rafmacalaba/datause-encoder-data huggingface dataset:
of these is the World Wealth and Income Database (WID), constructed by Thomas Piketty and his collaborators<sup>4</sup> , which reports summary statistics on average income and top income shares compiled from tabulated data on tax records. From this database, we retrieve time-series data on mean income and the bottom 90% income share for 19 countries with reasonably complete annual time series data during the post-World War II period. For the median country in this sample, we have 60 years of annual data on aggregate moments on which to base our estimates of mobility. The second data source is the compendium of household survey data for a large set of mostly developing countries in the World Bank’s PovcalNet database. This dataset provides summary statistics on the distribution of income (or consumption, depending on the survey) for over 1400 household surveys in a large cross-section of developing countries, in some cases extending back to the 1980s. We retrieve data on mean income (or consumption) and the Gini coefficient for these surveys, and restrict attention to the set of 28 countries for which we have at least 10 surveys. The median country in this sample has 16 household surveys spanning a period of 17 years.

We apply our methodology to find bounds on mobility for the countries in these two datasets. Our estimates confirm that among the high income countries in the WID database, the US ranks as the country with the lowest income persistence, while countries with high income persistence include all of the Scandinavian countries. Interestingly, when we correlate our estimates with GDP per capita, we find that lower income persistence is associated with higher income. This observation applies both to the WID and the PovcalNet estimates. As we discuss in more detail below, this observation is not an obvious consequence of our assumptions, and suggests that countries with greater persistence must have other characteristics conducive to more rapid growth.

We use these estimated bounds on mobility to generate bounds on the difference between “anonymous” growth rates of group average incomes (that do not require membership in the group to be stable over time) with corresponding “non-anonymous” growth rates (that track the performance of the same initial group of individuals over time). When we compare estimates of anonymous income growth rates among the bottom 40% (the World Bank’s measure of Shared Prosperity) to their non-anonymous counterparts, the difference is substantial: on average, non-anonymous growth rates exceed anonymous ones by four percentage points in the WID and three percentage points in PovcalNet in annual terms. This means that by tracking shared prosperity anonymously, policy makers could inadvertently overlook the

> 4 The full team behind the WID database includes Facundo Alvaredo, Lucas Chancel, Thomas Piketty, Emmanuel Saez, and Gabriel Zucman.

4
FIGURE A.2 Pass-through: Additional sign restriction to identify domestic demand shocks

# A. Monetary policy shocks

# B. Global demand shocks

<!-- Start of picture text --> Ratio Ratio Interquartile range Median<br>0.6 Interquartile range Median 1.5<br>1.0<br>0.4 0.5<br>0.0<br>0.2<br>-0.5<br>0.0 -1.0<br>All Advanced EMDEs All Advanced EMDEs<br>economies economies<br>C. Domestic demand shocks D. Global supply shocks<br>Ratio Ratio<br>0.1 Interquartile range Median 0.5 Interquartile range Median<br>0.0 0.0<br>-0.1 -0.5<br>-0.2 -1.0<br>All Advanced EMDEs All Advanced EMDEs<br>economies economies<br>E. Domestic supply shocks F. Oil price shocks<br>Ratio Ratio<br>0.2 Interquartile range Median 1.0 Interquartile range Median<br>0.1 0.5<br>0.0 0.0<br>-0.1 -0.5<br>-0.2 -1.0<br>All Advanced EMDEs All Advanced EMDEs<br>economies economies<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Add.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>sign restr.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change to shocks from country-specific factor-augmented vector autoregression models estimated for 51 economies (29 advanced economies and 22 EMDEs) over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. In the alternative specification, an additional sign restriction was imposed, assuming that a positive domestic demand shock leads to a contemporaneous increase in domestic interest rates. EMDEs = emerging market and developing economies.

40
TABLE 4 CREDIT MONTHS, MOSHAVIM'S DEBT TO MISHORIM RELATIVE TO SALES

|1970|12.1|1974|5.9|1978|6.0<br>:|
|---|---|---|---|---|---|
|1971|9.9|1975|5.3|1979|9.0|
|1972|7.5|1976|4.6|1980-|10.2|
|1973|5.9|1977|5.7|1981|10.7|

Source: Kislev, Marvid (1988).

Note: Credit months = (average annual debt of moshavim to Mishorim)/(annual moshavim's sales through Mishorim)x12.

blunted=. As a result, heavy financial inter-reliance of first and second order cooperatives lead to moral hazard behavior on part of many of the borrowing associations and was one of the major factors that exacerbated the severity of the current crisis. The alarm signals were building up for a long time and are worth examining further. Evidence comes from the third regional organization we are studying.

Data for Mishorim, a-supply cooperative with young moshavim similar to those of the Mountain Region, shovfthat not only across moshavim of different vintage, but over time for the same moshavim too--financial exposure is affected strongly by economic performance of the members of the regional organization. Mishorim's credit months ratio (Table 4) improved significantly in the mid 1970s with the expansion of export crops, in which the region specializes, but evidently the moshavim found it convenient not to cut back on their credit in the regional organization in 1979-1981 when agricultural terms of trade deteriorated with the weakening of the European currencies against the dollar and the region's production of exportables and products for the domestic market was reduced.

Differences are also found between moshavim in the same region. In a sample of 13 moshavim, members of Mishorim, one moshav owed nothing to the supply cooperative--it did all its procurement for cash. Another held the record for 1981 with 55 credit months. The next in °For a relevant theory of social power, see Harsanyi (1962).

17
Japan following the 2011 earthquake for the producers most dependent on Japan. This finding would suggest that global value chains reconfigured after the shock.

_Table 1: Impact on imports from Japan_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|**Panel A: All**||Au|to|||Elect|ronics||
||log of|log of|log of|log of|log of|log of|log of|log of|
||imports|imports|imports|imports|imports|imports|imports|imports|
|(Dummy share 2004-2010 > 15)|-0.814***|||-0.576***|-0.386***|||-0.108|
||(0.165)|||(0.181)|(0.057)|||(0.069)|
|(Dummy share 2004-2010 > 10)||-0.694***||-0.140||-0.469***||-0.221***|
|||(0.140)||(0.158)||(0.050)||(0.064)|
|(Dummy share 2004-2010 > 5)|||-0.594***|-0.345**|||-0.508***|-0.387***|
||||(0.128)|(0.138)|||(0.047)|(0.051)|
|Observations|5,336|5,336|5,336|5,336|50,194|50,194|50,194|50,194|
|R-squared|0.903|0.903|0.902|0.904|0.912|0.913|0.913|0.913|
|Treated|74|109|177|177|683|1024|1708|1708|
|Total country-products|405|405|405|405|3971|3971|3971|3971|
|Auto-Electronics >15|0.428**|||0.468**|||||
|Auto-Electronics >10||0.226||-0.081|||||
|Auto-Electronics >5|||0.085|-0.042|||||
|**Panel B: Auto**||Intermed|iate auto|||Fina|l auto||
||log of|log of|log of|log of|log of|log of|log of|log of|
||imports|imports|imports|imports|imports|imports|imports|imports|
|(Dummy share 2004-2010 > 15)|-0.748***|||-0.560***|-0.723*|||-0.171|
||(0.160)|||(0.178)|(0.433)|||(0.511)|
|(Dummy share 2004-2010 > 10)||-0.606***||-0.249||-0.752**||-0.035|
|||(0.146)||(0.165)||(0.334)||(0.346)|
|(Dummy share 2004-2010 > 5)|||-0.340**|-0.114|||-1.121***|-1.035***|
||||(0.152)|(0.155)|||(0.312)|(0.319)|
|Observations|3,749|3,749|3,749|3,749|1,587|1,587|1,587|1,587|
|R-squared|0.918|0.918|0.917|0.919|0.896|0.897|0.899|0.899|
|Treated|49|75|121|121|25|34|56|56|
|Total country-products|285|285|285|285|120|120|120|120|
|Intermediate-Final >15|0.025|||0.389|||||
|Intermediate-Final >10||-0.147||0.213|||||
|Intermediate-Final >5|||-0.781**|-0.921***|||||
|**Panel C: Electronics**|Inter|mediate elect|ronics||Final electro|nics|||
||log of|log of|log of||log of|log of|log of|log of|
||imports|imports|imports||imports|imports|imports|imports|
|(Dummy share 2004-2010 > 15)|-0.228**|||-0.092|-0.453***|||-0.122|
||(0.099)|||(0.126)|(0.070)|||(0.081)|
|(Dummy share 2004-2010 > 10)||-0.263***||-0.069||-0.549***||-0.277***|
|||(0.086)||(0.114)||(0.061)||(0.077)|
|(Dummy share 2004-2010 > 5)|||-0.386***|-0.336***|||-0.548***|-0.400***|

10
|**Y**|**= RIF estim**|**ate of Log [**|**Self-reporte**|**d Productivit**|**y (XOF/ha)]- **|**Export crop**|**s**|||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|Household head has Secondary<br>education|<br>(0.008)|<br>(0.059)|<br>(0.061)|<br>(0.049)|<br>(0.030)|<br>(0.027)|<br>(0.028)|<br>(0.022)|<br>(0.013)|
|Household head has Tertiary|0.000|0.000|0.000|0.000|0.000|-0.000|-0.000|-0.000|0.000|
|<br>education|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household head disability|0.291***|0.112|0.043|0.066|-0.004|-0.062|-0.009|-0.084*|-0.078|
||(0.104)|(0.087)|(0.087)|(0.075)|(0.063)|(0.063)|(0.058)|(0.050)|(0.051)|
|Dependency ratio|0.021|0.124|-0.034|-0.089|-0.228**|-0.149*|-0.132*|-0.217***|-0.358***|
||(0.121)|(0.117)|(0.120)|(0.113)|(0.096)|(0.078)|(0.072)|(0.080)|(0.093)|
|Household Male Labor Use (Number)|-0.005|-0.009|-0.042|-0.092|-0.098|-0.111*|0.056|0.049|-0.016|
||(0.068)|(0.067)|(0.069)|(0.069)|(0.060)|(0.060)|(0.054)|(0.053)|(0.050)|
|Household Female Labor Use|-0.023|-0.037|-0.036|-0.067|-0.030|0.029|-0.032|0.005|0.002|
|(Number)|(0.210)|(0.174)|(0.180)|(0.172)|(0.133)|(0.117)|(0.101)|(0.095)|(0.099)|
|<br>Household Child Labor Use (Number)|-0.048*|-0.050*|-0.072**|-0.017|0.006|-0.020|-0.003|-0.028|-0.074**|
||(0.027)|(0.028)|(0.032)|(0.038)|(0.028)|(0.024)|(0.023)|(0.021)|(0.032)|
|Hired Labor Use (Number)|0.110|0.040|-0.007|-0.030|-0.101|-0.015|0.046|-0.046|0.035|
||(0.068)|(0.098)|(0.099)|(0.093)|(0.081)|(0.080)|(0.061)|(0.046)|(0.042)|
|Number of Fields Managed by the|0.174|-0.016|-0.404|-0.093|-0.230|-0.082|0.107|-0.714***|-0.465**|
|<br>household|(0.241)|(0.258)|(0.275)|(0.308)|(0.233)|(0.215)|(0.152)|(0.162)|(0.189)|
|Total number of crops produced|0.020|0.047|0.486*|0.424|0.258|0.196|0.112|0.664***|0.480**|
||(0.301)|(0.306)|(0.294)|(0.322)|(0.257)|(0.239)|(0.220)|(0.200)|(0.212)|
|Household used any purchased seed|-0.021|-0.133|-0.047|0.014|0.002|0.030|-0.044|-0.057|-0.002|
||(0.078)|(0.082)|(0.083)|(0.084)|(0.073)|(0.064)|(0.058)|(0.051)|(0.048)|
|Plots that Use Chemical Fertilizer (%|0.031|0.060|0.101**|0.056|0.036|0.021|0.026|0.035|0.021|
|<br>of total)|(0.036)|(0.047)|(0.044)|(0.045)|(0.034)|(0.033)|(0.033)|(0.029)|(0.029)|
|<br>Plots that Use Pesticide (% of total)|-0.234**|-0.353***|-0.585***|-0.368***|-0.307***|-0.110|-0.163**|-0.137*|0.000|
||(0.111)|(0.119)|(0.127)|(0.116)|(0.091)|(0.084)|(0.080)|(0.080)|(0.100)|
|Constant|-0.224|-1.319|-0.807|-1.273|-0.342|-0.875|-1.077|0.773|-0.060|
||(0.661)|(1.005)|(1.475)|(1.237)|(1.066)|(0.993)|(1.039)|(0.925)|(0.465)|
|Observations|2823|2823|2823|2823|2823|2823|2823|2823|2823|

46
assume approximately zero pro...ts). However, in order to break down the productivity residual into components that re‡ect aggregate distortions, reallocation and technology growth we must obtain estimates of the markups and of technology growth. We will do that by assuming that the (gross) production function in sector j is Cobb Douglas: where i denotes ...rms (i = 1; ::; Ij), t time (t = 1; ::; Tj), and small case variables logs. �jt is an industry speci...c common component of productivity, �i a time invariant ...rm level component and !it an idiosyncratic component. In our application using the Amadeus data set, Tj is small and Nj large.

We will experiment with di¤erent estimation methods: OLS, LSDV, Olley and Pakes, Di¤erence and System GMM (assuming that !it is either serially uncorrelated, or that it follows an AR(1) process). The advantages and disadvantages of each choice are well known, although there is no agreement on which estimator one should ultimately choose. One fundamental estimation problem is the endogeneity of the input variables, which are likely to be correlated both with �i and !it. Correlation with !it may re‡ect both simultaneity of input choices or measurement errors. Given the shortness of the panel, elimination of �i through a within transformation is not the appropriate strategy. Di¤erencing of (35) and application of the di¤erence GMM estimator (Arellano and Bond (1991)) is a possibility, but appropriately lagged values of the regressors may be poor instruments if inputs are very persistent. Application of the GMM System estimator (Blundell and Bond (1998) and Blundell and Bond ( 2000) is probably a better option. An alternative approach is the one proposed by Olley and Pakes (1996). This estimator addresses the simultaneity (and selection) problem by using ...rm investment as a proxy for unobserved productivity and requires the presence of only one unobserved state variable at the ...rm level and monotonicity of the investment function. We are not interested to take a stand in this paper on which one is the preferable estimation strategy. Fortunately for us, the results of the decomposition are insensitive to the choice of a particular estimator.

Having obtained estimates of the output elasticity for each factor we will recover the ...rm speci...c markup from the ...rst order conditions for materials, equation (33). In the Cobb Douglas case, this can be expressed as: where s<sup>Qthetimeaverageofthe...rmspeci...crevenueshareofmaterialsfor...rmi:Ahat</sup> M;i<sup>is</sup> denotes estimated values. We have chosen to focus on the FOC for materials because they are likely to be the most a ‡exible input. Whereas the labor share, s<sup>Q</sup> L;i<sup>;canbeeasilyrecoveredfrom</sup> the data, the same is not true for the capital share, s<sup>Q</sup> K;i<sup>,unlessoneiswillingtomakeassumptions</sup> about the user cost of capital, which is problematic in the presence of ...rm heterogeneity in the cost of ...nance. We have recovered the capital share from estimates of the markup described above and 23
With respect to fertilizer, one piece of evidence that this input may be a binding constraint comes from a study on Uganda. Dolan (2004) finds that one of the most prominent barriers to increased agricultural activity is a lack of financial capital. Nearly all female-headed households reported a desire to expand agricultural activities but lacked the money to purchase land and inputs (seeds, fertilizer, pesticides) or to hire the labor needed to assist them. Credit constraints were also cited as a potential reason for the low uptake of fertilizer, even when it was subsidized in Malawi (SOAS et al., 2008). Due and Gladwin (1991) document the difference in overall fertilizer use and intensity in Malawi (approximately 50 percent more per hectare) and argue that social constraints prevent women from participating in the institutions (farmer clubs) that would provide them with access to credit. When land size and credit or cash are controlled, gender is not a significant determinant of demand for fertilizer.

As we will discuss below, women are less likely to farm cash crops (or are less likely to sell their produce at all). Therefore, the use of fertilizer may not be worthwhile. In itself, this finding does not indicate a problem with markets or institutions per se, but there is an underlying phenomenon in which women engage less in marketable agriculture. However, it is important to note that differences in fertilizer use (e.g., in Malawi and Burkina Faso) seem to persist even when men and women farm the same crop.

Finally, there are likely to be economies of scale for fertilizer. At one level, these issues of scale result because the time needed to procure knowledge and the fertilizer itself (e.g., significant transport costs) are nondivisible costs that may not make fertilizer economical for very small-scale farms, which are the type of farms women are more likely to have.

17
has become a net-exporter of people, which together with a reduced fertility rate have reduced population growth.

**Table 2.3:                    In-migration to the North during 1991-2000**

||**Non-nativep**|**opulation of th**|**e with less than 1**|**0 continuousyea**|**rs of residence**|
|---|---|---|---|---|---|
|**Large Regions and**<br>**Federal Units**|**Total**||**Continuous tim**|**e of residence**||
|||**Less than 1**||||
||**(1)**|<br>**year**|**1 to 2 years**|**3 to 5 years**|**6 to 9 years**|
|**Brazil**|**8 463 341**|**723 480**|**2 119 545**|**2 819 157**|**2 645 904**|
|**North**|**976 169**|**77 788**|**248 074**|**319 519**|**318 817**|
|Rondônia|181 479|16 076|41 948|60 479|60 942|
|Acre|24 471|3 264|4 170|8 756|8 097|
|Amazonas|141 580|8 075|33 225|61 128|37 160|
|Roraima|81 087|4 042|23 089|29 988|23 310|
|**Pará**|**272 664**|**12 531**|**89 016**|**69 465**|**96 493**|
|Amapá|97 854|8 909|15 774|40 219|31 946|
|Tocantins|177 034|24891|40 853|49484|60 868|

_Source_ : Census 2000.

**Table 2.4:                   Out-migration in the North during 1991-2000**

|**Previous residence**|**Non-nati**<br>**Ttl 1**|**vepopulation of**<br>|**the state with le**<br>**Continuous t**|**ss than 10years**<br>**ime of residence**|**of residence**|
|---|---|---|---|---|---|
||**oa**|**ess tan**||||
|||<br>**year**|**1 to 2 years**|**3 to 5 years**|**6 to 9 years**|
|**Brazil**|**8 463 341**|**723 480**|**2 119 545**|**2 819 157**|**2 645 904**|
|**North**|**672 718**|**55 074**|**181 268**|**239 790**|**188 971**|
|Rondônia|87 403|8 980|19 901|24 255|34 267|
|Acre|12 647|752|4 571|3 099|4 225|
|Amazonas|82 426|6 081|18 859|33 112|20 771|
|Roraima|18 943|1 083|7 936|7 465|2 459|
|**Pará**|**369 493**|**27 830**|**94 247**|**138 004**|**105 616**|
|Amapá|19 640|2 676|8 977|5 480|2 507|
|Tocantins|82 164|7 671|26 777|28 375|19126|

_Source_ : Census 2000.

Pará is becoming highly urbanized. In 1980 and 1991, Pará’s population was more or less equally distributed between rural and urban areas (see Table 2.5). This contrasts the development in the 1990s where urban areas expanded rapidly. In 2000, 67 percent of the _Paraenses_ lived in urban areas and more than half of the urban-dwellers lived in the state’s six largest cities, each with more than 100,000 inhabitants (Belém, Ananindeua, Santarém, Marabá, Castanhal, and Abaetetuba). According to IBGE’s definition of rural and urban areas, Pará is still far less urbanized than Brazil as a whole (where 81 percent lived in the urban areas in 2000 compared to 68 percent in 1980). But the urban population growth is increasing rapidly. In the 1970s, urban areas expanded at 5.0 percent annually, and during the 1990s they reached 5.3 percent 12
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