rafmacalaba/datause-encoder-data huggingface dataset:
# **_B. Surveys_**

The data set includes a face-to-face survey, two mobile phone surveys (baseline and end line), and call-in data collected in 2016. While the face-to-face baseline survey was conducted during March and the first two weeks of April, the baseline mobile phone survey was implemented in the last two weeks of April. The call-in data was recorded throughout the intervention period (May), and the end line mobile phone survey was carried out in June.

Prior to the baseline face-to-face survey, households were randomly assigned to a long survey or short survey.<sup>20,21</sup> The long survey collects comprehensive household and individual-level information, while the short survey includes only very basic listing information. Short survey participants were invited by the enumerator at the end of the face-to-face interview to participate in the second part of the baseline survey, which would be conducted shortly after the face-to-face interview via mobile phone using interactive voice response (IVR) technology.

Mobile phone surveys are required to be concise in order to achieve satisfactory response rates, usually limiting the number of questions to roughly ten. The mobile phone survey is restricted to the outcome variables described below. Enumerators briefly trained households in the use of the technology by conducting mock surveys and providing a written summary of instructions. Senegal is classified as a good candidate for phone surveys based on linguistic fractionalization and mobilephone penetration rate (Ben et al., 2015). The endline mobile phone survey consists of 15 variables and was administered to all households that had completed the (short) face-to-face baseline survey.

# **_C. Variable Description_**

The primary outcome of this experimental study is the increased adoption of solar lamps (Coville et al., 2015). Specifically, outcome indicators are binary indicators for the extensive and intensive margins of solar lamp adoption. For the extensive margin we use dummy variables for whether the household has ever owned a solar lamp. For the intensive margin, households were asked whether they currently own between one and five or more than five solar lamps. We construct two binary variables whether the household currently owns more than one solar lamp and more than five solar

> 20 Random assignment to survey type was stratified by village and enumerator. Only the first three households and the fifth, sixth, and seventh households surveyed by an enumerator in a given village were considered for assignment to the long survey. By implication, the fourth household (serving as buffer) and all households visited by the same enumerator after household seven automatically received the short survey.

> 21 Enumerators were instructed to conduct the face-to-face interview with the household head. If the household head was not available for the interview, the spouse of the household head, the oldest child of the household head above the age of 18, or any other adult who is a household member was interviewed (in this order).

11
## **Figure 4: One-step ahead forecasting scheme**

We use the following setup to make one-head ahead forecasts, in order to reflect the information available for decision makers at each point in time. The black lines and dots represent the observed data based on which the model is estimated. The red squares represent the one-step ahead forecasts. The estimation period is at least 15 time observations long which is about half of the total available data.

<!-- Start of picture text --> t t+1<br>... ...<br>... ...<br>... ...<br>T-1 T<br>... ...<br>Oct 2009 Apr 2013 Feb 2019<br>Minimum estimation period Expanding forecasting window<br>... ... ...<br><!-- End of picture text -->

# **5 Forecasting**

Using the selected variables and estimated model parameters we now demonstrate the model’s prediction performance. At each point in time we calculate the one-step ahead prediction and compare it to the famine risk distribution of the next FEWS NET release. This imitates the situation of policy makers, who decide based on the available information. Let I _푇_ denote all information since the beginning of recorded famine history in October 2009 until the hypothetical decision date _푇_ . Then the one-step ahead prediction is defined as In order to avoid incorporating future information in the one-step ahead predictions, which would not have been available, we estimate the model only using the information until _푇_ and then make a forecast for _푋푇_ +1| _푇_ . We then re-estimate the model with information until _푇_ + 1 and make a forecast for _푋푇_ +2| _푇_ +1. The minimal observation period is at least _푇_ = 15 such that there is sufficient information to fit the model. We note, however, that the set of selected variables in Tables 3 and 4 are taken as given. Unquestionably, the selection relies on the full sample and therefore on future information. Nonetheless, we decided against re-selecting the variables at each point in time to avoid model instability.

The results of the rolling forecasts using 1,000 hypothetical draws are shown in Figure 5 and associated accuracy in Table 5. We can see that the model is more accurate for higher IPC phases for almost all countries. The predictions are within the 50% confidence interval in the majority of cases. Lower IPC stages are associated with lower precision and broader confidence intervals. Interestingly, the model performs well in terms of predicting the correct direction of IPC3+ dynamics, for instance, in Afghanistan, Chad, Malawi, Mozambique and Zimbabwe.

14
Table 4: Baseline Results - Size heterogeneity in effects of different types of integration of SPS and TBT provisions

||Ex|port value of agricultu|ral products|Export value o|f any product|
|---|---|---|---|---|---|
||MR SPS|Harmonization SPS|Transparency SPS|Harmonization TBT|Transparency TBT|
|ln(1 +_τ_)|-1.602<sup>_∗∗∗_</sup>|-1.624<sup>_∗∗∗_</sup>|-1.621<sup>_∗∗∗_</sup>|-1.780<sup>_∗∗∗_</sup>|-1.808<sup>_∗∗∗_</sup>|
||(0.408)|(0.412)|(0.411)|(0.415)|(0.421)|
|ln(_GDP_)|1.350<sup>_∗∗∗_</sup>|1.287<sup>_∗∗∗_</sup>|1.304<sup>_∗∗∗_</sup>|2.159<sup>_∗∗∗_</sup>|2.160<sup>_∗∗∗_</sup>|
||(0.196)|(0.195)|(0.191)|(0.182)|(0.182)|
|Depth|-0.298<sup>_∗∗_</sup>|-0.110|-0.351<sup>_∗∗∗_</sup>|0.0559|0.124|
||(0.133)|(0.186)|(0.134)|(0.222)|(0.210)|
|1<sup>_st_ </sup>tercile _×_ Prov|0.352<sup>_∗∗∗_</sup>|0.444<sup>_∗∗∗_</sup>|0.616<sup>_∗∗∗_</sup>|-0.585|0.212<sup>_∗∗_</sup>|
||(0.0658)|(0.115)|(0.169)|(1.166)|(0.0908)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.00527|0.0905|-0.0690|1.001<sup>_∗∗_</sup>|0.0389|
||(0.0527)|(0.0943)|(0.140)|(0.486)|(0.110)|
|3<sup>_rd_ </sup>tercile _×_ Prov|0.143|0.203<sup>_∗_</sup>|-0.148|1.060<sup>_∗∗∗_</sup>|0.164|
||(0.111)|(0.119)|(0.281)|(0.248)|(0.130)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|
|N|1532134|1532134|1532134|6283454|6283454|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

34
insurance pays an indemnity in only 10.7 percent of phases, a point we return to below. The average estimated payout is Rs. 29.7, compared to an average premium of Rs. 99.9. This wedge presumably reflects, at least in part, the administrative and financing costs of designing, underwriting and selling insurance policies, especially given the small current size of the market and lack of associated economies of scale. Although the insurance is not actuarially fair, it may still be valuable to policyholders if it pays an indemnity in times when the household’s marginal utility of consumption is particularly high.

# **3. Distribution of Payouts**

Evidence on the distribution of payouts is presented in figure 2. The x-axis for the graph is ‘payout rank,’ which ranks payouts in increasing order of size, expressed on a scale from 0 to 1. Figure 2 plots payout amount against payout rank. The payout is zero up to the 89th percentile, indicating that an indemnity is paid in only 11 percent of phases. The 95th percentile of payouts is around Rs. 200, double the average premium. In a small fraction of cases (around 1 percent), the insurance pays the maximum indemnity of Rs. 1000, yielding an average return on the premium paid of 900 percent.

Figure 2 suggests that the ICICI Lombard policies we study primarily insure farmers against extreme tail events of the rainfall distribution. Confirming this graphical evidence, we calculate that around one-half of the value of indemnities is generated by the highest-paying 2 percent of phases. Without further evidence on the sensitivity of household consumption to rainfall shocks of different types, it is difficult to say whether this structure approximates the optimal insurance design. For example, Paxson (1992) and Jacoby and Skoufias (1998) are generally unable to reject that consumption of rural 7
_CHAPTER 4. AREA LEVEL MODELS_ 16 regression and _xd_ 2 _, . . . , xdp_ are the population means of _p −_ 1 auxiliary variables, obtained from census data.

Note that the true indicators _δd_ are not available and hence model (4.1), as it is, cannot be fit. However, direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> , _d_ = 1 _, . . . , D_ , are assumed to be available from the unit-level data in a (current) survey, where the areas may be identified. Traditional indicators of interest are the area means _δd_ = _Y_<sup> ̄</sup> _d_ , for which basic direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> and corresponding design variances var _π_ ( _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> ), are described in Chapter 2. Note that the direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> are subject to sampling error, which might be substantial, since they are based on the _nd_ observations from area _d_ , which is supposed to be too small for certain areas. Fay and Herriot (1979) proposed to account for the (important) sampling errors of the direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> of _δd_ , by considering the following model, known as _sampling model_ , in the second stage: where _ed_ , _d_ = 1 _, . . . , D_ , are supposed to be independent, and independent of the area effects _ud_ , following _ed ∼ N_ (0 _, ψd_ ), with variances _ψd_ = var _π_ ( _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> ), _d_ = 1 _, . . . , D_ , assumed to be known.

Replacing the linking model (4.1) in the sampling model (4.2) results in the linear mixed model with known and heteroscedastic error variances _ψd_ , _d_ = 1 _, . . . , D_ . The statistical theory behind linear mixed models is well described in Searle (1971), Searle, Casella and McCulloch (1997) and Jiang (2007). For _σu_<sup>2</sup> known, Henderson (1950) obtained the best linear unbiased predictor (BLUP) of a linear combination of the vector of fixed effects **_β_** and the vector random effects **u** _d_ = ( _u_ 1 _, . . . , uD_ )<sup>_′_</sup> in a linear mixed model. The BLUP of _δd_ = **x**<sup>_′_</sup> _d_<sup>**_β_**+</sup><sup>_ud_isthepredictor</sup><sup>_δ_ ̃</sup><sup>_d_thatisalinearcombination</sup> of **y** = ( _δ_<sup>ˆ</sup> 1<sup>_DIR_</sup> _, . . . , δ_<sup>ˆ</sup> _D_<sup>_DIR_</sup> )<sup>_′_</sup> , which is unbiased in the sense _E_ ( _δ_<sup> ̃</sup> _d − δd_ ), and
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 21. Sierra Leone’s existing infrastructure spending is average**

<!-- Start of picture text --> SSA<br>LIC-NoFragile<br>LIC-Fragile<br>MIC<br>ECOWAS<br>Resource-Rich<br>Zimbabwe<br>Liberia<br>Cape Verde<br>Ethiopia<br>Kenya<br>Ghana<br>Benin<br>Mali<br>Sierra Leone<br>Uganda<br>South Africa Capex O&M<br>Nigeria<br>Cote d'Ivoire<br>Congo, Dem. Rep.<br>% of GDP 0 2 4 6 8 10 12 14 16 18 20<br><!-- End of picture text --> _Source_ : Derived from Foster and Briceño-Garmendia 2009.

**Figure 22. Sierra Leone’s pattern of capital investment in infrastructure differs from that of comparator countries**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>Sierra Leone<br>3.5<br>3.0<br>2.5<br>2.0 LIC-fragile<br>1.5<br>1.0<br>0.5<br>0.0<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia and others 2009. _Note:_ Private investment includes self-financing by households.

42
**Figure 12: Foreign claims on Ireland, December 2011**

<!-- Start of picture text --> 140<br>120<br>100<br>Non-bank private sector<br>Banks<br>80<br>Public sector<br>60<br>40<br>20<br>0<br>Source: Bank for International Settlements.<br>US$ (billions)<br><!-- End of picture text --> 34. Fuelled by a rapid expansion of credit, Ireland‘s housing market began to expand in 2000, resulting in a boom in property investment and construction. The wealth effect from this boom spurred higher levels of consumption and helped sustain high growth rates. Boosted by the real estate boom, Ireland's banking system ballooned to five times the size of the economy, and its external debt to over 1000 percent of GDP at the end of 2010. When in the wake of the crisis funds from the US and Britain dried up, the banking system experienced a liquidity crunch, thus slowing credit to the real estate market. As borrowing became more expensive, the demand for housing started to decline, resulting in a fall in prices and an oversupply of housing. This put pressure on the balance sheets of banks many of which had relied extensively on profitable mortgage loans to boost their earnings. The authorities‘ extensive support as well as access to emergency support from the Central Bank was vital to address financial stability concerns. Yet, the bailout or purchase of failing banks also led to a crisis of confidence, as the government bailout package reached 20 percent of GDP and the budget deficit shot to 32 percent of GDP in 2010, leading to outflows of foreign assets. Figure 13 below shows the extent to which Ireland 18
# **<u>Stage 1 (probit):</u>**

ρij = PR(Ti,j = 1 ¦ observed variables) = αi + αj + αt + β1 lngdprep + β2 lngdppcrep + β3 lngdppar + β4 lngdppcpar + β5 lndistwces + β6 contig + β7 comlang_off + β8 colony + β9 lleither + β10 isleither + β11 wto + β12 fta + β13 mra( _sector_ ) + β14 hr( _sector_ ) + β15 hr( _sector_ )oecd_sgp + β16 hr( _sector_ )_dev + uij Before turning to the second stage of the estimation, it will be helpful to relax two assumptions made in the original model. HMR show that results do not depend on the assumption of a Pareto distribution of firm-level productivities. By making vij an ~~ˆ~~ * arbitrary increasing function of zij, they eliminate the non-linearity in _~~w~~ ij_ and thereby the need to estimate the second stage by maximum likelihood. Instead, stage 2 can be estimated by OLS. They further drop the normality assumption for the unobserved trade costs (which was needed to recover the<sup>_~~z~~_</sup> ~~ˆ~~ _ij_ *<sup>and</sup> ~~η ˆ~~ _ij_ * ) and work instead directly with the predicted probabilities ρ ˆ _ij_ . The actual gravity equation looks as follows:

# **<u>Stage 2 (OLS):</u>**

ln( _sector_ )exp = αi + αj + αt + β1 lngdprep + β2 lngdppcrep + β3 lngdppar + β4 lngdppcpar + β5 lndistwces + β6 contig + β7 comlang_off + β8 colony + β9 lleither + β10 isleither + β12 fta + β13 mra( _sector_ ) + β14 hr( _sector_ ) + β15 hr( _sector_ )oecd_sgp + β16 hr( _sector_ )_dev + ρ ˆ + _ij_ ~~η ˆ~~ _ij_ * + eij The predictions as to the direction of effects of individual variables are very similar for the two stages. Yet the interpretation is different to the extent that the first stage explains the impact on the probability that a certain firm is going to export (the extensive margin), while the second stage explains the increase in bilateral trade volume for firms already exporting before the liberalization took place (the intensive margin).

As for the variables of interest, some preliminary comments seem to be in order at this point. As far as MRAs are concerned, only effects on parties to the agreement were investigated. Third party effects are difficult to evaluate in this case, as some agreements are subject to rules of origin while others are not. In the latter case, MRAs should have a similarly positive effect on competitive third country producers as harmonization, while in the former case markets remain segmented for countries outside the liberalizing region. It could not always be established from the available documentation whether rules of origin were in place, so no analysis of the effect on third countries was conducted. For harmonization, on the other hand, these effects were taken into account. Hence, two further dummies were introduced to this effect, which take on the value “one” if the exporter is a developing country and the importer is member of a harmonizing region, or respectively, if the exporter is in the OECD<sup>16</sup> , but not harmonizing, while the importer is.

> 16 For the purpose of the analysis Singapore was included in this group

20
||Number of Months between the Start of HRS in|
|---|---|
||the County from December 1978|
||(1)<br>(2)|
|_Power Structure_||
|YRD|-1.647|
||(3.074)|
|YRD×GuerrillaNo|1.375|
||(3.615)|
|YRD×GuerrillaYes|-5.389|
||(3.428)|
|_Initial Conditions_||
|Ln_GVOPC_52|-6.783**<br>-7.467***|
||(2.561)<br>(2.291)|
|_Geography_||
|Share of Plain (%)|0.354<br>0.517|
||(0.340)<br>(0.358)|
|Distance to Xiamen|0.030*<br>0.025|
||(0.017)<br>(0.016)|
|Ln(Elevation)|17.834<br>23.913|
||(19.374)<br>(21.418)|
|Constant|22.934<br>16.822|
||(49.828)<br>(50.463)|
|Obs.|58<br>58|
|_R_<sup>2</sup>|0.169<br>0.238|

Table 20: The Effect of Power Structure on the Initiation of the Household Responsibility System (HRS) in Counties in Fujian Province.

obvious differences in mean population rates, though there is more dispersion among YRD-affiliated counties. This pattern is confirmed in the regression analysis in Table 19: the coefficients for power structure are never statistically significant in explaining population growth rates in all specifications.

# **5.8.2 Household Responsibility System (HRS)**

At the beginning of the rural reform in early 1980s, Chinese central government launched the _household responsibility system (HRS)_ , which allowed households to contract land from collective organizations. Households could make operating decisions independently within the limits set by the contract agreement, and freely dispose of surplus production over and above national and collective quotas. HRS represents one of the most significant institutional reforms in the Chinese agricultural sector. It gave the farmers the residual claim rights for production 49
The second problem is the identification problem. Even if we agree on the poverty line in the welfare space, we also need to know the expenditure function. Standard practice is to calibrate its parameters from data on consumer demands. The problem is that individuals vary in characteristics, such as their household size and demographic composition, which influence welfare in ways that may not be evident in consumer demand behavior.<sup>11</sup> (This too is a pervasive problem in applied economics, certainly not confined to poverty measurement.) As we will see, the methods found in practice address the referencing and identification problems by expanding the traditional information set used by economists in assessing welfare.

# **3.        Nutrition-based absolute poverty lines**

The main methods found in practice in developing countries are the food-energy-intake (FEI) method and the cost-of-basic needs (CBN) method. It is known that these methods can give radically different results even for the same country and date; using data for Indonesia, Ravallion and Bidani (1994) found virtually zero correlation between the geographic poverty profiles (given the poverty rates across geographic areas) produced by these two methods. Since policy choices (such as in geographic targeting) could depend critically on which method is used, it is important to probe carefully into the choice.

## **_The food-energy intake method_**

The FEI method can be interpreted as a special case of the functioning-based approach described above. The specialization is to focus on just one functioning, namely nutritional status as measured by food-energy intake relative to caloric norms. The method finds the consumption expenditure or income level at which food-energy intake is just sufficient to meet predetermined food-energy requirements for good health and normal activity levels.<sup>12</sup> To deal with the fact that food-energy intakes naturally vary at a given income level, the FEI method typically calculates an expected value of intake at given income (or the regression of food energy intake on income).

To see what this method entails in more formal terms, let _k_ denote food-energy (calorie) expenditure, which is distributed across individuals in some known way (as inferred from a sample survey), and let _y_ denote income (or total consumption expenditure) while _x_ denotes

> 11 If demands _q_ ( _pi_ , _yi_ , _xi_ ) can be integrated back to a utility function _u_ ( _qi_ ,<sup>_x_</sup> _i_ ) then the same demands also maximize _w_ [ _u_ ( _qi_ , _xi_ ), _xi_ ] . Thus there is no unique utility function that can be inferred from market demands alone. This is a well-understood problem; see Pollak and Wales (1979), Pollak (1991) and Browning (1992).

> 12 A widely used source for caloric requirements is World Health Organization (1985).

8
- 36 -

- Clewwe, Paul, and Dennis de Tray. (1989). "The Poor in Latin America During Adjustment: A Case Study of Peru". LSMS Working Paper No. 56, The World Bank, Washington, D.C.

- Clewwe, Paul and Kwaku Twum-Baah. (19°9). "The Distribution of Welfare in Ghana". Mimeo. The World Bank, Washington, D.C.

- Crootaert, Christiaan. (1982). "The Labor Market and Social Accounting: A Framewoork of Data Presentation. LSMS Working Paper No. 17, The World Bank, Washington, D.C.

- Crootaert, Christiaan. (1986). "Measuring and Analyzing Levels of Living in Developing Countries: An Annotated Questionnaire". LSMS Working Paper No. 26, The World Bank, Washington, D.C.

- Ho, Teresa J. (1982). "Measuring Health as a Component of Living Standards." LSMS Working Paper No. 15, The World Bank, Washington, D.C.

- Martorell, Reynaldo. (1982). "Nutrition and Health Status Indicators: Suggestions for Surveys of the Standard of Living in Developing Countries". LSMS Working Paper No. 13, The World Bank, Washington, D.C.

- Mehran, Farhad. (1980). "Employment Data for the Measurement of Living Standards". LSMS Working Paper No. 6, The World Bank, Washington, D.C.

- Musgrove, Philip. (1982). "The ECIEL Studv of Household Income and Consumption in Urban Latin America: An Analytical History". LSMS Working Paper No. 12, The World Bank, Washington, D.C.

- Scott, Christopher, P.T.A. de Andre and Ramesh Chander. (1980). "Conducting Surveys in Developing Countries: Practical Problems and Experience in Brazil, Malaysia, and the Philippines". LSMS Working Paper No. 5, The World Bank, Washington, D.C.

- Sullivan, Jeremiah, Susan H. Cochrane and William D. Kalsbeek. (1982). "Procedure for Collecting and Analyzing Mortality Data in LSMS". LSMS Working Paper No. 16, The World Bank, Washington, D.C.

- United Nations Statistical Office. (1980). "Towards More Effective Measurement of Levels of Living", and "Review of Work of the United Nations Statistical Office (UNSO) Related to Statistics of Levels of Liv:.ng". LSMS Working Paper No. 4, The World Bank, Washington, D.C.

- Visaria, Pravin. (1980). "Poverty and Living Standards in Asia: An Overview of the Main Results and Lessons of Selected Household Surveys". LSMS Working Paper No. 2. The World Bank, Washington, D.C.

- Wahab, Mohammed Abdul. (1980). "Income and Expenditure Surveys in Developing Countries: Sample Design and Execution". LSMS Working Paper No. 9, The World Bank, Washington, D.C.