rafmacalaba/datause-encoder-data huggingface dataset:
programs increasingly rely on mobile phones and money to distribute benefits (cf. Gentilini et al., 2020), CDR-based targeting methods will likely play a more prominent role in the set of options considered by policy makers and program administrators — particularly in contexts like Afghanistan, where traditional targeting benchmarks are missing or unreliable. In just the past few years, for instance, data from mobile phone operators was used in the design of social assistance programs in Colombia, the Democratic Republic of Congo, Pakistan, and Togo (Gentilini et al., 2020, 2021; Aiken et al., 2021). We conclude by highlighting a few policy considerations important for CDR-based targeting.

**Speed and cost** An advantage of CDR-based targeting is that it can be used in contexts where face-to-face contact is not feasible, dramatically reducing the time required to implement a targeted program. While it typically takes many months (or years) to implement a proxy-means test (PMT), community-based targeting (CBT), or consumption-based targeting, a CDR-based model can be trained in just a few weeks (see Appendix C). Likewise, the marginal costs per household screened are substantially lower with CDR-based targeting than with CBT, PMT, or consumption-based targeting. For instance, Table S9 uses cost estimates obtained from the literature (and detailed in Table S10) to estimate targeting costs for the TUP program.<sup>12</sup> Whereas the marginal costs of screening an individual with a CBT or PMT are estimated at $2.20 and $4.00, respectively, the marginal cost of screening with CDR is negligible (see Appendix C).<sup>13</sup> For the entire TUP program, which screened around 125,721 households in six provinces, CBT and PMT would add an additional estimated $276,586 and $502,884, respectively, corresponding to 2.18% and 3.97% of the total program budget.

**Data access and privacy** Access to phone data is necessary for CDR-based targeting. As we show, targeting performance degrades considerably when CDR are not available for subsets of the population. Encouragingly, the past several years have been characterized by a trend towards public sector access to CDR, particularly in the context of the COVID-19 pandemic, during which mobile network operators shared CDR with governments, researchers, 12In our cost calculations we obtain estimates for a CBT, rather than the hybrid approach used in the TUP program, as there is more information available on CBT-only costs in the literature. However, as the CBT cost can be interpreted as a lower bound for the cost of a hybrid approach, our qualitative results also apply to a hybrid approach.

> 13Marginal costs of CDR-based targeting are negligible because we assume no contact with screened individuals is required. In practice, it may be desirable to solicit informed consent to access CDR. If consent were collected in-person, the marginal costs would approach that of a PMT; if collected over the phone, there would still be significant cost savings, see Appendix Section C).

16
70 selection. Sathin training, Sathin newsletter, Sathin report and even Sathin village, establishes this point to a considerable extent.

8.14 The Sathin has emerged as the sole animator in the process of group-formation. Yet, an analysis of the WDP newsletter, Sathin Ro Kagad,shows that in some cases, the task of forming groups seems too intimidating for an individual worker. For these women, it may be more desirable after the initial Sathin training, to organize a subsequent training of a group of women from one village. These women should be preferably from different castes, and thus, able to form a core group of leaders and mobilize women of different communities. That groups of women from different communities can work as a team has been amply demonstrated at various points in WDP. Women from all castes are working as Sathins and the training is conducted for women from all castes together.

8.15 The women of the core-group could be selected by the Sathin and the Pracheta with the guidance of the DWDA and the IDARA, who would make sure that group is a representative one in terms of castes membership. This core group could then be a reference group for interaction among village women. Alternatively, a group comprising the Sathin, the Anganwadi worker and auxiliary nurse midwife (ANM) can be trained together. While no recipe for the building of such a core group can be
<mark>methodologies. In the first method, using the eligibility thresholds presented in Phadera et al. (2022) as hard cutoffs, inclusion errors of the PPMT models corresponding to the SEVAT and Legacy-VA models would be 15 and 21 percent, respectively, under the high confidence level. While these errors are well below the levels observed in practice, PPMT exclusion errors would be extremely high at 95 percent for both the models. This translates into referring only 2 percent of the extremely vulnerable humanitarian beneficiaries to the government program. However, exclusion errors under the second (ranking) approach, which is likely to overcome the differences arising from survey design and other data related disparities (further discussed in analytical strategy section 3.2), would be within the acceptable level of 30 and 15 percent for the PPMT corresponding to the SEVAT and Legacy-VA models. Both models would refer about 36 percent of the extremely vulnerable households to the MoLSA program.</mark> <mark>The choice between electing the PPMT models and collecting new data will depend on</mark> capacity, budget, and appetite for the uncertainty of eligibility but the choice comes with important trade-offs. <mark>Besides identifying the cross-eligibility with certainty, collecting new data may elicit greater insight into the possible transition. For instance, the field-test data showed that a smaller but a meaningful share of households (3 and 16 percent for SEVAT and Lgacy-VA models respectively) that are deemed nonvulnerable and, hence, ineligible for any humanitarian assistance, in fact, would be eligible for the MoLSA program when applying the full-PMT. This group, otherwise, will not even be considered for the crosseligibly assessment when relying purely on the PPMT models and the existing humanitarian database. Moreover, perceived ISIS affiliation and fear of persecution may mean some households do not want to be referred to the formal government system. One-third of the h</mark> ouseholds that are eligible for the humanitarian assistance in the field-test data are unwilling to be referred to the government support. Therefore, new data collection could be a preferred choice for the policy makers. However, the <mark>door-todoor survey sweeps are expensive, time consuming, and may not be possible to implement due to security, lack of capacity or other reasons. In such case, the</mark> ranking approach of the PPMT model could serve as an acceptable alternative.

The rest of the paper is organized as follows. The next section provides the background including the collaborative initiative between various study partners. Discussion on the humanitarian and government PMT models, field-test, and the analytical approach is presented in section 3. Findings from the analysis are presented in section 4 and concluding remarks are discussed in section 5.

6
(see the NOBUS website). Thus, we believe that, at the seven federal okrug level, the NOBUS survey is adequately statistically representative to assess household impacts of WTO accession. The data, questionnaires, summary statistics, descriptions and explanations of the variables and studies that use the survey are publicly available on a World Bank website.<sup>26</sup> Based on the reported incomes from the NOBUS survey, for each of the seven regions, we grouped the households into deciles, from the poorest to the richest. For households within the same decile, we aggregated their data, to form ten representative households in each region, giving us 70 representative households in all. We extracted the factor shares and consumption patterns for the 70 representative households from these data as well.

# **Reconciliation of the National Account and Household Budget Survey Data**

We have two sources of data for aggregate factor incomes: data from National Accounts and data from the NOBUS. In our Russian data, capital’s share of factor income is much larger in the National Account data than in the NOBUS. This is typical. Ivanic [2004] mapped income from the Living Standards Measurement Surveys (LSMS) surveys in 14 countries into factor shares and compared factor shares with the input-output tables in these countries. On average, capital’s share from the LSMS surveys was 21% of household income, but it was 52% of household income based on National Account information (based on the “GTAP” data set).

We must produce a balanced Social Accounting Matrix in order to implement our integrated model, which means we must reconcile these differences. There are biases in both the collection of National Account and Household Survey data so that neither source is clearly correct. A key problem with the factor share data from the national accounts is that capital’s share is calculated residually in the input-output tables. Then in sectors where labor payments are underreported, the share of capital is biased up. On the other hand, income estimates from LSMS surveys are known to be less than income estimates from National Accounts. Deaton (2003) explains that one of the most likely explanations of the difference is that households fail to respond to the survey, and that the probability of non-response plausibly increases monotonically with income. This presumed pattern of non-response to the household survey would also help explain this difference in capital’s share, since the rich are likely to have more capital than the poor.

> 26 For details on the NOBUS dataset, please see: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/RUSSIANFEDERATIONEXTN/0,,conte ntMDK:20919706~menuPK:2560592~pagePK:1497618~piPK:217854~theSitePK:305600,00.html

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Table E8: Firm Performance Impacts of GDP Decline in Firms’ Destinations - Ecuador

||Materials<br>(log)<br>(1)|Materials per<br>per worker (log)<br>(2)|Capital<br>(log)<br>(3)|Capital<br>per worker (log)<br>(4)|TFP<br>L-P (log)<br>(5)|Labor<br>productivity (log)<br>(6)|
|---|---|---|---|---|---|---|
|GFC frm shock*2009|-0.0408***<br>(0.0122)|-0.0243**<br>(0.0112)|-0.0497***<br>(0.0168)|-0.0423***<br>(0.0147)|-0.0327***<br>(0.0088)|-0.0204***<br>(0.0069)|
|GFC frm shock*2010|-0.0519***<br>(0.0123)|-0.0335***<br>(0.0106)|-0.0535***<br>(0.0178)|-0.0376**<br>(0.0159)|-0.0364***<br>(0.0083)|-0.0177**<br>(0.0069)|
|GFC frm shock*2011|-0.0430***<br>(0.0133)|-0.0375***<br>(0.0116)|-0.0616***<br>(0.0190)|-0.0511***<br>(0.0162)|-0.0198**<br>(0.0096)|-0.0158**<br>(0.0069)|
|GFC frm shock*2012|-0.0376***<br>(0.0125)|-0.0384***<br>(0.0109)|-0.0530***<br>(0.0178)|-0.0346**<br>(0.0161)|-0.0228***<br>(0.0081)|-0.0124*<br>(0.0074)|
|GFC frm shock*2013|-0.0380***<br>(0.0130)|-0.0322***<br>(0.0110)|-0.0522***<br>(0.0169)|-0.0267*<br>(0.0150)|-0.0280***<br>(0.0082)|-0.0153**<br>(0.0073)|
|GFC frm shock*2014|-0.0440***<br>(0.0140)|-0.0462***<br>(0.0115)|-0.0467***<br>(0.0177)|-0.0211<br>(0.0143)|-0.0217***<br>(0.0080)|-0.0171***<br>(0.0064)|
|GFC frm shock*2015|-0.0226|-0.0269**|-0.0485**|-0.0283*|-0.0220***|-0.0153**|
||(0.0145)|(0.0113)|(0.0192)|(0.0153)|(0.0085)|(0.0065)|
|GFC frm shock*2016|-0.0179|-0.0240**|-0.0408**|-0.0214|-0.0200**|-0.0155**|
||(0.0152)|(0.0114)|(0.0196)|(0.0160)|(0.0095)|(0.0070)|
|GFC frm shock*2017|-0.0293*|-0.0312**|-0.0452**|-0.0260|-0.0246**|-0.0150**|
||(0.0159)|(0.0124)|(0.0204)|(0.0164)|(0.0098)|(0.0072)|
|Observations|18,184|18,303|9,870|9,916|9,772|21,757|
|R-squared|0.5341|0.4691|0.5543|0.2855|0.6484|0.3925|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Note:_ Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for a vector of firm characteristics as of 2007 described in Appendix E. L-P refers to Levinsohn & Petrin (2003). FE stands for 64
**Table 5. Dynanuc Products in Middle East**<sup>**Countries' Exports to OECD Markets.**</sup>

|||OEC|D Imports ($ 0|00)|**Growt**|**h**Rate|
|---|---|---|---|---|---|---|
|Product (SITC)|Major 1992 Suppliers (share)|1986|1988|1992|1988-92|1986-92|
|Barley unmilled (043)|Cyprus (100)|18.6<br>|5,633.7<br>|1,534.9<br>|-28<br>|109<br>|
|Rice (042)|Egypt (88)|115.1|3.235.8|6,062.3|17<br>|94<br>|
|Cement and building products (661)|Turkey (89)|7,708.2<br>|42,472.8<br>|150,721.8<br>|37<br>|64<br>|
|Lead (685)|iLebanon (48). Israei (27)|90.8<br>|1,678.2<br>|1,397.2<br>|-4<br>|58<br>|
|Clay and refractory products (662)|Turkey (88)|4,740.4<br>|13,263.9<br>|62,029.4<br>|47<br>|54<br>|
|D<br>Iomestic electrical equipment (725)|Turkey (83)|5,905.6<br>|^'.513.6<br>|73,264.5<br>|-3<br>|52<br>|
|lEeathet (61 1)|Saudi Arabia (66,|2.714.8<br>|!**1.502.2**<br>|310,354.7<br>|26<br>|49<br>|
|Plumbing and heating equipment (812')|Turkey_(85)_|8,451.i<br>|21,736.1<br>|73,978.8<br>|36<br>|44<br>|
|Wire products (693)|Turkey (86)|4.320.8<br>|8.359.0<br>|34.496.7<br>|43<br>|41<br>|
|Woven textiles noncotton (653)|Turkey (74). Israel (21)|41,421.2|162.8i8 0|302,697.6|17<br>|49<br>|
|Electrical distributing machines (723)|Turkey (86)|32,646.4<br>|20,717.2<br>|236,913.0<br>|84<br>|39<br>|
|Natural abrasives (275)|Israel (60). Turkey (39)|4,920.7<br>|26.550.6<br>|34,625.0<br>|7<br>|38<br>|
|Silk (261)|Turkey (100)|105.0|1.464.3|718.5<br>|-16<br>|38<br>|
|Special textile products (655)|Israel (61). Turkey (36)|11.315.8|29,013.7<br>|75,066.7<br>|27<br>|37<br>|
|Leather manufactures (612)|Turkey (75)|982.8|3.495.4|6,571.2<br>|18<br>|37<br>|
|Wood in the rough (242)|Turkey (94)|1,057.0|580.5<br>|6,721.1<br>|84<br>|36<br>|
|Teleconmmunications equipment (724)|Israel (54), Turkey (26)|136,379.9|353,722.6|839,290.7<br>|24<br>|35<br>|
|Iron and steel castings (679)|Turkey (84)|1,211.2|3.203.1|7,469.3<br>|24<br>|I<br>|
|Zoo animals and pets (941)|Turkey (48), Egypt (33)|766.5|2,421.2|4,726.3|18<br>|35<br>|
|Radioactive materials (515)|Israel (92)|237.5|570.5|1,393.8<br>|25<br>|34<br>|
|Wood shaped (243)|Turkey (93)|1,687.5|1,729.2|9.767.5|54<br>|34<br>|
|Iron and steel wire (677)|Turkey (87)|327.7|5,418.0|1,882.4|-23<br>|34<br>|
|Non-alcoholic beverages (111)|Cyprus (35). Turkey (32)|1,189.8|3,770.1|6,283.1|14|32<br>|
|Wheat meal or flour (046)|Turkey (38). Lebanon (34)|143.3|498.9|677.9|8|30|
|Road motor vehicles and parts (732)|Turkey (70), Israel (13)|46.253.0<br>|91,420.1<br>|204,645.4<br>|22<br>|29<br>|
|Base metal household equipment (697)|Turkey (83)|11.753.0|20,578.0<br>|50,718.4<br>|25<br>|28<br>|
|WhIieat unmilled (041)|Saudi Arabia (96)|3,652.2|61,955.2<br>|15,850.5<br>|-29<br>|28<br>|
|Soaps and cleansing preparations (554)|Israel (58), Turkey (32)|2,789.4|2,907.1|12,012.1|43<br>|28<br>|
|Milk and cream (022)|Israel (50), Saudi Arabia (13)|416.5|400.1|1.454.3<br>|38<br>|27<br>|
|Iron and steel forms (672)|Turkey (80). Egypt (9)|17,784.6|149,733.8|73.519.7|-16|27|

Source: United Natiois Series. D trade tapes 1 o<sup>he included in the above tabulations OECD imports of the product had to total at least $500,000 in 1992.</sup>
ˆ ˆ _t_ ˆ ˆ * _T_  1  _<u>j</u>_  ˆ same as that of _cr_ , and we have defined _St_   _j_  1 _n j_ and  _nn_   _j_  _T_ 1 _k_   _ab_ ( _j_ ) , where _a_  _m_  and _b_ are any time series, _k(.)_ is a lag window, _m_ is the bandwidth parameter and ˆ  1 _T_  _ab_ ( _j_ )  _T_  _t_  1 _at_  _jbt_ . In large samples, the PC-bar test follows a standard normal distribution. If at least one of the individual ranks is less than the hypothesized value, the test asymptotically rejects the null. Hence, the _PC-bar_ statistic gives the minimum cointegration rank amongst all the cross-sectional units.

In short, we first use the _LR-bar_ test to estimate the maximum number of cointegration relations, and then we use the _PC-bar_ test to assess if for any country the number of cointegrating relations is less than the maximum given by the LR-bar test. If in the second step the null hypothesis cannot be rejected, the conclusion is that the number of cointegrating relations is the same for all cross-sectional units.

# **3.3 Heterogeneous panel data techniques**

As we report below, the panel cointegration tests indicate a common unit cointegration rank among GDP, physical capital, human capital and the composite index of infrastructure. We interpret the single cointegration vector (whose parameters may vary across countries) as a longrun production function. To estimate its coefficients, we adopt a single-equation approach. If there were more than one cointegrating relation, single-equation estimation would only determine a suitable combination of the various cointegrating relations. However, in the presence of a single cointegrating vector, Johansen (1992) shows that if the equations of the marginal model have no cointegration, the single-equation estimator is equivalent to the estimator resulting from system estimation of all the equations.<sup>24</sup> To estimate the coefficients in equation (1), we use the pooled mean group ( _PMG_ ) estimator developed by Pesaran, Smith, and Shin (1999).<sup>25</sup> In practical terms, we embed the production function equation (1) into an ARDL( _p,q_ ) model:

> 24 The single-equation analysis could be inefficient under certain circumstances (see Johansen 1992).

> 25 This estimator has been previously implemented in different contexts, for example Cameron and Muellbauer (2001) analyze the relationship between earnings, unemployment and housing in a panel of UK regions, while Égert et al. (2006) consider exchange rates, productivity and net foreign assets in a panel of countries.

12
among the self-employed, whereas in agricultural activities the decline is stronger for the wage-employed. As in the case of households who do not own land, the agricultural wage-employed households derive more income from agricultural unskilled labor than agricultural self-employed households. For non-agricultural households, it is the greater importance of skilled income for the wage-employed that makes poverty decline less strongly for this group.

_<u>Table 22: Poverty impact of trade, by occupation</u>_

||Population<br>%<br>2001|P0<br> <br>initial leve|% contr. to<br>P0<br>ls|<br>Population<br>% change<br>BaU 20|P0 change<br>01-15|Population<br>% change<br>Doha 2|% of BaU<br>P0 change<br>001-15|Population<br>% change<br>Full 2|% of BaU<br>P0 change<br>001-15|
|---|---|---|---|---|---|---|---|---|---|
|Agri wage-empl.|5.6|46.2|11.0|-1.5|-14.3|-1.5|101.5|-1.4|111.2|
|Agri self-empl.|10.2|48.0|20.7|-1.3|-13.7|-1.3|101.4|-1.2|106.7|
|Agri both|2.4|38.8|3.9|-0.5|-14.1|-0.5|101.3|-0.5|105.5|
|Not empl.|7.8|27.4|9.1|0.0|-4.3|0.0|100.0|0.0|100.0|
|Non-agri wage|48.5|17.2|35.3|1.6|-3.1|1.6|107.2|1.5|117.4|
|Non-agri self|15.9|22.7|15.3|1.1|-2.9|1.1|107.9|1.1|121.0|
|Non-agri both|9.6|11.5|4.7|0.6|-2.3|0.6|108.0|0.5|123.4|

Source: Authors’ calculations.

To sum up, the poverty changes under the deep Doha scenario are rather moderate and disappointing. With one exception, our analyses do not detect a particularly favorable effect on any of the poor and vulnerable groups that we have identified. This one exception is that the Doha scenario very slightly appears to favor the Northeast. Overall, income growth under the Doha scenario favors non-agricultural activities and, accordingly, urban areas. Since the population is concentrated in urban areas, some growth can already reduce poverty considerably, in particular if accompanied by a propoor distributional shift, as in the Doha scenario. Our analyses show that anti-poor changes in the distribution can easily dwarf the poverty reducing potential of growth. The income growth pattern under full liberalization tends to favor poor groups. Poor agricultural and less educated households benefit considerably more from full liberalization than from the Doha liberalizations.

# **5 Conclusions**

Our analysis is centered on the contributions to poverty reduction of one key long-term trend observed in the labor markets of many developing countries: the movement of workers out of agriculture and the consequent increase of the employment shares of the non-farm sectors of the economy. Econometric estimates for Brazil show that, with few 51
and labor),<sup>10</sup> this implies greater use of _Existing_ projects. Some of the most common services allocated through _Existing_ projects include:

- Management of distribution systems/assets - using performance-based incentive mechanisms to achieve greater efficiency in the management and replacement of assets, responding to end-user complaints, requests for new connections and other asset related services.

- Exploitation of commercial opportunities – certain assets, like airports and toll roads,  present opportunities for affiliated commercial undertakings, e.g. using available land, excess asset capacity, or selling additional goods and services to end-users.  This is of particular interest in airports and ports where a number of commercial services can be sold using the property available for the project.

- Billing and collection - public service providers are often inefficient when collecting tariffs from end-users due to a lack of incentives created for the public utility to improve its revenue stream and the potential political ramifications.  By outsourcing billing and collection, collection risk can be allocated to the private sector as can enforcement penalties, disconnection, etc. The incentive mechanisms for such arrangements need to be developed carefully to avoid overly vigorous application of police powers and the health and safety ramifications of overly aggressive collection methods leading to unnecessary disconnections or excessive (and possibly unhealthy) usage reduction.

- Reduction of operating costs – the project company, in its pursuit of profitability, is generally more experienced at maximizing efficiency than are public utilities.  By providing the project company with properly balanced incentives, the grantor can benefit from greater efficiency.

- Customer service - private sector commercial entities are often more focused on customer service, in order to improve profitability and attract a greater customer base.  By outsourcing such customer services, public utilities can benefit from the greater experience of the private sector.

- 10 Gassner, Popov and Pushak, “Does the Private Sector Deliver on its Promises? Evidence from a global study in water and electricity distribution”, (World Bank, December 2007). www.ppiafdev.org.

26
**Table 2 Urban population as a percentage of the total population, 1950-2010**

||**1950**|**1960**|**1970**|**1980**|**1990**|**2000**|**2010**|
|---|---|---|---|---|---|---|---|
|Armenia|40|51|60|66|67|65|64|
|Azerbaijan|46|53|50|53|54|51|53|
|Georgia|37|43|48|53|55|53|53|
|More developed regions*|55|61|67|70|72|74|77|
|Southern Europe|46|51|58|63|65|66|69|
|Eastern Europe|40|49|57|64|68|68|69|
|China|12|16|17|19|26|36|49|

**_Source:_** United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects: The 2014 Revision, CD-ROM Edition.

***** Includes Europe, Northern America, Australia/New Zealand and Japan.

25