rafmacalaba/datause-encoder-data huggingface dataset:
and in the recall periods can induce divergence in the measured consumption levels, as is now well documented in the literature (see Beegle _et al._ 2012 for a survey). In total, in this case, it is unclear whether any systematic divergence can be expected (Appendix S1 discusses this in more detail).

We observe that the total amount of annual expenditures per capita in PSF is significantly higher than in ESPS (328000 FCFA versus 285000, a difference of 15%). We can compare how this varies by household structure (appendix Table S1.3) and we show that for individuals living alone and for single cell households there are no significant differences to speak of, while households composed of more cells display larger differences. Distance between consumption estimates of the two surveys is maximal for households with the most complex structure, those with several cells among which at least one contains members that are neither a spouse, nor a child or a grand-child of the household head. This pattern is very consistent with the idea that the share of the consumption directly observed by the household head decreases when the number of potential decision makers in terms of consumption increases. Mobilizing several respondents would then allow a better measure of actual household consumption. When concentrating on food, PSF records higher consumption for most types of households, consistent with the fact that the decomposition between DQ and infrequent food purchases permits a better recall of food spending (appendix Table S1.2).<sup>11</sup> This difference in consumption estimates is in itself an important result. It suggests that traditional surveys in countries where individuals within the household do not fully share the information on their resources and on their expenditures are indeed likely to seriously underestimate consumption. In addition to varying with household structure, such underestimation is probably more important for households in the upper part of the consumption distribution. In fact, poor households have less often several income earners or transfer recipients, so that opportunities for individual, unnoticed, expenditures are plausibly less 13
premium following a new plant entry, with high-skilled workers experiencing a higher wage increase. In equilibrium, they respond by raising their labor supply and reducing the time spent shopping. Retailers respond by raising their mark-ups, leading to rising local prices. This generates relative real wage inequality between skill-groups, as high-skilled workers see a larger gain in nominal wages relative to prices compared to low-skilled workers.

The use of very detailed micro data on prices and time use enables us to empirically establish the mechanisms consistent with the theory. During the time period 2001-2011, we have data on MDP deals obtained from Bloom et al. (2019) who compile this data-set from the site-selection magazine and various other news articles. We collect data on prices, wages, and time-use from three different sources: Data on barcode level prices comes from the IRI data, an administrative dataset containing information on store-week-UPC sales and quantity information for products in 31 categories, representing roughly 15 percent of household spending in the Consumer Expenditure Survey across 7,200 department stores in the US. Data on wages, hours worked and time-use comes from the May Outgoing Group of the CPS and the American Time Use Survey (ATUS).

This paper contributes to three different strands of the literature. First, there is a lot of interest among policy makers to attract million dollar plants to their states. The economic rationale for attracting these firms often hinges on the argument of greater employment opportunities and higher wages. For example, in 2008, the state of Tennessee reached an agreement with Volkswagen to locate their new assembly plant in Chattanooga. Many papers since then have tried to study the effects of these large plant investments on the local community. The empirical evidence has been mixed. Million dollar plants can have large to moderate effects on Total Factor Productivity (Greenstone et al., 2010), moderate to negligible effects on wages, and almost no effect on house prices (Greenstone et al., 2010; Slattery and Zidar, 2020). More recently, Qian and Tan (2021) have shown that high-skilled incumbents, especially homeowners, benefit and low-skilled renters are harmed due to the entry of a large high-skilled firm. Hornbeck and Moretti (2018) find similar effects on renters and homeowners following large TFP increases in manufacturing. However, despite the wide acknowledgment that worker welfare ultimately depends on real wages, to date, we do not have any evidence on how MDPs affect local consumer price inflation, which ultimately affects local real wages. Our first contribution is to measure local inflation at the local regional level 4
years. The decline after 1982 is more puzzling in so far as the urbaLl LFSs 1982-85 tended to show increased unemployment. Capacity utilization, on the other hand, also tended to improve. The SPO estimate, however, does at least indicate aeceleration after 1980 and even decline in 1986. GDP growth did increase from 1985 and the auxiliary series may be leading indicators.

Finally we have the series for registered unemployed and new vacancies. These unfortunately cannot be combined into an excess demand indicator, the former being a stock, the latter a flow variable. Expressing both series as a percent of the so as dome_tic civilian labor forcq to normalize, the new vacancies rates are so small that they hardly lend themselves to economic analysis. For the record it can be mentioned that they declined from 0.3 percent in 1962-63 to 0.2 percent at which level they remained until 1978 when they declined further to 0.1 percent. They do thus indicate a downward trend in demand for labor. Registered unemployed were very few also until the mid-1970s whence they started increasing rapidly. Having stayed at a level of 0.2 percent from 1962 to 1969 the series increased to 0.3 percent through 1973 whence it steadily increased to reach 5.7 percent in 1986. This series may exaggerate the rate of increase because placement offices tend not to be fully informed about hiring of registered unemployed. Moreover, the strong increase during the 1980s may have been induced by government training programmes and the like that as a condition for participation 18 -

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relative price levels in the rural areas of different staces in a given year. Bhattacharya and Chatterjee (1974) worked out these state-wise price relatives for the year 1963-64, which alco formed the basis for the Bardhan (1973) study. These price relatives were for the entire rural population. In the present paper we have used the price relatives (obtained from Minhas 1989) which were based on the weighting diagram of the middle group of the rural population. These estimates were considered more appropriate for estimating Table 1: Consumer Price Index For Agricultural LBouRERts and State Specific Price Relatives

|States|Price<br>Relatives|Consu|mer Price|Indice|s|
|---|---|---|---|---|---|
||60-61|73-74|77-78|83|86-87|
|ANDHRA PRADESH|<br>100.9:|<br>242|297|426|484|
|ASSAM|110.8|260|315|508|606|
|BIHAR|102|337|341|545|594|
|GUJARAT|112.1|246|285|429|530|
|HARYANA|.104.7|273|<br>332|497|602|
|JAMMU-KASHMIR|104.2|263|<br>342|505|618|
|KARNATAKA|98.6|275|<br>308|490|557|
|KERALA|106.6|276|<br>317|531|665|
|MADHYA PRADESH<br>I|<br>93.8|309|<br>345|506|585|
|MAHARASHTRA|105.5|276|<br>320|482|58S|
|ORISSA|97.9|322|<br>351|555|616|
|PUNJAB|104.7|273|<br>332|497|602|
|RAJASTHAN|103.3|284|<br>327|459|565|
|TAMIL NADU|108.2|242|<br>306|496|575|
|UTTAR PRADESH|94.6|303|<br>336|506|593|
|WEST BENGAL|116|276|<br>321|512|607|
|ALL INDIA|100|283|<br>323|511|578|
|Source: For Price Re<br>and for Consumer Pr|latives: Mi<br>ice Indices|nhas and<br>:Monthly|Jain (19<br>Abstract|89)<br>of Stat|istics|
support in order to avoid costly liquidation.<sup>11</sup> Growing size and concentration of banks poses a critical challenge for ECA because of the economic and political ramifications connected to the failure of these large financial institutions. Although the banks in the ECA developing region are small in absolute terms, they are large in comparison to the local economies. Sberbank in Russia with assets over US$400 billion, for instance, is one of the largest banks in Europe. Since 2007, the top 5 banks by asset size have grown larger in all ECA sub-regions except in Central Europe and the Baltic countries (Figure 16).

# **Figure 16: Top 5 Bank Assets as a % of GDP**

<!-- Start of picture text --> 191<br>169<br>114<br>67<br>63 53 56 59 63<br>44 48<br>34<br>26 28 28 29<br>18 20<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017<br>200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>Total assets - Top 5 banks (% GDP) 40<br>20<br>0<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe.

Following guidelines and international standards set by the Financial Stability Board (FSB) and the Basel Committee on Banking Supervision (BCBS), new rules and regulations have been put in place to limit economic damage posed by SIFIs and to strengthen market discipline. Specifically, there are higher capital and liquidity requirements overall and additional surcharges for institutions deemed systemically important. There are also new rules for the orderly resolution of large banks and new requirements for these banks to hold bail-in debt.<sup>12</sup> These additional capital requirements are intended to provide enough equity cushion to make these institutions more resilient and to internalize the social impact of their failure.

> 11 Government interventions to support national banking systems were widespread during the global financial crisis, especially because of the systemic relevance of the distressed institutions. Systemic risk grows with bank size (Laeven et al., 2016) and with the degree of interconnection with the interbank network (Drehmann and Tarashev, 2013).

12 The BCBS set guidelines to identify both global and domestic systemically important banks. The assessment is based on the average of 12 indicators associated with five dimensions of systemic risk: size, interconnectedness, substitutability/financial institution infrastructure, complexity, and cross-jurisdictional activity (BCBS, 2018).

20
The system of pensions (retirement, widow, orphan, and disability), health insurance, work injury, non-pension disability benefits, and unemployment benefits constitute Turkey’s traditional model of social security. They are all linked to employment in the formal sector and are financed by employer and employee contributions. The widow and orphan pensions correspond to survivors’ benefits when relatives receive the retirement pension of the deceased person.

Retirement pensions amount to 6.2 percent of GDP and a fifth of total spending, and this increases to a quarter when adding in widow and orphan pensions. Contributory disability and unemployment benefits constitute a very small share of the overall contributory pension system, under 1 percent of total spending.

Finally, an important program within Turkey’s system is the Minimum Subsistence Allowance (AGI), with a budget almost as high as the social assistance transfers. The AGI program is the exclusion of tax for formal employees who are over 16 years of age, with the size of the benefit depending on marital status and the number of children, but not on level of income. AGI functions as a tax allowance by subtracting the amount of the entitled transfer from the payroll tax paid by the employee. The AGI is paid to the employee by the employer on behalf of the state and deducted from the employer's income tax. We treat AGI as a transfer instead of a tax allowance in the analysis.

# **3. Methodology**

To study the distributional impact of fiscal policy in Turkey, we use the CEQ methodology (Lustig 2018). The CEQ is a comprehensive incidence analysis that uses data from household surveys and national accounts to assess the impact of taxes and public transfers on household poverty and inequality. The approach has been applied in over 70 countries, which allows to benchmark Turkey’s performance with relevant peer countries.<sup>8</sup> The method is based on an accounting approach; it adds and subtracts taxes and transfers to household per capita income to measure income _before_ and _after_ each fiscal intervention. The per capita household income after transfers and taxes _Yh_ for household _h_ is given by

> 8 CEQ analysis has been completed in a total of 55 countries. In addition, 19 countries have a CEQ study ongoing. Source: www.commitmentoequity.org accessed January 30, 2020.

10
**TABLE 6: FULL VACCINATION COVERAGE, GROWTH MONITORING, BEDNET USE, STUNTING, UNDERWEIGHT AND WASTING AMONG CHILDREN†**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Fully<br>vaccinated<br>documented<br>by vaccine<br>card|Fully<br>vaccinated<br>by vaccine<br>card or self-<br>report|Growth<br>monitorin<br>g in the<br>last<br>month|Slept<br>under a<br>bednet|Stunting|Under-<br>weight|Wasted|
|Post indicator|0.127*|0.108**|-0.014|-0.181***|-0.008|-0.010|0.047**|
||[0.072]|[0.052]|[0.013]|[0.025]|[0.025]|[0.022]|[0.021]|
|PBF/Post interact|0.170*|0.164**|-0.002|0.001|0.008|0.046|-0.004|
||[0.095]|[0.069]|[0.017]|[0.042]|[0.033]|[0.028]|[0.028]|
|Control 1/Post interact|-0.054|-0.015|0.031*|-0.005|0.010|0.043|-0.029|
||[0.092]|[0.065]|[0.017]|[0.038]|[0.037]|[0.032]|[0.029]|
|Control 2/Post interact|0.018|0.029|0.022|0.003|0.037|0.018|-0.028|
||[0.092]|[0.073]|[0.019]|[0.036]|[0.033]|[0.028]|[0.027]|
|p-value PBF vs. C1|0.009|0.003|0.047|0.893|0.957|0.907|0.381|
|p-value PBF vs. C2|0.075|0.052|0.215|0.967|0.338|0.272|0.328|
|p-value PBF vs. C3|0.076|0.019|0.930|0.979|0.810|0.104|0.876|
|Baseline mean C3|0.599|0.645|0.048|0.809|0.444|0.147|0.067|
|N|1569|2448|7055|10107|8711|8672|8480|

* = p < 0.10,  ** p < 0.05,  *** p< 0.01

<mark>† Results from difference-in-differences regression models examining the effect of PBF on child vaccination among children (12 – 23 months), growth monitoring in the last month among children (12 – 59 months), having slept under a bednet the night before the survey and on child anthropometric outcomes (stunting, underweight and wasting) among children under 5 years of age included in the household survey</mark> . <mark>Regression model adjusted for individual (age, father in the household, religion, ethnicity) and household control variables (number of individuals in the household, housing type, house ownership, water source, and type of sanitation). Standard errors were clustered at the health facility level.</mark> 39
4 Laroque (1992) and by the studies of storage in particular by Williams and Wright (1991) and Deaton and Laroque (1996).

One way countries try to achieve their stabilization objectives is by varying the restrictions on their international trade in food according to seasonal conditions domestically and changes in prices internationally. Effectively this involves exporting domestic instability and not importing instability from abroad. A simulation exercise by Tyers (1991) suggests that between three-fifths and three-quarters of the global cost of OECD agricultural protection is due to the insulating component of high-income countries’ policies. That practice has continued unabated despite the signing of numerous multilateral and preferential trade-liberalizing agreements over the past two decades.

Another indication of how much governments are intervening for stabilization purposes is provided by annual estimates of national nominal rates of assistance (NRAs). A NRA captures the extent to which the domestic producer price differs from the most comparable international price at a country’s border. NRAs have been measured for all major farm products in more than 70 countries from the mid-1950s to 2007 in a recently completed study summarized in Anderson (2009). That study provides an ideal annual database for analysing government responses to food price spikes, which is fully detailed in Anderson and Valenzuela (2008).

As a prelude to looking at those distortion estimates, it is illustrative to compare the movements in their two components, namely domestic and border prices. Pursell, Gulati and Gupta (2009), for example, report pertinent prices for rice in India which reveal that the Indian government has been able to maintain an almost-constant real domestic rice price for decades despite huge fluctuations in the international price of rice. Similar if less-complete attempts have been made by most governments of South and Southeast Asia and in parts of Sub-Saharan Africa where rice is also a key food staple (Figure 2). When averaged across all developing countries, Figure 3 reveals that that insulating behavior of governments hugely reduced the rise in domestic prices relative to the spike in international prices for rice (and also for wheat). As a result, especially since Asia produces and consumes four-fifths of the world’s rice (compared with about one-third of the world’s wheat and maize), this marketinsulating behavior of Asian and to a lesser extent African policy makers means that very little rice production has been traded internationally: less than 7 percent in 2000-04 (and less than 5 percent pre-1990), compared with 14 and 24 percent for maize and wheat. This insulating behavior of governments also means international prices are much more volatile for rice than for those other grains, as is evident for the two periods shown in Figure 1.
change in the time evolution of world poverty (percentage change in the number of the world poor, or trend rate of decline of the global poverty headcount).<sup>8</sup> However, notice that in this case, as in the case of global inequality (as we shall show below), even if the overall trend may not be much affected, there are non-trivial annual revisions in the indicators because the weights of various countries in global output have changed due to the new ICP numbers. A nice example of this effect is presented by the December 2007 revision of the IMF global output projections for the next year (2008), merely a week after the preliminary ICP results were unveiled. As the weight of the Chinese economy in the world was reduced (from almost 15 to about 10 percent), and as China’s growth was expected to be higher than the world average, the global growth rate was revised downward on account of the change in weights only (that is, without any change in national real growth rate projections).

Up till now (spring 2009), only the World Bank and the IMF have revised the GDP accounts for all the previous years. Penn World Tables, according to its website accessed on 6 April 2009, is in the process of doing so. The third source of long-term GDP data (Angus Maddison series) has not proceeded to any revision yet.

# **2. Recalculated international and global inequality, 1952-2006**

Following Milanovic (2005) and World Bank (2006), we distinguish between international and global inequality. Unweighted international inequality (called also Concept 1 inequality) is inequality calculated across unweighted GDPs per capita of all countries in the world. It is similar to the so-called sigma convergence/divergence except that we use a more common measure of inequality, Gini coefficient, rather than the standard deviation of logs. Concept 1 inequality basically assesses whether there has been or not convergence among countries’ mean incomes. Weighed international inequality (Concept 2) also uses national GDPs per capita but weights them by population size of the countries.<sup>9</sup> It thus begins to approach global inequality (inequality calculated across

> 8 It has also led to the replacement of the well-known global poverty line of $PPP 1 per capita per day by a new one of $1.25 per capita per day (in 2005 international  prices).

> 9 Note that the use of  the term “international” here is not accidental. Concept 1 and 2 inequalities are truly those between national mean incomes (even when population-weighted), and not between individuals.

7
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* Based on aggregating impacts at the household level. North and Eastern Provinces excluded.
to: **_Xk_** The authors of this formula present results from a Monte Carlo experiment

**_j=1_**

showing that the linear segment approach outperforms the covariance-based formula only when applied to grouped data with 20 or fewer observations. They observe little or no difference in terms of bias and mean squared error when they test both methods on data set with 30 or more observations. The authors state their main conclusion as follows: _"The two estimators have similar properties when calculated from individual observations."_ They further claim: _"When using grouped data with 20 or fewer groups, our estimator has less bias and lower mean squared error than the_ covariance estimator. _When individual observations are used, or the number of groups is 30 or more, there is little or no difference in the performance of the two estimators."_ Box 2.3. Linear-Segment Estimator of the Extended Gini Coefficient LOAD %O SMPL @ALL SERIES xl={%1}/{%2} SORT XI SERIES PoPI={%2}*{%3} SERIES PI=POPI/@SUM(POPI) SERIES WXI=POPI*XI SERIES XSHARE=WXI/@SUM(WXI) SMPL @FIRST @FIRST SERIES CUMPI=PI SERIES CUMXSH=XSHARE SMPL @FIRST+1 @LAST CUMPI=CUMPI(-1 )+PI CUMXSH=CUMXSH(-1 )+XSHARE SMPL @ALL SERIES RATIO=XSHARE/PI SERIES QI=(1 -CUMPI) SERIES QH=(1 -CUMPI(-1)) !AvMAx=6 !B=2 !CF=!B-1 !RWS=(!B*!AVMAX)-!CF VECTOR(!RWS) GINU 'EXTENDED GINI !T=1 FOR !NU=1 TO !AVMAX STEP _1I!B_ SERIES QANU=QIA(!NU) SERIES QBNU=QHA(!NU) SERIES QCNU=QANU-QBNU SERIES QDNU=RATIO*QCNU SCALAR QDSUM=@SUM(QDNU) GINU(!T)=1 +QDSUM !T=!T+1 NEXT 'End of Program 10