rafmacalaba/datause-encoder-data huggingface dataset:
investment (5‐6). The savings payments of the different institutions enter new cells (7‐9). Given data on FDI (10), household investment financing is the residual (10‐11). By assumption, the household (as opposed to the government) pays for stock change (12). As it is part of its budget, the government finances its own investment (13). Government foreign borrowing is defined as the product of (i) total foreign borrowing (the difference between foreign savings and FDI); and (ii) the share of government foreign borrowing in total foreign borrowing (14). Remaining borrowing by the government must come from the household (15) while remaining borrowing of the household (the difference between total spending on its capital, i.e. the sum of its payments for private investment, stock change and to finance the government, and its savings) must come from abroad (16).

**Table 3.6. Macro SAM III for archetype low‐income country in 2015 (percent of GDP)**

||act‐prv<br>|act‐gov|com‐prv<br>c|om‐gov|f‐lab f‐cap|hhd<br>gov row|tax‐act<br>tax‐com<br>t|ax‐imp<br>tax‐exp|tax‐dir cssoc|cap‐hhd<br>|cap‐gov<br>cap‐row i|nv‐prvnv‐gov dstk|total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|act‐prv|||153.1|||||||||1|53.1|
|act‐gov||||11.7|||||||||11.7|
|com‐prv|63.6|8.4||||80.8<br>19.8||||||14.4<br>4.8<br>‐6.2 1|85.6|
|com‐gov||||||11.7|||||||11.7|
|f‐lab|49.1|3.3||||0.1|||||||52.6|
|f‐cap|40.0|||||0.2|||||||40.2|
|hhd|||||52.3<br>37.8|1.6<br>2.8|||||||94.5|
|gov||||||1.8<br>0.9|0.3<br>5.7|1.4<br>0.0|4.0<br>0.0||||14.1|
|row|||25.3||0.2<br>2.4|0.5<br>0.1|||||||28.5|
|tax‐act|0.3||||||||||||0.3|
|tax‐com|||5.7||||||||||5.7|
|tax‐imp|||1.4||||||||||1.4|
|tax‐exp|||0.0||||||||||0.0|
|tax‐dir||||||4.0|||||||4.0|
|cssoc|||||0.0||||||||0.0|
|cap‐hhd||||||7.4|||||0.4||7.7|
|cap‐gov||||||0.8||||2.5|1.5||4.8|
|cap‐row||||||4.9||||‐0.1|||4.7|
|inv‐prv||||||||||11.6|2.9||14.4|
|inv‐gov|||||||||||4.8||4.8|
|dstk||||||||||‐6.2|||‐6.2|
|total|153.1|11.7|185.6|11.7|52.6<br>40.2|94.5 14.1 28.5|0.3<br>5.7|1.4<br>0.0|4.0<br>0.0|7.7|4.8<br>4.7|14.4<br>4.8<br>‐6.2||

Source: Authors’ elaboration.

‐17‐
# **3 Construction of the Meta-Sample**

## **3.1 Studies’ Collection Strategy and Inclusion Criteria**

The first step in meta-analysis is to identify a sample of relevant studies. We use three research methods. First, we use reference snowballing techniques on two seminal reviews of the conflict literature: Couttenier and Soubeyran (2015), and Theisen (2012). Second, we collect relevant references from three meta-analysis on related topics: Blair et al. (2021) (on price shocks and conflicts), Vesco et al. (2020) (on natural resources and conflicts), and Hsiang et al. (2013, 2014) (on climate changes and conflicts). Third, we run keyword searches on Google Scholar and extract the results by web scraping using the R package ‘rvest’ (Wickham, 2022). We search for keyword associations in the format ‘ _”keyword1 shocks and keyword2”_ ’ (in whole documents) and ‘ _”keyword1 shocks” keyword2_ ’ (in reference titles), where _keyword1_ specifies the type of shock ( ‘income’, ‘price’, ‘natural resource’, ‘climate’, ‘climatic’ or ‘environmental’) and _keyword2_ indicates the type of conflict outcome (‘conflict’, ‘war’, ‘violence’, ‘unrest’). To be as comprehensive as possible, we also looked for plural or singular forms of keywords. A total of 112 different keyword associations were searched on Google Scholar in August and September 2022.

Our searches identify 565 potentially relevant studies (22 from literature reviews, 138 from meta-analysis, and 405 from keyword searches). To ensure the coherence of our sample, we choose seven inclusion criteria. We only select papers (i) published in peer-reviewed academic journals,<sup>8</sup> (ii) between 2010 and 2021, and (iii) reporting exploitable empirical results (including regression coefficients, sample size, standard errors and/or _t_ -statistics). We focus on studies (iv) where the outcome variable is the onset (i.e. conflict start), the incidence (i.e. number of conflicts), or the duration of a form of internal conflict,<sup>9</sup> and (v) where units of analysis are sub-national (e.g., grid-cells, municipalities, states, prefectures, or districts). We only select studies (vi) analyzing at least one income-related channel of transmission and (vii) in one or several non-OECD high income countries.<sup>10</sup> Sixty-four studies meeting our selection criteria compose our final sample. Figure A3

> 8We therefore exclude working papers that may be of high quality in terms of content but have not been subjected to the peer review process.

> 9We leave aside their intensity (i.e., number of fatalities) to limit causal heterogeneity between studies since these analysis refer to quite different explanatory mechanisms (see, for example, Lacina, 2006).

> 10Source: World Bank Country and Lending Groups, `https://datahelpdesk.worldbank.org/knowledgebase/ articles/906519-world-bank-country-and-lending-groups` , accessed in November, 2022.

8
29 These findings enable one to forecast a substantial stepping up of the effort to solve the housing problem if loans were to become available. In this case, the share of the households who plan to get better housing would rise from 23.5 to 29.4% in Vladimir and from 14.6 to 19.8% in Gorodets; the share of the households relying on their own resources would rise from 16.6 to 24.9% and from 11.1 to 17.3% respectively. As a result, the number of potential buyers of housing would increase 1.5 times and the waiting lists for free housing would become shorter. This suggests that a properly developed mechanism of mortgage lending could make a positive difference in the Russian housing market.

**Fig. 2.10.** Willingness to take out a long-term loan for the purchase of housing, by housing plans, % **Vladimir**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20 23 not prepared to<br>24 get a loan<br>15 E l1 13 prepared to get a<br>8.1 loan<br>10<br>545<br>0 , X<br>planning to get planning to get no plans, but<br>housing on a housing free of need housing<br>chargeable charge<br>basis<br>Gorodets<br>40 -<br>356<br>30-<br>25<br>20 32.2 D not prepared to<br>get a loan<br>15 El0 prepared to get a<br>10 - ~~~~~~~~~~~~~~loan<br>5 5<br>0<br>planning to get planning to get no plans, but<br>housing on a housing free of need housing<br>chargeable charge<br>basis<br><!-- End of picture text --> Source: Vladinmir and Gorodets Longitudinal Household Survey, December 1996.
51 .

## Table 15

_Administrative Expense In the Zambia National Provident Fund_ (Million Kwacha of each year)

|year|Administr.|Contribution|Revnue|Am.Expense|Cast perform|al worker*|
|---|---|---|---|---|---|---|
|ending|Expense|(M.Kw.)|(M.U.S. S)|Covered Wage|Kwachaty.|US Sty.**|
|1983|11.008|50.267|54.2|2.19 %|30.6|46.5|
|1984|13.303|53.709|42.9|2.48_%_|37.0|47.1|
|1988|53.614|123.800|13.9|4.33 %|148.9|33.2|
|1989|66.685|128.900|15.7|5.17 %|185.2|18.0|

: Obtained by dividing reported costs by the number of formal sector workers, which wa 360.000 in 1989 (ISSA. 1990 p. 61).

**: The dollar figure is obtained in a way that excludes the effects of changes **jr** the Zambian real exchange rate. All Kwacha figures are taken to 1982 using the Zambian CPL. then the exchange rate for that year is used to convert into U.S. dollars, and the resulting dollar figure is taken to 1991 using U.S. CPI inflation. The Zambian CPI used is that reported in the International Financial Statistics for the previous calendar year, because thd reported year ends in March 31 of each year. Source: Annual Reports of the Zambia National Provident Fund Board for 82/83. 83J84, 87AS and 58189.

We consider the average of 1983 and 1984 to be more representative of steady-state costs,. as the foreign exchange shortages of the latter period many of the normal activities to a stop.

We find that although annual costs per worker are bot large in real terms, they represent an unusually large fraction of contribution revenue. This suggests that the administrative cost of providing pension services should not be assumed to be a proportion of wages or of contributions in international comparisons.

# Cost structure

In the year ending in March 1988, the expenditure of the Provident Fund was allocated as follows:

|a) Salaries and Allowances|-% of expenses<br>44.1|
|---|---|
|b) Other staff costs (canteen, sports, staff pensions. others)|3.7|
|c) Rents paid to third parties|_5.7_|
|d) Rents:<br>Internal charge|2.0|
|c) Traveling & Motor vehicle expenses|11.0|
|f) Data processing hire|2.7|
|g) Postage,<br>telephone.<br>stationary,<br>printing|14.4|
|h)<br>Insurance|4.1|
|i) Depreciation<br>|8.6|
|j) Other|3.7|
headcount rate. These correlations are in line with our theoretical priors, although the association with the poverty rate is stronger than expected, given that no statistically significant correlation is found between poverty and income protection measures.<sup>5</sup> The magnitude of the elasticities between expenditure on job protection and the four economic outcomes is substantial. An increase in job protection expenditures of 1 percentage point of GDP is associated with an increase in GDP of about the same magnitude in 2021 (Table 3, column 2); an increase in employment of about 0.4 percent of the working-age population during 2020 (column 4) and an equivalent decrease in the inactivity rate in the same year (column 6).

These results suggest that preserving employment during 2020 by subsidizing firms’ wage bills brought levels of economic activity during 2021 closer to the pre-pandemic trend. The longer-term effects are unclear, as evidence from business enterprise surveys suggests that job protection measures were associated with less labor reallocation from low productivity firms to high productivity firms. This finding would imply a loss of efficiency in the economy in the longer run (Demirgüç-Kunt et al. 2022).

The share of the population with incomes under the $5.50 a day international poverty line is about 0.4 percentage points lower for every percentage point of GDP spent on job protection measures <u>(Table 3, column 8). From a cross-country perspective, this result suggests that preserving labor</u> earnings may have a stronger effect on short-term poverty reduction than direct income protection measures. However, income protection programs appear to be more effective in preventing extreme poverty in low-income settings during the pandemic (Bottan et al., 2021).

To assess the robustness of these results, we estimate a series of alternative specifications of equation (1), shown in <u>Table 4. In specification I, all dependent variables are expressed as the</u> difference between the values observed in 2020 and 2019. In specification II, the GDP and poverty headcount rate (at the $5.50 a day poverty line) are expressed as the difference between the value observed in 2020 and the pre-pandemic projection for the same year. In specification III, the GDP and poverty headcount rate ($5.50 a day) are expressed as the difference between the value observed in 2021 and the pre-pandemic projection for the same year. In specification IV, the

> 5 We replicated all the results presented in this paper by including the total number of COVID-19-related deaths per million of population in specification (1). This variable is insignificant is all estimations and the inclusion of it produces no meaningful changes in cooficinets compared to our baseline specification.

14
decade; subsequent surveys have found that state procurement accounts for 25–30% of rural households’ grain output, though in this sample, quota sales constitute only around 10% of grain production on average (Wang et al., 2003; Sicular, 1995).<sup>4</sup> More importantly, there was a substantial increase in quota prices beginning in 1993. The primary analysis in this paper will focus on the period 1993 to 2002; evidence in this sample suggests that at the beginning of the period, the quota price was 30% lower than the market price. In some years later in the period, however, the prices were nearly equal. This change is equivalent to a large reduction in the magnitude of the lump-sum tax imposed on farmers via the mandatory quota system (Huang, 1998). More recently, grain quotas have been widely eliminated. The timing of this change varied, but generally occurred post–2002, after the period of interest here (Huang, Wu and Rozelle, 2009).

It is also useful to note that the quota system treated different grain crops very differently, and rice was consistently the crop most heavily penalized. Procurement prices for rice were lower, and rose more slowly. In addition, the quota quantity constituted a higher proportion of total production for rice producers than for wheat producers (Huang, Rozelle, Ha and Li, 2002). The identification strategy in this paper will exploit the systematic cross-sectional variation in quota quantity that is correlated with cross-sectional variation in the propensity to cultivate rice, in conjunction with these fluctuations in the quota price over time, in order to identify variation in quota income.

In terms of the implementation of the quota system, Wang et al. (2003) provide a detailed analysis of the system’s operation during the period examined here. Quota prices were set by the central government and were constant at the national or provincial level, while quota quantities in the form of grain deliverable per household were generally set

> 4Sicular (1995) reports aggregate statistics on the percentage of farm output sold as quota sales between around 1985 and 1993; grain quotas fall rapidly in this period, accounting for the fact that the percentage of total output constituted by the quota in this sample, observed between 1993 and 2002, is lower. Wang et al. (2003) report data on the operation of the grain quota system assembled from secondary data sources (i.e., not household surveys) in 25 counties in Zhejiang, Jiangsu and Sichuan for the years 1980 to 1999. They report that the share of the quota in total output is less than 25% after 1995. Given that two of the three provinces included in the survey are relatively high-income, highproductivity provinces, the fact that quota sales constitute a relatively higher percentage of output may be unsurprising; there is in general a positive correlation between the level of agricultural production and the proportion of total output sold as quota sales.

6
# **Table A.1: Summary Statistics**

|||**All Chi**|**ldren**||**Co-resident**|**Children**||
|---|---|---|---|---|---|---|---|
||**Mean**|**Median**|**p/****c**|**N**|**Mean**<br>**Median**|**p/****c**|**N**|
|||||**BANGL**|**ADESH**|||
|Years of Education of|||**Both S**|**ons and Da**|**ughters Sample**|||
|Children|4.97|5.00||18587|5.52<br>5.00||5852|
|Father|3.39|2.00|0.92|14017|3.74<br>3.00|1.09|5599|
|Mother|1.46|0.00|0.55|14527|1.81<br>0.00|0.69|5523|
|Average of Parents|2.33|1.00|0.70|18505|2.78<br>2.00|0.82|5806|
|||||**Sons S**|**ample**|||
|Children|5.84|5.00||9056|5.56<br>5.00||3873|
|Father|3.38|2.00|0.85|7126|3.53<br>2.00|1.01|3713|
|Mother|1.45|0.00|0.51|7261|1.64<br>0.00|0.62|3648|
|Average of Parents|2.34|1.00|0.65|9010|2.59<br>1.50|0.75|3844|
|||||**Daughter**|**s Sample**|||
|Children|4.14|4.00||9531|5.44<br>5.00||1979|
|Father|3.41|2.00|1.05|6891|4.16<br>3.00|1.27|1886|
|Mother|1.47|0.00|0.63|7266|2.14<br>0.00|0.83|1875|
|Average of Parents|2.33|0.50|0.80|9495|3.14<br>2.50|0.96|1962|
|||||**IND**|**IA**|||
|Years of Education of|||**Both S**|**ons and Da**|**ughters Sample**|||
|Children|6.23|7.00||14877|6.97<br>8.00||9132|
|Father|4.37|2.50|0.91|14877|4.74<br>5.00|0.95|9132|
|Mother|1.83|0.00|0.65|14877|2.12<br>0.00|0.70|9132|
|Average of Parents|3.10|2.50|0.70|14877|3.43<br>2.50|0.74|9132|
|||||**Sons S**|**ample**|||
|Children|7.29|8.00||8341|7.54<br>8.00||6561|
|Father|4.31|2.50|0.92|8341|4.59<br>5.00|0.96|6561|
|Mother|1.82|0.00|0.66|8341|1.99<br>0.00|0.70|6561|
|Average of Parents|3.06|2.50|0.71|8341|3.29<br>2.50|0.74|6561|
|||||**Daughter**|**s Sample**|||
|Children|4.87|5.00||6536|5.54<br>6.00||2571|
|Father|4.46|2.50|0.96|6536|5.14<br>5.00|0.97|2571|
|Mother|1.84|0.00|0.68|6536|2.45<br>0.00|0.75|2571|
|Average of Parents|3.15|2.50|0.73|6536|3.79<br>3.25|0.78|2571|
# **_The response of Sweden’s wage-setting system to the global financial crisis_**

Contrary to the widespread view that highly unionized economies respond more slowly to adverse shocks, Sweden’s labor market dealt relatively well with the recent global financial crisis. The experience of the social partners with collective bargaining through many turbulent periods, the generally progressive and pragmatic attitude of the Swedish public in dealing with difficulties and the high level of cooperation between unions and employers’ associations as well as their recognition of each other’s interests were all among the factors that helped the Swedish economy work its way through the crisis (Ahlberg and Bruun, 2005; Blanchard et al., 2013). According to the _Global Competitiveness Report_ of World Economic Forum (2014), Sweden has among the highest levels of employee-employer cooperation in the world: the report ranked Sweden 17th in 2013 among 144 countries on its index on the degree of cooperation between employees and employers—above Germany, United Kingdom, Canada, Finland and United States as well as the averages for the EU15, OECD and G7 (Figure 2).

<mark>Figure 2: Sweden has one of the highest levels of cooperation between employees and employers</mark> <!-- Start of picture text --> 6<br>5<br>4<br>3<br>Denmark Norway Japan Netherlands Iceland Austria Luxembourg Ireland Sweden Germany Canada Finland EU15 average OECD average United States G7 average Portugal Belgium Spain Greece France Italy<br>Cooperation in labor-employer relations, 2013 Nordic average United Kingdom<br><!-- End of picture text --> Source: World Economic Forum (2014). Note: The index is based on the World Economic Forum’s Executive Opinion Survey, which gathers perception data from entrepreneurs. It ranges from 1 to 7; higher numbers indicate higher levels of cooperation. The Nordic average excludes Sweden.

<mark>During the global financial crisis Sweden had among the highest numbers of industrial agreements</mark> to cope with its adverse effects— <mark>along with B</mark> elgium, France, Germany, Italy, the Netherlands, Spain and the United Kingdom (Johansson and Linderoth, <mark>2012;</mark> Glassner et al., 2010). Social partners in Sweden’s metal and automotive industry signed agreements at the plant level for temporary layoffs as well as reduced working hours, pay and labor costs. To help mitigate the social 7
(A) Financial PV using Infrastructure Sharing (10 GB/Month Per User) Interval bars reflect estimates for low and high cost scenarios.

<!-- Start of picture text --> Cote D'Ivoire Mali Senegal<br>Baseline = Baseline= Baseline =<br>Passive = Passive£ Passive<br>Active f acive $ Active f<br>SRN £ SRN = SRN f<br>0 10 20 30 0 10 20 30 0 10 20 30<br>Kenya Tanzania Uganda<br>Baseline= Baseline= Baseline =<br>Passive= Passive= Passive =<br>Active = Active= Active £<br>SRN= SRN= SRN =<br>0 10 20 30 0 10 20 30 0 10 20 30<br>Financial Cost (Billions $USD)<br><!-- End of picture text --> <!-- Start of picture text --> ii High Hi Baseline ml Low<br><!-- End of picture text --> (B) Financial PV using Infrastructure Sharing (30 GB/Month Per User) Interval bars reflect estimates for low and high cost scenarios.

<!-- Start of picture text --> Cote D'Ivoire Mali Senegal<br>Baseline= Baseline= Baseline=<br>Passive= Passive= Passive=<br>Active £ Active= Active f<br>SRN= SRN= SRN=<br>0 10 20 30 0 10 20 30 0 10 20 30<br>Kenya Tanzania Uganda<br>BaselineES _ Baseline—S=— Baselinei<br>PassiveS_ Passivea Passive=<br>Active= Active= Active =<br>0 10 20 30 0 10 20 30 0 10 20 30<br>Financial Cost (Billions $USD)<br>Hi High | Baseline i Low<br><!-- End of picture text -->
# **Table A2 Data Sources**

|#<br>Series|Description|Source|Coverage|
|---|---|---|---|
|1 Capital Flows|Gross Asset and Liability Flows|IMF BOP|1970-2015|
|2 Nominal GDP in U.S. dollars|Nominal GDP in U.S. dollars|UN National<br>Accounts|1960-2015|
|3 VIX|CBOE Volatility Index|FRED|1990-2015|
|4 VXO|CBOE S&P 100 Volatility Index|FRED|1986-2015|
|5 10-year BAA spread|Moody's Baa Corporate rate minus Treasury<br>10Y rate|FRED|1970-2015|
|6 U.S. short-term real interest rate|Effective Fed Funds Rate minus GDP inflation|FRED|1970-2015|
|7 U.S. real GDP growth|Gross Domestic Product in constant US<br>dollars|IMF IFS|1970-2015|
|8 U.S. real exchange rate|Real Effective Exchange Rate REER|IMF IFS|1979-2015|
|9 World commodity price index|Commodity Price Index - Metals and<br>Minerals|UNCTAD|1960-2015|
|10 Financial openness|Chinn-Ito Index of Capital Account<br>Liberalization|Chinn-Ito|1960-2014|
|11 Trade openness (% of GDP)|Total Exports plus Imports over GDP|UN National<br>Accounts|1960-2015|
|12 Domestic credit (% of GDP)|Domestic credit to private sector over GDP|WB WDI|1960-2015|
|13 Exchange Rate regime|De Facto Aggregate ERA|Gosh, Ostry and<br>Qureshi (2015)|1980-2011|
|14 Commodity intensity|Net Exports of Commodities over GDP<br>(Leamer index)|Comtrade/UNCTAD|1960-2015|

38
Each region has independent institutions as production sectors, rural and urban households, a representative enterprise, and a local government; and, meanwhile, has relevant economic activities such as production, consumption, savings, and investment. Each region has 17 representative production sectors: one agricultural, five energy, seven non-energy industrial, and four service sectors (Table 1). The CE<sup>3</sup> MS database derives from the China 2007 regional social accounting matrices (SAMs).

Table 1. Sector declarations and descriptions.

|Sector codes|Description|
|---|---|
|Agri|Agriculture, forestry, animal husbandry and fishery|
|Coal|Coal|
|Coil|Crude oil and natural gas|
|Mine|Mining|
|Fpap|Manufacture of foods, beverage, tobacco, textile, wearing, apparel, leather,<br>wood, paper and publishing|
|Petro|Coking, gas and processing of petroleum|
|Chem|Chemical industry|
|Nmm|Manufacture of nonmetallic mineral products|
|Metal|Manufacture and processing of metals and metal Products|
|Omf|Other manufacture|
|Ele|Production and supply of electric, heat power|
|Gas|Production and supply of gas, water|
|Cons|Construction|
|Trans|Transport, storage, post, information transmission, computer services and<br>software|
|Wsale|Wholesale and retail trades, hotels and catering services|
|Esta|Real estate, leasing, business services and financial intermediation|
|Ots|Other services|

Export and import provides the linkages between each region and the rest of the world. Most importantly, a multi-regional model differs from a national model in its interregional linkages among all regions, including commodity trading, and the mobility of labor and capital. Unlike developed countries, the central government plays a quite important role in development decisions in China; therefore, a central government is described at the national level in this model. The basic modules of CE<sup>3</sup> MS are production module, emissions trading module, commodity trading module, 6