rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7:** Changes in Crashes Using a Placebo Test with 2017/2018 and 2018/2019

||A. 2018 to 2|017 Comparison|B. 2019 to 2|018 Comparison|
|---|---|---|---|---|
||Curfew Hours<br>(20:00-5:00)<br>(1)|Pre-Curfew Hours<br>(17:00-20:00)<br>(2)|Curfew Hours<br>(20:00-5:00)<br>(3)|Pre-Curfew Hours<br>(17:00-20:00)<br>(4)|
|Curfew Period (Post)|-0.610|-0.377|-0.344|-0.182|
||(0.645)|(0.382)|(0.515)|(0.451)|
|_IP lacebo_ (2018/2019)|-0.354*<br>(0.186)|-0.210<br>(0.163)|-0.165<br>(0.174)|-0.046<br>(0.136)|
|Curfew Period (Post) x_IP lacebo_|0.116<br>(0.275)|0.276<br>(0.218)|0.104<br>(0.271)|0.083<br>(0.215)|
|Constant|13.950|-2.765|-39.654|-4.962|
||(29.707)|(13.325)|(26.007)|(11.627)|
|Observations|294|294|294|294|
|R<sup>2</sup>|0.208|0.152|0.191|0.087|

_Notes_ . Robust standard errors are reported in parentheses. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind). For the placebo test, we compare 2017 and 2018, using 2018 as the treatment year (Panel A) and similarly we compare 2018 and 2019, where we treat 2019 as the treatment year (Panel B).

19
- 18 -

The regression results also confirmed earlier observations that region of residence and socio-economic status of the household were important determinants of welfare change. For example, for equal levels of endowments and for equal changes, households whose head worked in the public sector maintained better their welfare levels than households working in other sectors. The relative magnitudes of the relevant regression coefficients suggest that government policies towards certain regions or types of households can outweigh the effects from household endowments.

We also found that nationality and gender of the head of household are relevant characteristics, but not in the direction generally expected. Migrant non-Ivorian households tended to be better at preventing welfare losses than Ivorian households, while households headed by women did better than those headed by men (after controlling for differences in levels and changes in endowments.) Lastly, we addressed the question of measurement error in the data. Such errors are potentially more harmful to multivariate analysis of panel data than in the case of crosssectional data, because a given amount of measurement error will usually be a larger percentage of the change recorded in the panel than of the base value. We concluded that in the case of the C6te d'Ivoire Living Standards Survey, observed welfare changes were likely to be genuine and to reflect transitory components in household expenditure. If the data were dominated by measurement error, the regression results would imply a pattern of error (tendency towards zero, change in sign from one year to the next) which is not consistent with what is normally experienced in household surveys.

Our results provide some suggestions for policy makers. First, education is not only associated with higher welfare levels, but it also helps people cope better with economic declines. C6te d'Ivoire has recently experienced falling enrollment levels reflecting in part doubts about the value of education by parents. While curricula may need revision, policy emphasis on education should continue. Second, targeting the social safety net to larger households is justified in periods of economic decline. Providing support targeted at children,
**TABLE 1: PARAMETER ESTIMATES: PRICE INDICES**

|**_INDEX [weight]_**|**_log(POILt) _**|**_log(MUVt)_**|**_100*trend_**|**_Adj-R_**<sup>**_2_**</sup>|**_ADF_**|**_PP_**|
|---|---|---|---|---|---|---|
|Non-Energy [100.00]|0.16@<br>(4.32)|0.65@<br>(5.19)|-1.47@<br>(-4.09)|0.93|-3.40**|-3.60***|
|Metals [28.15]|0.11@<br>(2.28)|0.52@<br>(3.39)|-0.10<br>(-0.23)|0.91|-4.36***|-2.27|
|Fertilizers [2.71]|0.33@<br>(4.51)|0.24<br>(0.99)|-0.52<br>(-0.75)|0.84|-4.27***|-3.01**|
|Agriculture [69.08]|0.17@<br>(3.97)|0.72@<br>(5.07)|-1.98@<br>(-4.81)|0.91|-3.39**|-3.38**|
|Beverages [16.87]|0.26@<br>(2.82)|0.90@<br>(2.97)|-3.65@<br>(-4.21)|0.73|-4.26***|-3.26**|
|Raw materials [22.82]|0.04<br>(0.99)|0.81@<br>(6.64)|-0.88@<br>(-2.50)|0.93|-4.21***|-4.15***|
|Food [29.44]|0.18@|0.52@|-1.34@|0.85|-3.96***|-3.11**|
||(3.44)|(3.03)|(-2.71)||||
|Cereals [6.94]|0.18@|0.52@|-1.75@|0.76|-4.25***|-3.24**|
||(2.90)|(2.57)|(-3.01)||||
|Fats and oils [10.13]|0.19@|0.42|-1.39@|0.76|-2.88*|-3.68**|
||(3.15)|(2.11)|(-2.44)||||
|Other food [12.36]|0.17@|0.68@|-0.99|0.86|-3.70***|-3.49**|
||(2.53)|(3.16)|(-1.60)||||

**Notes** : ADF and PP denote the Augmented Dickey-Fuller (Dickey and Fuller 1979) and Phillips-Perron (Phillips and Perron 1988) statistics. One (*), two (**), and three (***) asterisks indicate stationary error term at the 10, 5, and 1 percent levels of significance. The @ sign denotes parameter estimate significant at the 5 percent level. The estimate of the constant term is not reported here. The coefficient of time trend has been multiplied by 100 in order to be interpreted as annual price change due to technical change. The numbers in square brackets denote weights (they add to 100). The structure of the indices is illustrated in Figure 2 while their commodity composition is discussed in the statistical appendix.

**_— 13 —_**
extent that banks either extended loans or overdrafts very early in the crisis to firms that they believed had long-term potential (if they could survive temporary liquidity problems) or ended lines of credit or loans for firms that they believed were the most likely to fail during the crisis, this could affect our estimates in the survival regressions. Still, it is likely that most of the repercussions of the crisis in terms of access to credit for firms in ECA had not been felt at the time of the last round of the BEEPS survey. Moreover, if a firm reported having a loan at the time of the BEEPS it must have applied for and received it prior to the survey.

Because the financial constraints regressions are designed to summarize firms‘ access to financial services, this set of variables represented by F is obviously less relevant. However, and as noted above, we do include proxies for the availability of internal funding in those regressions. C represents a matrix of country characteristics. Because the survival analysis is based on a cross-section and because the FCS was done in only six countries, it is not possible to include a full set of country-level controls in the survival regressions. In the survival analysis, we therefore include country dummies instead of country controls.

Because the BEEPS data offer time series variation and cover a much broader set of countries, we are able to include a rich set of country-level variables in the financial constraints regressions. These include GDP per capita, the KKM index of institutional development,<sup>20</sup> inflation, and GDP growth. All except inflation are likely to be associated with less severe financial constraints. We also include population and population density in the constraints regressions, though it is less clear how those variables are expected to affect financial constraints. We interact the GDP growth variable with dummy variables for the year of the survey to test whether that variable had differential effects on financial constraints in crisis and non-crisis years, in line with some predictions from the literature.

The country variables also include the share of banking sector assets in state-owned and foreign owned banks. For the reasons described above, the foreign bank participation variable is measured in 1999. Foreign bank participation in 1999 is therefore our proxy for the more stable, rooted local lending by foreign banks. In a robustness check described in section VI, we include

> 20  This is the measure of broad institutional development created by Kaufmann, Kraay, and Mastruzzi (2007).

17
We find that the CDR-based method (precision and recall of 42%) is close in accuracy to methods relying on assets (precision and recall of 49%) or consumption (precision and recall of 45%). To evaluate the trade-off between inclusion errors and exclusion errors resulting from selecting alternative cut-off thresholds, Figure 1c shows the ROC curve associated with each classification method. The Area Under the Curve (AUC) scores for these curves, listed in Table 2, are comparable among methods, with assets (AUC=0.73) slightly superior to consumption (AUC=0.71) and the CDR-based method (AUC=0.68). The corresponding Precision-Recall curves are shown in Figure S5.

# **3.2 Comparison of Errors across Methods**

To better understand the nature of the mis-classification errors arising from the different datasets used for targeting, Table 3 compares the characteristics of correctly and incorrectly classified households for three different methods (targeting on assets, consumption, and CDR). Panel A highlights differences between ultra-poor households correctly classified as ultra-poor (True Positives) and ultra-poor households _mis_ -classified as non-ultra-poor (False Negatives, also referred to as exclusion errors). Likewise, Panel B highlights differences between non-ultra-poor households correctly classified as non-ultra-poor (True Negatives), and non-ultra-poor households mis-classified as ultra-poor (False Positives, or inclusion errors). This analysis uses the _matched_ sample (see Table 2) to highlight differences that arise when switching from one targeting dataset to another, on a population of households that are observed in all three datasets.<sup>7</sup> Across methods, false negatives (exclusion errors) have higher levels of food security, – financial inclusion, and psychological well-being than true positives that is, all three targeting methods misclassify ultra-poor households as non-ultra-poor when those ultra-poor households are better-off, according to other observable characteristics not used in the targeting. Likewise, false positives (inclusion errors) tend to fare worse than true negatives across these same measures. The exact pattern of differences depends on the targeting method; for instance, asset-based targeting (first set of columns) tends to misclassify ultra-poor as non-ultra poor when they have assets (the difference of -2.21 is large), but errors are not systematically correlated with consumption (the difference of -0.19 is relatively small). The CDR-based method in particular tends to prioritize households that score low on these alternative measures of well-being. These patterns suggest that the CDR-based targeting method

> 7Similar analysis could also be performed using the balanced sample or the full sample; however, results would conflate differences caused by the targeting data (the current focus of Table 3) with the differences that arise from considering (or excluding) households without mobile phones (the current focus of Table 2).

12
gives the coverage rate by region and for each reference year; for this purpose, a country is defined as being covered if there was a survey (in our data base) within two years of the reference date (a five-year window). The coverage rate in 2008 varies from 47% of the population of the Middle East and North Africa (MENA) to 98% of the population of South Asia. Naturally, the further back we go, the fewer the number of surveys—reflecting the expansion in household survey data collection for developing countries since the 1980s. And coverage deteriorates in the last year or two of the series, given the lags in survey processing. Most regions are quite well covered from the latter half of the 1980s (East and South Asia being well covered from 1981 onwards). Unsurprisingly, we have weak coverage in Eastern Europe and Central Asia (EECA) for the 1980s; many of these countries did not officially exist then. More worrying is the weak coverage for Sub-Saharan Africa (SSA) in the 1980s; indeed, our estimates for the early 1980s rely heavily on projections based on distributions around 1990. The weak coverage for EECA, MENA and SSA in the 1980s is evident in Table 1. Our estimates for these regions in the 1980s are heavily dependent on the extrapolations from NAS data.<sup>24</sup>

# **4. Measures of absolute poverty**

Table 2 gives the absolute poverty rates—the percentage of the population living below $1.25—at three yearly intervals during 1981-2008. Table 3 gives the corresponding results for $2.00 a day, which is the median poverty line amongst developing countries as a whole (RCS).<sup>25</sup> Over the 28 year period, we find that the percentage of the population of the developing world living below $1.25 per day was halved, falling from 52% to 22%. The number of poor fell by 600 million, from 1.9 billion to 1.3 billion over 1981-2005 (Table 2). The trend rate of decline in the $1.25 a day poverty line over 1981-2008 was 1% point per year. (Regressing the poverty rate on time the estimated trend is -1.03% per year with a standard error of 0.06%, with

> 24 Note that there is a ―hole‖ in coverage for South Asia in 1999. This reflects the well-known comparability problem due to India’s National Sample Survey (NSS) for 1999/2000. (Further discussion and references can be found in Datt and Ravallion, 2002). We decided to drop that NSS survey round given that we now have a new survey for 2004/05 that we consider to be reasonably comparable to the previous survey round of 1993/94. We also decided to only use the 5-yearly rounds of the NSS, which have larger samples and more detailed and more comparable consumption modules (aside from the 1999/00 round). Unfortunately, this leaves a 10-year gap in our survey coverage for India; the estimates for India over the intervening period use our interpolation method. Including all available survey rounds for India adds to the variability in the series but does not change the trend.

> 25 Further details on these estimates and results for other poverty lines and for the poverty gap index can be found in Chen and Ravallion (2012).

13
# **Conclusion**

Increases in the level and volatility of food prices over the past several years have led to a severe erosion of purchasing power in developing countries where the poor often spend the majority of their budgets on food. Increases in the price of staple foods in particular can have deleterious effects for households living at subsistence levels.

In this paper, we use the case of Afghanistan during the 2007-08 global food price crisis to examine the impact of high staple food prices on household food security. We estimate the marginal effect of increases in wheat flour prices on measures of total food consumption, caloric intake, and dietary diversity. We use the unconditional quantile regression estimator, proposed by Firpo, Fortin and Lemieux (2009b), to examine the marginal effects at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> quantiles of the unconditioned distribution of each food security-related dependent variable, as well as the marginal effects associated with key policy-relevant thresholds.

The key findings of our analysis illustrate the value of distribution-sensitive analysis. We find large differences in the behavioral response of a household to wheat flour price increases based on its location on the unconditional distribution of each of our measures of food security – differences that are obscured when using OLS estimation techniques. Households at the 75<sup>th</sup> quantile of the distribution of real food consumption experience the largest percentage decline in real food consumption for a given increase in wheat flour prices – over two and a half times larger than that of households at the 25<sup>th</sup> quantile. Analogously, those at the 75<sup>th</sup> quantile of the caloric intake distribution experience the largest percentage declines in caloric intake for a given increase in wheat flour prices. Households at the bottom of the caloric intake distribution make very small reductions in caloric intake due to the price increases. Such households live near subsistence levels and are forced to make adjustments to the quality of their diets in order to maintain energy levels; households at the 25<sup>th</sup> quantile of the dietary diversity distribution make the largest reduction in dietary quality – about one and a half times larger than that of households at the 75<sup>th</sup> quantile.

The findings have several implications. First, if policymakers focus exclusively on changes in caloric intake that result from price shocks, they may miss an important component of the big picture. While poorer households do not cut back on calories very much, it is likely that they reduce dietary quality. Such findings underscore the importance 21
|Physical investment (billions of|||0.538<sup>*</sup>|0.478<sup>*</sup>|0.542<sup>**</sup>|
|---|---|---|---|---|---|
|dollars)|||(0.311)|(0.269)|(0.245)|
|Physical investment squared|||–0.0744|–0.0633|-0.0784<sup>*</sup>|
|(billions of dollars)|||(0.0510)|(0.0439)|(0.0464)|
|Executive recruitment concept||||0.151<br>|0.103<br>|
|||||(0.121)|(0.152)|
|Executive constraints concept||||–0.478<sup>***</sup>|-0.442<sup>***</sup>|
|||||(0.153)|(0.125)|
|Political competition concept||||0.114|0.141|
|||||(0.121)|(0.111)|
|Annual GDP per capita growth rate|||||0.0937|
|(detrended, previous year)|||||(0.0658)|
|Annual depreciation|||||0.0142<sup>***</sup>|
|(detrended, previous year)|||||(0.00455)|
|Banking crisis occurred|||||1.267<sup>**</sup>|
|(previous year)|||||(0.541)|
|Debt crisis occurred|||||1.060<sup>**</sup>|
|(previous year)|||||(0.540)|
|Exchange rate crisis occurred|||||-0.666|
|(previous year)<br>|||||(0.552)|
|γ1|2.096<sup>***</sup>|2.093<sup>***</sup>|2.094<sup>***</sup>|2.101<sup>***</sup>|2.082<sup>***</sup>|
||(0.284)|(0.283)|(0.286)|(0.282)|(0.240)|
|γ2|0.120<sup>***</sup>|0.120<sup>***</sup>|0.120<sup>***</sup>|0.120<sup>***</sup>|0.114<sup>***</sup>|
||(0.0242)|(0.0245)|(0.0246)|(0.0241)|(0.0223)|
|Constant|–9.928<sup>***</sup>|–9.661<sup>***</sup>|–9.851<sup>***</sup>|–9.195<sup>***</sup>|–9.344<sup>***</sup>|
||(0.674)|(0.697)|(0.738)|(0.881)|(0.818)|
|Number of projects|3,976|3,976|3,976|3,976|3,976|
|Number of cancelled projects|167|167|167|167|167|
|Number of countries|86|86|86|86|86|
|Akaike information criterion (AIC)|1,563.7|1,535.1|1,531.0|1,520.6|1,484.9|
|Bayesian information criterion (BIC)|1,598.0|1,581.9|1,584.0|1,582.9|1,562.9|
|Project-years|39,365|39,365|39,365|39,365|39,365|

_Note_ : Robust standard errors clustered by country are in parentheses. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

The estimation results suggest that the probability of early termination of brownfield projects is not statistically different from that of greenfield projects. Private sponsors tend to express a preference for brownfield projects over greenfield projects because the returns of greenfield 14
household, μ is an unmeasured determinant of the credit demand that is time-invariant and fixed within a village, and _ε_ is a non-systematic error.

The conditional demand for outcomes ( _Yij_ ) (such as consumption, children’s schooling or women’s labor supply) conditional on the level of credit demand ( _Cij_ ) is where δ _f_ and δ _m_ are the “common” effects for female and male credit, respectively.<sup>14</sup> Our interest is to estimate the impact of male and female credit separately on outcomes of particular interest such as household per capita expenditure, non-land asset, boys’ and girls’ schooling, and so on. With cross-sectional data (where _t_ =1), the endogeneity arises as a result of the possible correlation among μ _cjf_ , μ _cjm_ , and μ _jy_ , and among ε _ijmc_ , ε _ijfc_ and ε _ijy_ (Pitt and Khandker 1998). Pitt and Khandker (1998) used a village-level fixed-effect method to resolve program placement (or village-level) endogeneity of the 1991/92 data since a household-level fixed-effect method could not be applied with cross-sectional data. So Pitt and Khandker (1998) adopted a two-stage instrumental variable (IV) type method to resolve the endogeneity of a household’s participation. In this IV framework, they used exogenous gender- and landholding-based exclusion restrictions to create a discontinuous household program choice variable. That variable was interacted with household’s observable characteristics to create instruments.

With the availability of panel data ( _t>_ 1), the household-level fixed-effects method can be used and two-stage identification restriction is not needed. This is simply done by differencing out the unobserved village and household attributes, which are the sources of correlation between the

> 14 Here we do not differentiate the time-varying or dynamic effects of credit, e.g., effects of past and present credit. We’ll relax this assumption later.

12
|Table 6: Empirical results for Multinomial Lo|git Model explainingPPP(d<br>|ependent variable)<br>|
|---|---|---|
||(1)|(2)|
|1. Management and lease contracts|||
|IRA|0.0403|0.0819|
||(0.1110)|(0.1159)|
|GDP per capita||‐0.00003***<br>(0.00001)|
|Inflation|‐0.0114|‐0.0189**|
||(0.0089)|(0.0089)|
|2. Greenfield contracts|||
|IRA|‐0.1547*|‐0.2112*|
||(0.0865)|(0.1104)|
|GDP per capita||8.44e‐06|
|||(8.67e‐06)|
|Inflation|0.0082|0.0142*|
||(0.0054)|(0.0079)|
|3. Brownfield contracts|||
|IRA|0.0869|0.0304|
||(0.1316)|(0.1579)|
|GDP per capita||0.00005***<br>(0.00001)|
|Inflation|0.0144|0.0269**|
||(0.0095)|(0.0130)|
|4. Divestiture contracts|||
|IRA|0.0033|‐0.0035|
||(0.0524)|(0.0398)|
|GDP per capita||5.34e‐06|
|||(3.97e‐06)|
|Inflation|0.0007|0.0018|
||(0.0035)|(0.0032)|
|Basic Statistics and Tests|||
|Observations|60|60|
|������ �<sup>�</sup>|0.0512|0.1885|
|Log Likelihood|‐81.9|‐70.05|
|LR�<sup>�</sup><br>|8.84|32.55|
|Prob <�<sup>�</sup>|0.3561|0.0011|

Standard errors in parentheses ‐ *** p<0.01, ** p<0.05, * p<0.1 The model estimated shows goodness‐of‐fit as suggested by the ������ �<sup>�</sup> . The model is a better fit when income per capita is controlled for (0.19 vs. 0.05), but remains just outside the good fit interval (0.2 to 0.4). This is one of the reasons why Model (2) is our preferred model. In addition, the log likelihood is maximized when controlling for income per capita (model 2), and the probability of obtaining the �<sup>�</sup> statistic if there is in fact no effect of the independent variables, is also lowest for this model (0.001). The analysis of the coefficients allows the following conclusions.

First, the relation between the type of PPP contract in the water and sanitation sector and the existence or not of an IRA is only significant when the contract is a greenfield project.

Second, in the only contract type for which an IRA matters, the impact is actually negative. Investors seem not to be keen on being regulated by an independent agent, potentially with discretionary powers. Indeed, we find that having an IRA decreases the probability of having a greenfield project, 11