rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Table A.3 Definition of variables used from weekly/triweekly interviews</mark>**

|Variable|Definition|
|---|---|
|Phone weekly|Dummy variable = 1 if respondent was assigned to weekly phone<br>interview treatment|
|In person weekly|Dummy variable = 1 if respondent was assigned to weekly in person<br>interview treatment|
|Phone triweekly|Dummy variable = 1 if respondent was assigned to triweekly phone<br>interview treatment|
|Working|Dummy variable = 1 if respondent completed work over the reference<br>period|
|Days|Days worked per week|
|Days conditional on work|Days worked per week conditional on having reported any work that<br>week|
|Hours|Hours worked over the reference period (e.g. in case the reference<br>period is a week it is number of days worked per week X number of<br>hours worked per day; in case the reference period is a day it is the<br>number of hours worked that day)|
|Hours conditional on work|Hours worked per week over the reference period (if the reference<br>period is a week the number of hours conditional on work is calculated<br>by multiplying number of days worked per week times number of<br>hours worked per day; if it is per day it is simply the number of hours)<br>conditional on havingreported anywork duringthe referenceperiod|

|**Table A.4 Definition of variables used from**<br>Variable|**endline survey**<br>Definition|
|---|---|
|Self-employed|Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed|Dummy variable = 1 if primary work activity is wage-employment, 0 if<br>primary work activity is self-employment, out of labor force, or<br>unemployed|
|**Table A.5 Definition of variables used from**|**3-month follow up survey**|
|Variable|Definition|
|Fixed Location Outside of the House (FLOH)|(Self-employed only) Dummy variable=1 if respondent’s place of work is<br>outside of the home in a permanent structure, 0 otherwise (e.g. when<br>the respondents works either at home or in a mobile location outside of<br>the home).|
|Informal|(Wage employees only) Wage employees who do not get paid for doing<br>overtime, do not have a pension, do not get paid sick leave, nor paid<br>holidays. This definition of informality is ad hoc but allows us to identify<br>roughlyeven-sizedgroups of informal and formal wage workers.|

29
31 With perfect foresight the DES subsidy immediately translates into a higher price of equity q. The higher equity price, in turn, fosters equity investment. The government fixes the subsidy so as to encourage enough equity investment to repurchase the desired amount of debt. With constant returns to scale, once the subsidy is cut q drops back to one and the economy reaches a new steady state with a higher level of equity.

With decreasing returns to scale, as equity grows (following the subsidy announcement) the physical return on capital drops and, thus, drives down the price of equity. Once q falls below one it induces equity disinvestment, e.g., via depreciation, up to the point where the equity stock returns to its initial level and the price of equity becomes one again. At the new equilibrium the share of foreigners in domestic equity is likely to increase.

# 6. Some Applications to Latin America.

To illustrate the model result about the DES impact on the steady state price of debt we parametrize inequality (27) employing data from Argentina, Brazil, Chile and Mexico. According to the model, a positive (negative) sign of the inequality implies that DES raise (lower) the steady state price of sovereign debt. Table 3 provides the parameter values and the estimated value of inequality (27) for each country.

To approximate the parameter t */f we employ the average trade surplusdebt ratio of 1986-87. The parameter sf/f is measured by the ratio of the stock of foreign investment to total foreign debt for the last year for which foreign investment stock figures were available. The parameter gr is
|Mean dependent variable|0.807|4.279|37.660|
|---|---|---|---|
|Observations|3,803|3,800|3,800|
|R‐squared|0.514|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which they were planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. All specifications include individual fixed effects. Columns 1 and 4 are estimated by OLS and columns 2, 3, 5, and 6 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
consumption rule which charges households for a preset minimum volume of consumption even if they use less. Alternatively, utilities require households to pay a fixed monthly charge on top of the volumetric charge. At least half of the water and electricity utilities surveyed for this study apply a minimum consumption rule, a fixed charge, or both (Komives et al 2005).

A second reason the rates of subsidization for the poor and non-poor is similar is that the volume of consumption does not vary significantly among poor and non-poor households (esp. for water service). Figures 1 and 2 examine water and electricity consumption patterns by quintile in a variety of cases. The average consumption levels in the poorest quintiles are not as different from the middle quintiles as one would perhaps expect. In the electricity sector, the richer households tend to have significantly higher average consumption levels, while water consumption does not follow this pattern to the same degree.

Where consumption patterns of the poor and the non-poor do not differ substantially, the fifth and final determinant of benefit targeting performance – the ratio of the average quantity consumed by poor subsidy recipients to the average quantity consumed by all recipients --would have no effect on benefit incidence. When the poor do in fact consume less than the population as a whole (and when nearly all residential customers are subsidy recipients), this factor tends to _decrease_ the size of the total subsidy received by poor recipients relative to the total subsidy received by the population as a whole. Ironically, this means that the less the poor consume relative to the non-poor the _less likely_ the subsidy is to deliver the bulk of the benefits to the poor.

**Figure 1. Electricity consumption by quintile**

<!-- Start of picture text --> 350.0<br>300.0<br>Colombia, Bogota<br>250.0 Guatemala<br>Peru<br>200.0<br>Uruguay<br>M exico - CFE<br>150.0<br>Rwanda<br>100.0 Cape Verde<br>Sao Tome<br>50.0<br>Yemen<br>0.0<br>1 2 3 4 5<br>Quintile<br>kWh per month<br><!-- End of picture text --> 14
1997) which used similar surveys to analyze the effects of privatization in Central and Eastern European transition economies. The questionnaire was offered to general managers in Georgian (Modlovan respectively) and Russian, as a number of directors in both countries were still ethnic Russians and did not use the local language.

The survey in Georgia covered only enterprises located in Tbilisi (the capital city). This was because the Georgian Statistical Office (our counterpart in the survey) did not have regional branches and could not conduct the survey without large travel-related expenses. Since 72% of all manufacturing activity in Georgia in 1997 was concentrated in Tbilisi, the representativeness of the sample was not discredited significantly. The selection of enterprises in the main survey was based on the 1996 census of manufacturing firms in Tbilisi which covered 5,543 business entities. We chose only enterprises which reported more than 25 employees in 1991, i.e., we do not cover small enterprises or new private firms that started operations following 1991. This category comprised of 1,326 firms. We then calculated the number of firms in each manufacturing sector as a share of the sub-sample. We next multiplied this share by 100 (the desired sample size for the survey) to come to the target number of firms in each manufacturing sector. This process was designed to ensure a distribution of the enterprises in the survey sample that was representative of the industry mix in Tbilisi.

One hundred questionnaires were hand-delivered to General Directors in September, 1997. The survey team re-visited the enterprises in the first week of November, 1997 and collected the responses. In four cases the General Manager was either on sick leave or a business trip. Four other questionnaires were left unanswered since the enterprises were closed down and we excluded them from the subsequent analysis. The total number of surveys included in the analysis was 92. The survey asked managers to identify the main sector of activity of their enterprise. More than a third of all enterprises were in the food and beverage sector (35), the next largest sectors were apparel (13), and industrial machinery (13). The rest of the sample came from metals (9), chemicals (7), construction materials (6), pharmaceuticals (5), and wood and furniture (4).

7
# **Table 2.  Oil subsidies & CO2 emissions both directly & via CO2 consumption: Sequential G Estimation**

|**Dependent**||**CO2 emissions: full an**|**d demediated effects**||
|---|---|---|---|---|
|**Variable**|||||
||**Full sample**|**Full sample after**<br>**CO2-intensive**<br>**consumption is**<br>**demediated)**|**MENA oil group**<br>**subsample**|**MENA oil group**<br>**subsample after CO2-**<br>**intensive**<br>**consumption is**<br>**demediated**|
|**population**|0.00000433<sup>***</sup>|0.00000433<sup>***</sup>|0.00000990<sup>**</sup>|0.00000990<sup>**</sup>|
||(3.21)|(3.21)|(3.44)|(5.16)|
|**GDP**|-1.15e-10<sup>**</sup>|-1.15e-10<sup>***</sup>|1.54e-10|1.54e-10<sup>*</sup>|
||(-2.46)|(-4.01)|(1.61)|(2.88)|
|**CO2-intensive**|0.735<sup>***</sup>||0.545<sup>**</sup>||
|**consumption**|(8.03)||(3.26)||
|**subsidy share of**<br>|93195887.4|93195717.3|728710544.3<sup>**</sup>|728710648.8<sup>**</sup>|
|**GDP**|||||
||(0.52)|(0.51)|(3.71)|(3.27)|
|**lag subsidy share**<br>**of GDP**|-155731716.5|-155731727.8|571732359.3<sup>***</sup>|571733000.8<sup>***</sup>|
||(-1.12)|(-1.23)|(13.88)|(13.82)|
|**co2 emission via**|1.849|1.849|3.530|3.530<sup>*</sup>|
|**flaring**|||||
||(1.22)|(1.16)|(2.27)|(3.00)|
|**Constant**|-235.9|-235.9|-325.2<sup>*</sup>|-325.2<sup>**</sup>|
||(-1.54)|(-1.58)|(-3.10)|(-4.09)|
|**_N_**|271|271|32|32|

_t_ statistics in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. All regressions control for country & time fixed effect & use robust error. Using Sequential g estimation in fixed effects panel, by comparing the full model (columns 1 and 3) with demediated model (columns 2, 4), in which we remove the influence of co2-intesnive consumption, strong direct effect of oil subsidies on co2 remains, likely arising from other sources, e.g., manufacturing.

39
|Multi-Sector Demographic Project|Djibrilla Karamoko|
|---|---|
|Multi-Sector Demographic Project|Djibrilla Karamoko|
|Multi-Sector Demographic Project|Djibrilla Karamoko|
|Multi-Sector Demographic Project|Djibrilla Karamoko|
|Multi-Sector Demographic Project|Djibrilla Karamoko|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Land Registration|Gavin P. Adlington|
|Development PolicyLoan(DPL)|Erjon Luci|
|Development PolicyLoan(DPL)|Erjon Luci|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|Power Transmission Project in Support of the EnergySector Reform|Dmytro Glazkov|
|NATIONAL WATER SUPPLY & SANITATION|Hadji Huseynov|
|NATIONAL WATER SUPPLY & SANITATION|Hadji Huseynov|
|NATIONAL WATER SUPPLY & SANITATION|Hadji Huseynov|
|NATIONAL WATER SUPPLY & SANITATION|Hadji Huseynov|
|NATIONAL WATER SUPPLY & SANITATION|Hadji Huseynov|
|NATIONAL WATER SUPPLY & SANITATION|Hadji Huseynov|
Dehejia, Rajeev, and Roberta Gatti. 2005. “Child Labor and Agricultural Shocks.” _Journal of Development Economics_ 81 (1): 80–96.

De Janvry, Alain, Frederico Finan, Elizabeth Sadoulet, and Renos Vakis. 2006. “Can Conditional Cash Transfer Programs Serve as _Safety_ - _Nets_ in Keeping Children at School and from Working when Exposed to Shocks?” _Journal of Development Economics_ 79 (2): 349–73.

Duryea, Suzanne, David Lam, and Deborah Levison. 2007. “Effects of Economic Shocks on Children’s Employment and Schooling in Brazil.” _Journal of Development Economics_ 84 (1): 188–214.

Edmonds, Eric. 2006. “Child Labor and Schooling Responses to Anticipated Income in South Africa.” _Journal of Development Economics_ 81 (2): 386–414.

Ermisch, John F., and Marco Francesconi. 2001. “Family Structure and Children’s Achievements.” _Journal of Population Economics_ 14 (2): 249–70.

Evans, David, and Edward Miguel. 2007. “Orphans and Schooling in Africa: A Longitudinal Analysis.” _Demography_ 44 (1): 35–57.

Fiszbein, Ariel, Norbert Schady, Francesco Ferreira, Margaret Grosh, Niall Keleher, Pedro Olinto, and Emmanuel Skoufias. 2009. _Conditional Cash Transfers: Reducing Present and Future Poverty_ . Washington, DC: World Bank.

Gennetian, Lisa. 2005. “One or Two Parents? Half or Step Siblings? The Effect of Family Composition on Young Children.” _Journal of Population Economics_ 18 (3): 415–36.

28
# Comparison Between Real GNP and Good Paved Roads per Million Inhabitants (1984 and 1989)

<!-- Start of picture text --> $/capita km/mil. inhab<br>Botswana 1984 960 - 1770<br>1989 1600 - 1802<br>Mauritania 1984 450 289<br>1989 500 446<br>Ghana 1984 350 56<br>1989 390 113<br>Zaire 1984 140 E 14<br>1989 260 31<br>10000 1000 100 500 5000<br>GNP/Capita Good Paved Roads<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 9<br><!-- End of picture text -->
14 Colombia. By 1988 the interest/export ratio was not only lower than in the debt crisis year of 1982 but also either lower than or equal to the ratio in the pre-crisis year of 1981 for Brazil, Mexico, Chile, and Colombia. For Latin America as a whole, the ratio stood at the same lavel in 1988 (28 percent) as in 1981, and well below the peak 1982 level of 41 percent.<sup>6</sup> Even the absolute level of debt, and the ratio of debt to exports and goods and services (which fails to take account of the sharp decrease in the price of debt, the interest rate) showed moderating trends by 1988. As indicated in figure 3, in 1988 the dollar value of total external debt for Latin America as a whole fell (from $410 billion to $401 billion) as the consequence of debt-equity conversions and substantial discounted debt buybacks in the private sector. In real terms (deflating by US wholesale prices), by 1988 debt for the six countries stood only 13.5 percent above the 1982 level, for average annual growth of only 2.1 percent. And the ratio of debt to exports of goods and services declined from its 1986 peak for the six countries at 424 percent to only 339 percent, a level moderately lower than in 1982 (354 percent) and not far removed from the pre-crisis 1981 level (331 percent).

In view of these absolute and relative trends, the summary view presented in the World Bank's 1988 debt report was misleading: .. most of the indebted countries are still no better off than in 1982 -- when the debt crisis erupted. Debt disbursed and outstanding has doubled, and debt service 6 ECLA 1988, table 17.
# **Table A10: Impacts of mental health on labor outcomes: Impacts of mental health on labor outcomes – Dummy indicator**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Serious mental illness|-0.064***<br>(0.014)|-0.423***<br>(0.159)|
|_First stage of 2SLS (dependent va_<br>Trauma experience*Year after<br>arrival|_riable is serious me_<br>-0.023***<br>(0.005)|_ntal illness)_<br>-0.026***<br>(0.010)|
|Kleibergen-Paap F stat.|17.312|16.988|
|AR 95-CIs|[-0.304, -0.039]|[-1.414, -0.244]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,687|
|Numberof individuals|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

42
<!-- Start of picture text --> 3.5<br>_x<br>gy 30<br><=S25<br><<br>a 2.0<br>=<br>©315<br>-1s)<br>4 ee .<br>o<br>0.0 oe ee we tl<br>252SSL<ELSSEFFESILEERgREeeseEESE RBESERER= = cc SSSCRSESESERPSERSESes bof)<br>sete sSseeBZBdeEtrstzroarezassSttzet<br>Osc2:4a Be eo bELVIz > ¢ a o-<br>= osoc SFo oOa= zs afanZo =4> 5<br>Y = = a<br>Ss x<br>ao Ss<br>Oo<br><!-- End of picture text -->
# **A Note on Rising Food Prices**<sup>**1**</sup>

Donald Mitchell<sup>2</sup>

## **I. Introduction**

Internationally traded food commodities prices have increased sharply since 2002 and especially since late-2006, and prices of major staples, such as grains and oilseeds,<sup>3</sup> have doubled in just the past two years. Rising prices have caused food riots in several countries and led to policy actions such as the banning of grain and other food exports by a number of countries and tariff reductions on imported foods in others. The policy actions reflect the concern of governments about the impact of food price increases on the poor in developing countries who, on average, spend half of their household incomes on food. This paper examines how internationally traded food commodities prices (maize, wheat, rice, soybeans, etc.) have changed, and analyzes the factors contributing to these increases. In particular, it looks at the contribution of biofuels production to food price increases. In this paper biofuels refer to ethanol and biodiesel.<sup>4</sup>

## **II. The rise in global food prices**

**The IMF’s index of internationally traded food commodities prices**<sup>**5**</sup> **increased 130 percent from January 2002 to June 2008 and 56 percent from January 2007 to June 2008 (Figure 1).** Prior to that, food commodities prices had been relatively stable after reaching lows in 2000 and 2001 following the Asia financial crisis. The low levels of global grain stocks had been identified as a cause for concern in a number of fora<sup>6</sup> and the risk of higher food prices was highlighted in a recent World Bank publication<sup>7</sup> and online.<sup>8</sup>

> 1 The views expressed in this paper are those of the author and should not be attributed to the World Bank or its Executive Directors.

> 2 Lead Economist, Development Prospects Group (DECPG), World Bank, Washington. Comments should be sent to <u>dmitchell@worldbank.org.</u> Thanks are expressed to Hans Timmer, Ron Steenblik, Harry de Gorter, and Masami Kojima for useful comments. Any remaining errors or omissions are the sole responsibility of the author.

> 3 Oilseeds are crops with high oil content such as soybeans, rapeseed, sunflower, flax and cottonseed.

> 4 Ethanol is produced from sugar crops, such as sugar cane or beets, or starchy crops such as maize. Biodiesel is produced from vegetable oils or animal fats.

> 5  A nominal dollar index of food commodity prices using global export value weighs.

> 6 “Are we facing a food price spike”, session at Rural Week 2004, Mitchell and Le Vallee (2005) Food Price Variability in Global Markets.

> 7 Global Development Finance 2007, May 2007,

> 8 Mitchell, Donald “A coming spike in grain prices?” Focus Topic, April 2007.

2
Table 7: Household assets

||Dependent varia|ble = 1(Ever B|een Beaten)||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Work|0.186|0.214|0.115|0.136|0.530***|0.526***|
||[0.170]|[0.164]|[0.176]|[0.168]|[0.200]|[0.200]|
|Log(HH assets)|-0.017**||-0.015*||-0.017**||
||[0.009]||[0.008]||[0.008]||
|Work X Log(HH assets)|-0.016||-0.002||-0.012||
||[0.014]||[0.015]||[0.015]||
|Log(HH assets per capita)||-0.021**||-0.017**||-0.021**|
|||[0.009]||[0.009]||[0.009]|
|Work X Log(HH assets per capita)||-0.02||-0.004||-0.014|
|||[0.016]||[0.016]||[0.016]|
|Age First Marriage|||-0.015***|-0.014***|||
||||[0.005]|[0.005]|||
|Work X Age First Marriage|||-0.020**|-0.019**|||
||||[0.008]|[0.008]|||
|Education|||||-0.009|-0.008|
||||||[0.006]|[0.006]|
|Work X Education|||||-0.025**|-0.024*|
||||||[0.012]|[0.012]|
|Observations|1,324|1,324|1,324|1,324|1,323|1,323|

_Standard errors in brackets, clustered at the level of the village. *** p<0.01, ** p<0.05, * p<0.1. Household assets are the sum of the current value of agricultural land, homestead land (including house), other real estate, rickshaw, cart/van, cows/buffaloes/goats, fan, radio/cassette player, tv, bicycle, wall/table clock, furniture, sewing machine, freezer, mobile phone, and other assets_