by servicing the lower end of the market - informal financing typically consists of small, unsecured, short term loans restricted to rural areas, agricultural contracts, households, individuals or small entrepreneurial ventures. Informal financial institutions rely on relationships and reputation and can more efficiently monitor and enforce repayment from a class of firms than commercial banks and similar formal financial institutions can.<sup>3</sup> According to this view however, informal financial systems cannot substitute for formal financial systems because their monitoring and enforcement mechanisms are illequipped to scale up and meet the needs of the higher end of the market.<sup>4</sup>

> 1 This literature includes cross-country studies (King and Levine, 1993; Levine and Zervos, 1998; Levine, Loazya and Beck, 2000; and Bekaert, Harvey and Lundblad, 2001 and 2005), firm level studies (DemirgucKunt and Maksimovic, 1998; Love, 2003; Beck, Demirguc-Kunt, and Maksimovic, 2005), industry level studies (Rajan and Zingales, 1998; Wurgler, 2000; Beck, Demirguc-Kunt, Laeven, and Levine, 2006) and individual country case studies (Guiso, Sapienza, and Zingales, 2002 in Italy and Bertrand, Schoar and Thesmar, 2004 in France)

2 By informal financial institutions, we refer to the entire gamut of non-market institutions such as credit cooperatives, moneylenders, etc. that do not rely on formal contractual obligations enforced through a codified legal system.

3 A large economics literature has also argued that informal institutions have a comparative advantage in monitoring (the peer monitoring view as in Stiglitz (1990) and Arnott and Stiglitz (1991)) and enforcement capacity. Theoretically the informal sector has been modeled as both, a competitor to its formal counterpart (as in Bell et al., 1997; Jain, 1999; Varghese, 2005) as well as a channel of formal funds, where informal lenders who offer credit acquire formal funds to service entrepreneurs’ financing needs (Floro and Ray, 1997; Bose, 1998; Hoff and Stiglitz, 1998). Both strands of literature emphasize that informal lenders hold a monitoring advantage over the formal lender.

4 There is a direct parallel with the prevalence of angel finance in the US. Business angels are typically wealthy individuals who provide the initial funding to young new firms with modest capital requirements.. However, Lerner (1998) argues that there is no evidence that angel financing generates positive financial returns or improves social welfare.

2
26

- 12/ In private communication, Aw and Roberts confirmed that their breakdowns were for broadly defined price categories (less than $2.50 per pair and over $2.50 per pair) which, to some extent, would minimize the possibility of measuring as quality upgrading, what is, in effect, a price rise induced by the quota.

- 13/ Our use of July to June data years allows us better to capture the effects of the OMA, which operated from July 1977 to June 1981. However, to compare with Aw and Roberts we repeated our calculations on calendar years. This made very little difference to our US results, and hence to table 5, but it did affect those for the FRG and Japan having the effect of increasing their rate of price increase over the restricted period. Aw and Roberts define their restriction as starting with 1978 data expressed relative to 1977 values.
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commodity prices, and short-term interest rates. The fact that inflation rises following a demand shock but falls with a supply shock allows us to disentangle one type of shock from the other.

A global monetary shock – i.e., an expansionary shift in global monetary policy – increases output, inflation, and commodity prices, but reduces the short term interest rate on impact. This latter sign allows us to distinguish demand from monetary shocks. Finally, a global commodity shock, understood as a decline in global excess demand for commodities, is assumed to increase global output and to reduce inflation and commodity prices on impact. Note how the opposing impacts on commodity prices allow us to disentangle a commodity price shock from a (noncommodity) supply shock. In both cases, we leave the response of the interest rate unrestricted.

It is important to stress that sign restrictions, unlike conventional parametric (zero) restrictions, only achieve what has been termed set identification. That is, they help identify a set of models, rather than a single model, consistent with the assumed restrictions (see, e.g., Fry and Pagan 2011). Below we describe how we deal with this issue when reporting the empirical results.

# **3. Empirical implementation**

The empirical sample is dictated by data availability. We take all countries with complete GDP data in PWT 8.1 over the period 1970-2011. After dropping countries with anomalous observations, high-income, and very small economies, we are left with 82 developing and emerging countries. In terms of the World Bank 2014 classification, 79 belong to the ‘middle income’ category, and 3 are low- income countries. They are listed in table A1 in the Appendix.<sup>11</sup>

> 11 From the overall sample of 167 countries, we retain only those with complete GDP data over the period 1970‐ 2011. Next, we drop 11 countries exhibiting anomalous and/or possibly erroneous observations, as well as 24 countries with population fewer than 500,000 in 2005. Lastly, we exclude 24 OECD or high‐income economies.

11
Several key conclusions emerge from this analysis:

- Although Latvia belongs to the middle group of the countries, within this group it ranks lowest in terms of both KEI and GDP.  Furthermore, the gap that Latvia needs to bridge in order to move to a leading position within this middle group is almost as large as the gap between leading NMS’s (Czech Republic, Slovenia, Hungary and Estonia) and Ireland, Denmark, Finland and Sweden.  In other words, Latvia’s task is daunting.

- Although Latvia has significantly improved it’s Economic and Incentive Regime score, it is still only in the middle rank among NMS’s.

**Figure 18 Economic and Institutional Regime in Global Context**

<!-- Start of picture text --> 11 .0 0<br>10 .0 0<br>E ston ia<br>9.00<br>F inland D en m ark<br>8.00 Irelan<br>S w eden<br>d<br>7.00 L ithu ania<br>H u ngary C zech R epublic<br>6.00<br>S lo ven ia S lo vakia<br>L A T V IA<br>5.00<br>4.00<br>B ulgaria R om ania<br>3.00<br>R ussia<br>2.00<br>1.00<br>0.00<br>0 .00 1 .00 2 .00 3.00 4 .00 5.00 6 .00 7.00 8 .00 9.00 10 .0 0 1 1.00<br>1995<br>Most Recent<br><!-- End of picture text --> _Source_ : WBIKAM

- All NMS’s rank relatively high on the education index and they all cluster together at the high end of the scale.  In other words, they all do relatively well on this dimension.  But as the next section will highlight, the key issue for Latvia and other NMS’s is not educational attainment per se but rather, their ability harness this educated labor force for high value added, knowledge intensive goods and services production.  In this respect, as we will demonstrate below, Latvia is not doing so well.

30
# **table B. Exogenous variables for 2nd child arrival in Turkey**

|||**no 2nd child**<br>**arrival**|**2nd child**<br>**arrival**|**Significance**<br>**of**<br>**difference**|
|---|---|---|---|---|
|**Stability on the labour market**|_Stable full‐time employment_|0,160|0,078|***|
||_Stable part‐time employment_|0,019|0,012||
||_Stable full‐time self employment_|0,099|0,053|**|
||_Stable part‐time self employment_|0,043|0,072|*|
||_Stable unemployment_|0,014|0,003||
||_Stable retirement_|0,009|/||
||_Stable student_|0,003|/||
||_Stable inactivity_|0,610|0,748|***|
||_Stable military service_|0,000|/||
||_Change in activity status within the observed_<br>_three‐monthsperiod)_|0,045|0,034||
|**Partner information**|_Partner in stable employment_|0,745|0,798|*|
||_Partner not in stable employment_|0,146|0,146||
||_No partner_|0,110|0,056|**|
||_Partner and married_|0,890|0,944|**|
||_Partner but not married_|0,000|/||
|**Both in stable employment**||0,244|0,181|*|
|**Household wage income**|_Zero household wage income_|0,126|0,087|*|
||_Low household wage income_|0,309|0,371|*|
||_Medium household wage income_|0,262|0,327|*|
||_High household wage income_|0,303|0,215|**|
|**Educational attainment**|_Low education (illiterate, prim.not completed)_|0,537|0,601|*|
||_Medium education (primary and secondary)_|0,360|0,324||
||_High education(tertiary)_|0,103|0,075||
|**Age**|_15‐24_|0,238|0,380|***|
||_25‐34_|0,420|0,573|***|
||_35‐45_|0,342|0,047|***|
|**Age of first child**|_0_|0,149|0,100|*|
||_1‐2_|0,237|0,393|***|
||_3‐6_|0,213|0,386|***|
||_7+_|0,401|0,121|***|
|**First child is female**||0,437|0,483||
|*p<0,05,**p<0,01,***p<0,001|||||
|Data Base: EU-SILC LT 2006-2011, w omen|aged 15-45 w ith one child||||

29
where _yit_ refers to the outcome of interest for principal _i_ in time _t_ ; _αborder_ is the border fixed-effects as defined for Eq. (1); _αt_ is a time trend; and _αct_ is a municipality-specific time trend. _Ceara_ is a dummy for schools at _Ceará_ ’s borders and zero for schools on the other side of the border (Figure 1); therefore, _β_ 0 measures the DD in the baseline period. _αyear_ is a set of exclusive dummies per year, i.e., from 2007 to 2017. The variable _DiDt_ interacts _αyear_ and _Ceara_ ; _γXi_ is a set of controls for principals’ characteristics (five age categories, race, and gender); and _εit_ is the error term. Because principals manage schools of different sizes, Eq. (2) weights the coefficients by school size, according to the school census. As before, the parameters of interest are the yearly differences in outcomes ( _βt_ ) at the border _relative_ to the difference during the baseline year of 2007.

Figure 4 plots the DD coefficients of interest for principal selection. Selection types are merged in four categories: "Election", "Designated", "Formal Selection", and "Hybrid".<sup>17</sup> Thus, Figures 4 _.A_ and 4 _.B_ present the _βt_ , and their respective 95% confidence intervals, relative to the difference in the baseline, in this case 2007.

The first result from Figure 4 is the opposite behavior shown by designations and formal selection of principals. Figure 4 demonstrates that municipalities in _Ceará_ had around a 10 _pp_ decrease in the probability of school principals being designated during the RBF period (2009 _−_ 2011) and almost a 20 _pp_ decrease after the RBF was reformulated (2013 _−_ 2017). In contrast, there is an increase in formal selection: when the RBF was introduced, principals in Ceará were around 10 _pp −_ 15 _pp_ more likely to go through a formal selection process than principals on the other side of the border. The second finding is that either "Hybrid" or “Election” selection processes present no significant change. Overall, these findings are in accordance with the interpretation

> 17The referred question in SAEB is " _Did you assume this position by:_ ". The outcome "Election" equals one when principals respond " _Election only_ " and zero otherwise. The outcome "Designated" equals one when principals answer " _Designation only_ " and zero otherwise. From SAEB 2011 backwards, this variable also includes principals who respond " _Political Indication_ " and " _Technical Indication_ ". "Formal Selection" considers principals admitted by " _Selection Only_ ". "Hybrid" encompasses the combinations of " _Selection and Election_ " or " _Selection and Indication_ ". This combination was chosen because these options remain in SAEB from 2007 to 2017.

29
for the utility was the number of voltage dips which were consistently higher than the limits set by NRS-048 (figure 12).<sup>44</sup> From Eskom’s perspective its inability to meet the dip voltage limits set by NRS048 implied that such limits were unrealistic and could not be achieved without substantial upgrading investment in the network. In response to these difficulties of managing dip performance, and after extensive consultations with the various stakeholders (utilities, equipment manufacturers, end-users, standards bodies, etc.), NER, in 2002, issued a new Directive on Power Quality.<sup>45</sup> Under this new PQ framework, the transmission and distribution utilities were no longer required to meet global dip limits. Instead the focus shifted away from the number of dip events on how the utility interacts with its customers, and the causes of specific events that affect sensitive customers. Network planning, operation, and maintenance management received increased attention. The implementation of high priority maintenance and reliability projects led to improvements in waveform quality and disturbance indicators (table 3).

**Table 3  Eskom’s power quality indicators**

||**Wa**|**veformquali**|**ty**||**Distur**<br>|**bance**<br>||
|---|---|---|---|---|---|---|---|
||**Regulation**|**Unbalance**|**Harmonics**|**Type X**<br>**dips **|**Type S**<br>**dips **|**Type T**<br>**dips **|**Type Z**<br>**dips **|
|2000|95.8|99.1|100|86.8|92.6|97.1|94.1|
||_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|
|2001|95.8|99.1|100|92.6|95.6|100|98.5|
||_(95)_|_(95)_|_(95)_|_(95)_|_(95)_|_(95)_|_(95)_|
|2002|98|98.4|100|98.8|96.4|100|95.2|
||_(95)_|_(95)_|_(95)_|_(95)_|_(95)_|_(95)_|_(95)_|
|2003|98.7|99.2|100|98.6|100|100|100|
||_(97)_|_(97)_|_(97)_|_(96)_|_(96)_|_(96)_|_(96)_|
|2004|98.9|99.5|100|97.6|99.4|99.4|99.4|
||_(96.2)_|_(97)_|_(98)_|_(96.2)_|_(96.2)_|_(96.2)_|_(96.2)_|
|2005<sup>*</sup>|99.5|100|100|96.3|96.3|99.4|98.8|
||_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|_(n.a.)_|

_Source_ : Eskom, Annual Report (various years).

# **Operating Efficiency**

This section examines Eskom’s labor and capital productivity and compares them to other utilities from around the world.

## **Labor Productivity**

In many developing and transition economies (DTEs), state ownership in the electricity sector has been associated with extraordinarily high excess employment.

> 44 Koch, R., Sigwebela, I. and H. Geldenhuys. 2003.  “A Summary of South-African Experience with Power Quality Management”. _CIRED 2003, Round Table on Power Quality at the Interface T&D._

> 45 National Electricity Regulator. 2002. “NER Power Quality Directive”. _Directive of the South African Electricity Regulator,_ Revision 1.5, April 2002.

28
**15**

countries and only slightly positive (and not statistically significant) for Latin American countries. Although both the micro data and this cross-country evidence suggest that the linkage effects of agricultural growth appear to be true for Africa, the opening up of African economies might for example undermine the linkage effects from any expansion in rural demand (that demand possibly being increasingly met by imports). We therefore update the data set examined by Bravo-Ortega and Lederman and revisit their results taking an African focus and using a somewhat modified specification.

In particular, in our specification non-agricultural GDP per capita ( _Y_<sup>_n_</sup> _it_<sup>) in country i at time t</sup> is assumed to depend on both lagged levels of per capita non-agricultural GDP and lagged levels of per capita agricultural GDP. In addition, we consider a vector _Xit_ of country-specific exogenous explanatory factors, yielding: where h _i_ reflects unobserved country specific characteristics that determine the sectoral output, and vit a white noise error term. Similarly, agricultural GDP per capita ( _Y_<sup>_a_</sup> _it_<sup>) is expressed as a linear</sup> function of lagged agricultural and non-agricultural GDP per capita as well as observed and unobserved country specific exogenous explanatory factors.

In the empirical application, we estimate the agricultural and non-agricultural equations separately, regressing non-agricultural (change in) GDP on lagged (changes in) agricultural and non-agricultural GDP and vice versa. We introduce a time indicator to capture period-specific shocks common to all countries (e.g. global up or down turn of the economy) as exogenous variable in the non-agricultural regression and the yearly deviation from long run average rainfall in each country<sup>12</sup> as exogenous variable in agricultural regression. The estimation employs the Arellano-

> 12 The meteorological data have been constructed by Dr. T.D. Mitchell at the Tyndall for Climate Change Research. Based on a comprehensive set of high-resolution grid data of monthly climate indicators 1901-2000 (version TYN CY 1.1), yearly means of precipitation and year-to-year variations are available for each country. For further information, see Mitchell et al. (2003) and <u>http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.html.  Bravo-Ortega and</u> Lederman do not include rainfall shocks in their specification.
from ownership loss, there is a drop in the rate of successful completion of six years of primary education for individuals between 10 and 22 years.

# **5.4. Estimating impact of war on sub-samples: The North versus the South**

As the final robustness check, we compare empirical outcomes from sub-samples: the North and the South. Table 5.4.1 reports the simple difference-in-difference outcome of average years of education on age-cohorts between the North and the South sub-populations. Out of a total of 50 departments the North has 16 which were located to the north of the United Nations peacekeeping line. The rest of the departments are classified as being in the South. As is evident from Table 5.4.1, for both the old and the young cohorts, the average years of education is lower in the North. However, the difference in years of schooling between the old cohort (aged between 23 and 32 years) compared to the younger one (aged between 10 and 22 years) is twice as big. As a result the difference-in-difference outcome for years of education is 0.72 years. This implies individuals in the young cohort have on average 0.72 more years of education compared to the old cohort in the North. This is also supported by the fact that the difference in average years of education between the young and the old cohort is larger in the South compared to the North. These findings contradict those of many studies documenting that the impact of war on education was more severe in the North. Rather it suggests that despite the historically lower educational outcome in the North, the impact of war on education fell most heavily on the school going agecohort that was exposed to the war.

Table 5.4.1 Means of Years of Education by Cohort and region

||Ye<br>|ars of edu<br>|cation<br>|
|---|---|---|---|
||South|North|Difference|
|Old Cohort (Aed 23 to 32 in 2008)|9.13|7.81|1.33|
|g|(0.08)|(0.15)|(0.16)|
|Youn cohort (Aed 10 to 22 in 2008)|6.56|5.96|0.61|
|g g|(0.04)|(0.06)|(0.07)|
|Difference|2.58|1.85|0.72|
||(0.08)|(0.14)|(0.17)|

Note: Standard errors are in parenthesis, all estimated coefficients are statistically significant at 1 percent 23