rafmacalaba/datause-encoder-data huggingface dataset:
15

- That insulation tendency appears to be no less in the two decades following the traderelated policy reforms that began in the mid-1980s than it was in the previous two

decades.

- NACs were substantially lower in the two upward price spike periods than in adjacent non-spike periods, and higher for the downward price spike period around 1986, with both export and import measures contributing to that finding.

- The extent and speed of the annual NAC changes during an upward price spike was greater in the early 1970s than in the recent period to 2008, consistent with the fact that international food prices rose proportionately less per year in the latter period.

- The extent and speed of NAC changes in each spike period are similar for foodexporting and food-importing countries, suggesting both types of countries actively insulate their domestic market from international food prices spikes.

- The percentage change in the NAC was less for developing countries and more for high-income countries in the mid-1980s’ downward price-spike period than in the two upward spike periods, suggesting that in developing countries consumers are more likely to be protected from an upward price spike than producers would be from a downward price spike, and conversely in high-income countries.

True, the above findings are based in part on distortion estimates for the most-recent food price spike that are preliminary and cover just border measures for the two main food staples. However, the behavior of policy makers indicated by those estimates is so similar to that indicated by past responses to price spikes that tentative policy implications can be drawn with reasonable confidence, pending the availability of a more-comprehensive update of distortion estimates.

Trade policy interventions are varied in response to international food price spikes to achieve various stated or hidden objectives of governments. The most commonly stated one
In this analysis it was decided to include a dummy for urban and rural area, the idea being that the inclusion of this extra dummy will capture part of the local variation due to differences in durable ownership and housing characteristics due to the place of residence. To our knowledge no other previous studies have included locational dummies in the model. An alternative would be to estimate two separate asset indices, one for rural area and one for urban area. This would allow us to rank urban and rural differences in ownership of amenities. However, this approach would preclude any national analysis as the ranking of urban and rural household would necessarily need be performed separately (see Appendix A, Table A1-A4 for the results of principal components analysis done separately for urban and rural area and Figure A1-A4 for the comparisons of the household ranking between the per capita monthly consumption expenditure and urban or rural asset indices).

Given that the Census collects information on 15 asset type variables, the potential range of combinations is 2<sup>15</sup> (i.e. 15 dimensions). As it is not straightforward to visualize any data with more than three dimensions, the PCA allows the reduction of the number of variables, and thus dimensionality without losing too much information in the process. The PCA technique achieves this by creating a smaller number of variables which explain most of the variation in the original variables. The new variables (which are created such that they are uncorrelated with each other) are linear combinations of the original variables (factor score). They are derived in decreasing order of importance so that, for example, the first new variable will account for as much as possible of the variation in the original data. An illustration of PCA is presented below.

Suppose we have _p_ variables (in our case _p_ data on household asset), _X_ 1 , _X_ 2 ,..., _X p_ , for _n_ individuals.

The first principal component is the linear combination of these variables The coefficients _a_ 11 , _a_ 12 ,..., _a_ 1 _p_ are chosen such that the variance of _Z_ 1 is maximised.

58
# Table 5. Impact by household location and income-level: Multivariate results

|Countries:||Crisis,Hi|gh-income|||Crisis,mid|dle income|||Non-|crisis||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent<br>variable:|Income<br>shock S|taple foods|Other<br>consumption|Health care|Income<br>shock S|taple foods|Other<br>consumption|Health care|Income<br>shock S|taple foods|Other<br>consumption|Health care|
|Rural|0.0389***|0.0217**|-0.01|0.01|-0.0902***|0.00|-0.0406***|-0.0194**|-0.0371***|0.0172*|-0.0188**|0.00|
||[0.0148]|[0.00971]|[0.0140]|[0.00738]|[0.0122]|[0.0120]|[0.0119]|[0.00963]|[0.00920]|[0.00898]|[0.00896]|[0.00651]|
|Low income|-0.01|0.119***|0.02|0.0402***|-0.119**|0.200***|-0.0381**|0.104***|-0.0348**|0.188***|-0.0642***|0.127***|
||[0.0253]|[0.0166]|[0.0239]|[0.0126]|[0.0535]|[0.0170]|[0.0167]|[0.0136]|[0.0150]|[0.0146]|[0.0146]|[0.0106]|
|Income shock||0.106***|0.296***|0.0718***||0.193***|0.303***|0.106***||0.188***|0.320***|0.0755***|
|||[0.00891]|[0.0128]|[0.00677]||[0.0119]|[0.0117]|[0.00954]||[0.00930]|[0.00928]|[0.00674]|
|Observations|5'412|5'412|5'412|5'412|6'988|6'988|6'988|6'988|10'995|10'988|10'988|10'988|
|Countries|5|5|5|5|6|6|6|6|10|10|10|10|
|CountryFE|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Method|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|R2|0.07|0.06|0.22|0.03|0.01|0.11|0.14|0.09|0.13|0.12|0.17|0.07|

_Notes: The reported coefficients are estimates from a linear probability model. All regressions include country fixed effects. See the Appendix for a definition of all dependent and explanatory variables._ 20
Notes: Countries are classified as unfriendly if less than 50 percent of Americans believe country is a friend or ally. Imports are identified with six-digit Harmonized System (HS) codes: face masks (630790), penicillin (300410), infant formula (190110), crude oil (270900), electric car batteries (850760), and semiconductor chips (854231).

First, in the category of health products, face masks, of which 73 percent come from China in 2022, is an outlier in terms of reliance on non-friendly countries. ‘Non-friendly’ countries provide less than 20 percent of penicillin imports, and less than 5 percent of infant formula imports. Evenett (2020) makes a similar point in his study of prepandemic international sourcing patterns of medical goods and medicines for France, Germany, the United Kingdom, and the United States.

Second, as noted earlier, in the case of face masks, imports from China provided important relief and increased resilience during the pandemic. Note the spike of imports from non-friendly nations during the second quarter of 2020 in Figure 7. Hence, decoupling from unfriendly nations may increase resilience to specific geopolitical shocks, but decrease resilience to other shocks unrelated to politics. The general point is that it is impossible to think about resilience without specific reference to the type of shock to which resilience is sought.

Third, Panel B of Figure 7 shows how import dependence on non-friendly nations has evolved in the past decade for some strategic goods. The sharp increase in the case of semiconductor chips reflects the rising popularity of the “foundry” model in the past two decades, with many foundries located in Malaysia, Vietnam and China (see Table A1 in the Appendix) – countries that are all perceived as non-friendly by a majority of Americans in the YouGov survey. Similarly, in the case of electric batteries, the growing dependence on China is evident in the figure. However, import shares for some goods appear responsive to policy. The share of crude oil sourced from ‘non-friendly’ countries has fallen from 60 percent to about 20 percent, as the shale boom made the US a petroleum exporter. Though in 2020 about 80 percent of semiconductor chips were imported from non-friendly countries, in 2022, during which the U.S. articulated a friendshoring policy with regard to the sector, this share had fallen dramatically to 55%. This reflects growth in the import share of Taiwan (China) from 5 percent to 10 percent; Ireland, from 2 to 8; and Israel, from 1 percent to 4 percent.

Dependence on non-friendly countries is only one among several possible factors affecting supply chain resilience. High concentration in the market of a specific product will in general imply high dependence of buyers on suppliers making this market less resilient to supplier-side risk – be it risk associated with exogenous shocks (e.g., natural disasters) or risk 30
which is accepted as a basic principle of income and consumption, is strongly evident in the Azeri household data for all three years considered in this analysis. As expected, rural areas spend a bigger share of their total expenditures on food. Households with significantly higher human capital such as educational attainment have higher income generating opportunity, other things being equal. Per capita income, expenditures and wage earning for households with household heads having higher level educational attainments are consistently higher than those corresponding to less educated household heads. These observations and empirical regularities hold true across time and geographic locations (see Ersado, 2006 for details).

**Table 2.** <u>Consumption share of income decreases with income</u>

||||||**INCOME**|**DECILES**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|
|**2002**|||||||||||
|Income (2003<br>prices)|95,205|125,080|141,808|155,152|167,062|178,533|191,894|208,677|235,342|332,082|
|Total consumption<br>(% income)|144|112|102|96|93|89|87|86|83|80|
|Food(% income)|93|72|65|60|58|55|53|51|50|44|
|**2003**|||||||||||
|Income (2003<br>prices)|90,468|120,808|138,132|152,568|166,420|181,555|199,594|221,262|252,612|355,712|
|Total consumption<br>(% income)|161|121|110|105|99|94|90|85|82|73|
|Food(% income)|107|81|73|67|62|58|54|51|47|40|
|**2004**|||||||||||
|Income (2003<br>prices)|109,357|134,651|149,794|163,564|175,837|188,862|202,804|220,803|254,302|365,693|
|Total consumption<br>(% income)|132|110|105|100|99|97|93|91|88|79|
|Food(% income)|91|76|71|67|65|62|59|55|52|42|

Source: Azerbaijan HIES data.

Another good aspect of the Azerbaijan data is that the survey made a reasonable capture of the Gross National Income (GNI). For instance, the average consumption per capita in the 2002 survey is $386 annually vs. the 2002 GDI per capita of $742. The capture ratio is thus 52 percent, which is slightly below the average 57 percent capture ratio for Eastern Europe and former Soviet Union countries. But the capture ratio of the Azerbaijani survey is much better than Armenia’s (34 percent) and is similar to Georgia’s (54 percent).

However, as discussed above, the extremely low inequality in the data is implausible for Azerbaijan. The ratio of per capita consumption of the top 5 percent of people compared to the bottom 5 percent (also known as ventile ratio) is extremely low. The ventile ratios are respectively, 4.2, 4.1 and 3.5 in 2002, 2003 and 2004 (Table 3). The corresponding ratio for the top and bottom 10 percent of the people was only 3.2, 3.1 and 2.8 respectively for 2002, 2003 and 2004. These values are significantly smaller than in comparable countries. For example, in 2002 consumption ventile ratio was 7 in Armenia, 22 in Georgia, 10 in Russia, and 7 in Ukraine (see Milanovic, 2005). It is not obvious whether this low value in Azerbaijan is due to an unrealistically high income of the bottom, or due to an unrealistically low income of the top.

4
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# Table of Contents

||Page|
|---|---|
|The Data ........................................................|2|
|Food Prices in 1992 .............................................|4|
|Price Differences Between Cities .........................................|11|
|Causes of City-to-City Price Differences .<br>...................................|16|
|Trends in Resi.ual Food Price Variation .<br>...................................|21|
|Regional Market Integration ...........................................|23|
|Conclusions ............................................|29|
|Appendix: Average Prices and Volume in Monitored Outlets of<br>19 Commodities in 132 Russian Cities .<br>...................................|31|
35

# TABLE 7: Basic new planting equation

Dependent variable Is NPBAHR2 18 observations used for estimation froa 1968 to 1985 Regressor Coefficient T-Ratio CONS -.0163 3 3.4623 PRICOCO(-1) .0735xlO 3 5.2300 PRICOCO(-2) .0437xlO 2.6948 CHPRI .0117 -2 2.2021 kRALINT -.104x10 -10.0122 NEWCAPR2(-i) -.0198 -1.9917 R-Squared .9663 F-statistic _FP_ 5, 12) 68.7542 R-Bar-Squared .9522 s.E. of Regression .0070318 3 Residual **Sum** of Squares .5934x10 Mean of Dependent Variable .0369 S.D. of Dependent Variable .0322 DW-statistic 1.67fit R-Squared

# Diagnostic Score (LK) Test Statistics

A:Serial Correlation CHI-SQ( 1)= .2901 B:Functional Form CHI-SQ( 1)= .2783 C:Normallty CHI-SQ( 2)= .8643 D:Heteroscedasticity CHI-SQ( 1)= .7206

# TABLE 8 : Tests for Mon-Nested Models of New Planting

Dependent variable is NPBAHR2 Regressors for model M1: CONS, PRICOCO(-1), PRICOCO(-2), REALINT, NEWCAPR2(-1) Regressors for model N2: CONC, AREVPROD(-1), AREVPROD(-2). REALINT, CKPRI, NEWCAPR2(-1) CHPRI, Test Statistic Ml against K2 K2 against Ml COX-PESARAN -.6627 -1.4303 ADJUSTED COX-PESARAN -.4068 -.9602 DAVIDSON-MACKINNON J-Test .9022 1.4457 ADJUSTED JA-Test .1700 .4942 Model Ml: DW 1.6761 ;R-Bar-Squared .9522 Model K2: DW 1.5614 ;R-Bar-Squared .9482 Akalke's Information Criterion of Ml versus M2= .7343 favours Ml
opportunities. We use a (pseudo-) Tobin's Q (TOBINQ), which is a measure of the market's assessment of a firm's investment opportunities, to determine the market's perception of future growth.'<sup>0</sup> We find that firms that filed for bankruptcy had lower (pseudo-) Tobin's Q, which may have effected their inability to access credit during financial distress and increased their likelihood of entering bankruptcy.

We also include two dummies to account for differences in corporate ownership. FAMILY is equal to 1 (0 otherwise) if the firm is a member of a family of firms. BANK is equal to 1 (0 otherwise) if the firm is directly owned by a bank or belong to a family that also owns a bank." Only 10% of the bankrupt firms are owned by banks or owned by a group that also owns a bank, compared to 40% of the firms which do not file. Bankrelated firms thus appear significantly less likely to file for bankruptcy.

In addition to firm-specific variables, we include country-specific indicators. These measures allow us to test the effect of variations in institutional features, macroeconomic performance, and judicial efficacy on the likelihood of filing for bankruptcy. We include country dummies to identify differences in institutional characteristics. We use the growth of GNP in 1998 as an ex-post measure of economic activity and degree of general financial distress in each country following the crisis. Furthermore, we use our index of creditor rights to study the influence of legal protection on the decision to file. Alternatively, we use dummies to identify the legal-origin in each country, as in La Porta et al. (1998). Finally, we use the interaction term between

> **10** Tobin's Q is approximated by the book value of debt plus the market value of equity divided by total assets.

11 This is an improvement on previous literature that examined bank-related firms in Japan that only took account of direct ownership.

14
the different tax instruments as well as trade and transport margins on domestic sales, imports and exports.

**Table 1.** Disaggregation of SSA country CGE and SAM.

|Category‐ #||Item|Category‐ #|Item|
|---|---|---|---|---|
|**Sectors**|**Agriculture (3)**|Agriculture|**Factors (7)**|Labor|
|**(activities and**||Forestry||Private capital|
|**commodities)**||Fishing||Land|
|**(25)**|**Mining (2)**|Petroleum and gas<br>Mining||Timber<br>Fish|
||**Manufacturing**|Food||Extractive res in Pet and gas|
||**(13)**|Beverages||Extractive res in Mining|
|||Tobacco products|**Institutions**|Households|
|||Textiles and leather|**(3)**|Government|
|||Wood||Rest of the world|
|||Paper|**Auxiliary**|Taxes on production|
|||Refined petroleum products|**accounts (7)**|Taxes on sales|
|||Chemical products||Taxes on imports|
|||Rubber and plastic||Taxes on income|
|||Non‐metalic mineral products||Trade and transp marg, dom|
|||Metal products||Trade and transp marg, imp|
|||Machinery and vehicles||Trade and transpmarg,exp|
|||Other manufactures|**Savings and**|Savings<br>|
||**Services (7)**|Electricity and gas|**Investment**|Private (non‐government)|
|||Construction|**(4)**|Government|
|||Trade, hotels and resturants||Stock change|
|||Transport|||
|||Communications|||
|||Government|||
|||Other services|||

_Source_ : Authors’ elaboration.

On the basis of the SAM data, Table 2 summarizes the sectoral structure of the illustrative SSA economy: sectoral shares in value-added, production, employment, exports and imports, as well as the split of domestic sectoral supplies between exports and domestic sales, and domestic sectoral demands between imports and domestic output. For instance, while (primary) agriculture represents a significant share of employment (around 66 percent), its shares of value added (VA), production, and exports are much smaller (in the range of 15-25 percent). The share of its output that is exported is around 10 percent while only some 5 percent of domestic demands are met via imports.

12
they commonly ask “what is the most important thing that governments should do”; to pose “agree/disagree” questions following statements about what governments should do;<sup>20</sup> to frame questions as a direct referendum on where public spending should be allocated—“if you were to allocate $X of public spending, where would you spend it?”; or to ask respondents to express preferences from among a menu of public goods. Another strand of literature has focused on citizens’ views of the redistributive role of the state (Alesina and Glaeser, 2004). None of these approaches allows us to infer changes in citizen demand along the private good-public good spectrum.

Keefer and Khemani (2014) design a survey vignette aimed at measuring citizen demand for a private good—jobs—that prior research has identified as a salient currency of clientelist political strategies, relative to their demand for public goods like health and education whose benefits are widely shared. The vignette imposes an explicit trade-off in the form of a budget constraint faced by leaders such that when they choose to allocate resources to government jobs for some members of the community, this would come at the cost of inputs into broadly delivered health or education services that benefit the entire community. This vignette was purposefully designed to contrast jobs as a private good, and health as a public good, based on prior research findings on the characteristics of government jobs and public health. With regard to health, public spending on vaccination and the distribution of subsidized antimosquito bed nets has been regarded as contributing to the public good of combatting the spread of contagious diseases (Cohen and Dupas, 2010). The vignette is thus designed to focus on health inputs of vaccination and bed nets for all children to highlight the public good characteristics of the proposed spending.

Public employment or government jobs, on the other hand, have been documented as a classic form of provision of private benefits in weak institutional contexts where public bureaucracies are not sufficiently professionalized and rules-based (Wantchekon, 2003; Robinson and Verdier, 2003; Chandra, 2004; Kitschelt and Wilkinson, 2007; Grindle 2012; Gibson, Hoffman and Jablonski 2015). The dismantling of political patronage in government jobs is at the heart of accounts of transition from weak to strong institutions, such as in the history of the United States during the Progressive Era (Glaeser and Goldin, 2006). Furthermore, there is systematic correlation between failures in delivering broad public services and the provision of patronage jobs, and clientelist forms of vote mobilization more generally (Callen et al, 2014; Khemani, 2015; Andersen et al, 2015). That is, when government jobs are provided as political patronage, the job-holders have weak incentives to exert effort and perform well in delivering public services (World Bank, 2004).

> 20 Such questions are contained in the Afrobarometer surveys, for example.

33
Control in the East Asia and Pacific Region. Discussion Paper, the World Bank.

Lofgren, Hans, Martín Cicowiez, and Carolina Diaz-Bonilla. 2013. “MAMS – A Computable General Equilibrium Model for Developing Country Strategy Analysis.” In Dixon, P.B., and D.W. Jorgenson, eds., _Handbook of Computable General Equilibrium Modeling._ North Holland, Elsevier B.V., pp. 159-276.

- Lofgren, Hans, Rebecca Lee Harris, and Sherman Robinson, with assistance from Moataz ElSaid and Marcelle Thomas. 2002. “A Standard Computable General Equilibrium (CGE) Model in GAMS." Microcomputers in Policy Research, Vol. 5. International Food Policy Research Institute (IFPRI), Washington DC.

- McDonald, S., K. Thierfelder, and S. Robinson. 2007. “GLOBE: A SAM-based global CGE model using GTAP data,” Economics Working Paper, US Naval Academy, Annapolis, USA.

- McDonald, S., K. Thierfelder, and T. Walmsley. 2013. “GLOBE2_DYN: Technical document and user guide.” Processed.

- McKibbin, W.J. and A.A. Sidorenko. 2006. “Global Macroeconomic Consequences of Pandemic Influenza,” Lowy Institute for International Policy, Sydney Australia.

- Narayanan, B., Aguiar, A., and McDougall, R., 2012. _Global Trade, Assistance, and Production: The GTAP 8 Data Base,_ Center for Global Trade Analysis: Purdue University, West Lafayette IN.

- O’Neill, J., 2016. “The Review on Antimicrobial Resistance, Tackling Drug-Resistant Infections Globally: Final Report and Recommendations.” Independent review, commissioned by UK Prime Minister David Cameron in 2014.

- Rudholm, Niklas. 2002. “Economic implications of antibiotic resistance in a global economy _,” Journal of Health Economics_ , 21: 1071–1083.

- Smith, Richard D. Milton Yagoa, Michael Millarb, Jo Coastc. 2005.” Assessing the macroeconomic impact of a healthcare problem: The application of computable general equilibrium analysis to antimicrobial resistance, _Journal of Health Economics_ , 24: 1055– 1075.

- Stern, Nicholas. 2007. _The Economics of Climate Change: The Stern Review._ Cabinet Office – HM Treasury, Cambridge University Press, UK.

Taylor, J., M. Hafner, E. Yerushalmi, R. Smith, J. Bellasio, R. Vardavas, T. Bienkowska-Gibbs, and J. Rubin. 2014. _Estimating the economic costs of antimicrobial resistance – model and results._ Cambridge, UK: RAND Europe.

- Timmer, M.P. (Ed.). 2012. “The world input-output database (WIOD): Contents, sources and methods.” WIOD Working Paper Number 10.

21