rafmacalaba/datause-encoder-data huggingface dataset:
``` Dm2gdpratio = + 0.508*Drgpspc_1 - 0.00672*DRealInterest_1 (SE) (0.242) (0.0105)
- 0.00897*Dm2gdpratio_1 + 0.351*Drgpspc_2 - 0.00629*DRealInterest_2
(0.112) (0.241) (0.0114)
- 0.156*Dm2gdpratio_2 + 0.176*CIa_1 - 0.134*CSeasonal
(0.104) (0.0593) (0.00704) + 0.000735*CSeasonal_1 - 0.0297*CSeasonal_2 - 0.0606*dumm2002q3 (0.0157) (0.0163) (0.0211) + 0.0104*dumm1998q4 + 0.0547*dumm1999q1 + 0.00534*dumm2004q1 (0.0196) (0.0197) (0.0199) + 0.0563*dumm2002q4 - 0.0138*dumm2008q2 + 0.0653*dumm2002q1 (0.0209) (0.0197) (0.0198) ``` The speed of adjustment in the short run money demand equation is negative, statistically significant and lies between zero and unity. It therefore satisfies the needed criteria to set up a robust model that converge to equilibrium in the long run. This also translates to the fact that real gross private savings per capita is above or below its long run equilibrium level. Finally, the negative sign and the magnitude that is less than unity ensure that real private savings converges to the long run equilibrium.

# **Reduction of the General Model and Parsimonious Model**

The estimation of the short term system with unrestricted constant and dummies indicated that a number of variables are statistically insignificant. Since our empirical analysis reached a general statistical model that captures the key characteristics of the underlying data set, we proceed to reducing the complexity of the general model by eliminating statistically insignificant variables and checking the validity of the reductions at every stage to ensure congruence and parsimony of the final selected model (See Hoover and Perez (1999) and Campos, Ericsson, and Hendry (2005)).

Throughout the reduction process, 32 regressors were eliminated based on their statistical insignificance. Throughout the process, a progress test was conducted to test for the validity of the elimination in each step. This test is built on the likelihood ratio of the restricted (after reduction) versus the unrestricted model (before reduction).

The parsimonious short term equations included some significant lagged variables in the general model in addition to the first lag of exchange rate and US treasury bills rate. This is in addition to contemporaneous growth in real per capita income, inflation, and real interest rates.

Moreover, a comprehensive test was conducted to ensure that no critical information was lost throughout the process. The parsimonious model was accordingly formulated based on minimizing the Schwartz (SC) and Hannan–Quinn (AIC) information, which was found to encompass the general unrestricted system as well as all the reduced systems. It also minimized the three information criteria.

18
91

* controlling access to congested inner city areas.

The appropriateness of these interventions depends on the physical layout of the urban area, the density of street space, and the characteristics of the urban transport system. A detailed discussion of urban traffic management approaches to minimize air pollution impacts is provided in the ECMT report on transport policy and the environment. For example, different forms of bus priorities can reduce exhaust emissions from 7% for priority turns and other minor measures to as much as 60% for exclusive bus streets and freeway privileges. Emissions can also be reduced by increasing the proportion of the traffic in 30 kph to 70 kph range and reducing the proportion in the ranges above and below these limits. Thus, maximum speed limits, if effective, can reduce emissions significantly [ECMT 1990]. The elements of a comprehensive and environmentally-sound traffic management system implemented in Singapore are outlined in Box 6.

On-street and roadside parking is a common practice in major cities in many developing countries. Since roads are not wide enough, on-street parking interferes with traffic, often causing stop-and-go operations that result in increased vehicle emissions. A program of offstreet parking would greatly improve traffic flow in many of these cities.

Lack of coordinated traffic signal systems is quite common in cities in many developing countries, resulting in prolonged stop-and-go traffic operations which in turn cause increased pollutant emissions (Tables 20 and 21). Centrally computerized control systems can greatly enhance the efficiency and timing of traffic signals to help maintain uniform speed and stable traffic flows. Even mechanical traffic signal synchronization and individual sign control arrangements can help to improve traffic circulation. For example, the progressive and simple linking of signals along arterial roads can minimize the number of acceleration/deceleration operations, while maintaining the cruising traffic speed within a range at which emission rates are lowest [UNEP 1981, UNEP 1986].

Other measures may be needed to reduce obstructions to smooth traffic flow caused by construction and maintenance works and the presence of non-mechanized forms of transport such as bicycles, and human- and animal-drawn crafts, which often serve as major means of personal and goods transport in some developing countries. Such obstructions slow the general traffic stream and lead to increased fuel consumption and emissions. In simulated urban traffic operations, a comparison between free-flowing and heavily congested peak-hour traffic flows indicated a 31 % saving in fuel consumption and a reduction of 54%, _52%,_ and 2 % in HC, CO, and NOx emissions for hot start [UNEP 1981, UNEP 1986].

# **Auto Restrictions and Vehicle Free Zones**

Auto restriction measures include the designation of selected streets or areas in central cities as auto-free zones or zones that only allow high occupancy automobiles and public buses. In some cases, certain areas may be designated as pedestrian precincts and kept free of all
by aggregating across plots belonging to the same farm, are weighted using the probability weights described in a separate section below ( _Sampling weights_ ).

# ii. _Disaster type and frequency_

Similarly, our estimates of the prevalence of different shock types is conducted at the plot-level and involves the estimation of frequencies at the national level and pooled across countries using the household sampling weights. Our estimates of the most common shock type within enumeration areas are based on simple, unweighted frequencies.

# iii. _Heterogeneity in disaster impacts across farms_

Our multivariate analysis focuses on two main outcome variables: a binary variable indicating any disaster crop loss and a continuous variable denoting the percentage share of the total potential harvest that was lost to disasters. We estimate all models with the binary crop loss indicator as outcome variable <mark>via maximum likelihood using logistic regression. Models with the percent share of harvest lost as outcome variable are estimated via ordinary least squares regression. Our independent variables for these multivariate regressions are comprised of plot characteristics (plot size, elevation, a topographical wetness index, an indicator for ownership of the plot, and main crop fixed effects), as well as plot management (hired labor and fertilizer input use, irrigation, intercropping), plot manager (age, gender, and education), and household characteristics (urban/rural residence, an indicator for livestock farming, and electricity access). Models pooling the sample across countries further include country fixed effects.</mark> We also conduct multivariate analysis using a binary variable capturing the gender of the plot manager as outcome variable and plot- and plot-management characteristics as independent variables. As before, all multivariate regressions are weighted using the sampling weights.

# **iv.** _Different disaster impacts on neighboring plots_

Our analysis of differences in drought impact within the same enumeration areas first determines whether some but not all plots belonging to the same enumeration area recorded a drought loss and then estimates the simple, unweighted proportion of enumeration areas for which this is the case. Our analysis of within-household differences in shock impacts first limits the sample to households with multiple maize plots and where at least one of the household’s maize plots recorded a drought shock. We then calculate the share of remaining maize plots belonging to the same household that also record a drought loss and report a simple, unweighted average of this share across households for each country and year.

# v. _Comparison of survey micro data with aggregate sources_

Our estimates of the number of people affected by disasters and aggregate economic losses are totals at the national level and employ the household-level sampling weights.

14
# **5.2 Transition Matrices in Egypt in Comparison with Mexico**

Mexico and Egypt are both developing countries with comparable levels of GDP per capita at purchasing power parity (as of 2020, $18,444 in Mexico and $12,607 in Egypt). Importantly, notable similarities exist between both labor markets. Both have large informal sectors that make up just under two-thirds of total employment (Wahba and Assaad 2017). Both have a young labor force, but also face low labor force participation rates, especially among women (OECD 2018).

These underlying similarities make for an informative comparison of labor market transitions in the two countries, which also differ in significant respects. Egypt has a considerably larger public sector segmented from the rest of the labor market and a much smaller private formal sector. Workers in Mexico also regularly transition from the informal sector to the formal sector as they secure better employment (Gong, van Soest, and Villagomez 2004).

As described in Section 4, we simulate six-yearly labor market transitions in Mexico based on observed annual ones. Figure 8 below firstly shows the empirical annual transition for men aged 20 to 53 in Mexico over one year, on which we base our simulations.

**Figure 8: Transition matrices by employment status, formality and sectors in Mexico, 2005 Q1 – 2006 Q1**

<!-- Start of picture text --> Private informal 57 29 4 3 7 30% (35%)<br>Private formal 23 66 2 3 5 36% (43%)<br>Public formal 27 31 35 5 6% (5%)<br>Men<br>Unemployed 33 38 3 13 13 3% (4%)<br>Out of labor force 28 28 3 6 34 24% (13%)<br>Private Informal Private Formal Public Unemployed OLF<br>Private informal 39 16 5 2 39 21% (21%)<br>Private formal 17 49 5 2 28 19% (21%)<br>Public formal 12 15 45 26 7% (7%)<br>Women<br>Unemployed 23 26 4 5 42 2% (2%)<br>Out of labor force 17 13 4 2 64 50% (48%)<br>Private Informal Private Formal Public Unemployed OLF<br><!-- End of picture text --> _Notes_ . This figure relies on panel data from the ENOE in 2005 Q1 and 2006 Q1. We restrict our analysis to individuals aged 20 to 53 who were interviewed in both rounds. We use transition matrices to examine individuals’ transition of their work status/sector between the two dates. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2005 Q1, with the shares in 2006 Q1 reported in brackets. Panel weights are used.

Even on an annual basis, transitions are more frequent in Mexico than in Egypt on a sixyearly basis. And even the stickiest private formal sector only retains two-thirds of workers after one year. Those out of the labor force also find employment rapidly. The two largest stocks of 16

**Official Use**
from top) is exactly the opposite: the households in heavy rainfall areas pay on average 182 taka as payment without receipt, while, the households in other areas pay 271 taka.

A related question that may come to a reader’s mind at this point is whether it is likely that the heavy rainfall a¤ects the income of the teachers negatively, and thus they ask for more bribes. Note, however, that the teacher salary is paid by the central government according to a national pay scale that does not vary by geographic location. This is true even for the teachers in the so-called private schools (although the number of private schools is much smaller in rural areas), thus their income is immune to rainfall di¤erences or other weather shocks across regions. This implies that they are more likely to help smooth the consumption of the farmers in the village, rather than demanding money from poor households hit with a negative weather shock.

# Risk Averseness and Bad Health

One might also worry that the households that live in heavy rain areas may su¤er from bad health, and may be less risk averse. Evidence in Table 1 using two health indicators (chronic illness and ‘sick or injured’in last thirty days) clearly shows that there is no signi...cant di¤erence between heavy rainfall and other areas (see rows 4 and 5 in Table 1). It is, however, important to appreciate that even if there were negative health e¤ects of heavy rainfall, it would in no way constitute a rejection of our conclusions. This is because bad health lowers the demand for schooling, and thus reduces the propensity to pay bribes, opposite to what we ...nd from both the OLS and IV regressions reported later.

A less risk averse person is more likely to migrate, but there is no evidence that migration propensities are signi...cantly higher in heavy rainfall areas. We provide additional evidence on this issue by looking at precautionary grain stocks and land rental.<sup>24</sup> The evidence in row 6 of Table 1 shows that grain stocks due to precautionary motives are not di¤erent in heavy rainfall areas. The evidence in Table 1 also shows that there is no signi...cant di¤erence in the incidence of land rental in heavy rainfall and other areas.

Note also that, similar to the point made above regarding bad health, even if the households in the heavy rainfall (‡ood-prone) areas were less risk averse, it would not constitute an argument against our main ...ndings. According to the standard bargaining models, a less risk averse

> 24 A household’s grain stocks would primarily be determined by its land and household size. To get a reasonable estimate of precautionary grain stocks, we thus need to partial out the e¤ects of operated land and household size.

19
roughly 9 percentage points reduction in poverty rates. The positive soil fertility effects on poverty reduction are in line with the agricultural literature that connects farm income and soil erosion, see for example Hazarika and Honda (2001).

Aside from land use, we also control for soil type, which is a very important control measure in the cross section, as it allows us, along with land use, to isolate the intensive margin changes, and control for poverty reduction effects due to changes in land use.

**_Table 3: Dependent Variable ‐ Poverty Headcount Ratio_**

|VARIABLES|(1)<br>OLS|(2)<br>IV|(3)<br>IV - no<br>outlier|(4)<br>IV - SSA|(5)<br>IV- rural|(6)<br>IV- rural<br>SSA|
|---|---|---|---|---|---|---|
|Top Soil Carbon|-4.634***|-0.920|-23.46**|4.116|-21.27***|-93.74**|
||(1.092)|(5.718)|(11.41)|(13.08)|(6.944)|(39.28)|
|Precipitation|0.761<br>(1.244)||||||
|Ruggedness|<br>0.375|0.615|-1.016|-1.745|-0.449|-2.325|
||(0.399)|(0.547)|(1.094)|(1.976)|(0.668)|(2.915)|
|Road Density|-3.247***|-3.190***|-3.453***|-1.769|-2.249*|-0.698|
||(0.752)|(0.749)|(0.942)|(3.417)|(1.353)|(6.701)|
|Share Cropland|5.335*|5.762**|0.502|12.55|-7.192|-41.08|
||(3.169)|(2.870)|(4.270)|(8.749)|(6.062)|(25.89)|
|Share Urban|<br>-5.325|<br>-4.374|<br>-14.48|<br>-1,219***|<br>-36.89**|<br>-269.1|
||(7.556)|(7.178)|(9.897)|(227.2)|(17.74)|(274.4)|
|Share Grassland|<br>1.271|<br>2.226|<br>-16.56*|<br>8.183|<br>7.265|<br>-37.47|
||(4.475)|(4.341)|(9.805)|(18.57)|(9.766)|(32.39)|
|Share Forest|4.408|3.738|4.530|-2.272|-11.09|-43.33**|
||(3.250)|(3.625)|(4.783)|(8.170)|(7.012)|(20.90)|
|Population|<br>-1.235**|<br>-1.314***|<br>-1.157*|<br>-2.472|<br>-0.439|<br>-5.638|
||(0.489)|(0.466)|(0.621)|(2.495)|(0.722)|(3.762)|
|soil_type = 2, Andisol|5.178**|3.188|14.30*||||
||(2.461)|(4.040)|(7.359)||||
|soil_type = 3, Ardisol|-0.561|-0.234|-7.278*|10.19|||
||(2.351)|(2.459)|(4.367)|(7.561)|||
|soil_type = 4, Entisol|0.555|0.474|0.740|3.855||3.411|
||(1.749)|(1.658)|(2.431)|(4.268)||(4.020)|
|soiltype = 5 Gelisol|-5.893*|-7.451**|-1.303||||
|_ ,|(3.128)|(3.706)|(5.060)||||
|soil_type = 6, Histosol|<br>4.658**<br>|<br>0.729<br>|<br>24.53**<br>||||
||(2.311)|(6.567)|(12.31)||||
|soiltype = 7, Inceptisol|<br>-1.272|<br>-1.966|<br>3.569|-8.919||16.14*|
|_|(1.170)|(1.583)|(3.168)|(7.673)||(8.951)|
|soil_type = 8, Mollisol|<br>1.011<br>|<br>0.450<br>|<br>-0.406<br>||||
||(1.131)|(1.175)|(1.549)||||
|soiltype = 9, Oxisol|<br>-6.385**|<br>-6.398**|<br>-5.728|-3.552||48.02**|
|_|(2.907)|(2.784)|(4.342)|(7.083)||(19.63)|
|soiltype = 10, Rock|<br>3.414|<br>2.975|<br>10.50**||||
|_|(3.195)|(3.057)|(4.527)||||
|soiltype = 11, Sand|<br>2.961|<br>3.428|<br>-5.060|10.78**|||
|_|(6.370)|(5.782)|(9.317)|(4.916)|||
|soiltype = 12, Spodosol|<br>-2.232|<br>-4.123|<br>6.588||||
|_|(1.826)|(3.456)|(6.506)||||
|soiltype = 13, Ultisol|<br>1.342|<br>1.315|<br>2.058|0.986||23.20***|
|_|(1.902)|(1.825)|(2.551)|(4.467)||(6.570)|
|soiltype = 14, Vertisol|<br>2.128|<br>1.440|<br>-1.785|<br>-4.657||<br>35.69**|
|_|(2.754)|(2.908)|(3.911)|(11.76)||(17.23)|
|Constant|65.17***|53.55**|147.0***|107.5|132.6***|501.4***|
||(10.48)|(23.31)|(47.92)|(78.21)|(28.55)|(153.1)|

14
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30 1997<br>20 2000<br>10<br>0<br>2 32 62 92 122 158 267<br>Monthly per capita expenditure<br>Cumulative distribution of population<br><!-- End of picture text --> Source: Latvia Household Budget Surveys (HBS 1997, 2000).

This exceptional improvement in welfare in the region of Kurzeme has implications for the configuration of the Latvian poverty map (see Figure 4, to be contrasted with Figure A. 2 in the Annex, which provides a mapping of poverty in 1997). Figure 4 provides the geographical and socioeconomic mapping of poverty in Latvia in the late 1990s. Vidzeme and Latgale are now the poorest regions of Latvia, despite the slight improvement in the latter, which witnessed a rapid decline in its headcount index, from 30 to about 24%. In spite of the improvement, it continues to exhibit the largest poverty gap, an indication of persistence of the high vulnerability of its population and its relatively low income. The region of Latgale has the lowest wage rate and the highest unemployment rates, which may partly explain the continued high vulnerability of its population.

Unemployment is indeed a key determinant of poverty in Latvia, with implications for the regional distribution of welfare. Latgale, the most poverty-stricken region, has the highest rate of jobseekers (over 22%), the highest rate of registered unemployment (over 15%), and long-term unemployment (over 7%) (Republic of Latvia [2003]). Moreover, the contrast between wage and non wage income on the poverty mapping scale is significant across all geographical regions. This contrast is illustrated by Figure 5, which provides the mapping of poverty incidence across regions for the wage income earners and self-employed workers, and Figure 6, which provides the same mapping of poverty for households living on social transfers. For the poverty-stricken region of Latgale, the poverty incidence gaps between wage income earner households and households deriving their income from direct transfers in the form of social security and unemployment benefits is about 33%. This gap is about 23% in Vidzeme and much lower in all the other regions.

The updated geographical mapping of poverty is actually stable with respect to differing assumptions on economies of scale. Latgale and Vidzeme remain the two poorest regions under moderate economies of scale assumptions (see Table 3). This result is consistent with the last World Bank’s Poverty Assessment (World Bank [2000a]). Despite this relative stability, it is worth pointing out the slight increase in the incidence of poverty and the poverty gap in the region of Vidzeme. Also, note that under moderate economies of scale 16
39

|Country|Findings|Quintile dependence<br>pattern as a function of<br>quintile.|Urban vs. rural|
|---|---|---|---|
|Mali|49% of households reported<br>at least one blackout over the<br>last 7 days. The number of<br>days with blackouts averaged<br>2.1 days, the number of times<br>per day averaged 1.5, 33%<br>reported that blackouts lasted<br>15 to 30 minutes at a time,<br>21% reported 30 to 60<br>minutes, and 20% reported<br>less than 15 minutes, while<br>11% reported longer than 3<br>|The bottom 2 quintiles had<br>too few sampled households<br>for meaningful statistics.<br>There was no consistent<br>pattern by quintile.|Rural households reported<br>more frequent blackouts. The<br>largest difference was in<br>answer to the question on<br>whether the household had<br>experienced at least one<br>blackout over the last 7 days:<br>46% of urban and 60% of rural<br>households replied “yes.”|
||hours.|||
|Niger|Questions were identical to<br>those in Mali. 91% of<br>households reported at least<br>one blackout over the last 7<br>days. The average number of<br>days with blackouts was 4.4,<br>the number of blackouts per<br>day was 2.2, 34% reported<br>that blackouts lasted 30 to 60<br>minutes, followed by 25%<br>reporting 15 to 30 minutes,<br>17% reporting 1 to 3 hours,<br>14% reporting less than 15<br>minutes, and 11% reporting<br>more than 3 hours.|The bottom 2 in urban and<br>the bottom 4 quintiles in rural<br>areas had too few sampled<br>households for meaningful<br>statistics. The frequency of<br>blackouts increased with<br>quintile in urban areas.|Meaningful comparison was<br>not possible because even the<br>top quintile in rural areas had<br>a total of only 31 samples.|
|Nigeria|57% of households reported<br>daily blackouts in their areas,<br>and 31% reported several<br>blackouts over the last 7 days.|The percentage of households<br>reporting daily blackout<br>declined from 62 in the<br>second to 54 in the top<br>quintile.|55% of urban and 59% of rural<br>households reported daily<br>blackouts.|
|Senegal|Over the last 4 weeks,<br>households connected to the<br>grid reported blackouts 15<br>days on average, or 53% of<br>the time.|The number of days with<br>outages decreased slightly<br>from the second to the top<br>quintile.|The average number of days<br>with blackouts over the last 4<br>weeks was 14 in urban and 18<br>in rural areas.|

Source: World Bank staff analysis of household surveys _Note_ : All percentages are percentages of households connected to the grid who replied to the questions on blackouts.

# Gender of Head of Household

Is there evidence that female‐headed households tend to be poorer? If they are, examining the relationship between the gender of the head of household and access could be misleading, confusing poverty with gender. Are female‐headed households less likely to have access to electricity, or buy less electricity, than their male‐counterparts, after accounting for per capita and household expenditures?