rafmacalaba/datause-encoder-data huggingface dataset:
everywhere the same among identical individuals, it would be difficult to value benefits since individuals are forced to consume more or less than they would like. A simple example of the first point is that a school is worth much more to a family with young children than to one without them. But there are many other examples. A supplementary feeding program will benefit a household in a region or period in which food prices are high more so than a household facing low food prices. The second factor is less obvious but may be just as important. When goods are supplied in fixed quantities by ration shops the available quantity is unlikely to equal the desired quantity, so the price paid may not reflect the true value to the consumer. The same can happen with public goods which are intrinsically lumpy-a road cannot be supplied only on the day of the week in which you happen to want to use it-it is there all the time.

Two general approaches to measurement can be identified. Neither is ultimately able to adequately resolve the twin difficulties of variable individual and household characteristics and quantity constraints. The first approach is found in benefit incidence studies and assumes that the value of the benefits of a public service to the individual equals the unit cost of providing the service. The second approach attempts to value benefits using various measures related to the notion of consumer surplus, in the attempt to arrive at behaviorally consistent measures. I discuss these in turn.

# 3.1.1 Benefit incidence

Benefit-incidence studies-several of which are included in PSP-typically proceed by ranking individuals (or more typically households) by some indicator of welfare (most commonly per capita household income or expenditures). In order to make valid distributional comparisons, it is clearly important that the welfare indicator be suitably normalized for cost-of-living differentials and household demographics. Next the unit costs of providing the public service are attributed across subgroups according to household utilization information. This exercise reveals how the governrment's 6
Piselli, Elisabetta. 2003. “The Establishment and Development of a Public Procurement System for Kosovo,” in Arrowsmith and Trybus.

- Rose-Ackerman, Susan. 1999. _Corruption and Government: Causes, Consequences, and Reform._ Cambridge University Press.

- Rose-Ackerman, Susan.  1995a. "The Political Economy of Corruption," _Viewpoint_ , No. 74. Washington DC: World Bank (April).

- Rose-Ackerman, Susan.  1995b. "Redesigning the State to Fight Corruption," _Viewpoint_ , No. 75.

- Rothery, Robert. 2003. "China's Legal Framework For Public Procurement." _Journal of Public Procurement_ . Volume 3, issue 3, pages 370-388.

- Sabel, Charles and Sanjay Reddy. 2002. “Learning to Learn: Undoing the Gordian Knot of Development Today,” Columbia University, mimeo (www.sopde.org/discussion.htm).

- Schooner, Steven L., and Christopher R.Yulkins. 2003. “Model Behavior? Anecdotal Evidence of Tension Between Evolving Commercial Public Procurement Practices and Trade Policy,” _International Trade Law and Regulation._ Volume 9.

- Segal, Geoffrey, and Michael Taylor. 2001. “Electronic Procurement: How Technology is Changing Government Purchasing.” E-Brief number 111. Reason Public Policy Institute.

- Seong, Si  Kyung, and Jae Yong Lee. 2004. "Developing e-procurement systems: a case study on the government e-procurement systems in Korea," _Public Finance and Management_ . Volume 4(2), pages 136-166.

- Srivastava Vivek. 1999. _India’s Accession to the GPA: Identifying the Costs and Benefits”_ NCAER, New Delhi.

- Tian, Jingbin. 2001. “Public Procurement in China: The Way Forward.” _Public Procurement Law Review_ , volume 10.

- Tian, Jingbin. 2003. “Enforcement of Public Procurement Rules in China,” in Arrowsmith and Trybus.

- Transparency International. 2002. “Karachi Integrity Pact Saves 75% in Consulting Fees,” Press release. 27 February 2002.

- Trepte, Peter. 1998. “Procurement Regulation and Emerging Economies: The Examples of Laos and Bhutan.” In Arrowsmith and Davies.

- Verhage, Rob, Johan van de Gronden, Kofi Awanyo, and Samuel Boateng. 2002. "Procurement Reform in the Ghana Health Sector." _Journal of Public Procurement_ . Volume 2, issue 2, pages 261-268.

- Wade, R. 1982 “The System of Administrative and Political Corruption: Canal. Irrigation in South India,” _Journal of Development Studies_ . Volume 18: 287–328.

- Watermeyer, Ron. 2004a. “Facilitating Sustainable Development Through Public and Donor Procurement Regimes: Tools and Techniques,” _Public Procurement Law Review_ 13(1): 30-55.

- Watermeyer, Ron. 2004b. “Transparency within the South African Public Procurement System,” presented at the 2<sup>nd</sup> IWOGDA brainstorming meeting, CUTS, Jaipur June 28-29.

- Wiseman, Alan E. 2000. _The Internet Economy, Access, Taxes and Market Structure_ . Brookings Institution Press.

- Wittig, Wayne. 1999. “Building Value through Public Procurement: A Focus on Africa.” International Trade Centre.

- World Economic Forum (2002). _Global Competitiveness Report 2001-2002_ . Oxford University Press, New York.

- WTO (2002). World Trade Organization. “Report On The Meeting of 29 May 2002.” WT/WGTGP/M/14. 13 August 2002.

36
# **6. Conclusions**

Using three waves of Business Pulse Survey data collected in South Asia during the COVID19 crisis, we disentangle the impact of the shock on informal firms, their adjustment response, and the reasons underlying their fragility. We construct a measure of informality that captures its multidimensionality. In our analysis, we also incorporate the various views characterizing informality by comparing the pre-pandemic productivity distribution of these firms – necessity, De Soto, and parasite firms – with that of formal firms. These features allow us to test the heterogeneity in impact, recovery, and response to the pandemic by the degree of informality (our composite index) and the type or motivation for informality.

Before delving into analyzing the impact of the crisis, we compare the stylized facts on informality using our survey data. In line with the existing literature, our data confirm that informal firms, as defined by our composite index, tend to be smaller in size, relatively younger, and more prevalent in sectors with low entry costs. Additionally, informal firms have lower labor productivity, possibly driven by poor management capabilities and digital readiness. Such lack of capabilities potentially makes informal firms more vulnerable to shocks even after accounting for external attributes. That is, informality by itself leads to worse outcomes and, in particular, for firms driven by necessity that remain subsistence. These factors also limit the adjustment response to shocks. For instance, the gap between formal and informal in terms of digital adoption is not only salient prior to the pandemic but also widening.

Given our finding that size cannot proxy for informality due to the multi-dimensional nature of the problem, we propose considering the following aspects in supporting the private sector in the context of informality: : _Targeting – Who to support_ In general, sector or firm selection can be distortionary; however, during shocks the efficiency of instruments can be most effective for the industry or group of firms that are most affected. Based on our analysis, we suggest that considering the severity and type of informality is critical in assessing the modality and instrument for support. For instance, although necessity firms are particularly vulnerable, support for these firms should consider approaches similar to those used for helping households (e.g., cash transfers). By comparison, the De Soto and Parasite firms lag behind in their adjustment response to the pandemic and may be eligible for support on digitization. The selection of sector or firm groups should be carefully evaluated after supply-chain analysis and focus on strengthening the weak linkages, where the failure of a few key firms in a value chain can have big repercussions for the rest of the economy.

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# **5.4 Welfare Analysis**

Now, I explore the question of whether accumulating reserves is welfare improving. On one hand, the central bank accumulates international reserves to undo government over-borrowing. On the other hand, this is costly because using reserves to pay back debt reduces spreads. To quantify the welfare gains of accumulating reserves, I contrast the baseline model with the consolidated government economy (i.e. _β_<sup>_M_</sup> = _β_<sup>_F_</sup> = 0 _._ 939). Table 7 shows that, by accumulating international reserves, an independent central bank can transfer resources towards the future in a way that cannot be undone by an impatient government, reducing the net debt position of the whole economy.

Table 7: Welfare Gains

||Independent|Consolidated|
|---|---|---|
||Central Bank|Government|
|mean B/y(%)|44_._4|41_._1|
|mean A/y(%)|6_._5|0_._3|
|net debt position(%)|37_._9|40_._8|
|**change in welfare**(%)|**0**_._**1**|**0**_._**0**|

To calculate the welfare gains of having an independent central bank that accumulates reserves, I proceed as follows. First, I take as a starting point a draw from the ergodic distribution of the consolidated government economy ( _y, κ, B, A_ ). Then, I simulate a series of shocks _{_ ( _y, κ_ ) _}t_<sup>_T_</sup> =1<sup>for</sup> _T_ = 1000. Using policy functions for the consolidated government economy and the baseline model, I compute the consumption path for both economies _{_ ( _ct_<sup>_NoInd_</sup> _, ct_<sup>_Indep_</sup> ) _}t_<sup>_T_</sup> =1<sup>.Then,Itake</sup> _N_ = 1000 draws of these consumption paths _C_ = _{{_ ( _ct_<sup>_NoInd_</sup> _, ct_<sup>_Indep_</sup> ) _}t_<sup>_T_</sup> =1<sup>_}N_</sup> _n_ =1<sup>and define:</sup> and 33
where conflict in t−1 denotes conflict events only during the previous year relative to the second year of each specific wave and, in the case of food insecurity, the previous calendar year. This specification has its own drawback because we are omitting the peak year of the Boko Haram insurgency (2014) and therefore are omitting some of the conflict events.<sup>28</sup> The timing is illustrated in appendix F, figures F.1 and F.2.

Tables F.1 and F.2 present robustness checks for the results on food insecurity (tables 2 and 3) and consumption (tables 4 and 5), respectively. We can see that the results are robust to this alternative lag structure. The effects are even slightly stronger for food insecurity, which is a more volatile measure of well-being than consumption, and violence also seems to matter in this case, a result arising clearly from such shorter-term dynamics. The stronger effects on food insecurity suggest that households might have already recovered from the events that occurred further in the past, and their effect on food insecurity in period t is diminishing over time, perhaps suggesting that there is recovery from the initial shock when it comes to food insecurity and violent events. There might be an initial drop in welfare, as we find in our robustness check, but over time the household might start recovering. Overall, our main results remain unchanged with the alternative lag structure.

# 6.   Conclusions

In this paper, we have analyzed the relationship between victimization and household wellbeing by using panel data from Nigeria. We exploit the time and spatial variation of victimization to analyze these effects within a household fixed effects framework using up to six rounds of data. We find that victimization negatively affects household welfare, measured by consumption and food insecurity. We also find that property-related events are more detrimental to household welfare than are violent events. Both types of acts are perpetrated by different groups. Insurgents, bandits and criminals, and clashes between farmers and herders all involve both violence and property-related crimes. However, these different perpetrators might have different motivations for their acts, which might lead to different consequences for the household. Indeed, we find that consumption and food insecurity are

> 28 For the analysis with the wave-based consumption aggregate measured in 2010–11, 2012–13, and 2015–16, we consider events that took place in 2010, 2012, and 2015. For the food insecurity analysis, the outcome variables are measured in 2011, 2012, 2013, 2015, and 2016. Therefore, we use conflict information from 2010, 2011, 2012, 2014, and 2015, and we are only dropping 2013 conflict events. As in the main analysis, here food insecurity in 2010 also is not part of the analysis as we do not have conflict data preceding that year .

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|**Reasons**|**Table A5: Rea**<br>**Ma**|**sons for pend**<br>**lawi**|**ing vaccinati**<br>**Nig**|**on by gender**<br>**eria**|**Tan**|**zania**|
|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Currently ineligible|1.6|2.1|3.1|3.6|0.8|2.1|
||(-0.5 to 3.6)|(-0.7 to 4.8)|(-1.2 to 7.4)|(1.9 to 5.3)|(-0.2 to 1.7)|(0.5 to 3.8)|
|Don't know how to get|2.1|3.9|39.5|39.7|10.1|7.0|
||(-0.7 to 4.9)|(-0.2 to 8.0)|(30.0 to 49.0)|(34.4 to 45.0)|(4.1 to 16.0)|(4.0 to 9.9)|
|Too crowded at vax site|1.4|5.3|8.5|6.2|1.5|0.5|
||(-0.4 to 3.2)|(1.1 to 9.5)|(3.4 to 13.5)|(3.8 to 8.6)|(-0.6 to 3.6)|(0.0 to 1.0)|
|Facility not disability friendly|||1.2|0.6|0.6|1.0|
||||(-0.2 to 2.6)|(0.0 to 1.2)|(-0.6 to 1.9)|(0.1 to 1.9)|
|Too far away|12.4|14.3|8.0|13.2|8.1|13.7|
||(4.5 to 20.3)|(8.3 to 20.3)|(2.8 to 13.3)|(10.0 to 16.4)|(3.4 to 12.9)|(10.1 to 17.3)|
|Not enough vaccines|12.6|22.3|10.6|9.4|6.4|10.1|
||(4.9 to 20.2)|(14.7 to 29.9)|(4.6 to 16.5)|(6.3 to 12.4)|(2.3 to 10.6)|(7.2 to 13.1)|
|Unable to register|0.0|0.3|0.5|2.5|1.2|1.4|
|||(-0.2 to 0.7)|(-0.1 to 1.2)|(0.9 to 4.1)|(-0.3 to 2.7)|(0.1 to 2.7)|
|Work commitments|0.7|8.1|8.1|12.8|20.1|31.0|
||(0.0 to 1.4)|(3.4 to 12.9)|(2.6 to 13.6)|(9.1 to 16.4)|(13.7 to 26.5)|(26.2 to 35.8)|
|Domestic commitments|6.4|4.2|2.2|1.3|12.0|5.4|
||(2.0 to 10.8)|(0.1 to 8.3)|(-0.1 to 4.4)|(0.0 to 2.6)|(6.2 to 17.8)|(3.3 to 7.5)|
|Religious reasons|0.0|0.8|0.5|0.1|0.8|0.2|
|||(-0.4 to 1.9)|(-0.3 to 1.2)|(-0.0 to 0.2)|(-0.2 to 1.7)|(-0.1 to 0.5)|
|Medical reasons|17.0|2.4|9.4|1.8|13.4|4.4|
||(7.4 to 26.5)|(-1.0 to 5.7)|(3.3 to 15.5)|(0.5 to 3.1)|(7.4 to 19.5)|(2.7 to 6.1)|
|Waiting for appointment|1.7|2.3|3.8|10.6|2.4|3.6|
||(-0.1 to 3.4)|(0.1 to 4.5)|(1.5 to 6.1)|(7.4 to 13.9)|(0.6 to 4.2)|(1.6 to 5.5)|
|Afraid of side effects|42.9|30.4|7.0|5.4|0.0|0.0|
||(30.6 to 55.2)|(22.2 to 38.5)|(3.2 to 10.8)|(2.9 to 7.9)|||
|Other reason|9.5|7.7|10.8|6.9|28.0|25.4|
||(0.7 to 18.3)|(3.1 to 12.4)|(4.4 to 17.2)|(4.7 to 9.1)|(20.5 to 35.5)|(20.8 to 30.0)|
|_N_<br>Note: Reasons for pending<br>95% confidence intervals i|177<br>vaccination despi<br>n parentheses.|316<br>te being willin|270<br>g to get vaccin|830<br>ated, by gende|277<br>r. All values i|715<br>n percent.|

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and a constant adjustment α of 15.824. We can then use this estimated relationship to convert scores from countries which only took part in regional assessments to an international scale. For example, Argentina has a score of 501.32 in primary science in 2013 on LLECE and would thus have an equivalent international score of around 425.

We can also use an alternative approach called linear linking when only one country participates in pairwise assessments. This approach uses information on within-country standard deviations and mean scores to estimate α and β as follows: where α= μY - βμX, β= σ<sup>σ</sup> X<sup><u>Y</u></sup> ~~,~~ and σ denotes within-country standard deviations on test X and Y. Both methods adjust test scores by a constant as well as relative standard deviations across tests.

By producing a linking function and placing regional scores on an international scale, we are able to compare learning outcomes on a global scale. On this scale, 625 represents advanced attainment and 300 represents minimum attainment. This interpretation is derived by taking establish benchmarks already used on international and regional assessments. For the high-performance benchmark on the upper end of the distribution, we use the TIMSS benchmark of 625. For the lowperformance benchmark on the lower end of the distribution, we use 300, which is the equivalent on the HLO scale of the minimum benchmarks on regional assessments such as LLECE and PASEC. This approach enables us to capture performance across the distribution and accounts for floor and ceiling effects that would be introduced by taking either international or regional benchmarks on both ends of the distribution. Supplement Table VI includes descriptions of each assessment to enable derivation of linking functions.

# _B. Sensitivity Tests_

We conduct a series of sensitivity tests. First, we examine the degree to which linking functions are stable across countries using two approaches. For tests where we have multiple participating countries and for which we use the regression method we can also produce linking functions using country-fixed effects.

SUPPLEMENT TABLE I SCORES USING REGRESSION WITH AND WITHOUT COUNTRY-FIXED EFFECTS

||(1)|(2)|(3)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|
||EGRA reading|LLECE math|LLECE reading|LLECE science|PISA math|PISA science|
|HLO|348.4|383.3|454.1|423.2|490.0|496.2|
|HLO - Country Fixed Effects|329.4|368.4|462.7|414.5|474.0|489.0|
|Correlation|1.000|1.000|1.000|1.000|1.000|1.000|

_Notes_ : HLO references Harmonized Learning Outcomes produced with a linking function without country-fixed effects. HLO- Country Fixed Effects refers to HLO scores produced from with a linking function derived using a 25
# Figure 17: SSA: Investment growth slowdown

# A. Investment growth

# B. Share of SSA EMDEs with weak investment

growth <!-- Start of picture text --> Percent<br>2000-21 average<br>12<br>6<br>0<br>-6<br>SSA EMDEs<br>C. Gross foreign direct investment inflows<br>to SSA, excluding South Africa<br>US$, billions<br>50 Oil exporters<br>Metal exporters<br>40<br>Rest of SSA<br>30<br>20<br>10<br>0<br>E. Chinese loans to SSA economies<br>US$, billions<br>30<br>Angola<br>25 Zambia<br>Ethiopia<br>20<br>Kenya<br>15 Rest of SSA<br>10<br>5<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br>2010 2012 2014 2016 2018 2020 2022 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> C. Gross foreign direct investment inflows to SSA, excluding South Africa <!-- Start of picture text --> Percent of countries<br>100 Below long-term average<br>Contracting<br>75<br>50<br>25<br>0<br>2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> D. General government debt in SSA <!-- Start of picture text --> Percent of GDP<br>Median<br>120 Interquartile range<br>100<br>80<br>60<br>40<br>20<br>0<br>F. International bond issuance by SSA<br>governments<br>US$, billions<br>20<br>15<br>10<br>5<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text -->

# F. International bond issuance by SSA governments

Sources: Dealogic; Haver Analytics; Global Development Policy Center (Boston University); International Monetary Fund; World Bank; United Nations Conference on Trade and Development. Note: EMDEs = emerging market and developing economies; SSA = Sub-Saharan Africa. A. Weighted averages. Includes 98 EMDEs, of which 38 are SSA.

D. Median values. Dotted lines indicate interquartile range.

E. Loan commitments to SSA governments and state-owned enterprises from Chinese commercial banks, government entities, companies, and other financing sources.

F. Last observation is July 2022.
negative binomial counterpart of the Poisson distribution. In addition, given the large number of zeros in the household survey, this leads us to formally test the use of the zeroinflated negative binomial model for the household survey.

**Table 2: Frequency of visitation**

|**Person-day-trips**|**Household**<br>**frequency**|**Percent**|**Tourist**<br>**frequency**|**Percent**|
|---|---|---|---|---|
|0|2516|74.93|0|0.00|
|1|455|13.55|185|47.56|
|2|152|4.53|94|24.16|
|3|84|2.50|41|10.54|
|4|30|0.89|25|6.43|
|5|37|1.10|14|3.60|
|6|12|0.36|5|1.29|
|7|7|0.21|0|0.00|
|8|5|0.15|0|0.00|
|9|0|0.00|0|0.00|
|10|26|0.77|5|1.29|
|10 to 15|12|0.36|6|1.54|
|15 to 20|10|0.30|6|1.54|
|20 to 30|3|0.09|4|1.03|
|30 to 40|3|0.09|2|0.51|
|40 to 50|1|0.03|2|0.51|
|50 to 100|5|0.15|0|0.00|
|Total|3358|100.00|389|100.00|
|Mean|0.81||3.17||
|Standard deviation|3.95||5.75||

# **IV. Estimation Results**

# _(i) Determinants of visitation_

The household sample was initially modeled using the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB). The onsite sample was modeled using the truncated Poisson, truncated negative binomial (TRNB) and the truncated negative binomial with endogenous stratification (TRNBES). Comparative tests between each model were performed and are reported below. For brevity, only the estimation results for the household (NB and ZINB) and on-site models 10
number of remaining indicators for which direct data was not available, such as: the annual growth in ship fleets, the maximum size of the fleet over time, the costs of baits, crew provisions, cost of ice (or refrigerating systems), costs of maintenance and repair, depreciation costs (of the hull and engine – in the case of motorized boats –), other unspecified costs (such as permits). Future costs have been assumed constant but adjusted for inflation over time. It is important to highlight that the rents calculated for the fishery sector are based on the assumption that the stock behaves as a finite resource. This is not (yet) the case, although risks persist due to the overexploitation of several species (see page 17).

# **_Natural Gas and Crude Petroleum_**

The World Bank team met with representatives from the Société Mauritanienne des Hydrocarbures (Hydrocarbon Agency of Mauritania, SMH) and with officials from the Ministry of Energy and Mines, who provided very useful cooperation and precious background information about the sector. With regard to natural gas, specific data on unit prices, costs of production, upfront capital costs, total annual production, and related estimates for the future were sourced from internal World Bank data based on a recent WBG project on the Banda gas field<sup>50</sup> . In the case of crude oil, historic data on production and prices were obtained by the SMH. Basing on that, future production is estimated at roughly 2,5 million barrels per year, and price is assumed to be equivalent to average USD 100 per barrel, adjusted for inflation over time. In the absence of better direct data availability, total unit costs of production have been assumed equivalent to the average for the African Total Upstream Costs, as per the estimates provided by the United States Energy Info Administration (EIA) of the US Department of Energy<sup>51</sup> .

# **_Pasture Land_**

Information on pasture land consisted in data on production and prices of milk, meat and a large set of animal derivatives produced in Mauritania. This data was retrieved with the very precious cooperation of the Ministry of Rural Development, which provided historic statistical figures. On the basis of such figures, staff proxied the estimates for future prices, productions and rents, basing on the average of the last available 5 years data.

> 50 For more information on this Project, please see : <u>http://documents.worldbank.org/curated/en/docsearch/projects/P145664</u> 51 http://www.eia.gov/tools/faqs/faq.cfm?id=367&t=6

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