rafmacalaba/datause-encoder-data huggingface dataset:
As shown in columns 3 and 4, the war caused 10 and 15 percentage points decline in non-farm business activities and wage related activities, respectively. On the contrary, participation in farm employment increased by a modest 3 percentage points (10 percent). These findings appear to suggest that as the war led to wide-ranging economic decline in Tigray, and especially in urban centers where non-farm business and wage employment are concentrated, households have increasingly found economic refuge in farm activities. This might be because battles mainly took place in areas around the main highways in the region, where incidentally all larger urban centers are located. Thus, precipitating displacement of livelihood activities from non-farm to farm based modes. These developments may have lasting dire implications to the slowly evolving structural transformation that was taking shape. This potential undoing of achievements of the last two decades in gradually reducing the share of agriculture in GDP and employment is likely to set the region back decades.

**Table 5: The Impact of violent conflict on labor market and livelihood activities**

||(1)<br>Any activity|(2)<br>Farm activity|(3)<br>Non-farm<br>business|(4)<br>Wage related<br>activities|
|---|---|---|---|---|
|Wartime dummy|0.016<br>(0.010)|-0.031<sup>***</sup><br>(0.007)|-0.013<sup>*</sup><br>(0.008)|0.020<sup>***</sup><br>(0.008)|
|Tigray × Wartime dummy|-0.101<sup>*</sup>|0.032<sup>*</sup>|-0.097<sup>***</sup>|-0.151<sup>***</sup>|
||(0.057)|(0.018)|(0.025)|(0.029)|
|Household fixed effect|Yes|Yes|Yes|Yes|
|R-squared|0.001|0.003|0.002|0.003|
|Mean dep. var (pre-war)|0.801|0.313|0.241|0.417|
|No. observations|21940|28558|28558|21940|

Notes: The outcome variable in the first column is a dummy variable for participation in any economic activity. The variables in columns 2-4 are binary indicators assuming a value of 1 for those households participating in these activities. All estimations use sampling weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

# **5.2 Disaggregated DID Results**

The availability of multiple pre- and post-war-onset survey rounds enables estimation of a more disaggregated DID model using round dummies instead of before and after dummies. This uncovers potential temporal heterogeneities in the impact of the conflict while also allowing testing pre-war trends in food security and labor market outcomes. Table 6 provides estimates of disaggregated impacts of the conflict on overall food insecurity. The full set of FIES questions 28
Two adaptation measures are selected for reducing flood risks to the Masonry and Mixed Wall dwellings. The cost estimates are based on FEMA adapted to account for labor cost differences in the United States (Teicholz, 1998; Davis, 2002):

- Measure 1: Improve flood resilience and resistance of the property. Approximate cost is 3,100 USD

- Measure 2: Elevate the house by 1 meter. Costs are estimated to be 9,345 USD.

# **_Benefit/Cost Calculations_**

Using data on expected average annual loss (AAL) and estimates of AAL reductions resulting from the application of each aforementioned measure, Table 1 shows the results of the benefitcost- calculations for the two options. The results are highly sensitive to the choice of the discount rate, the assumed length of life of the residential structure and the hazard level. We show the results for discount rates of 5 percent and 12 percent and for an expected lifetime of the structures of 10 and 25 years.

**Table 1: Flood Risk in Jakarta - Summary of Selected B/C Ratios**

|Adaptation Measure|Time||Mas|onry|||Mixe|d Wall||
|---|---|---|---|---|---|---|---|---|---|
||Horizon|Min H|azard|Max H|azard|Min H|azard|Max|Hazard|
||(years)|Disc<br>Ra|ount<br>te|Disc<br>Ra|ount<br>te|Disc<br>Ra|ount<br>te|Disc<br>R|ount<br>ate|
|||5%|12%|5%|12%|5%|12%|5%|12%|
|1. Improve flood|10|0.49|0.36|0.63|0.46|0.10|0.07|0.11|0.08|
|resilience|25|0.90|0.50|**1.16**|0.64|0.18|0.10|0.21|0.11|
|2. 1 m elevation|10|0.83|0.61|**1.18**|0.86|**2.06**|**1.51**|**3.69**|**2.70**|
||25|**1.51**|0.84|**2.15**|**1.20**|**3.77**|**2.10**|**6.73**|**3.75**|

_Note: numbers above 1 are in bold and indicate that the benefit is higher than the cost of these measures_ The results show benefit-cost ratios to homeowners that are substantially higher among mixed wall structures than among masonry structures, due to a greater hazard level. Elevating the property by 1 meter also has mostly favorable results, with benefit/cost ratios ranging from 0.61 to 6.73. Note however, that this is only the direct economic benefit to the homeowner. This does not include the indirect private benefit s/he can derive from a more resilient house (i.e., reducing the need for temporary housing, need for health care, evacuation cost), nor the broader benefits to society if all houses in a disaster prone area are resilient.

12
While none of these issues are new, our approach improves on previous literature by using a larger sample and applying panel data econometric techniques. A number of contributions (Hatting et al. 1998, Savadogo et al. 1995, etc.) have demonstrated that- farm level data can provide useful insights to examine these issues. However, these studies are characterized by small samples (122 and 150 households, respectively), a restricted set of variables (e.g. no information on household education, non-agricultural assets, or soil quality is available), and reliance on cross-sectional analysis. This limits not only the ability to generalize from the results obtained and make broader inferences but, more importantly, may also bias the results significantly. The reason is that there is likely to be a strong correlation between the large number of unobserved household and farm characteristics and input use. As, to take just one example, more educated farmers would tend to use inputs more intensively, the coefficients on observed inputs in a traditional production function will be biased upwards. Thus, the impact of purchased inputs would be overstated and cross-sectional evidence alone would appear to suggest that farmers "'underuse" these inputs.

To overcome these limitations of cross-sectional analysis, we apply panel data techniques. Under the condition that farmers' unobserved characteristics are time-invariant, use of panel data techniques allows to overcome these limitations that have plagued the earlier literature and are likely to have led to an upward bias on the coefficients for purchased inputs. Instead of making it appear these inputs are more effective and farmers inefficient in not applying the optimal quantities, they allow to retrieve structural parameters regarding the impact of specific inputs in the production function. Furthermore, use of instrumental variable estimators allows us to estimate the impact of time invariant productivity shifters such as producers' endowments of physical assets, human capital, and their access to public services. Both the structural parameters for specific inputs and the productivity shifters enable us to derive more precise policy recommendations.

Instead of relying on a survey that was specifically fielded for this purpose and the small sample that is generally associated with such efforts, we use data on farm production from a panel of 4853 farm households from the Central Statistical Office's Post Harvest Surveys for 1993/94 and1994/95. The data were collected sufficiently long after initiation of the reforms for them to have shown at least some impact. While the time period covered is rather short, it coincides with significant variability in the economic and agro-climatic environment such as the final closure of official credit institutions, and a drought in 1994/95. As a result, there is sufficient "within" household variation in input use for panel data methods to be meaningful and allowing us to
For MDG 2, the treatment is slightly more complex. In this case, the arguments in Table 3.3 determine the shares of children that enter basic school (out of the cohort of six-year olds), and successfully complete their current grade (among those enrolled in the first basic cycle). The shares that repeat their current grade or drop out from it are determined residually. The service level is measured per enrolled student, an indicator of educational quality. For the secondary and tertiary cycles, student behavior depends on a similar set of determinants. No continuation rate is defined for the tertiary cycle (as it is the terminal cycle).

In MAMS, the net (on-time) primary completion rate is the indicator that is targeted in relation to MDG 2, the goal of ensuring that children everywhere by 2015 are able to complete a full course primary schooling. The net enrolment rate, which is the official indicator, is a less informative measure of the extent to which the relevant age group is able to complete the six-year primary cycle. More specifically, in any year, the net completion rate is defined as the share of the students that would complete primary school on time if that year’s net intake and grade promotion rates were to prevail during the coming six years.<sup>11</sup> Considering the recent evolution of the school system in Yemen and the definition used for the MDG 2, the target for the net on time primary completion rate was adjusted downwards to around 92.5 percent, a rate that still is very ambitious. It could, for example, be the outcome of a 99 percent net intake rate and 99 percent promotion rates throughout the six years of primary schooling.

Generally speaking, the functions for educational outcomes and the other (i.e., noneducation) MDGs have all been calibrated to assure that, under base-year conditions, base-year indicators are replicated and that, under a set of other conditions identified in the Yemen ―Needs Assessment‖ (MOPIC, 2005), the target is fully achieved. Specifically, the ―Needs Assessment‖ provides estimates of government sectoral spending needs (current and capital) for the period 2006-2015. In MAMS, these are used to identify parameters for real services in functions that define MDG achievements. The

> 11 _y_ Mathematically, _NPCR_ t = _NIR_ t∙( _PR_ t) where _NPCR_ = net primary completion rate; _NIR_ = net intake rate (0 ≤ _NIR_ ≤ 1); _PR_ = promotion rate (0 ≤ _PR_ ≤ 1); and _y_ = number of grades in the primary cycle. As a simplification, MAMS assumes a uniform _PR_ for all primary grades. This version of the _NPCR_ is a period measure; the corresponding cohort measure would use the relevant rates over a series of six years.

19
lowest PMT. This approach ensures that the results we obtain in terms of targeting effectiveness are not due to a substitution of households across districts.

Table 3 shows that holding coverage levels constant, using the UDB to select beneficiaries leads to a meaningful decrease in both undercoverage and leakage compared to baseline targeting mechanisms. For the Jamkesmas program, using baseline targeting mechanisms for the same number of beneficiaries predicted to be eligible by the UDB would increase leakage from 23% to 28%, and severe leakage from 17% to 24%, as shown by comparing Tables 2 and 3. For the BLT program, using the UDB at constant baseline coverage levels would reduce undercoverage from 51% to 48% and leakage from 32% to 29%. Figure 5 provides the semiparametric benefit incidence curves for the two columns, analogous to the previous figure.

The reduction in exclusion errors under this constant coverage counterfactual further suggests that the main reason for the increase in undercoverage noted earlier in this section is the concurrent decrease in coverage levels, compared to baseline. Extrapolating, this improvement in targeting implies over 12,000 additional households from the poorest 30 percent with at least one child under the age of sixteen receiving this program in our study districts.

To summarize, holding coverage levels constant, the UDB is predicted to improve both undercoverage and leakage relative to baseline. Although the percent gains seem modest, the returns to improved coverage of the poorest members of society are potentially quite significant.

# **IV. Disentangling Misenumeration and Misclassification**

As described earlier, targeting errors in the UDB can be attributed to two factors: (1) misenumeration, or undercoverage of poor households during the enumeration process, and (2) misclassification of households during the ranking stage. In this section, we attempt to disentangle these two sources of targeting errors in order to highlight distinct policy implications. We focus first on errors resulting from the enumeration process using ‘reconstructed’ PMT scores calculated for all households in the SUSETI sample, instead of focusing only on those matched households who are actually registered in 19
||Table3B:S<br>|ummarystatist<br>|icsforgradua<br>|tingcountries,<br>|1961-1987<br>||||
|---|---|---|---|---|---|---|---|---|
||<br>()<br>heretDA <br>graduation|<br>Q)<br> °a‘Hine <br>_graduation|<br>8)<br> Difference<br>2)-()|<br>4<br>Percentage <br>cnaige|<br>6)<br> _—" <br>graduation|6<br> bent<br>_graduation|0)<br>Difference<br>©)-@)|8)<br>Percentage<br>eunee|
|Alllending(2010dollars)|||||||||
|<br>Total (millions)|99.47|206.33|106.86***|107.43|71.34|216.98|145.64***|204.16|
|Total per capita|14.30|22.87|8.57%**|59.90|11.51|22.34|10.83***|94.16|
|HDlending(2010dollars)|||||||||
|<br>Total (millions)|7.62|13.50|5.88|77.21|5.62|12.67|7.05**|125.28|
|Sectors (millions)|6.38|10.05|3.67|57.48|4.98|12.26|7.28%***|145.99|
|Total per capita|1.35|2.71|1.36*|101.12|0.96|2.62|1.65***|171.88|
|Sectors per capita|1.16|2.55|1.39*|119.47|0.87|2.57|1.71***|197.32|
|HD Practice Areas (2010 dollars)|||||||||
|Education (millions)|7.40|12.28|4.88|65.94|5.51|11.46|5.95**|107.89|
|Health, Nutrition andPopulation (millions)|0.22|1.22|1.00|459.42|0.11|1.21|1.10*|975.11|
|Social Protection and Labor (millions)|0.00|0.00|0.00||0.00|0.00|0.00||
|Numberofprojects|||||||||
|<br>All|1.62|2:29|0.67***|41.14|1.23|2.29|1.06***|85.62|
|HD|0.23|0.23|0.01|3.33|0.16|0.23|0.07*|46.85|
|Education|0.22|0.20|-0.02|-7.00|0.15|0.20|0.05|31.98|
|Health, Nutrition andPopulation|0.01|0.03|0.02|210.00|0.01|0.03|0.02*|448.22|
|SocialProtectionand Labor|0.00|0.00|0.00||0.00|0.00|0.00||

results from testing for statistical significance for difference in the two means. * Significant at 10%, ** Significant at 5%, *TM* Significant at 1%.
scarcity prevalent in the Sahel in comparison to other countries in Sub-Saharan Africa. This suggests a significant role for infrastructure investments in the water sector aimed at reducing the effects of climate variability, such as irrigation, in mitigating conflict risks. However, the benefits from infrastructure are also constrained by the economic and institutional environment. In that respect, we document evidence of a sudden onset of conflict that occurred in the group of countries known at the G5 Sahel after the Arab Spring, and how the political context of the Sahel region may have led to higher risks of conflict in areas with irrigated land, possibly resulting from an increase in the “potential lootability” of those areas, population density, or changes in local livelihood dynamics.

# _5.1 Climate Variation, Livelihood Shocks and Conflict_

First, this section revisits the relationship between conflict incidence and climate-related variability in soil water-content documented by Harari and Ferrara (2018) – henceforth HF. Their research provides evidence that climate-induced negative income shocks in the African continent generate persistent increases in conflict incidence, and that such conflict spills over into neighboring regions. Using HF’s gridded data on conflict and variations in water content of soil captured by the SPEI, we find the existence of a sharp negative relationship between the SPEI and conflict specifically for the countries of the G5 Sahel.

_Figure 2: Relationship between mean conflict and SPEI for G5 Countries and the Rest of Africa, usig data from Harari and Ferrara (2018)._ <!-- Start of picture text -->
-1 -.5 0 .5 1<br>Mean SPEI of gridcell (1997 - 2011)<br> Rest of Africa  Sahel<br>.4<br>.3<br>.2<br>.1<br>Fraction of years with conflict per gridcell<br>0<br><!-- End of picture text -->

To build on this analysis, we start by looking at the cross-sectional relationship between the average annual SPEI of a grid-cell during years 1997 to 2011 and the fraction of years in which a conflict event occurred in that cell, separately for the G5 Sahel countries and the rest of Africa (Figure 2). The evidence here suggests the presence of a strong negative relationship between the SPEI and frequency of conflict for a given cell, but the correlation between _mean_ conflict and SPEI does not account for country-specific differences in average climate and conflict experience that may be driving the relationship. It may well be that regions which have a higher average SPEI experience more conflict due to unobserved characteristics that are correlated with both variables, such as type of terrain.

To address the issue of unobservable covariates, the data on annual occurrence of conflict per grid-cell and annual variation in the SPEI are next analyzed through the following regression: 8
# **Table A26:  World Bank Country Policy and Institutional Assessments (PIA)**

<!-- Start of picture text --> Data Provider The World Bank.<br>Description Multilateral development bank headquartered in Washington, United States<br>Website www.worldbank.org<br>Data Source Country Policy and Institutional Assessments<br>Type Expert Assessment<br>Respondents World Bank country economists subject to centralized review for comparability<br>Frequency Annually since 1978<br>Coverage World Bank client countries<br>Public Access Since 2005, only for countries eligible for concessional lending from the International<br>Development Association.<br>Description Indicators on 16 dimensions of policy and institutional performance. Responses are coded<br>on a 6-point scale. CPIA indicators are used to allocate concessional lending across<br>countries.<br>2007 2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>NA .. .. .. .. .. .. .. .. ..<br>Political Stability and Absence of Violence<br>NA .. .. .. .. .. .. .. .. ..<br>Government Effectiveness<br>Management of external debt X X X X X X X X X<br>Quality public administration X X X X X X X .. ..<br>Budget management X X X X X X X X X<br>Efficiency of revenue mobilization / public expenditures X X X X X X X X X<br>Regulatory Quality<br>Business regulatory environment X X X X X X X X ..<br>Trade policy X X X X X X X X X<br>Rule of Law<br>Property rights X X X X X X X X ..<br>Control of Corruption<br>Transparency, accountability and corruption in public sector X X X X X X X X ..<br>Country coverage 140 136 134 135 136 136 136 136 131<br>Year of Publication 2007 2006 2005 2004 2003 2002 2000 1998 1996<br><!-- End of picture text --> 64
Table 3: Regression of Wages on Sanitation Coverage

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|sanitation in birth year|0.00296***|0.00296<sup>_†_</sup>|0.00181***|0.00322<sup>_†_</sup>|0.00286<sup>_†_</sup>|
||(0.000509)|(0.001611)|(0.000509)|(0.00169)|(0.00169)|
|district fxed efects|✓|✓|✓|✓|✓|
|year of birth fxed efects|✓|✓|✓|✓|✓|
|state _×_ urban fxed efects||✓||✓|✓|
|state-specifc linear time trends||✓||✓|✓|
|social group _×_urban indicators||✓||✓|✓|
|job category fxed efects|||✓|✓|✓|
|education indicators|||||_F_16_,_322 = 5_._98|
||||||(_p <_0_._001)|
|_n_ (adult men)|6,134|6,134|6,134|6,134|6,134|

Note - _p_ values: _†_ = 0 _._ 1 _, ∗_ = 0 _._ 05 _, ∗∗_ = 0 _._ 01 _, ∗∗∗_ = 0 _._ 001. Standard errors clustered at the district level.

Table 4: Regressions of Schooling and Literacy on IMR/Sanitation

||(1)<br>schooling|(2)<br>schooling|(3)<br>literacy|(4)<br>literacy|
|---|---|---|---|---|
|IMR in birth year|0.00175||0.00057||
||(0.00508)||(0.00048)||
|sanitation in birth year||0.0215||0.00102|
|||(0.0154)||(0.00148)|
|district fxed efects|✓|✓|✓|✓|
|year of birth fxed efects|✓|✓|✓|✓|
|_n_ (adult men)|12,718|6,134|12,747|6,142|

Note - _p_ values: _†_ = 0 _._ 1 _, ∗_ = 0 _._ 05 _, ∗∗_ = 0 _._ 01 _, ∗∗∗_ = 0 _._ 001. Standard errors clustered at the district level.

41
Table 3b: Tests for discontinuity on selected covariates: survey data

||(1)<br>**Full**|(2)<br>(3)<br>**2011-2012**<br>**2013-2014**|
|---|---|---|
|||**Panel A: Community survey**|
|Ln(km to any road)|0.228*|0.322<br>0.305*|
||(0.138)|(0.250)<br>(0.171)|
|Control Mean|7.613|7.648<br>7.588|
|Ln(km to major city)|0.154**|0.085<br>0.181**|
||(0.070)|(0.126)<br>(0.085)|
|Control Mean|4.601|4.664<br>4.557|
|Ln(members with rights to comm. land)|0.138|0.201<br>0.253|
||(0.137)|(0.263)<br>(0.166)|
|Control Mean|4.641|4.616<br>4.659|
|Ln(poverty index 2010)|0.148*|0.340**<br>0.057|
||(0.081)|(0.161)<br>(0.102)|
|Control Mean|0.079|0.015<br>0.124|
|Ln(average slope in degrees)|0.133**|0.092<br>0.124|
||(0.060)|(0.110)<br>(0.076)|
|Control Mean|2.222|2.360<br>2.126|
|Mean canopy cover 2000|-0.498|-3.768<br>1.436|
||(3.076)|(5.743)<br>(3.911)|
|Control Mean|47.318|40.318<br>52.166|
|Observations|862|357<br>505|
|||**Panel B: Household survey**|
|Household head educ: sec school or above|0.015|0.001<br>0.019|
||(0.019)|(0.037)<br>(0.024)|
|Control Mean|0.160|0.172<br>0.152|
|Observations|8350|3442<br>4908|
|Household depends upon subsistence agric|0.001|-0.058<br>0.047*|
||(0.021)|(0.038)<br>(0.027)|
|Control Mean|0.197|0.185<br>0.205|
|Observations|8413|3466<br>4947|

> 1 Regressions include the threshold dummy, the first and second order polynomials of the re-centered point score and their interactions with the threshold dummy, as well as state fixed effects. For the community level analysis, robust standard errors are presented in parentheses. For the household level analysis, standard errors were clustered at the community level.

> 2 In Panel B, the number of obs for the household being dependent upon subsistence agriculture matches the full number of observations, while the number of observations for the household head education has some missing data.

35
The above list roughly describes the alternatives open to the public since the reform of 1977. Unfortunately we do not have reliable data describing assets holdings prior to that reform. Loosely speaking, however, we can say that the Argentine economy has experienced a sustained process of demonetization (dollarization?) in recent decades. The real amount of Ml (noninterest bearing money) has systematically dEcreased since 1970 as shown in Figure 7 (the apparent real incrtvase in Ml during 1973-75 was really due to the price freeze imposed during the period that resulted in the inflationary explosion known as "Rodrigazo" in mid-1975). The time path of real M2 (Ml plus time deposits) has depended more on the evolution of interest rates paid on time deposits.

**FIGURE 7**

**REAL UALUE OF MI (MONTHLY 1960-89)**

<!-- Start of picture text --> 175 m<br>750 . . . . . . .. . . . .. t<br>?5 . .... ..... . ........<br>2 5 . . . . . . . . . . . . . . . . .. . .<br>1969 1965 1979 1977 1989 1985 1999<br>.MIR<br><!-- End of picture text --> 25