rafmacalaba/data-use-mentions-extended huggingface dataset:
The results in this section are derived from micro-data in the Population and Housing Census (RGPH) 2014 and the National Survey of Budget, Consumption and Standard of Living of Households (EBCNV) of 2015, which has the typical structure of HBSs. Details on the regressors included in the models are reported in INS (2020).

Mapping the poverty rates in Tunisia demonstrates that there is a high concentration of poverty in the North-West and Center-West landlocked regions. Poverty rates decrease moving towards the coastal North-East, Center-East and the Greater Tunis regions, although there are pockets of relatively high poverty rates there as well. Southern regions (South West and South East) are characterized by diverse levels of poverty.

The performance of models is tested by comparing the poverty rates obtained with the poverty mapping method with the EBCNV 2015 survey estimates at the regional and governorate levels. This comparison is feasible because the EBCNV 2015 is representative at both regional and governorate levels.

As shown in Table 1, poverty rates estimated on the basis of a household survey are more precise at the national level only. In fact, at regional levels, estimates from poverty mapping (Census 2014) show coefficients of variation (in the range of 8%-19‰) that are lower compared to survey estimates (EBCNV 2015), with the coefficient of variation in the range of 24%-69‰.

**Table 1. Poverty rates** **<u>by region</u>**

|**Region**|**E**<br>**Poverty**<br>**rate**|**BCNV 2015**<br>**Standard**<br>**error**|**cv**|**Pove**<br>**Poverty**<br>**rate**|**rty mappin**<br>**Standard**<br>**error*
De Mel, Suresh, David McKenzie and Christopher Woodruff (2009a) “Measuring microenterprise profits: Must we ask how the sausage is made?:, _Journal of Development Economics_ , 88(1): 19-31.

De Mel, Suresh, David McKenzie and Christopher Woodruff (2009b) “Are Women more Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns.” _American Economic Journal: Applied Economics_ 1(3): 1-32.

Drexler, Alejandro, Greg Fischer, and Antoinette Schoar (2012) "Keeping it Simple: Financial Literacy and Rules of Thumb", Mimeo. LSE Duflo, Esther, Rachel Glennerster, and Michael Kremer. (2008). “Using randomization in development economics research: A toolkit.” In _Handbook of Development Economics_ , Vol. 4, ed. T. Paul Schultz and John Strauss, 3895–3962. Amsterdam, NH: North Holland.

Fafchamps, Marcel, David McKenzie, Simon Quinn and Christopher Woodruff (2012) “Using PDA consistency checks to increase the precision of profits and sales measurement in panels“, _Journal of Development Economics_ , 98(1): 51-57.

Fajnzylber, Pablo, William Maloney, and Gabriel Montes Rojas (2006) “Microenterprise Dynamics in Developing Countries: How Similar are they to those in the Industrialized World? Evidence from Mexico,” _World Bank Economic Review_ 20(3): 389-419.

Field, Erica, Seema Jayachandran and Rohini Pande (2010) “Do traditional institutions constrain female entrepreneurship? A field experiment on business training in India”, _American Economic Review Papers and Proceedings_ 100(2): 125-29.

Gibson, John and David McKenzie (2007) “Using global positioning systems in household surveys for better economics and better policy”, _World Bank Research Observer_ 22(2): 217-41 Giné, Xavier and Ghazala Mansuri (2011) “Money or Ideas? A field experiment on constraints to entrepreneurship in rural Pakistan”, Mimeo. World Bank.

Glaub, Matthias and Michael Frese (2011) “A critical
.6|1.2|0.9|0.9|0.8|
|Portfolio Investment|0.9|1.6|2.4|1.0|1.4|3.2|19.5|7.5|3.3|2.8|
|Other Long-term Flows|-2.6|-10.8|-4.3|-1.8|1.3|-1.9|-30.9|1.8|0.4|**6.2**|
|Other Short-term Flows|-4.1|0.7|-1.2|2.2|2.0|-0.8|2.9|0.8|3.2|5.6|
|Net Error& Omissions|2.0|-6.8|3.2|-2.4|-2.3|5.5|1.8|-2.0|-2.1|-15.5|

Source: Own calculation based on monthly central bank balance of payments statistics.
Cameroon|47.91|19|
|Congo|51,037|Central African Rep.|26.44|28|
|Burundi|45,490|Togo|18.99|29|
|Burkina Faso|29,234|Burkina Faso|18.61|30|
|Angola|23,783|Zambia|13.45|34|
|Zambia|23,594|Mali|11.78|37|
|Togo|20,613|Congo, Rep. of|11.35|38|

Source: Authors’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014).

18
commodity prices has been well established by numerous authors. See, for example, Lamm (1980), Gardner (1981), and Baffes and Dennis (2015) for agriculture and Gilbert (1989), Baffes (1997), and Akram (2009) for metals.

Among the sectoral fundamentals, the effect of a rise in the _stock‐to‐use ratio_ is, as expected, negative and highly significant, with a panel estimate of ‐0.37. The estimates are remarkably similar to findings reported elsewhere. For example, Bobenrieth, Wright, and Zeng (2012) estimated correlation coefficients between stock‐to‐use ratios and real de‐ trended prices for wheat, maize, and rice of ‐0.40, ‐0.50, and ‐0.17, respectively (compared to ‐0.46, ‐0.48, and ‐0.49 in the present study). Similarly, FAO (2008, p. 6) reported corre‐ lation coefficients between the cereals price index and various measures of stock‐to‐use ratios, ranging from ‐0.47 and ‐0.65.

Likewise, the effect of a rise in the _real oil price_ is found to be significantly different from zero for all six commodities. The panel estimate at 0.15 implies that a 10 percent in‐ crease in oil prices is associated with a 1.5 percent increase in agricultural prices. The strong relationship between energy and non‐energy commodity prices was established in the lit‐ erature long before the post‐2004 price boom. Gilbert (1989), for example, using quarterly data from 1965‐86, estimated the transmission elasticity from energy to non‐energy com‐ modities at 0.12 and that from energy to food commodities at 0.25. Hanson and others (1993), based on a general equilibrium model, found a significant effect of oil price changes on agricultural producer prices in the United States. Borensztein and Reinhart (
Ravallion (2010) measures the extent to which the existing distribution of income would constrain the redistribution needed to eliminate poverty in 90 countries, using a measure of the marginal tax rate on the rich. Lind and Moene (2011) develop a miser index to measure the extent to which there is poverty in the midst of affluence, by comparing absolute poverty to the total amount of resources available. Finally, Ceriani and Verme (2014) develop an income lever index to measure of the monetary capacity of countries to eliminate poverty by implementing different distribution rules.

Similarly, to develop a method to measure the potential DRM constraints related to the political equilibrium of countries, this paper looks at the _politics_ of DRM from the perspective of the concentration of resources in the hands of few individuals. It seeks to measure how that concentration may impact tax policy decisions and proposes a Political Influence Concentration Index. It builds in particular on three recent efforts to measure such influence. Gilens and Page (2014) use data from the United States to quantify the differences in policy influence between average citizens, economic elites, and business interests. Karabarbounis (2011) measures the relationship between inequality and redistribution and finds a one dollar-one vote political characterization in which income is associated with political influence. Finally, Igan and Mishra (2011) construct a database of political connections and contributions and find that they are positively associated with the likelihood that legislators change their position on financial deregulation.

Consider a population _N_ made of _i_ = 1 _,_ 2 _, . . . , n_ individuals, _n ∈_ N. Each individual is endowed with a level of welfare _yi ∈_ R+, which is the monetary value according to which poverty is measured (it can either be income or consumption according to the prevailing methodology in the country). Without loss of generality, let us assume that incomes are ordered, such that _y_ 1 _≤ y_ 2 _≤ y_ 3 _≤· · · ≤ yn_ .

Population _N_ can be divided into
<!-- Start of picture text --> Figure 3. Demographic characteristics<br>Household sizes have declined... ..while the share of adults has increased<br>6.0 90<br>5.2<br>5.0 4.5 4.4 4.2 4.4 4.1 80 73.3<br>4.0 68.4 68.0 70.7 69.7<br>3.1 2.9 3.0 2.9 70 63.4<br>2.7 2.6<br>3.0<br>60<br>2.0<br>50<br>1.0<br>40<br>0.0<br>2000 2010 2004 2010 2000 2009 30<br>2000 2010 2004 2010 2000 2009<br>Bangladesh Peru Thailand<br>Bangladesh Peru Thailand<br>Average household size<br>Number of adults per household Share of adults per household<br>Source: Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4. Labor Income Growth Incidence Curves<br>Bangladesh, 2000 to 2010 Peru, 2004 to 2010 Thailand, 2000 to 2009<br>6.00 0.14 0.8<br>5.00 0.12 0.6<br>4.00 0.1 0.4<br>0.08 0.2<br>3.00<br>0.06 0<br>2.00<br>-0.2<br>0.04<br>1.00 Labor Income per capita<br>-0.4<br>0
|0.15|
|P-Value|0.36|0.24|0.70|

Robust t statistics in parentheses. Standard errors corrected for clustering * significant at 10%; ** significant at 5%; *** significant at 1% Instruments: Malaria Endemicity* % area under inland water, Malaria Endemicity*% of Area above 1000 feet of elevation, difference in slope, difference in slope*malaria Endemicity*inland water

**Table 6: The Importance of Interaction Effect**

|Travel Time|Travel|Marginal|Elasticity|
|---|---|---|---|
|Percentile|time(hour)|Effect||
|p20|0.57|-1.84|-8.8%|
|p30|0.93|-1.72|-8.3%|
|p40|1.40|-1.57|-7.5%|
|p50|1.88|-1.41|-6.8%|
|p60|2.32|-1.27|-6.1%|
|p70|3.01|-1.04|-5.0%|
|p80|3.58|-0.86|-4.1%|
|p90|5.89|-0.10|-0.5%|
Figure A6: Share of migrants in essential jobs by health exposure <!-- Start of picture text --> 911<br>912<br>816<br>921<br>933<br>532<br>832<br>221<br>251231 213 751 511<br>833<br>531 323 222<br>351250215252313235352311214315232 312611 516321322541961325 226<br>233 622835831314 234<br>612 224<br>350 230310620 610613830 530920910540320 220<br>0 .2 .4 .6 .8 1<br>Exposure index (no-telework*F2F)<br>.4<br>.3<br>.2<br>.1<br>Share of foreign workers per occupation<br>0<br><!-- End of picture text --> Source: Own elaboration based on data from the 2018 EU-LFS and combining Dingel and Neiman (2020), RioChanona et al (2020) and Avdiu and Nayyar (2020) methodologies.

Note: The exposure index is the combination of the share of jobs not amenable to telework in each 3-digit occupation and the intensity of face-to-face interactions. The size of the circles represents the share of each occupation in the total employment in the EU.

52
<!-- Start of picture text --> SOE Data Benavich<br>Parameters<br>Income |<br>Macro & Market Costs |<br>Scenario Debt Cascade<br>Debt Payments |<br>Create a scenario Calculate cashflows for that scenario Record results<br>oili Debt ServiceIncome<br>—<br>GDP Default? a<br>Loss Given Default 25%<br>Exposure at Default $435<br>Year Payment $27<br>Ratesa________» Year DSCRISCR O7os<br>Year<br><!-- End of picture text -->
**Figure 2 - Reservation and market wage distribution, by skill level**

<!-- Start of picture text --> a) Market wages b) Reservation wages<br><!-- End of picture text --> <!-- Start of picture text --> Low skilled Low skilled<br>High skilled High skilled<br>Medium skilled Medium skilled<br>2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Log(Wage) Log(Wage)<br>2 2<br>1.5 1.5<br>1 1<br>Density Density<br>.5 .5<br>0 0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._ Despite the general observation that reservation and market wages in North Macedonia do not significantly differ, still the process of reservation wage formation may be affected by individual characteristics whose role for already employed individuals is either attenuated or does not exist. **Figure 3** presents simple correlations of the reservation wages with the duration of the unemployment spell, the household income and the number of children living in the household. All three could capture, or be related with, the job searching costs. Apparently, the longer spells are related with declining reservation wages, while household income with rising reservation wage. The relation with the number of children is not clear-cut.

**Figure 3 - Reservation wage and some personal characteristics of the unemployed**

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and  39 countries (Russian Federation is excluded due to prevalence of commodity exports).  Data come from the World Input-Output Database (Timmer et al, 2015), obtained by interacting Census data (interpolated for years between Censuses) with yearly trade data. Vertical specialization indicators are computed based on the methodology described by Wang, Wei, Zhu (2015), using an algorithm developed in Quast and Kummritz (2015).
Table 10: Parameters and data used to solve the model

|Parameters||
|---|---|
|Productivity dispersion<br>_θ_<sup>_j_</sup> = 3_._29<br>|Caliendo, Parro (2015)|
|Trade costs by industry<br>_τ _<sup>_j_</sup><br>_ni_|Survey|
|Consumption expenditure shares<br>_α_<sup>_j_</sup>|PCBS (National Accounts)|
|Land input shares<br>_β_<sup>_j_</sup>|Survey|
|Share of value added in gross output<br>_γ_<sup>_j_</sup><br>_n_|EORA (Jordan 2010)|
|Input-output coefcients<br>_γjk_|EORA (Jordan 2010)|
|Data<br>||
|Total employment by industry and location<br>_L_<sup>_j_</sup><br>_i_|PCBS (Census 2007)|
|Wages<br>_wi_|PCBS (Labor Force Survey)|

trading partner in location _d_ . We regress this probability on origin fixed effects, destination fixed effects, and the (log of the) distance between origin and destination. Second, for the estimation of the productivities, we invert an equation linking the demand of traded goods with its supply. We argue that the solution is unique and that we describe an approach to find it numerically. Notice that we only need estimates for productivities for manufacturing in order to recover trade shares. The other sectors are considered non-tradable, and we will not have estimates for industry-specific productivities in that case.

In the survey we ask the following information: From the largest to the smallest commercial partner (buyers or sellers), specify town, district, and average shipping time. This question allows us to compute the probability that a firm in location _i_ has a client or seller in location _n_ . We
# **2.4.1 Weather Descriptives**

In addition to estimating mismeasurement from the choice of remote sensing products and extraction method, we examine variation arising from the choice of different weather metrics. In total, we test 22 different ways to measure precipitation and temperature. A complete list of these variables with their exact are in Table 4.

To provide a sense of the variation in measurement induced by the remote sensing product, we present summary figures for a subset of the 22 weather metrics we examine. While our focus in the econometric analysis is country, because that is the relevant unit of analysis for most economists, we present descriptive evidence by global agro-ecological zone as these agro-climatic divisions are more relevant when seeking to assess difference in weather patterns (absent the connection to household data). The Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) develop agro-ecological zones to assess agricultural resources and potential. The measurement integrates available land and water resources, agroclimatic resources, and general conditions about agricultural suitability to determine a set of zones, which differ across these metrics. We use a revised AEZ surface for Africa (Sebastian, 2009) that incorporates high resolution climatology (WorldClim) and elevation (SRTMv4) surfaces to produce a downscaled version of the agro-ecological zones. In our data, six agro-ecological zones are covered, including (1) tropic-warm/semi-arid, (2) tropic-warm/sub-humid, (3) tropic-warm/humid, (4) tropic-cool/semi-arid, (5) tropic-cool/sub-humid, and (6) tropic-cool/humid.

Figure 4 presents the distribution of total season rainfall (measured in millimeters), by remote sensing product and agro-ecological zone. Within any given agro-ecological zone there are substantial differences in the distribution of rainfall as reported by each remote sensing product. In tropic-warm/semi-arid regions all products report about the same mean (between 550 and 650mm) but some report a bimodal distribution while others report a unimodal distribution.
**_<u>________________________________________________________</u>_**

# **2.  Existing Data Sets and Analytical Tools**

Performance indicators are necessary to encourage accountability by public agencies, measure the quality of their management, and evaluate the service to users. In addition, performance indicators can help in comparing a country’s efficiency with other countries as well as with acceptable benchmarks. They are also helpful in monitoring the impact of policy reform and other measures taken to reduce costs and facilitate trade.

Trade facilitation has most often been proxied by a single indicator, such as (1) import prices, (2) international transport costs, or (3) the productivity of a particular transportation mode (air, maritime, road). The traditional measures have to be replaced by something more consolidated that can be used more widely and that reflects transport and logistics efficiency. In a broader context, however, trade facilitation clearly involves policies and activities that accelerate the flow of goods and services across borders. This includes customs regimes, port infrastructure, transport systems and related infrastructure as well as information technology (as it relates to transport and logistics). Transport infrastructure includes road, railway, and inland waterway networks, as well as warehousing facilities and supporting communication systems that are essential for efficient transport services. With modern inland transportation networks countries are in a better position to supply foreign markets. Exporters can choose among different transport modes, and alternative ports and other transport platforms. Other barriers to trade are institutional and involve unreasonable customs delays or demands for bribes to expedite the movement of goods across borders.

A set of benchmark indicators to measure progress and outcomes based upon comparisons can provide a starting basis for quantifying trade facilitation. A compendium of these data sets and indicators of trade facilitation is provided in the following sections.

## **2.1 General Economic Indicators 2.1.1 Investment Climate Indicators**

## _<u>World Bank’s Rapid Response Unit</u
# **Figure 4: Diet quality (restricted nutrients separately), by socioeconomic neighborhood**

<!-- Start of picture text --> Fat (absolute) Fat (per price) Fat (per 100g)<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>Carbohydrates (absolute) Carbohydrates (per price) Carbohydrates (per 100g)<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>Sodium (absolute) Sodium (per price) Sodium (per 100g)<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>low mid-low<br>mid high<br>.2 .01 .02<br>.1 0 .010<br>0 -.01 -.01<br>PDRNI (mean) -.1-.2 PDRNI/price (mean) -.02-.03 PDRNI/100g (mean) -.02-.03<br>2 .4 .4<br>.3 .3<br>1 .2 .2<br>PDRNI (mean) .1 .1<br>0 PDRNI/price (mean) 0 PDRNI/100g (mean) 0<br>2 .4 .4<br>.3 .3<br>1 .2 .2<br>PDRNI (mean) .1 .1<br>0 PDRNI/price (mean) 0 PDRNI/100g (mean) 0<br><!-- End of picture text --> _Source:_ Estimates based on data of ENCONUT 2013.

_Note:_ The diet quality index, expressed in the Proportional Difference of Relative Nutrient Intake (PDRNI) index on the restricted nutrients, measures the distance to the ideal intake. The further away the value is from zero (ideal value), the less nutritious is the average menu offered in restaurants in the nutrients considered. Data are weighted.

In order to understand the potential relevance of the diet
of overall short-run economic activity than official GDP statistics which may be biased due to any number of reasons including under-reporting of output by enterprises (to avoid taxes) and old reporting systems used by statistical offices. We find that the GDP troughs identified in the crisis episodes coincide with troughs in manufacturing value added and in power consumption, though the power consumption data do not exhibit a collapse as large as that of the GDP.

While we find no evidence that the GDP statistics are not representative, several papers have suggested that there may be statistical measurement error in computing macro numbers. For instance, Bartelsman and Wolf (2009) show that productivity forecasts using micro data are more accurate and better than the macro alternative. Miron and Zeldes (1989) examine two measures of monthly manufacturing production – the index of industrial production and a second measure constructed from the accounting identity that output is given by the sum of sales and changes in inventories and find several discrepancies. They conclude substantial measurement error exists in the macro (industry level) price indices as compared to those constructed from micro data. Similar discrepancies between macro-level and household consumption data in national account statistics have been noted by Ravallion (2003) and Young (2009). Hence the need for examining the recovery process from a crisis using detailed firm-level data.

# _3.2.2  Sales versus Credit Recovery_

In this section we examine if we observe the phenomenon of Phoenix Miracles at the micro-level by looking at the recovery in corporate sales and short-term borrowing. In Figure 5A we present averages across the 9 episodes and find that there does not seem to be any evidence of a Phoenix Miracle. Both Sales (bold line) and Short Term Borrowing (dotted line) recover simultaneously albeit one period after GDP recovery.

When we start looking at individual countries, we can split the crisis episodes into two main categories: (a) **_Phoenix Miracles at the Micro Level_** : This is the case of Argentina, Malaysia, Korea and Thailand (see Figure 5B)
# **III.   Eruption, Evolution and Consequences of the Crisis**

## **_Eruption_**

11. The global financial crisis erupted in September 2008 with the collapse of Lehman Brothers, largely as a result of accumulating defaults on mortgages and derivative products. The ensuing financial sector crisis quickly led to a significant decline in credit to the private sector as well as to a sharp rise in interest rates. The resulting collapse in U.S. financial institutions led to a collapse of equity markets (Figure 3) and of international trade and industrial production and spread to other advanced economies as well as to emerging markets and developing countries. Real growth around the world declined sharply below projections and advanced economies, including the U.S., entered into a recession (Figure 4). Only China and developing Asia maintained strong growth.

<!-- Start of picture text --> Figure 3: Collapse of Equity Markets<br>350<br>United States<br>300<br>Japan<br>250<br>200<br>United<br>150 Kingdom<br>China<br>100<br>50<br>Hong Kong<br>0 SAR, China<br>Canada<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Source: World Bank, Global Economic Monitor database.<br>Equity Market Indices, 2000=100<br><!-- End of picture text --> 7