rafmacalaba/datause-encoder-data huggingface dataset:
**h2 _ .376, I** - .306, DW - .473, **ay** - 528.5, Data - 1973:1 - 1987:4 Estimated Equation for PRDBEEF

PRDBEEF **-** 5005. + 132.6 01 - 3.275 Q2 - 30.32 Q3 + .04418 CATPL (536.8) (113.7) (185.7) (99.21) (.08028) - .4125 CCOWKE + 34570. RPCPB (.07123) (11990.) R<sup>2</sup> _ .516, I2 - .462, DW - .915, cry = 269.6, Data - 1973:1 - 1987:4 Estimated Equation for COF Estimated Equation for CATPL CATPL - 3968. - 1743. Q1 - 1514. Q2 - 1298. Q3 - .09603 TCOWKE (1116.) (165.2) (163.1) (162.7) .02569) - 146900. RPCPB (23680.) R<sup>2</sup> = .790, R2 = .770, DW = 1.59, ay - 442.1, Data = 1973:2 - 1987:4 The pork supply component of the meat supply block includes the following estimated equations.

Estimated Equation for BRHGGKE RI = .925, R2 = .916, DW = 1.74, acy 200.3, Data = 1973:1 - 1987:4 Estimated Equation for PRDPORK R<sup>2</sup> - .826, R2 = .802, DW - 1.41, ay = 171.1, Data = 1973:1 - 1987:4
# **Executive Summary**

There are tremendous disparities in economic development across regions in large developing countries such as China, India, Indonesia and Brazil. Globalization may have different impact on urban and coast developed areas and rural and inland less developed regions. A major obstacle in conducting policy analysis for regional economic development under globalization is the lack of consistent, reliable regional data, especially data on interregional trade and interindustry transactions. This study implements and tests a mathematical programming model to estimate interregional, interindustry transaction flows in a national system of economic regions based on an interregional accounting framework and initial information on interregional shipments. A complete national input-outputtable plus regional sectoral data on gross output, value-added, exports, imports and final demand are used as inputs to generate an interregional input-output system that reconciles regional market data and interregional transactions. The model is tested on a four-region,10-sector example against data aggregated from a multi-regional global input-output database, and test results from seven experiments are evaluated against eight mean absolute percentage error indexes. The model has capacity to discover the true interregional trade pattern from highly distorted initial estimates. The paper also discusses some general guidelines for implementing the model for a large–dimension. multi-regional account based on real national and regional data.

2
Policy Research Working Paper 4961

# **Abstract**

This paper provides a new definition of ‘time poverty’ as working long hours and having no choice to do otherwise. An individual is time poor if he/she is working long hours and is also monetary poor, or would fall into monetary poverty if he/she were to reduce his/her working hours below a given time poverty line. Thus being time poor results from the combination of two conditions. First, the individual does not have enough time for rest and leisure once all working hours (whether spent in the labor market or doing household chores such as cooking, and fetching water and wood) are accounted for. Second, the individual cannot reduce his/her working time without either increasing the level of poverty of his/her household (if the household is already poor) or leading his/her household to fall into monetary poverty due to the loss in income or consumption associated with the reduction in working time (if the household is not originally poor). The paper applies the concepts of the traditional poverty literature to the analysis of time poverty and presents a case study using data for Guinea in 2002–03. Both univariate and multivariate results suggest that women are significantly more likely to be time poor than men.

This paper—a product of the Gender Group, Poverty Reduction and Economic Management Network , and the Development Dialogue on Values and Ethics, Human Development Network—is part of a larger work program on gender and time use. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at ebardasi@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
preferences are quasi-homothetic, the indirect utility function can be written where _γ_ denotes the ‘subsistence’ bundle and _φ_ ( _·_ ) is an exact (K ̈onus) cost-of-living index, which includes the opportunity cost of leisure, even if this does not vary. Hence, Aggregating over all households in the village yields the total ‘commercial’ benefit _B_<sup>_c_</sup> = _M_<sup>_c_</sup> _H_ . It is seen that transactions between villagers, whether in the form of tenancy, or labor services, draught animals, animal feed, or whatever, simply net out; so that the resulting sum is just the village’s domestic product plus any net factor income from its dealings with the rest of the economy, that is to say, gross village income (analogously to gross national income). Any changes in the aggregate production plan involve mainly changes in the purchases of inputs that the village cannot supply itself, such as artificial fertilizers, pesticides, motor fuel, certified seeds and the like, with an allowance for the costs of additional credit when needed. There is, however, a snag in employing (5) in practice. Whereas an exact cost-of-living index will duly incorporate the value of leisure, the standard indices do not; nor, moreover, is leisure measured in the surveys available for present purposes. Changes in the real cost of the subsistence bundle are also ignored. Hence, estimates of _B_<sup>_c_</sup> derived from (5) using, say, the consumer price index for agricultural laborers (CPIAL) and data on household incomes, as the surveys define income, will involve errors; but if the changes in leisure are small, these are unlikely to be serious.

Each of these alternatives is now illustrated with actual examples, drawing on Bell (2009a). The community in Himachal Pradesh enjoys a good standard of living and is heavily commercialized, the principal crop being apples. A stretch of some 12 km. of all-weather road was completed under PMGSY in the middle of 2009, at a cost of about Rs 30 million. It serves five villages, whose combined population is about 2000 persons, living in some 350 or more households. Ten percent have no orchard; the rest own, on average, about 400 trees, which yield about 500 boxes at 25 kg. to the box in a normal year. Before the road was constructed, there was just a _kutcha_ track, which truckers were rarely prepared to enter, and portage (usually by Nepalis) over footpaths was arduous, often exceeding 1.5 km. In 2008, the average cost was Rs 20 a box; in the 2009 season, thanks to the road, it fell to Rs 7 a box, the average 12
**Table 6: Correlates of price charged (private interactions)**

||**(1)**|**(2)**|**(3)**<br>**Fees i**|**(4)**<br>**n Rs.**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||**Representa**|**tive sample**|**Dual pract**|**ice sample**|**Pooled**|**sample**|
||Binary|Multiple|Binary|Multiple|Binary|Multiple|
||regressions|regression|regressions|regression|regressions|regression|
|Time spent with SP (minutes)|1.763***|0.665|2.625***|2.286***|1.530***|0.762*|
||(0.454)|(0.467)|(0.587)|(0.685)|(0.363)|(0.390)|
|Percentage of checklist items|0.411***|0.366***|0.364***|0.050|0.396***|0.313***|
||(0.091)|(0.097)|(0.100)|(0.128)|(0.073)|(0.089)|
|Correct diagnosis (unconditional)|-3.749|-4.162**|7.504|5.534|3.026|1.985|
||(4.212)|(1.922)|(9.350)|(9.022)|(4.678)|(4.113)|
|Correct treatment|7.065***|-0.985|7.744*|4.312|7.978***|1.134|
||(1.789)|(2.504)|(4.145)|(5.039)|(1.895)|(2.183)|
|Palliative treatment|8.036***|3.618*|10.435**|7.376|7.890***|4.399**|
||(2.056)|(2.105)|(4.242)|(4.682)|(1.807)|(1.923)|
|Unnecessary treatment|14.039***|0.970|14.973***|5.149|14.283***|3.241|
||(2.395)|(2.878)|(5.032)|(6.214)|(2.126)|(2.532)|
|Dispensed medicines|17.643***|18.552***|16.118***|14.176**|14.948***|17.872***|
||(3.470)|(1.601)|(6.070)|(5.692)|(2.631)|(1.750)|
|Number of medicines|5.198***|0.897|5.863***|2.664|4.989***|0.380|
||(0.874)|(1.058)|(1.783)|(2.837)|(0.809)|(1.128)|
|Referred/Asked to see child|-19.161***|-9.762***|-9.882**|-4.112|-16.737***|-11.350***|
||(4.115)|(3.659)|(4.763)|(4.606)|(3.366)|(3.100)|
|Has MBBS|24.325***|29.595***|||14.504***|23.850***|
||(6.644)|(8.185)|||(4.608)|(4.275)|
|Has some qualification|4.444|6.077***|||2.317|6.819***|
||(3.276)|(2.350)|||(2.930)|(2.417)|
|Patient load during visit|0.736|0.640**|-0.073|-0.281|0.402|0.240|
||(0.665)|(0.315)|(0.807)|(0.817)|(0.621)|(0.534)|
|Age of provider|-0.150|-0.075|0.267|0.251|-0.091|0.011|
||(0.144)|(0.089)|(0.239)|(0.219)|(0.119)|(0.081)|
|Gender of provider (1=Male)|-8.164**|-4.668|-1.284|-3.976|-7.555***|-3.384|
||(3.497)|(5.102)|(4.882)|(5.598)|(2.924)|(4.082)|
|Constant||5.524||-12.433||-0.785|
|||(6.854)||(12.367)||(5.936)|
|R2||0.414||0.444||0.378|
|Number of observations||543||154||697|
|Mean price charged||27.460||32.740||28.627|
|SD||26.241||28.592||26.846|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. Observations are standardized provider-patient interactions. Interpretation of coefficents in "Binary regressions" needs caution. Each coefficient represents a separate regression of prices on the row variable and SP, case and district fixed effects. Multiple regressions include SP, case and district fixed effects. The covariate number of medicines includes all medicines recommended to the patient (dispensed and/or prescribed).

38
- help prospective suppliers (domestic as well as foreign) to set up production facilities;

- provide technical assistance or information to raise the quality of suppliers' products or to facilitate innovations;

- provide or assist in purchasing of raw materials and intermediaries;

- provide training and help in management and organization; and

- assist suppliers to diversify by finding additional customers.

In his empirical study, Lall (1980) examines two Indian truck manufacturers (one MNC and one joint venture) and finds significant backward linkages of all five types mentioned above. In particular, he notes that the truck manufacturers had been active in the establishment of supplier firms: of the 36 sampled supplier firms, 16 had been launched by the principals.<sup>7</sup> Behrman and Wallender (1976), who examine the operations of General Motors, ITT, and Pfizer in several host countries, find similar linkages. They emphasize the ongoing character of the contacts and information flows between MNCs and their local suppliers. Evidence on the development of linkages is also provided by e.g. Watanabe (1983a, 1983b) and UNCTC (1981).8 Apart from demonstrating various types of linkages that create a potential for spillovers, these studies also suggest that the local content in MNC production is one of the determinants of the strength of linkages. Reuber et al. (1973), in a comprehensive survey of MINC affiliates in developing countries, note that over a third of the total value of goods and services purchased 1970 by all affiliates included in their survey were provided by local firms. However, there were systematic differences in local purchases depending on the affiliates' market orientation, the parent's nationality, and the host country. Local-market oriented affiliates purchased more from local firms than did export-oriented affiliates (perhaps because import licenses are easier to obtain ifor exporters); European MNCs relied more on local firms than US or Japanese firms (perhaps because they are generally older and have already built up local supplier networks); and affiliates in Latin America and India purchased more local inputs than affiliates in the Far East (probably because of differences in local content requirements). In addition to these factors, it seems that the technical capability of potential local suppliers must be important to take into account.

13
In many respects, the changes in outcomes that directly followed this policy were remarkable. The most salient of these is that the primary net enrollment rate across Sub-Saharan Africa increased from 61 to 90 percent between 1990 and 2010—whereas it had taken currently high-income countries 60 years to make a similar improvement (1875 to 1935; World Bank 2018 figure 2.1). This pattern of rapid improvement has been observed in a number of countries (for example, see Avenstrup, Liang, and Nellemann 2004 for Kenya, Lesotho, Malawi and Uganda). Studies in selected countries have documented the explicit link between the policy change and increases in access, enrollment, and grade attainment.<sup>2</sup> Uganda was one of the first countries to implement FPE, and early studies established the role it played in lowering age at school entry (Grogan 2009), increasing enrollment (Deininger 2003), and increasing grade attainment (Nishimura, Tamano, and Sasaoka 2008). Analyses of more recent episodes find similar impacts, including in Lesotho (Moshoeshoe, Ardington and Piraino 2019), Kenya (Lucas and Mbiti 2012a) and Tanzania (Hoogeveen and Rossi 2013). In Tanzania impacts were largest for children from poorer families and for girls, while in Kenya impacts on attainment were also largest for children from poorer backgrounds (Lucas and Mbiti 2012a) but the gender difference favored boys (Lucas and Mbiti 2012b).<sup>3</sup> In addition to analysis of education sector impacts, the “shock” of FPE has also been used to study the impact of education on desired and realized fertility. For example, one study shows (using a regression discontinuity approach) that FPE reduced women’s reported desired number of children in Ethiopia, Malawi, and Uganda (Behrman 2015). The study also argues that it is not just the education of the women that mattered, but that of their partners did as well. Other studies have used similar methods, or other approaches that exploit differences in the timing and geographic deployment of FPE (using difference-in-difference approaches), to document impacts on realized fertility. For example, research on Ethiopia (Chicoine 2021) highlights the complementary role of women’s access to markets; and evidence from Ghana (Boahen and Yamauchi 2018), Malawi (Behrman 2015), Nigeria (Okonkwo Osili and Long 2008), Uganda

> 2 Also see the discussion in Evans and Acosta (2021).

> 3 There is a literature on the political economy of the conceptualization and implementation of FPE. This is beyond the scope of this paper but interested readers could see: Bennel (2021); MacJessie-Mbwewe (2002); Nudzor (2013); Oketch and Rolleston (2007); Sifuna (2007); or Somerset (2009).

3
# **I. Introduction**

In recent years, the U.S. military has increasingly used development projects as a strategic weapon to fight ongoing counterinsurgency efforts in Afghanistan, Iraq, and other theaters. The approach is predicated on a hypothesis that such projects - which are commonly implemented by the domestic government and allied entities and deliver basic services and infrastructure - can improve economic outcomes, build support for the government, and ultimately reduce violence as sympathies for the insurgency wane. As evidenced by its prominence in the U.S. Army’s _Counterinsurgency Field Manual_ , the hypothesis now constitutes a major component of current U.S. counterinsurgency doctrine (U.S. Army / Marine Corps, 2006).

Despite the ongoing application of the strategy, there is limited empirical evidence on the effectiveness of development projects in countering insurgencies. In this paper, we use results from a large-scale randomized field experiment involving Afghanistan’s largest development program - the National Solidarity Program (NSP) - to test mechanisms by which development projects can potentially affect counterinsurgency outcomes. We find that villagers residing in communities which have received projects are more likely to hold positive perceptions of their economic situation and exhibit positive attitudes towards the government. We also find that the areas around villages which receive NSP become safer, although this effect is limited to regions with moderate levels of initial violence.

Between 1960 and 2010 more than half of the world’s countries were affected by civil conflict, 20 percent of which had been at war for at least ten years (Blattman and Miguel, 2010). Insurgencies are a subset of civil conflicts that are largely irregular, asymmetrically-fought, yet prolonged attempts by anti-government elements to overthrow the government or win autonomy for a region or territory (Iyengar and Monten, 2008).<sup>2</sup> Counterinsurgency, in turn, refers to all economic, political, and military steps taken by the government and allied forces to defeat the insurgency.

According to theories of civil conflict, the strength of an insurgency depends primarily on its level of popular support, as this determines the ease by which insurgents can recruit additional members and whether the population is willing to share intelligence with government agents. Development projects can form part of a counterinsurgency strategy if they are successful in increasing support for the government and weakening support for insurgents. The effectiveness of this approach will depend on

> 2 Alternative but closely related definitions of insurgency can be found in Fearon and Laitin (2003) or in the US counterinsurgency manual (U.S. Army/Marine Corps, 2006).

2
|**Lithuania**|**Yes**|**Annex: Secured Transaction Regimes for Movables in the EU 8**<br>**OVERVIEW**<br>• Has a modern and efficient regime for secured transactions (EBRD<br>2003 Transition Report).<br>• Pledges are registered in the Register of Mortgages administered by<br>the mortgage departments of district courts.<br>**TYPES OF PLEDGES**<br>• Types of collateral include:<br>• any movable object defined by individual or species characteristics<br>except objects subject<br>• to mortgage under the Law on Mortgage;<br>• securities;<br>• intellectual property rights;<br>• money in the bank account of the debtor;<br>• rights in rem;<br>• rights in person arising from a written agreement.|
|---|---|---|
|**Poland**|**Yes**|**OVERVIEW**<br>• Has a Pledge Registry system.<br>• In Poland the pledge registries are maintained by district courts.<br>• The pledge registry is maintained in an electronic form and data are<br>accumulated nationwide.<br>• The system has problems of excessively long periods of processing<br>request in the pledge registries. The registered pledge as a form of<br>collateral does not significantly diminish credit risk. In fact, the long<br>processing times may even contribute to/create higher risk.<br>**TYPES OF PLEDGES**<br>• The collateral may be any movable object or property right which is<br>transferable. In particular, a registered pledge may encumber:<br>• objects which may be specifically identified,<br>• objects identified as to type, if in the pledge agreement their quan-<br>tity or means for differentiating them from other objects of the<br>same type is specified,<br>• a collection of movable objects or rights constituting an economic<br>entity, even though the individual object may be replaceable,<br>• claims,<br>• intellectual property rights,<br>• rights from securities.<br>• A registered pledge may also encumber objects or rights, which the<br>pledgor [debtor] is to acquire in the future. In such case the encum-<br>brance of these objects or rights shall be effective at the moment title<br>is acquired by the pledgor [debtor].|

**37**

**THE WORLD BANK**
Notably, the data used to evaluate performance with the SDGs can come from different sources. This can pose a cause for concern, since it is common knowledge that even for the same variable, different sources can provide different numbers. As an example, trusted data providers, such as the United Nations, World Bank, and IMF, may publish different country estimates of macroeconomic aggregates such as GDP. As these statistics are founded in the same conceptual framework for all UN member states,<sup>15</sup> why does this happen? There are two reasons. First, many countries with lower statistical capacity publish GDP estimates with a time lag. As international organizations use GDP estimates for operational purposes (e.g., means testing the World Bank’s member countries for IDA eligibility, or informing development policy discussions with member state policy makers), they employ different approaches to fill the data gap between the latest published number and the present. Second, international organizations also forecast economic time series, and the schedules for revising (recalibrating) model estimates can vary among the different organizations.

Similarly, two household surveys implemented by a national statistical office to collect data on the same employment characteristics may not produce the same statistics (see, e.g., Dang, Lanjouw, and Serajuddin (2017) for a discussion for Jordan). Furthermore, this issue is not only pertinent to poorer countries but is frequently observed for richer countries as well.<sup>16</sup> We discuss next a specific example of how analyzing data coming from different sources can complicate the conclusions. In particular, shared prosperity as measured by growth in the income

> 15 This framework is available at <u>https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf. See also the IMF</u> (2018) for an interesting discussion on some potential differences between their macroeconomic databases. 16 A salient case is the U.S., where the inconsistency between different surveys is well documented in the literature. For example, Abraham _et al._ (2013) examine the differences between employment data between the Current Population Surveys (CPS) and employer-reported administrative data. Bavier (2014) finds spending and income poverty in the Consumer Expenditure Survey (CES) to be an outlier compared with those in other surveys including the Panel Study of Income Dynamics (PSID).

17