rafmacalaba/datause-encoder-data huggingface dataset:
propose the following process to model exchange rate returns and conditional volatility assuming that the error terms are drawn from a double exponential (DE) distribution:<sup>13</sup> _rt_ =φ0 + φint _er INTER_ +φ _sales SALES_ +φ _purchs PURCHS_ + φ _sign SIGN_ 2 + φ _brady BRADY_ +φ _ON ON_ + ε _t_ ; ε _t_ ~ _DE_ (,0σ _t_ ), εt = _et_ σ _t_ , _et_ ~ _iid_ ()1,0 (1) 2 2 ln _(_ σ _t_ ) = _w_ + α(| _et_ −1 | +γ _et_ −1 ) + βln(σ _t_ −1 ) + δint _er INTER_ + δ _sales SALES_ + δ _purchs PURCHS_ + δ _sign SIGN_ + δ _brady BRADY_ + δ _ON ON_

where INTER, SALES, PURCHS stand for, all in millions of USD, central bank intervention, sales of foreign exchange, and purchases of foreign exchange, respectively.<sup>14</sup> SIGN is a dummy variable with a value of unity on the day of a public report, and is intended to signal exchange rate policy intentions on dates where there was a modification of the contractual terms of the auctions. Information on this variable is recorded from the central banks monetary reports, annual reports and press releases.

In an attempt to directly account for the effect of intervention in the money market, we include the policy instrument for each country, denoted as ON. For Mexico, we use the actual daily stance or target for cumulative balances in millions of pesos,

> 13 A preliminary analysis suggested the use of the Generalized Error Distribution (GED). The estimated tail thickness parameter (ν) could not reject the hypothesis Ho: ν= 1, which corresponds to the Laplace distribution whose distribution function is _f_ ( _x_ ) = _e_ −| _x_<sup>|</sup> / 2 . In addition, Akaike and Bayes criteria preferred this conditional density over a GED, normal or _t_ distributions. This analysis is not included in the paper, although the results are readily available upon request.

> 14 In the case of Mexico, given that investors will decide to exercise the put options in appreciating trends, Werner (1997b) noticed that the variable PURCHS cannot be an exogenous variable since it is correlated with the error term (εt) in equation (1).  In order to address the inconsistency problem, he uses the two period lag of the variable as instrumental variable. In this paper, we also follow this approach (see Werner (1997b) for more details).

13
Policy Research Working Paper 6166

# **Abstract**

This paper describes stylized facts about internal migration and the labor force in Egypt, and shows how internal migration in the country is low compared with international standards. Using aggregate labor force survey data, the paper shows how individuals migrate to governorates with higher wages. With a Mincerian equation, the analysis finds that migrants earn premiums with respect to non-migrants, except for those migrants with low education levels. The aggregate labor statistics reveal lower unemployment rates among migrants, a phenomenon that is verified by an employment equation. According to the econometric results, migrants are more likely to be employed, even after controlling for other observable individual characteristics. Finally, the paper estimates a Probit model for the decision to migrate, finding that more educated individuals are more likely to migrate, agricultural workers have a lower probability of migrating, and individuals from governorates in which food production for own consumption is higher are less likely to migrate. These results suggest that low educational attainment and the “food problem”, which ties resources to food production to meet subsistence requirements, are at the root of low migration in Egypt.

This paper is a product of the Poverty Reduction and Economic Management Unit, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at sherrera@worldbank.org and kmohamed@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 6: The Impact of Reference Period on Weekly Labor Market Reporting**

<mark>The impact of the reference period on labor market reporting Phone triweekly vs phone weekly</mark> <u><mark>(omitted category)</mark></u>

|Phone triwe|eklyvsphon|e weekly (|omitted catego<br>Days-|ry)|Hours-|
|---|---|---|---|---|---|
||Working|Days|conditional<br>on work|Hours|conditional<br>on work|
|Panel A: Main effects||||||
||(1)|(2)|(3)|(4)|(5)|
|Phone triweekly|0.07**|-0.14|-0.59***|-0.84|-4.70**|
||(0.03)|(0.20)|(0.12)|(2.19)|(1.94)|
||[0.05]|[0.61]|[0.00]|[0.70]|[0.04]|
|Mean phone weekly|0.75|3.90|5.19|33.41|44.45|
|N|3220|3220|2530|3220|2530|
|Adjusted R2|0.008|0.005|0.043|0.003|0.015|
|Panel B: Heterogeneity by self-em|ployment st|atus||||
||(6)|(7)|(8)|(9)|(10)|
|Phone triweekly|-0.01|-0.52*|-0.60***|-5.37*|-6.28**|
||(0.05)|(0.27)|(0.17)|(3.01)|(2.68)|
||[0.86]|[0.09]|[0.01]|[0.10]|[0.07]|
|Self-employed|-0.09**|-0.54**|-0.12|-7.53***|-4.78*|
||(0.04)|(0.24)|(0.13)|(2.84)|(2.60)|
||[0.07]|[0.07]|[0.42]|[0.07]|[0.10]|
|Phone triweekly*Self-employed|0.13**|0.70*|0.04|8.39**|3.50|
||(0.06)|(0.36)|(0.23)|(4.13)|(3.70)|
||[0.07]|[0.09]|[0.86]|[0.09]|[0.42]|
|Mean wage employed phone||||||
|weekly|0.81|4.24|5.26|37.85|46.97|
|Triweekly effect self-employed|0.13|0.17|-0.56|3.02|-2.78|
|p|0.00|0.50|0.00|0.31|0.30|
|<br>Number of observations|3220|3220|2530|3220|2530|
|Adjusted R2<br>Note: *** p<0.01, ** p<0.05, * p|0.015<br><0.10, standa|0.011<br>rd errors c|0.043<br>lustered by ho|0.014<br>usehold are|0.021<br>presented in|

Note: *** p<0.01, ** p<0.05, * p<0.10, standard errors clustered by household are presented in parentheses. Multiple testing adjusted FDR-adjusted q-values are presented in square brackets. The omitted category are individuals who were working at baseline and completed weekly in person interviews. Regressions are attrition weighted (with weights corresponding to 1/number of weeks for which we have data for each individual). All regressions include week of interview dummies.

26
**Appendix 4. Data** **<u>Used to Calibrate the Model (continued)</u>**

|Variable/parameter|Symbol|2010/11|2011/12|<br>Unit|Source|
|---|---|---|---|---|---|
|PRICES||||||
|Price of gasoline (volumetric)|PG|1.05|1.10|$/liter|ANP|
|Fuel price (volumetric)|PF'|2.47|2.66|$/liter|ANP|
|Fuel price (energy)|PF|2.68|2.88|$/GEEL|PF= PF'/λF|
|Price of anhydrous ethanol (volumetric)|PA'|1.18|1.42|$/liter|CEPEA|
|Price of anhydrous ethanol (energy)|PA|1.76|2.12|$/GEEL|PA= PA'/λA|
|Price of hydrous ethanol (volumetric)|PH'|0.96|1.19|$/liter|CEPEA|
|Price of hydrous ethanol (energy)|PH|1.43|1.78|$/GEEL|PH= PH'/λH|
|Consumer price of hydrous ethanol (volumetric)|PE100'|1.54|1.88|$/liter|ANP|
|Consumer price of hydrous ethanol (energy)|PE100|2.29|2.80|$/GEEL|PE100= PE100'/λH|
|Price of sugarcane|PSC|56.11|67.83|$/tonne|IEA|
|Price of sugar|PS|700.93|884.00|$/tonne|ERS|
|Marketing margin for fuel (volumetric)|mF'|0.41|0.53|$/liter|mF' = PF' -αv(PA' + tA') - (1 -αv)(PG+ tG)|
|Marketing margin for fuel (energy)|mF|0.44|0.57|$/GEEL|mF' = PF-αE(PA+ tA) - (1 -αE)(PG+ tG)|
|Marketing margin for E100 ethanol (volumetric)|mE100'|0.31|0.39|$/liter|mE100' = PE100' - PH' - tH'|
|Marketing margin for E100 ethanol (energy)|mE100|0.47|0.57|$/GEEL|mE100= PE100- PH- tH|
|Price gap between fuel and hydrous ethanol|θ|0.393|0.082|$/GEEL|θ = PF- PE100|
|QUANTITIES||||||
|Consumption of anhydrous ethanol (volumetric)|CA'|7.51|8.49|billion liters|CA' = DF' - CG|
|Consumption of anhydrous ethanol (energy)|CA|5.03|5.69|billion GEELs|CA= λACA'|
|Consumption of hydrous ethanol (volumetric)|DH'|15.29|10.19|billion liters|UNICA|
|Consumption of hydrous ethanol (energy)|DH|10.24|6.83|billion GEELs|DH= λHDH'|
|Consumption of gasoline (volumetric)|CG|22.99|28.31|billion liters|UNICA|
|Consumption of fuel (volumetric)|DF'|30.50|36.80|billion liters|UNICA|
|Consumption of fuel (energy)|DF|28.02|34.00|billion GEELs|DF= λFDF'|
|Production of sugarcane|SSC|0.62|0.57|billion tonnes|CONAB|
|Production of anhydrous ethanol (volumetric)|QA'|8.32|8.59|billion liters|UNICA|
|Production of anhydrous ethanol (energy)|QA|5.58|5.76|billion GEELs|QA= λAQA'|
|Production of hydrous ethanol (volumetric)|QH'|19.05|14.05|billion liters|UNICA|
|Production of hydrous ethanol (energy)|QH|12.77|9.42|billion GEELs|QH= λHQH'|
|Residual anhydrous ethanol (volumetric)|IA'|0.40|1.22|billion liters|UNICA|
|Residual anhydrous ethanol (energy)|IA|0.27|0.82|billion GEELs|IA= λAIA'|
|Residual hydrous ethanol (volumetric)|IH'|0.68|1.62|billion liters|UNICA|
|Residual hydrous ethanol (energy)|IH|0.46|1.09|billion GEELs|IH= λHIH'|
|Quantity of sugarcane devoted to sugar production|C<sup>S</sup><br>SC|0.29|0.28|billion tonnes|Derived from equation (8)|
|Domestic consumption of sugar|D<sup>D</sup><br>S|0.01|0.01|billion tonnes|D<sup>D</sup><br>S<sup>= (1-σ)φ</sup>S<sup>CS</sup><br>SC|
|Foreign consumption of sugar|D<sup>W</sup><br>S|0.03|0.02|billion tonnes|D<sup>W</sup><br>S <sup>= σφ</sup>S<sup>CS</sup><br>SC|

Note: The data sources are documented below.

46
treatment as dependent on the set of X variables listed in Appendix C.<sup>16</sup>

**Table 6. Characteristics of Program and Non-Program Samples at Baseline, Philippines**

**<u>ECD Study</u>**

|Level of Aggregation|Program|Non-program|
|---|---|---|
|**_Municipality_**|||
|Municipality in poorest income class|63.0|69.6|
|_Municipal Mayor:_|||
|Has completed college+ level of schooling|17.5|40.2*|
|Born in sample municipality|77.1|51.8|
|Belongs to civic or political group|95.1|97.3|
|_ECD and Health Services per 1000 population_|||
|Doctor|.032|.033|
|Nurse|.078|.077|
|Midwife|.271|.226*|
|Barangay Health workers|3.24|3.75|
|**_Barangay_**|||
|_Barangay Captain_|||
|Years of schooling|11.8|11.3|
|Born in sample barangay|68.1|56.2|
|Belongs to civic or political group|80.1|81.8|
|_ECD and Health Services per 1000 population_|||
|Health Centers|.39|.629|
|Public Hospitals|.022|.117|
|Private Hospitals|.021|.117|
|Day Care Centers|.699|1.3*|
|Public Elementary Schools|.447|.722|
|_Other infrastructure in barangay:_|||
|Market|32.9|8.0*|
|Piped water connection|32.7|33.7|
|Paved Roads|36.1|39.2|
|**_Household_**|||
|Piped water|27.6|32.5|
|<br>Flush/water seal toilet|58.2|57.3|
|Mean Income (SE)|69,174 (5,502)|59,792 (4,646)|
|Number of rooms|2.66|2.67|
|Electricity available|57.0|45.8*|
|Minutes to nearest road|7.2|9.6|

________________ 16 The non-program area, particularly Region 8, would be a less desirable control group, conditional on matching children on X, if there were important ongoing ECD-related activities (e.g., related to external donors or non-governmental organizations) distinct from the government’s ECD package of services. However, only four of the 96 barangays in the non-program region report that there are other such funding agencies providing assistance to them (OPS 2005). This affirms the comparison between the ECD program and the non-program areas as being one between areas that have received the program inputs and those that have not received these inputs or any other significant ECD-related interventions.

16
data since many other changes (eg, general trade and investment liberalization) also impact agriculture in the period under review. Nevertheless, aggregate data provide a context for discussions about productivity and production for maize and other selected outputs.

# **4.1** Overview **of Agricultural Growth 1961 to 1990s**

Growth Indices: FAO indices for agriculture, crop, and livestock production in Turkey (see Figure 4.1) show little change in rate of growth from years before to years after 1980. For all three indices, average rates of growth for 1961-63 to 1978-81 fall in the range 2.2-3.2 percent per year, while from 1979-81 to 1991-93, growth rates ranged from 2.3-2.5 percent per year.

Inputs and Outputs: Aggregate data mask important changes in source of growth. Table 4.1 shows that fertilizer use and tractor numbers grew at double digit annual rates in the 1960s and 1970s, declining to 2.9 and 4.4 percent, respectively, from 1981. This suggests that new varieties and other new technology (improved planting equipment, new pesticides, better match of fertilizers to soil deficiencies) contributed relatively more after 1980 than before. Irrigation has not had a major impact on growth; latest figures report only 7 percent of cultivated area irrigation.

Aggregate data also mask important differences in rate of growth for various outputs. Table 4.1 shows production for maize, sunflower, soybeans, chickpeas, potatoes, and tomatoes growing significantly faster after 1980 than FAO's crop index. Among animal products, poultry meat and egg production grow much faster than FAO's livestock production index both before and after 1980 (see Figure 4.5).

Agricultural Terms of Trade and Other Factors: During the 1980s, government commitment to industrial development led to policies that squeezed agriculture, so that "agriculture was taxed in aggregate rather than subsidized as in the 1970s" (WB 1990, vol 1, p 3). Despite many liberalizing reforms, government continued export restrictions on agricultural outputs, forcing domestic prices below world prices, ensuring cheap food and inputs for the rest of the economy. During the 1990s, policy-based movements in domestic input and output prices have reduced if not eliminated the 1980s bias against agriculture.

Other factors affecting agricultural performance in the years after regulatory reforms include weather (eg, drought in 1993 and 1994) and wars in Irag and the former Yugoslavia that interfered with sales to Iraq and Western Europe.

# **4.2 Benefits through Selected Outputs**

For maize and sunflower, we use international prices to calculate economic benefits from actual production compared to a counter-factual estimate of what would have happened without reform. For several other outputs we present data to show that reform has had an impact, but we do not estimate any monetary estimate of the magnitude of that impact.

For many agricultural commodities, Turkey's farm-level prices have been somewhat higher than import-parity prices over most of the last several decades. Taken alone, this suggests that Turkey

-21-
particular reason being remarkably important in all three categories. The market factors and client relationship are slightly more important than other items.

**Table 18: Main reason for locating in the city (%)**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Home-base|29|5|53|27|
|Infrastructure|14|25|7|16|
|Hub|14|15|7|12|
|Proximity to markets/production base|22|35|33|31|
|Client relationship|14|60|27|37|
|Culture amenity|14|25|13|18|
|Government|43|-|7|14|
|International city|-|5|7|4|
|Exposure|-|-|-|-|
|Other|7|30|27|22|
|_Shanghai_|||||
|Home-base|29|-|20|18|
|Infrastructure|14|-|5|8|
|Hub|10|11|20|14|
|Proximity to markets/production base|24|33|30|28|
|Client relationship|19|22|30|24|
|Culture amenity|5|11|10|8|
|Government|24|22|10|18|
|International city|10|33|15|16|
|fouse by media|10|11|15|12|
|other|29|67|30|34|

_Note_ : The percents may not add to 100% as multiple responses are included.

Table 19 shows how the firms assessed the business environment of their current locations when the interview was conducted. Most of the firms were satisfied with their current locations for business purposes. Some firms considered relocating elsewhere, but do not intend to move away, but to find a better location within the same city.

31
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places. We find no evidence that built-up cells are located at more or less regular distances from each other. The same is true for cells located along a road – where distance better represents travel cost and time using road transport. Why this is the case is unclear: after all, any look at a map or a Google Earth picture indicates that towns are seldom located next to each other. But in practice, small differences in terrain and obstacles to travel seem to offer enough disruption to blur the distance regularity predicted by theory. This is true even in Ghana where the terrain is fairly uniform, compared to other parts of the world, and lacks major rivers other than the Volta.

A related strand of literature has sought to characterized the size distribution of cities, taking Zipf’s Law as starting point (Gabaix 1999, Arshad et al. 2018). While some evidence has been offered that city sizes follow an exponential distribution, more recent evidence has cast doubt on the idea, arguing that the distribution of city sizes diverges from Zipf’s Law when more complete data are used at a higher degree of disaggregation. We contribute to this literature by showing that at the very smallest level – i.e., partial buildup of a small geographical cell, Zipf’s Law certainly does not apply: the log of the buildup proportion of cells is far from forming a linear relationship with the log of the rank of that buildup proportion. We see this as extending earlier work to the lowest level of geographical disaggregation.

We start in section 2 by explaining how the data are constructed. A brief conceptual framework is introduced in Section 3. Empirical results are presented in Section 4.

# **2 Data**

Our main variable of interest _yit_ measures the proportion of a cell _i_ in year _t_ that is covered by buildings, as inferred from a high-definition satellite image of Ghana. This variable comes from the Global Human Settlement Layer (GHSL) data that have been put together by the European Commission, Joint Research Centre. A Mollweide projection was used to divide the earth’s surface into square cells of approximately 250 by 250 meters, which is rather small for an analysis of builtup area. The proportion of buildings on each cell was predicted using a machine learning algorithm trained on observations of actual buildings made on the ground (see Pesaresi et al. 2016 for details). This algorithm predicts buildup in squares of 30x30 meters. These predictions are then aggregated using GIS into 250x250m cells, thereby generating an accurate prediction of the proportion of that 5
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