rafmacalaba/datause-encoder-data huggingface dataset:
# **_DAU 2006: Any Innovations?_**

23. Effective in 2006, the minimum allocation will now cover the total wage bill instead of proportional wage bill (PWBA) and lump sum (LS). In practice, the allocation for wage will be done first and then the rest of the DAU pool will be distributed based on the fiscal gap formula.

24. The Fiscal Gap formula has undergone some major innovations as well. First the components of fiscal capacity will now be fully weighted;

* (11). FCi = PADi + STXi + SDAi

Second, the poverty gap indicator is substituted by an inverse of Human Development Index (HDI) and Gross Regional Domestic Product per capita AV (12). IEN = (0.3*IPOPi +0.1*1/HDIi + 0.15*IAREAi + 0.3*ICOSTRELi+0.15*IPCGRDPi)*EN The rest of the process remains the same with previous years, including the hold harmless provision.<sup>38</sup> 25. Third, is the removal of hold harmless condition by fiscal year 2008. Folloring the formula final DAU allocation will be (13). DAUi = BAi + FGi The basic allocation (BA) equals to 100 percent of wage bill. The final adjustments will be as follows: (i) If a region has fiscal gap equal to zero, it shall receive DAU in the amount of the basic allocation (BA). ( FG=0; DAU = BA).

(ii) If a region has a negative fiscal gap that is lower than the basic allocation, it shall receive DAU in the amount of the basic allocation less the value of the fiscal gap. (if CF<0, [CF]<AD; DAU < AD).

(iii) If a region has a negative fiscal gap equal to or bigger than the basic allocation, it shall receive zero allocation of DAU. (if CF<0, [CF] >= AD; DAU = 0)

> 38 Please note that if a region has a minus or zero fiscal gap, its DAU 2006 before hold harmless will be equal to its total wage bill. If the amount is bigger than the previous year DAU, the final DAU allocation after the hold harmless adjustment will ultimately be less than the 100 percent of total wage bill. In 2006, for example, regions such as Kabupaten Badung (Bali), Kabupaten Penajem Paser Utara (East Kalimantan) and DKI Jakarta receive DAU less than their total wage bills.

34
change mitigation efforts in developing countries. In so doing, we have been able to offer some important insights that may help shape future policies as well as ongoing research in this area.

A first point is the key role of potential earnings effects precipitated by market responses to climate change. To date, most studies have focused solely on food availability and prices, yet higher food prices in the wake of adverse climate change can also translate into higher incomes for producers. In some regions, this could lead to poverty reductions, particularly those areas: (i) which are less severely affected by climate change, (ii) where poverty is concentrated in rural, agricultural areas, and (iii) where adverse climate shocks boost the demand for unskilled labor (e.g., through the need to replant fields). A further complication arises due to the prevalence of non-priced goods in the consumption and earnings bundles of poor households. Adverse climate impacts on publicly accessible forests and wildlife resources could have a significant adverse impact on the poor who rely on them for firewood, food, and other consumption items. In short, the poverty consequences of climate change are much more nuanced than has been suggested in most discussions to date. It is not simply a question of food price impacts on low income consumers.

With the proliferation of studies assessing the likely agricultural productivity impacts of climate change, policy makers are in desperate need of guidance regarding the comparability and reliability of such reports. We have evaluated the three main approaches to assessing these impacts and conclude that each has its own limitations and should not be used in isolation. Decision makers must press those doing crop simulation analysis for more regional and national scale validation of their work, while the statistical studies (including the Ricardian) analyses need to be carefully evaluated for robustness. In general, too little attention has been paid to the 43
<!-- Start of picture text --> 45<br>Chart 4a: Gender gap in Science score at percentiles - 2003 vs.<br>40 2007<br>35<br>30<br>G<br>e 25<br>n 20<br>Sience-2007<br>d<br>15 Science-2003<br>e<br>r 10<br>5<br>G<br>0<br>a<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>p<br>Percentile<br>10<br>Chart 4b: Characteristics component of the gender gap in<br>Science score: 2003 vs. 2007<br>5<br>G<br>0<br>e Sience-2007<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>n<br>Science-2003<br>d<br>-5<br>e<br>r<br>-10<br>G<br>a<br>-15<br>p Percentile<br><!-- End of picture text --> 16
<!-- Start of picture text --> 0.275-<br>0.250-<br>0.225 - ar}<br>0.200-<br>0.175-<br>0.06-<br>fo}<br>0.04- ©33io}<br>=<br>0.02-<br>0.00-<br>03-<br>0.2-<br>a°g<br>O1-<br>0.023-<br>0.021 -<br>=<br>0.019- &Fi<br>0.017-<br>0.10-<br>0.08 - 2€a3Ffz<br>0.06-<br>2000 2005 2010 2015 2020<br>Year<br>Cluster 1 2<br><!-- End of picture text -->
TABLE 3: Impact of text messages on Early Childhood Development outcomes

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|**_Panel A: Intention to treat_**||||
|ITT|0.00|-0.01|0.00|
||(0.03)|(0.06)|(0.04)|
|**_Panel B: Heterogeneity by caregiver education_**||||
|ITT|-0.12<sup>_∗∗_</sup>|-0.24<sup>_∗_</sup>|-0.07|
||(0.06)|(0.13)|(0.07)|
|ITT X Medium educ (4-6 years of education)|0.17<sup>_∗∗_</sup>|0.18|0.13|
||(0.08)|(0.16)|(0.09)|
|ITT X High educ (more than primary education)|0.18<sup>_∗∗_</sup>|0.41<sup>_∗∗∗_</sup>|0.04|
||(0.08)|(0.15)|(0.09)|
|Observations|2485|774|1711|
|_P-values joint signifcance test_||||
|ITT + ITT X Medium educ = 0|0.35|0.48|0.22|
|ITT + ITT X High educ = 0|0.27|0.07|0.74|

_Note:_ All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months old, and the first principal component of all 7 tests for children 36-84 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, Memory and self-control) of children aged 36 to 83 months. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 37
|Northern Gaza EmergencySewage Treatment(NGEST)Project|Jean-Martin Brault|
|---|---|
|BURUNDI- ERC|Jean-Pascal Nganou|
|BURUNDI- ERC|Jean-Pascal Nganou|
|BURUNDI- ERC|Jean-Pascal Nganou|
|BURUNDI- ERC|Jean-Pascal Nganou|
|Montenegro Environmental Infrastructure|Andreas Rohde|
|Montenegro Environmental Infrastructure|Andreas Rohde|
|Montenegro Environmental Infrastructure|Andreas Rohde|
|Montenegro Environmental Infrastructure|Andreas Rohde|
|Montenegro Environmental Infrastructure|Andreas Rohde|
|CHILE - Chacabuquito Hydro Power Project|Javier Freire Coloma|
|CHILE - Chacabuquito Hydro Power Project|Javier Freire Coloma|
|CHILE - Chacabuquito Hydro Power Project|Javier Freire Coloma|
|CHILE - Chacabuquito Hydro Power Project|Javier Freire Coloma|
|CHILE - Chacabuquito Hydro Power Project|Javier Freire Coloma|
|CHILE - Chacabuquito Hydro Power Project|Javier Freire Coloma|
|Tanzania EmergencyPower Supply|Mohua Mukherjee|
|Tanzania EmergencyPower Supply|Mohua Mukherjee|
|Tanzania EmergencyPower Supply|Mohua Mukherjee|
|Tanzania EmergencyPower Supply|Mohua Mukherjee|
|Tanzania EmergencyPower Supply|Mohua Mukherjee|
|NBI-ISP NELSAP-CU Institutional Strengtheningand ScalingUp|Barbara A. Miller|
|NBI-ISP NELSAP-CU Institutional Strengtheningand ScalingUp|Barbara A. Miller|
|NBI-ISP NELSAP-CU Institutional Strengtheningand ScalingUp|Barbara A. Miller|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|Education for All Project|Venkatesh Sundararaman|
|EmergencyDemobilization and Reintegration Project|Adriaan Verheul|
|EmergencyDemobilization and Reintegration Project|Adriaan Verheul|
|EmergencyDemobilization and Reintegration Project|Adriaan Verheul|
|EmergencyDemobilization and Reintegration Project|Adriaan Verheul|
|Second HIV/AIDS Project|Shiyan Chao|
|Second HIV/AIDS Project|Shiyan Chao|
|Second HIV/AIDS Project|Shiyan Chao|
|Second HIV/AIDS Project|Shiyan Chao|
|Second HIV/AIDS Project|Shiyan Chao|
# **ACRONYMS AND ABBREVIATIONS**

|ai|active ingredient|
|---|---|
|CIMMYT|<br>International Maize and Wheat Improvement Center|
|DONATIM|see TZDK|
|dus|distinctive, uniform, and stable|
|GDAR|General Directorate of Agricultural Research|
|GIFAP|International Group of National Associations of Manufacturers of Agrochemical<br>Products|
|ha|hectare|
|ICD|Industry Council for Development|
|IPM|integrated pest management|
|kg|kilogram|
|MARA|Ministry of Agriculture and Rural Affairs|
|**OPV**|open-pollinated variety|
|PVP|plant variety protection|
|SEKER|Sugar Factories Company|
|SIS|State Institute of Statistics|
|SOE|state owned enterprise|
|t|metric ton|
|TEBD|Seed Industry Association|
|TIGEM|Agricultural State Enterprises|
|TISIT|Association of Pesticide Manufacturers, Importers and Agents|
|TDF|Technology Development Foundation|
|TKV|Development Foundation of Turkey|
|TL|Turkish lira|
|TZDK|Agricultural Supply Organization|
|UPOV|International Convention for the Protection of New Varieties of Plants|

ii
A. Sanghi and D. Johnson <!-- Start of picture text --> 800<br>600 China<br>India<br>400 Nertherlands<br>UK<br>200 US<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Year<br>Investment (USD Millions)<br><!-- End of picture text --> Figure 19: Investment from China rising; investment from UK and US falling Source: KenInvest 2015 <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>China France Italy Japan Luxembourg Switzerland<br>Country<br>FDI inflows (USD Millions)<br><!-- End of picture text --> Figure 20: China and France top sources of FDI inflows for Kenya (2012) Source: UNCTAD FDI/TNC database 2015 22
sampling to save the travel cost [cancel out the cluster-specific errors]. Further, because _rJ_<sup>_∗_[</sup><sup>_r_</sup> _J_<sup>+]</sup> is an increasing [a decreasing] monotonic transformation of _ρ_ , the signs of its partial derivatives with respect to _c_ , _wc_ , _τ_ , and _wh_ are the same as [the opposite of] those in Lemma 16.

It is also interesting to note that the number of households to be sampled in each cluster is the same between the main and dual problems as the following equation shows: Both equations show that the cluster size tends to get smaller when the errors due to the cluster-level variations become more pronounced. On the other hand, the cluster size tends to get larger (and the number of clusters to be visited get smaller) when the travel cost is larger. The ratio _K_ 2 _/K_ 1 = 1 _/_ ~~�~~ (1 + _wh_ ) _τ_ , which is no less than one when eq. (21) is satisfied, represents the change in the cluster size when one switches from optimal single sampling to optimal double sampling. When the parameters are unfavorable to double sampling (i.e., when _wh_ and _τ_ are high), the ratio of cluster sizes tends to be smaller.

# **4.2 Realistic estimates of gains from double sampling**

Let us now evaluate the potential benefits of double sampling using a realistic set of parameter values ( _c, τ, wc, wh_ ) taken from existing surveys. While the parameter values are clearly context-dependent and cannot be readily extrapolated to other contexts, this exercise is still useful as it gives practitioners a sense of how much they could reasonably expect to gain from double sampling. It also facilitates comparisons between the sample direct estimator and the optimal single sampling estimator.

Ideally, all the parameter values should come from a single survey. However, we are unable to do so due to the lack of data. In particular, the information on survey costs is typically unavailable to the public. Therefore, we collect empirical values of _c_ , _τ_ , , _wc_ , and _wh_ from various sources. For each of these parameters, we specify low, mid, and high values. The low and high values are close to the minimum and maximum observed in our data sources. The mid value is either the arithmetic or geometric mean of minimum and maximum. The parameter values are presented in Table 1. The details of the data sources and assumptions are provided in 22
To perform practical inference we suggest the use of resampling techniques to approximate the limiting distributions and obtain critical values. To obtain the critical value for the first two criteria we use a bootstrap procedure, and for the inequality test we make use of subsampling.

# **3.4.2 Practical implementation of testing procedures**

Implementation of the proposed tests in practice is simple. First, we discuss the test _H_ 0 in (11). To implement the tests one needs to compute the statistics of test _V_ 1 _n_ or _V_ 2 _n_ . Analogously, when _r_ ( _τ_ ) is unknown, one computes _V_<sup> ̄</sup> 1 _n_ or _V_<sup> ̄</sup> 2 _n_ . We suggest the use of a recentered bootstrap procedure to calculate critical values. The steps for implementing the tests in practice are as follows.

First, the estimates of _β_ ( _τ_ ) are computed by solving the problems in equations (6)–(8) and calculating _β_<sup>�</sup> ( _τ_ ). Second, _Wn_ is calculated by centralizing _β_<sup>�</sup> ( _τ_ ) at _r_ ( _τ_ ), and _V_ 1 _n_ or _V_ 2 _n_ is computed by taking the maximum over _τ_ ( _V_ 1 _n_ ) or summing over _τ_ ( _V_ 2 _n_ ). For the general case with unknown _r_ ( _τ_ ), the tests are computed in the same fashion. The only adjustment is the use of _r_ �( _τ_ ) to compute _W_<sup> ̄</sup> _n_ . Third, after obtaining the test statistic, it is necessary to compute the critical values. We propose the following scheme. We use the test statistic _V_ 1 _n_ as an example, but the procedure is the same for the other cases. Take _B_ as a large integer. For each _b_ = 1 _, . . . , B_ : Let � _c_<sup>_B_</sup> 1 _−α_<sup>denotetheempirical(1</sup><sup>_−α_)-quantileofthesimulatedsample</sup><sup>_{V_�</sup> 1<sup>1</sup> _n_<sup>_, . . . ,_�</sup><sup>_V_</sup> 1<sup>_B_</sup> _n_<sup>_}_,</sup> where _α ∈_ (0 _,_ 1) is the nominal size. We reject the null hypothesis if _V_ 1 _n_ is larger than � _c_<sup>_B_</sup> 1 _−α_<sup>.</sup> In practice, the maximum in step (iii) is taken over a discretized subset of _T_ .

A formal justification the simulation method is stated as follows. Consider the following conditions.

**QG.IIB**<sup>_√_</sup> _<u>n</u> n_<sup><u>1</u></sup> � _ni_ =1<sup>[(</sup><sup>_τ −_</sup><sup>**1**</sup><sup>_{Yi< qt_(</sup><sup>_τ_)</sup><sup>_}_)(</sup><sup>_w_�</sup> _t_<sup>_∗_(</sup><sup>_Ti,_</sup><sup>**_X_**</sup><sup>_i_)</sup><sup>_−w_�</sup><sup>_t_(</sup><sup>_Ti,_</sup><sup>**_X_**</sup><sup>_i_))] converges weakly to a tight</sup> random element G in _l_<sup>_∞_</sup> ( _T_ ) in _P_<sup>_∗_</sup> -probability.

20
In (13), growth in urbanization is a function of growth in moisture, where the growth specification removes the effect of time-invariant district characteristics (distance to markets, soil quality and the like) on urbanization _levels_ . Of course, some of these factors ( ) such as aridity and the likelihood that local towns have a manufacturing base may also affect urban share growth rates, yielding heterogeneous effects. We control for country-year fixed effects to account for national time-varying conditions driving urbanization overall in a country. This also controls to some extent for variation between countries in the definition of urban areas, which poses a significant problem in cross-country urban analysis. What we are doing is demanding on the data—identification of climate effects must come from within-country differences across districts in annualized growth rates of moisture.

We smooth the moisture levels over three years, on the assumption that potentially permanent decisions are more likely to be based on average recent experience rather than one good or bad year. As an example of the smoothing, the annualized rate of change in urban share between the 1965 and 1980 censuses is estimated as a function of the annualized rate of change in moisture between the average for 1963, 1964 and 1965 and the average for 1978, 1979 and 1980. Although this smoothing period is somewhat arbitrary, our results are robust to reasonable adjustments as shown below.

Our theoretical model suggests that the effect of moisture growth on local urbanization will depend on two factors. First is how arid the area is and thus how dependent agriculture is on rainfall variation (the magnitude of _f_ 2<sup>/</sup> _f_ in equation 10). We expect a smaller response to changes in climate in the more humid parts of Africa where, potentially, _f_ 2  0 . We therefore run regressions distinguishing effects for countries located in arid regions, by adding an interaction between moisture growth and a dummy for a country being moist. We set the moisture threshold as country average value exceeding 1.0 for 1950-1969.

The second factor is whether a district’s cities have an industrial sector producing goods for export to national or international markets. In this section we focus more on the arid versus moist country division, whereas in Section 6 the industrial distinction will be critical. There are no relevant district-level data on industry, so we try two strategies to designate industrial cities. First, we use a threshold of agriculture’s share of national GDP net of resource rents. Besides the fact that we are using a national measure to proxy for local conditions, this division is problematic for three inter-related reasons. First, our panel extends up to 42 years for any one country; and a country’s industrial share and likelihood of cities having manufacturing changes over time. Second, we do not have the relevant sector GDP data at even the country level for most countries until the mid-1980s, so we use values from the 15
Table 5 Trust in the Technology

|||_Dependen_|_t variable_||
|---|---|---|---|---|
||Bank a|ccount|Mobile mo|ney account|
||Leave BDT<br>1,000 in account<br>(1)|Leave BDT<br>5,000 in account<br>(2)|Leave BDT<br>1,000 in account<br>(3)|Leave BDT<br>5,000 in account<br>(4)|
|Bank or mobile payroll|0.049|0.042|0.168***|0.146***|
||(0.030)|(0.028)|(0.031)|(0.028)|
||[0.128]|[0.153]|[0.001]|[0.001]|
|Bank or mobile account|0.030|0.047|0.082**|0.035|
||(0.049)|(0.044)|(0.036)|(0.033)|
||[0.321]|[0.241]|[0.049]|[0.241]|
|Baseline controls|X|X|X|X|
|Sample|Bank+Contl|Bank+Contl|Mobile+Contl|Mobile+Contl|
|Test: payroll = account|0.688|0.913|0.014|0.001|
|Observations|1,159|1,363|1,311|1,559|
|R-squared|0.103|0.096|0.112|0.103|
|Mean control [endline]|0.556|0.558|0.380|0.377|

Notes: The table reports treatment effects on trust in the technology. The dependent variable in columns (1) and (3) is a dummy equal to one if a respondent indicates that they would be fully comfortable leaving an amount of BDT 1,000 in a bank or mobile money account respectively, for a period of one month. The dependent variable in columns (2) and (4) is a dummy equal to one if a respondent indicates that they would be comfortable to leave an amount of BDT 5,000 in a bank or mobile money account respectively, for a period of one month. The sample in columns (1) and (2) consists of the control group and respondents assigned to a bank account, the sample in columns (3) and (4) consists of the control group and respondents assigned to a mobile money account. Controls include stratification dummies, marital status, number of dependents, dummies for savings, formal savings, years of work experience, and years with current employer. Robust standard errors in parentheses, sharpened q-values in brackets. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.

45
region-specific data and further highlights the potential of new data sources in shedding light on the intricate relationships between climate change, poverty, and inequality.

# _2.2.2. Empirical Models_

Previous studies often rely on three primary types of econometric models to examine the economic effects of climate change and natural disasters: cross-sectional models, panel models, and long differences models. The first two models have been used to evaluate the effects on poverty and inequality (Arouri _et al._ , 2015; Azzarri and Signorelli, 2020; Paglialunga _et al._ , 2022), while the last model has been used recently on other outcomes such as agriculture, health, and labor reallocation (Burke and Emerick, 2016; Obradovich _et al._ , 2018; Liu _et al._ , 2023).

Cross-sectional models offer the starting point where the relationship between climate change and poverty (inequality) is commonly formulated as C C C (1) ii ii ii ii In such models, the dependent variable (YY = αα + ββ TT) typically represents a socio-economic indicator, + γγ PP + εε ii such as poverty (inequality), in a specific location (YY _i_ ) during a given period. These models factor in variables like annual temperature ( ) and precipitation ( ) (Azzarri and Signorelli, ii ii 2020).<sup>10</sup> TT PP A potential omitted variable bias is an inherent challenge in Equation (1), which refers to a situation where unobserved correlations between temperature and other factors like technological changes or labor productivity, could influence the outcome variable. To

> 10 While rainfall is not the central focus of our review, it is worth noting that a substantial body of literature has applied similar models to explore the impacts of (various metrics of) rainfall on different outcomes. For example, previous studies have used total annual precipitation (Damania _et al._ , 2020), rainfall deviations from the historical means (Corno and Voena, 2023), and extreme rainfall events exceeding specific thresholds (Shah and Steinberg, 2017). Each of these measures provides a distinct lens through which the multifaceted impacts of rainfall can be understood.

17