rafmacalaba/datause-encoder-data huggingface dataset:
lifted above this level of utility, they are no longer in poverty. The framework uses the AIDADS system to represent consumer preferences, and is calibrated using Tanzania’s Household Budget Survey 2000/2001<sup>10</sup> . Households are stratified into seven groups based on earnings sources. The poverty line in Tanzania is taken to match the observed national poverty headcount ratio reported by the World Bank (2006). This in turn dictates the poverty level of utility in the initial equilibrium.

# **3.2 EXPERIMENTAL DESIGN**

The patterns of historical production deviations from trend for each of the five case study years highlighted in the previous section – Scenarios 1995 and 1971 (“Tanzania +”), Scenario 1980 (“Tanzania 0”), and Scenarios 1982 and 1983 (“Tanzania –”) are reproduced in the model via an appropriate combination of international productivity shocks<sup>11</sup> . We then consider the impacts on trade and poverty in the context of a range of different trade scenarios to explore the interplay between trade regimes and the poverty impacts of climate shocks<sup>12</sup> .

In order to mimic the short run/transient nature of these productivity shocks, these simulations are conducted under the assumption that agricultural land and capital are immobile across sectors. That is, farmers are limited in their ability to adapt to different climate outcomes. For example, they are able to mobilize labor in response to a climate shock and adjust harvesting dates, but are not able to bring new irrigation infrastructure online in response to a single climate outcome. The allocation of cropland to a given crop is also pre-determined in this short run model closure.

The production deviations for the five case study years are simulated under two different trade regimes, as perturbations from the benchmark world economy. Conducting all simulations as perturbations from trend allows for comparability across trade regimes. The two trade regimes are:

- Regime 1 (Baseline) – The trade regime prevailing in the 2001 world economy.

> 10 The poverty model is fully documented in Ahmed et al. (2011), including parameter estimates for the AIDADS demand system used.

> 11 To obtain the appropriate scale for the productivity shocks, we perform a pre-simulation in which output is exogenous and productivity endogenous.

> 12 The production deviations for Mozambique, Rest of Sub-Saharan Africa, Southern Africa, Zambia, and Zimbabwe are not implemented under any scenario. The magnitudes of their deviations from trends were too large for the model to converge on a solution. Production in these countries can thus be assumed to be at trend for any given year simulated.

13
<!-- Start of picture text --> 44000 Distribution of land value change<br>ME land value decrease<br>12000 GE land value increase<br>300<br>_.. 10000<br>D 250<br>8<br>8000 200<br>a<br>= 150<br>SD<br>[eo] fax<br>oO<br>+= 6000 |<br>4000<br>2000 5 0 |<br>0 100 200 300 400 500<br>0 pmWM| ds<br>-100 -50 0 50 100 150 200 250 300 350 400 450 500<br>Land value change due to resilience investments ($/m?)<br><!-- End of picture text -->

<!-- Start of picture text --> 44000 Distribution of land value change<br>GRE land value decrease<br>12000 GE land value increase<br>100<br>_.. 10000<br>2 80<br>8 8000<br>2<br>2 60 |<br>fe}<br>oO<br>i= 6000 40 |<br>2000 0<br>25 100 200 300 400 500<br>0 pee),eedT<br>-100 -50 0 50 100 150 200 250 300 350 400 450 500<br>Land value change due to resilience investments ($/m?)<br><!-- End of picture text -->
**Figure 7: Perceptions of Iraq's biggest challenge**

**(% of adults)**

<!-- Start of picture text --> Other 1%<br>Religious extremism 3%<br>Public services 6%<br>Fighting terrorism 8%<br>Political issues 11%<br>Foreign interference 10%<br>Security 14%<br>Economic situation 18%<br>Corruption 33%<br><!-- End of picture text --> Source: Arab Barometer 2018, Wave V.

# **_4.2 Perceptions of corruption_**

Perceptions of corruption are relatively high in Iraq compared to other countries. In Iraq, 90 percent of adults believe that corruption is widespread throughout their government, which is about 15 percentage points higher than the average in oil-exporting countries (Figure 8).

Adults in Iraq believe that corruption is widespread in businesses and government. While perceptions of private and public corruption have both increased since 2013, more adults in Iraq believe the government is corrupt than believe businesses are corrupt (figure 9).

Overall, men are about 5 percentage points more likely than women to believe that the government is corrupt, while the poor and rich are equally likely to believe this. All ethno-religious groups believe government corruption is widespread (figure 10), and that perception has deepened over time.

11
_CHAPTER 5. UNIT LEVEL MODELS: LINEAR INDICATORS_ 34 effects. GLMMs were also studied by Hobza, Marhuenda and Morales (2020). Ghosh et al. (1999) extended the GLMM to include spatial correlation and applied it to disease mapping. Jiang and Larihi (2001) obtained empirical best (EB) predictors under a logistic GLMM at the unit level. The optimal EB predictors under a unit-level logistic mixed model do not have closed-form expressions and hence require computationally intensive methods such as Monte Carlo simulation. Given that the likelihood does not either have a closed form and complex numerical methods are also needed for model fitting, a resampling procedure for MSE estimation of EB predictors might become too computationally intensive. A way to reduce this computational burden is to consider suboptimal predictors such as plug-in predictors. EB and plug-in predictors are compared by Hobza and Morales (2016) and by Molina and Strzalkowska-Kominiak (2020). Hobza, Morales and Santamar ́ıa (2018) estimated poverty proportions under unit-level temporal Binomial-Logit mixed models. Berg and Chandra (2014) studied small area prediction for a unit-level log-normal model. Berg (2022, 2023) proposed a unit-level Poisson-Gamma model for counts and Tzavidis et al. (2015) proposed robust small area prediction for counts.

GLMs are also used by Isidro, Haslett and Jones (2016) in the so called extended structure preserving estimation (extended SPREE) method for updating small area estimates of poverty in intercensal years. A couple of methods for SAE estimation under GLMMs for counts are implemented in the R package `saeeb` (Fauziah and Wulansari, 2020).

Models with measurement error in the covariates are classified in Fuller (1987) into functional measurement error models, where the true value of the covariate is assumed to be fixed, and structural models, when it is regarded as random. The former were developed by Ghosh and Sinha (2007) and Datta, Rao and Torabi (2010), while the latter were firstly studied by Fuller and Harter (1987), Ghosh, Sinha and Kim (2006) and Torabi, Datta and Rao (2009).
<!-- Start of picture text --> In -<br>210<br>9 17<br>1%<br>! I~~I<br>IS<br><!-- End of picture text --> Fi.gre 4: Growt Rate of Retail Price Indexc: 1979-1993 <!-- Start of picture text --> 4s<br>U.__<br>79 S OB1 X 3 9t 95 S 17 1B 89 9 91 92 93<br><!-- End of picture text --> Figure 5. Growth Rate of Real GNP: 1979-1993 47
Policy Research Working Paper 7646

# **Abstract**

There is wide variation in how consumption is measured in household surveys both across countries and over time. This variation may confound welfare comparisons in part because these alternative survey designs produce consumption estimates that are differentially influenced by contrasting types of survey response error. Although previous studies have documented the extent of net error in alternative survey designs, little is known about the relative influence of the different response errors that underpin a survey estimate. This study leverages a recent randomized food consumption survey experiment in Tanzania to shed light on the relative influence of these various error types. The observed deviation of measured household consumption from a benchmark is decomposed into item-specific consumption incidence and consumption value so as to investigate effects related to (a) the omission of any consumption and then (b) the error in value reporting conditional on positive consumption. The results show that various survey designs exhibit widely differing error decompositions, and hence a simple summary comparison of the total recorded consumption across surveys will obscure specific error patterns and inhibit the lessons for improved consumption survey design. In light of these findings, the relative performance of common survey designs is discussed, and design lessons are drawn to enhance the accuracy of item-specific consumption reporting and, consequently, the measures of total household food consumption.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jfriedman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **<u>Table 6c.</u>** <u>Multivariate Analysis of Trees for Timber or Fuelwood</u>

|||_Adoption An_|_alysis (Probit)_|_Dete_|_rminants of sha_|_re of farmland with trees_|
|---|---|---|---|---|---|---|
||_Dep. Vari_|_able: Trees fo_<br>_farm (_|_r timber or fuelwood on_<br>_yes=1)_|_Dep. Vari_|_able: Share of f_<br>_for timbe_|_arm area with presence of trees_<br>_r or fuelwood_|
||(I)|(II)|Shapley Value|(III)|(IV)|Shapley Value|
|**_Household Controls_**|||**0.007 (2.70%)**|||**0.010 (3.32%)**|
|Household Size|-0.000|-0.001||-0.005|-0.015||
||[0.002]|[0.002]||[0.015]|[0.018]||
|Number of Children (<14 years old)|<br>0.003|<br>0.003||<br>0.014|<br>0.019||
||[0.003]|[0.003]||[0.013]|[0.014]||
|Head's Age (years)|0.001**|0.000***||0.001|0.002||
||[0.000]|[0.000]||[0.001]|[0.001]||
|Head Female (yes=1)|-0.002|-0.004||0.082|0.051||
||[0.006]|[0.005]||[0.075]|[0.057]||
|Head education (years)|0.001|0.001||-0.002|-0.005||
||[0.001]|[0.001]||[0.005]|[0.006]||
|**_Assets and land_**|||**0.001 (0.37%)**|||**0.122 (55.22%)**|
|Tropical Livestock Units (TLU)|0.001|0.000||0.019|0.013||
||[0.001]|[0.001]||[0.019]|[0.017]||
|Land Owned (area - ha)|0.001|0.001||0.144|0.145||
|<br>|[0.001]|[0.001]||[0.101]|[0.100]||
|**_Geo- and climate variables_**<br>|||**0.084 (30.35%)**|||**0.007 (3.34%)**|
|Log Population Density around 20km (people/km<sup>2</sup>) (2010)|-0.000|-0.000||-0.002|0.001||
||[0.001]|[0.001]||[0.002]|[0.002]||
|Tree Cover % around 20km (mean) (2010)|-0.000|0.000||-0.005|-0.005||
||[0.000]|[0.000]||[0.003]|[0.003]||
|Fertile Soil % around 20 km (mean) (2010)|0.002|0.001||0.012|-0.001||
||[0.003]|[0.003]||[0.013]|[0.010]||
|Log. Annual Mean Temperature ( C )|-0.005***|-0.003**||0.002|0.012||
||[0.001]|[0.001]||[0.012]|[0.013]||
|Log. Annual Precipitation (mm)|0.000|0.000||0.000|0.000||
||[0.000]|[0.000]||[0.000]|[0.000]||
|**_Country Fixed Effects (Baseline Country: Ethiopia)_**|||**0.11 (42.02%)**|||**0.023 (10.43%)**|
|<br>Malawi|-0.040***|-0.038***||0.227*|0.273||
||[0.005]|[0.005]||[0.127]|[0.160]||
|Nigeria|||||||
|Tanzania|0.245***|0.165***||0.571***|0.415***||
||[0.065]|[0.050]||[0.154]|[0.128]||
|Uganda|0.018|-0.001||0.097*|0.035||
||[0.017]|[0.011]||[0.057]|[0.056]||
|(pseudo) R-Square|0.250|0.300||0.182|0.221||
|Observations|8728|8697||8728|8728||
|District/Zone Fixed Effect|No|Yes||No|Yes||

_Note:_ This table presents the multivariate results for tree for timber or fuelwood on farms. Robust standard errors in brackets, clustered at strata level. Sampling weights used for all regressions. * p<0.10 ** p<0.05 *** p<0.01.

31
<!-- Start of picture text --> Each pillar receives equal 1/5th weight in overall score<br>~ on a<br>income—— oe _—<br>— oo y<br>- oe -_<br>i?) 25 50 75 100<br>level<br>Pillar | | Pillar 1: Data Use | | Pillar 2: Data Services | | Pillar 3: Data Products<br>(0) Pitar4: Data Sources [lJ Pillar 5: Data Infrastructure<br>SPI Overall Score scale = 0 - 100 points.<br><!-- End of picture text -->

Each pillar receives equal 1/5th weight in overall score <!-- Start of picture text --> High income<br>Upper middle income<br>income<br>Lower middle income<br>Low income<br>i?) 25 50 75 100<br>percentage<br>Pillar | | Pillar 1: Data Use i Pillar 2: Data Services i Pillar 3: Data Products<br>|| Pillar 4: Data Sources | Pillar 5: Data Infrastructure<br>SPI Overall Score scale = 0 - 100 points.<br><!-- End of picture text -->
Figure 4: Annex B CFC Event Study Around Signature of RTA <!-- Start of picture text --> −3 −2 −1 0 1 2 3<br>Event Time (RTA Signature)<br>0.2<br>0.1<br>0.0<br>% Deviation from MP<br>−0.1<br>−0.2<br><!-- End of picture text -->

**Notes:** This figure presents event study coefficients for CFC emissions from Annex B countries (represented as percent deviations from target). Black lines correspond to coefficient estimates and 95% confidence intervals for RTAs without ODS provisions, red lines correspond to coefficient estimates and 95% confidence intervals for RTAs with ODS provisions.

31
Table 1: Descriptive statistics

|**Variable**|**Mean**|**Std.**<br>**Dev.**|**Min**|**Max**|**% of**<br>**zeros**|
|---|---|---|---|---|---|
|**Patents**|1132|3509|0|31104|6.45|
|**ln R&D**|20.90|2.33|11.25|25.82||
|**ln US trade**|15.20|1.68|7.82|19.05||
|**Natural Resources**|0.21|1.66|-4.74|14.88||
|**Quality of Academic Institutions**|4.93|0.97|2.80|6.30||
|**Collaboration with the Private Sector**|3.97|0.95|2.54|5.78||
|**Education**|7.05|2.23|2.21|11.84||
|**IPR**|2.93|0.94|0.33|4.57||
|**ln (Mean Patents)**|3.48|2.70|-2.56|8.39||

25
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<!-- Start of picture text --> 010-<br>0.05<br>00——<—$<br>1 2 3<br>0.000 ————___—.<br>-0.025-<br>-0,050-<br>-0,075-<br>01005<br>1 2 3<br>0.00<br>1 2 3<br>0.02<br>0.01,——<br>-0,01:<br>1 2 3<br>0.10-<br>0.05<br>0,00<br>-0.05-<br>1 2 3<br>03-<br>=<br>OL<br>PY<br>; 1 2 3<br><!-- End of picture text --> <!-- Start of picture text --> 2}<br>a<br>1 2 3<br>10:<br>ost<br>i ——————<br>0.0<br>-05-<br>1 2 3<br>0.0—__=<br>Se“oe<br>1 2 3<br>05+<br>0.0<br>eE=_——<br>-05:<br>1 2 3<br>10<br>5<br>0<br>1 2 3<br>10-<br>VF —<br>-10,<br>er<br>1 2 3<br><!-- End of picture text -->
# **Table 5: Do Owner Characteristics Predict Innovation?**

Dependent Variable: Any Form of Innovation, Marginal Effects from Probit Estimation Shown

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Firm Size Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm Characteristics Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Owner is Female||0.0128|0.0141|0.0135|0.0136|0.0135|0.0198|0.0131|0.0230|
|||(0.0238)|(0.0239)|(0.0239)|(0.0239)|(0.0239)|(0.0242)|(0.0239)|(0.0243)|
|Owner's Age||-0.00136|-0.00133|-0.00114|-0.00136|-0.00136|-0.00164|-0.00143|-0.00145|
|||(0.00130)|(0.00130)|(0.00131)|(0.00131)|(0.00130)|(0.00132)|(0.00131)|(0.00132)|
|Owner is Married||0.00972|0.0106|0.0113|0.00845|0.00985|0.00779|0.00957|0.00901|
|||(0.0286)|(0.0287)|(0.0287)|(0.0287)|(0.0286)|(0.0288)|(0.0286)|(0.0289)|
|Owner is Tamil||-0.0963***|-0.0953***|-0.0946***|-0.0967***|-0.0958***|-0.0964***|-0.0967***|-0.0938***|
|||(0.0317)|(0.0319)|(0.0317)|(0.0317)|(0.0318)|(0.0317)|(0.0318)|(0.0318)|
|Owner's Years of Education||0.0118***|0.0100***|0.00979**|0.0110***|0.0118***|0.0118***|0.0115***|0.00797**|
|||(0.00379)|(0.00389)|(0.00384)|(0.00380)|(0.00380)|(0.00379)|(0.00380)|(0.00393)|
|Risk Seeking Aptitude||0.000638|0.000404|0.000349|-0.000403|0.000596|0.000260|-0.000162|-0.00155|
|||(0.00363)|(0.00363)|(0.00363)|(0.00366)|(0.00363)|(0.00363)|(0.00366)|(0.00369)|
|Log of Owner's Discount Rate||0.0318***|0.0325***|0.0338***|0.0334***|0.0317***|0.0318***|0.0318***|0.0354***|
|||(0.00839)|(0.00841)|(0.00845)|(0.00842)|(0.00839)|(0.00838)|(0.00839)|(0.00849)|
|Hyperbolic Discounter||-0.0211|-0.0224|-0.0194|-0.0269|-0.0210|-0.0222|-0.0216|-0.0265|
|||(0.0214)|(0.0214)|(0.0214)|(0.0216)|(0.0214)|(0.0214)|(0.0214)|(0.0216)|
|Digitspan Recall of Owner|||0.0126*||||||0.00966|
||||(0.00681)||||||(0.00696)|
|Raven test Score of Owner||||0.0105***|||||0.00946**|
|||||(0.00389)|||||(0.00393)|
|Optimism of Owner|||||0.0388**||||0.0314*|
||||||(0.0182)||||(0.0190)|
|Polychronicity of Owner||||||-0.00486|||-0.00726|
|||||||(0.00876)|||(0.00897)|
|Owner has 3 or more previous jobs|||||||0.0678*||0.0664*|
||||||||(0.0350)||(0.0352)|
|Tenacity of the Owner||||||||0.0375|0.0218|
|||||||||(0.0261)|(0.0275)|
|Observations|2461|2461|2461|2461|2461|2461|2461|2461|2461|
|Pseudo R <sup>2</sup>|0.0618|0.0734|0.0745|0.0756|0.0748|0.0735|0.0747|0.0741|0.0791|
|P-value for test individual characteristicsjointlyzero||0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ All probits also include the same firm size and firm characteristic controls as column 6 of Table 4. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

- 30 -
Figure 2: SPS and TBT Mutual Recognition and Harmonization Provisions in PTAs <!-- Start of picture text --> MRA provision of SPS Harmonization provision of SPS<br>MRA provision of TBT Harmonization provision of TBT<br>Number of new agreements (left axis)<br>Number of new agreements with specific provisions (see note)<br>Cumulated number of agreements (right axis)<br>1960 1970 1980 1990 2000 2010 2020 1960 1970 1980 1990 2000 2010 2020<br>1960 1970 1980 1990 2000 2010 2020 1960 1970 1980 1990 2000 2010 2020<br>20 300 20 300<br>10 150 10 150<br>0 0 0 0<br>20 300 20 300<br>10 150 10 150<br>0 0 0 0<br><!-- End of picture text --> Note: the graph plots the number of new RTAs entering in force over the period 1958-2017. It disantangles RTAs including at least one provision on mutual recognition of SPS (upper-left), RTAs including at least one provision on harmonization of SPS (upper-right), RTAs including at least one provision on mutual recognition of TBT (bottom-left) and RTAs including at least one provision on harmonization of TBT (bottom-right). The red line shows the cumulated number of RTAs.

Source: Mattoo, Rocha, Ruta (2020).

30
(2006c), using longitudinal data from West Bengal, find that district and/or state allocated development grants to villages are strongly negatively related to the percentage of low-caste poor households in the village. However, neither this study nor any other econometric analysis of which we are aware addresses whether devolution of resource control can exacerbate conflict and inequality _between_ communities.

More broadly, this paper is related to, and the results consistent with, Acemoglu and Robinson (2008) who study the interaction between de jure and de facto political power: “A change in political institutions that modifies the distribution of de jure power,” they argue, “need not lead to a change in equilibrium economic institutions if it is associated with an offsetting change in the distribution of de facto political power.” Our question, in particular, is whether an institutional change (decentralization) reduces economic inequality or is instead thwarted by increased investments in political capture. We also follow Baland and Robinson (2008) and Anderson et al. (2015), among others, in associating land ownership in developing countries with political power or influence, although our mechanism (rent-seeking) is distinct from theirs (clientilism).

The next section of the paper presents the institutional backdrop and data for our analysis. Section 3 develops the model of corruption and rent-seeking along a canal system. Section 4 lays out the empirical methodologies and presents the main impacts of irrigation management reform in Punjab. Section 5 turns to the empirical analysis of power asymmetry along a channel and how it interacts with decentralization. Section 6 concludes the paper.

# **2 Context and Data**

## **2.1 Indus Basin irrigation system**

The Indus Basin irrigation system, which accounts for 80% of Pakistan’s agricultural production, lies mostly in its most populous province, Punjab, wherein it encompasses 37,000 5