rafmacalaba/datause-encoder-data huggingface dataset:
T **able 8: Investment in land improvement by** **<u>phase, year, and participation status</u>**

||<br>**Partic**|**Phase 1**<br>**ipant**||<br>**Partic**|**Phase 2**<br>**ipant**||**P**<br>**Partic**|**hase 3**<br>**ipant**||
|---|---|---|---|---|---|---|---|---|---|
||**No**|**Yes**|**Diff**|**No**|**Yes**|**Diff**|**No**|**Yes**|**Diff**|
|**Period 3 (2006–08)**||||||||||
|Invested|0.30|0.46|***|0.26|0.72|***|0.40|0.55||
|...with NREGS support|0.23|0.38|***|0.21|0.66|***|0.34|0.50||
|Number of observations|1,305|977||554|260||664|159||
|**Period 2 (2004–06)**||||||||||
|Invested|0.11|0.10||0.09|0.08||0.06|0.08||
|Number of observations|1,192|872||506|236||663|160||
|**Period 1 (2000–03)**||||||||||
|Invested|0.16|0.15||0.08|0.08||0.48|0.60||
|Number of observations|1,367|1,000||581|258||734|163||

_Source:_ Authors’ computation from household survey and administrative data.

_Notes:_ Survey periods are July 2006 to June 2008; Jan. 2004 to June 2006; and June 2000 to Dec. 2003. Significance level: *: 10%, **: 5%, ***: 1%.

23
Gregorio Impavido

# **Figure 3: Evolution of Average Underwriting Performance**

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>-<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br>LR OR UR<br>Source : CNSF<br><!-- End of picture text --> It is important to notice how the worsening underwriting performance of pension annuity companies in Mexico is not due to inefficiencies. The real reason for the worsening underwriting performance since 2002 is the drastic fall in gross premium income (the reasons for which are discussed in detail in section III) and the delayed cost structure adjustments that companies have introduced. The next table reports the evolution of gross premium income and underwriting costs divided in pure underwriting costs and costs related to premiums for additional benefits. It essentially reports the UR data showed in the previous figure.

**Table 15: Evolution of Average Underwriting Costs, MXN Million, 2001–2005**

||**2001**|**%**|**2002**|**%**|**20**<br>**03**|**%**|**4**<br>**200**|**%**|**2005**|**%**|
|---|---|---|---|---|---|---|---|---|---|---|
|GPI|16,640.59||10,261.26||3,501.71||5,224.7||4,504.33||
|Pure Underwriting|403.90|2.4|288<br>.70|2.8|173.9<br>6|5.<br>0|.5<br>162|3.1|203<br>.26|4.5|
|Additional Benefits|1,178.58|7.1|695.98|6.8|33<br>2.21|9.5|14<br>156.|3<br>.0|177.6<br>7|3.9|
|Total Underwriting|1,152.48|9.5|984.68|9.6|506.18|14.5|318.64|6.1|380.93|8.5|

**Notes** : 1/ Real 2005 pesos **Source** : CNSF

The sudden increase in the underwriting expense ratio (the same could be said as far as operational expenses, not reported in the table, are concerned) in 2003 was due to the sudden drop in premiums from 2002 onwards that was only partially offset by the decrease in underwriting costs over the same period. Gross premium income in 2003 was 34 percent of gross premium income of 2002. Pure underwriting costs (agent fees) in 2003 were only 60 percent of the value of 2002 while premiums for additional benefits decreased in 2003 to 48 percent the values of 2002. In other words, companies were

- 22 -
Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project. The IHS3 data were collected within a two-stage cluster sampling design, and are representative at the national, urban/rural, regional, and district levels, covering 12,271 households in 768 enumeration areas (EAs). The IHS3 instruments included Household, Agriculture, Fishery, and Community Questionnaires.

All sample households were administered the multi-topic Household Questionnaire. The sample households that were involved in agricultural activities (through ownership and/or cultivation of land, and/or ownership of livestock) were administered the Agriculture Questionnaire. The Agriculture Questionnaire solicited information on land areas, physical characteristics, labor and non-labor input use, and crop cultivation and production at the plot level, separately for the reference rainy and dry seasons. Table C1 in the electronic Appendix C presents the definitions of the variables used in the analysis.

# 5. RESULTS

# 5.1 DESCRIPTION OF GENDER DIFFERENCES

The descriptive statistics and the results from the tests of mean differences sorted by the head of household’s gender are presented in Table 1. The full sample consists of 14,044 plots managed by the head of household, 23 percent of them are female headed households. The sample has been restricted to include only plots that are managed by the head of household and in which the manager works at least one hour per day on the plot. In Malawi where households cultivate an average of 1.8 plots, most of the plots (94%) are managed by the head of household. Only 6.8% 19
_5_ accounted for a once-and-for-all decline in prices of about 40%. Figure 1 presents the evolution of the Grilli-Yang commodity price index, which has been updated to 1992.4 All the above papers tested the secular decline in commodity prices using a simple trend and cycle model, which assumes that prices, P,, can be appropriately characterized by a TS representation of the form:

## _LogP, = PT, + ARMA(p,q)e,_

where T, is a detenninistic trend variable, e, is an i.i.d. random shock and parameter B can be estimated by standard econometric procedures. The mixed autoregressive moving average (ARMA) term for the residual, used by Grilli and Yang (1988) and Sapsford, Sarkar and Singer (1992), eliminates the potential misspecification caused by higher-order serial correlation. In this model the only information required to predict the long-mn evolution of the price is its mean growth rate (ps) because shocks, being totally transitory, do not affect long-run forecasts.

This methodology has been criticized after the work of Dickey and Fuller (1981) and Nelson and Plosser (1982), which suggest that the implicit assumption of stationarity is frquently rejected by economic data. If the variable is non-stationary, standard econometric procedures cannot be applied directly because the estimated parameter ps, although not biased, has a non-trivial distribution that invalidates inferences on it. Nevertheless, the first difference of prices may conform to a DS model of the form:

# _ALogPt =_ PI _+ ARMA(p',q')1t,_

where A is the first-difference operator and parameter ps' is now the mean growth rate of the variable. The presence of 1,, an i.i.d. random variable, will induce the trend of the level of commodity prices to have a stochastic behavior; hence, an important implication of this

> ' The original Grilli-Yang index comprises 24 commodities (aluminum, bananas, beef, cocoa, coffee, copper, cotton, hides, jute, lamb, lead, maiz, palm oil, rice, rubber, silver, tea, timber, tin, tobacco, wheat, wool and zinc), weighted by their share iu world exports in the 1977-79 period. Appendix 1 describes the data. Cuddington (1992) expanded the database to include oil and coal. In this paper, the original Grilli-Yang data was extended to include the 1987-92 period; unformmately, data for oil and coal was not available.
**Table 2. Difference in share of the population living below the national poverty line between the lean season and non-lean season waves**

||Burkin|a Faso|Ni|ger|Sen|egal|
|---|---|---|---|---|---|---|
||No controls|Full controls|No controls|Full controls|No controls|Full controls|
|Lean season<br>wave|0.1369***|0.1158***|0.0659**|0.0697***|0.0810***|0.0816***|
||(0.0332)|(0.0206)|(0.0274)|(0.0199)|(0.0306)|(0.0164)|
|N|7017|6936|6020|5919|7153|7107|
|R-squared|0.0198|0.3093|0.0045|0.2421|0.0071|0.3441|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the ‘grappe’ level are in parentheses. Dependent variable is a dummy variable taking 1 if a household’s consumption level is below the national poverty line and 0 otherwise. Individual-level sampling weights are applied. ‘Full controls’ specifications include controls for the gender, age, and education level of the household head, household size, the dependency ratio, household walls material, household roof material, household floor material, access to electricity, access to improved drinking water, and access to improved sanitation as well as an urban-rural dummy and region fixed effects. Source: EHCVM and World Bank estimates.

Month-by-month results suggest that the effects of seasonality may be even larger than the estimates above, which simply compare the lean and non-lean season waves of the EHCVM. To show this, we modify Equation 1 but rather than including a single dummy variable for the lean season as a regressor, we include four dummies that capture whether or not the date of the interview was in April, May, June, or July of 2019. The base category remains the non-lean season wave. For this model, the specification with household and location controls is preferred because the EHCVM is not designed to be representative at the month level, so there could be systematic differences in the sub-samples from April, May, June, and July. Pooling the data from Burkina Faso, Niger, and Senegal, the drop in consumption compared to the non-lean season wave increases between April and July, although this is driven principally by Burkina Faso and Niger (Table 3). This corroborates the notion that the agricultural lean typically continues to September or October each year and could still be yet to reach its peak in July; the effects of seasonality may therefore be understated by simply comparing the lean and non-lean season waves.

14
Policy Research Working Paper 8223

# **Abstract**

This paper evaluates the early impact of introducing a performance component into Jakarta’s school grant program on learning outcomes. Using administrative data, it applies difference-in-differences and regression discontinuity approaches to identify the impact of the grant by exploiting differences in program coverage over time, as well as by comparing changes in test scores between schools that received the additional performance award with schools that did not. The paper finds that the introduction of the performance component had different impacts on government primary and junior secondary schools. The program improved learning outcomes for primary schools at the bottom of the performance distribution and narrowed performance gaps across schools. However, improvements in equity were also driven by negative impacts of the program on better performing primary schools. Overall, the program reduced primary examination scores albeit by a small amount. In contrast to the results at the primary level, the performance component improved examination scores in government junior secondary schools. However, the impact seemed to be greatest among better performing schools and has therefore widened performance gaps. The findings also suggest that program impact was largely through competition between schools to receive the performance component. There is little evidence that the additional resources schools received from the award had any additional impact. The evaluation utilized preexisting administrative data and the paper offers some suggestions on how education information systems can be strengthened to create more robust feedback loops between research and policy.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2018: LEARNING to Realize Education’s Promise_ —is a product of the Education Global Practice Group. 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 salsamarrai@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 A11: Effects of temperature on agriculture**

|Crop yield|Ric|e|Ma|ize|Soy|bean|W|heat|
|---|---|---|---|---|---|---|---|---|
||Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Temperature|-0.197***|-0.111***|-0.183***|-0.115***|-0.042***|-0.041***|0.010|-0.000|
||(0.021)|(0.014)|(0.013)|(0.008)|(0.011)|(0.011)|(0.014)|(0.010)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|No|Yes|No|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|Yes|No|Yes|No|
|Mean crop yield<br>(tonnes/hectare)|3.215|3.215|2.412|2.412|1.719|1.719|3.350|3.350|
|Number of countries|74|74|101|100|33|33|90|90|
|Number of regions|660|660|955|955|189|189|670|670|
|Observations|8,566|660|12,392|955|2,452|189|8,663|670|
|Equality test (Panel vs.<br>long differences)|p = 0.|000|p = 0|.000|p =|0.000|p =|0.628|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Crop yield data is provided by Iizumi and Sakai (2020). *** p<0.01, ** p<0.05, * p<0.1 59
52 The Iraqi government frequently carries out summary executions, enforced disappearances, and mutilation of corpses. Government‐affiliated institutions unlawfully detain citizens after screening hundreds of families. Deportation of residents and destruction of Arab homes or even villages occur. Internally displaced people are being restricted in their movement; they are not allowed to travel to a certain region of Iraq and the disputed territories and are required to stay in camps<sup>79</sup> .

Iraq’s largely state‐run economy is dominated by the oil sector. According to the CIA’s Factbook<sup>80</sup> , oil sector provides “more than 90% of government revenue and 80% of foreign exchange earnings.”

# Economic Opportunity

With the improvement of security and stability in Iraq, its GDP annual growth exceeded 10% in 2016, which is the best performance in a decade. Iraq also was able to lower its unemployment rate from the highest 28% to about 15 % in the past few years<sup>81</sup> . Although government revenue relies heavily on the oil sector, only a small percentage of Iraqis are employed in the sector while the majority of Iraqis still report living in poverty<sup>82</sup> . Lack of essential services like electricity, clean water, sanitation, and health services is also a major concern for refugee’s return. Detailed statistics on Iraqi refugee returnees is not yet available, but media report that small but growing influx of returnees is going back to Iraq due to asylum rejections in Europe.

# **<u>Myanmar</u>**

According to UNHCR<sup>83</sup> , the total estimated population of concern in Myanmar is about 1.3 million, including 926,000 Stateless people and 375,000 IDPs. Myanmar’s nascent democracy and its newly‐elected government in 2016, faces major political and economic challenges. These include furthering the national reconciliation process, strengthening respect for human rights, protecting civilians in armed conflict and addressing some deeply rooted divisions within the country<sup>84</sup> . Refugees from South‐East Myanmar that are residing in Thailand and the region’s IDPs have still not returned in number. The Northeast region of Myanmar is still mired in fighting between military and ethnic armed groups. People in Rakhine State in Myanmar are still in danger of armed attacks and lockdowns. Operations by Myanmar security forces had led to 43,000 stateless Muslims to flee to Bangladesh and displaced estimated 30,000 within Maungdaw district.

EIU<sup>85</sup> predicts that Myanmar will achieve real GDP growth to average 7.3% a year, continuing to be one of the faster‐growing economies in Asia.

> 79 ‐ ‐ Human Rights Watch, Iraq, <u>https://www.hrw.org/world report/2017/country chapters/iraq</u> 80 CIA World Factbook https://www.cia.gov/library/publications/the‐world‐factbook/geos/iz.html

> 81 Trading Economics, <u>https://tradingeconomics.com/iraq/unemployment‐rate</u>

> 82 UNDP <u>http://www.iq.undp.org/content/iraq/en/home/countryinfo.html</u>

> 83 UNHCR, Myanmar, http://reporting.unhcr.org/node/2541

> 84 UNHCR, Iraq, <u>http://reporting.unhcr.org/node/2547</u>

> 85 EIU, Myanmar, Key development, July 6th 2017
**Table 1: Variables in the district level data base for Peru**

|**Variable**|**Unit**|**Source**|
|---|---|---|
|Total population per district|-|2005 National Census –<br>INEI Peru|
|Urbanization rate<br>(Percentage of population living in<br>urbanareas)|%|2005 National Census –<br>INEI Peru|
|Literacy rate<br>(Percentage of the adult population<br>that can read and write)|%|Human Development<br>Report – Peru 2006|
|Life expectancy|Years|Human Development<br>Report– Peru2006|
|Per capita income|Nuevos Soles<br>per month|Human Development<br>Report– Peru2006|
|Latitude|Decimal degrees|Department of Energy and<br>Mines – Peru<br>GoogleEarth|
|Longitude|Decimal degrees|Department of Energy and<br>Mines – Peru<br>Google Earth|
|Elevation|Kilometers<br>above sea level|Data Bank of District<br>Information–INEI Peru|
|Normalaverage annualtemperature|Degrees Celsius|World Climate|
|Normal annual rainfall|Milimeters|New et.al (2002): 10’<br>latitude/longitude data set<br>of mean monthly surface<br>climate over global land<br>areas|

As we did not have meteorological data for each and every district in Peru, this information was estimated. Since average annual temperature in any particular location depends principally on distance from the equator and elevation above sea-level, we estimated a simple model (see Table 2) using information on average annual temperature, latitude, and altitude for all the Peruvian stations for which we could obtain “normal” temperature data (from www.worldclimate.org).

**Table 2: Model used to estimate temperature** **<u>in districts with missing data</u>**

|**Variable**|**Coefficient**|**t-value**|**P-value**|
|---|---|---|---|
|Elevation|-2.1518|-4.48|0.0000|
|Latitude|-0.3883|-8.87|0.0000|
|Constant|27.2469|17.25|0.0000|
|**No. of observations**<br>**R**<sup>**2**</sup>|27<br>0.7669|||

5
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

# Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

- Republique du Mali. 2010. _Note sur le Secteur des Télécommunications au Mali: Etude des situations de concurrence sur le marché des Télécommunications au Mali_ . Technical assistance report. Bamako, Mali.

# Irrigation

FAO (Food and Agriculture Organization). 2004. ―Aquastata: Senegal Country Profile.‖ www.fao.org/nr/water/aquastat/main/index.stm.

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Briceño-Garmendia, Cecilia, and Maria Shkaratan. 2010. ―Power Tariffs: Caught between Cost Recovery and Affordability.‖ Working Paper, First draft, February 2010.

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank,

47
# **Table A3: Alternative measure of poverty – Multidimensional poverty**

||||Percen<br>|tage ofpopulatio|n deprived|||
|---|---|---|---|---|---|---|---|
||Monetary<br>poverty|Educational<br>attainment|Educational<br>enrolment|Electricity|Sanitation|Drinking water|Headcount<br>ratio|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Temperature|0.737***|0.726**|0.119|1.137***|0.682*|0.097|1.374***|
||(0.203)|(0.282)|(0.172)|(0.355)|(0.373)|(0.199)|(0.395)|
|Precipitation|-0.011|-0.192***|0.049|0.509**|0.461**|-0.133|-0.155|
||(0.097)|(0.064)|(0.044)|(0.210)|(0.200)|(0.117)|(0.120)|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,478|2,464|2,260|2,437|2,315|2,321|2,478|
|R-squared|0.419|0.089|0.232|0.259|0.146|0.047|0.412|
|Number of regions|1,179|1,179|1,109|1,172|1,163|1,169|1,179|
|Mean headcount<br>poverty rate (percent)|7.651|10.51|3.516|7.311|21.83|7.909|11.31|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Column (1) measures the percentage of the population living on less than $2.15 a day at 2017 international prices, Column (2) measures the percentage of population deprived of primary educational attainment; Column (3) measures the percentage of population deprived of school enrolment; Column (4) measures the percentage of population deprived of electricity; Column (5) measures the percentage of population deprived of sanitation; Column (6) measures the percentage of population deprived of drinking water; Column (7) is the share of people who are considered multidimensionally deprived. *** p<0.01, ** p<0.05, * p<0.1 50