rafmacalaba/datause-encoder-data huggingface dataset:
Out of the 37,801 families who applied for the program, the poorest 15,952 families were selected based on their PMT scores. This meant that the cut off lines or the eligibility thresholds for selection was a PMT score of 660 for the two rural Upazilas and 690 for the Narayanganj City Corporation (NCC).<sup>9</sup> The list of eligible beneficiaries was then validated by community leaders, and verified by UP chairmen, and the Mayor’s office in the case of NCC. UP Chairmen, members, leaders of communities also played a role in explaining the project objectives to the targeted families. The eligible families were requested to enroll into the project, and this enrollment process was completed in April 2012. The final number of enrolled households was 14,125. Among this roster of eligible households, there were 7,004 children who were eligible to receive the nutrition benefit and 15,774 children who were eligible to receive the education benefit. The breakdown of the beneficiary families in terms of their location and the type of benefit is provided in Table 1 below.

**Table 1: Distribution of Beneficiaries**

|Total beneficiaries|14,125 families|
|---|---|
|Jaldhaka|7,735 families|
|Hatibandha|4,238 families|
|Narayanganj|2,154 families|
|Nutrition-related benefits|7,004 children|
|Education-related benefits|15,774 children|

Once enrollment was completed the project delivered cash payments every two months to the enrolled households from April 2012 to December 2013. Three types of transfers were set up depending on the household demographic composition. Beneficiary households received: (i) a transfer of BDT 400 (approximately USD 5) per household if the household members included

> 9 The two cutoff points, 660 and 690, are given without any unit, as they are a score. These cut-offs represent respectively the 19 and 47 percentiles of the national income distribution (data from HIES 2005). These cut offs were determined based on both the resource envelop and the capacity of the implementing agency to monitor the project. A higher cut off was used for Narayanganj as it is relatively wealthier than rural areas in Rangpur and to minimize undercoverage rates. Using a higher cut off line for urban areas relative to rural areas is also consistent with the recommendations in Sharif (2009).

9
reflecting the political economy in their countries that resulted in pressure to increase wages at a time when the adoption of the euro seemed to herald a new era of prosperity.

24. Wage increases and growth in government spending in non-core countries led to large increases in aggregate demand. In Greece, for example, aggregate demand grew between 2002 and 2007 by an average of 4.2 percent compared to a growth of only 1.8 percent in the Euro Area (Dadush (2010) p.34). This increase was financed by foreign borrowing and resulted in higher inflation with consumer prices in Greece rising since 1997 by 47 percent compared to an increase of only 27 percent in the Euro Area. Only over the past four years, the real effective exchange rate appreciated by 17 percent eroding Greece‘s competitiveness and resulting in a widening current account deficit.

25. In a quasi Dutch disease-type phenomenon, this loss of external competitiveness also led to a shift away from manufacturing sectors towards service and non-tradable sectors. Table 7 shows that, between 2000 and 2007, the share of manufacturing in GDP fell by 10 percent in Ireland, 3 percent in Italy, 4 percent in Spain, and 3 percent in Portugal and 1 percent in Greece (from its already low level of 10 percent). At the same time, the share of the service and construction sectors in Spain increased by 3.4 percentage points of GDP, while it increased by 2.3 percent points of GDP in Ireland. The shares of the construction sector did not increase in Greece and Portugal.

26. Yet, the share of the manufacturing sector rose by 1 percentage point of GDP in Germany. This increase in the relative size of Germany‘s manufacturing sector (from its already very high level) was also reflected in a rise of Germany‘s overall surplus position with the Euro Zone member countries.

**Table 7: Manufacturing Value Added,** **<u>percent of GDP</u>**

||**2007**|**Change since 2000**|
|---|---|---|
|**Germany**|24|+1|
|**Ireland**|22|-10|
|**Italy**|18|-3|
|**Spain**|15|-4|
|**Portugal**|14|-3|
|**Greece**|10|-1|
|Source: World Bank|||

27. The emerging divergence in competitiveness between core and non-core countries was partly conditioned by countries‘ desire to observe Maastricht stability criteria. In fact, prior to the global financial crisis most Euro Zone countries maintained their fiscal deficits at or below 3 percent of GDP (with the notable exception of Greece and—to an extent—Portugal, although Portugal managed to substantially reduce deficits in 2007 and 2008). Several countries, such as Ireland and Spain even had surpluses. Yet with core countries able to pursue wage moderation 13
Let _I_ 2 denote the set of control variables chosen in this step.

_Step 3_ : Then the PDS Lasso estimator of the treatment effect comes from regressing y on the treatment indicator, along with the union of the set of controls selected in the first two steps. That is from the union of _I_ 1 and _I_ 2. Belloni et al. (2014b) note that researchers may also wish to force the model to include additional variables from a set _I_ 3, that he or she has theoretical reasons for wishing to include for robustness, even if they are not selected in either of the first two steps. They call this third set of variables the amelioration set _I_ 3. The full set of controls selected is then the union of _I_ 1, _I_ 2 and _I_ 3, which is denoted I. The treatment effect is estimated by the following least squares regression: Inference then uses the standard heteroscedasticity robust standard errors from this linear regression. Belloni et al. (2014b) show that the resulting estimator allows for imperfect selection of the controls (does not assume that the approximation is exact), and provides confidence intervals that are valid uniformly across a large class of models, and achieves the semi-parametric efficiency bound under homoscedasticity.

The most common way of implementing this estimation in practice is through code developed by Ahrens et al. (2018), who developed the Stata program _pdslasso_ . Stata versions 17 and up also include the builtin command _dsregress_ which also implements double-selection Lasso. The two packages will give slightly different results when using their defaults for two reasons. First, the standard errors reported in _pdslasso_ are slightly smaller because they do not adjust for the degrees of freedom from the selected controls.<sup>2</sup> Second, the default tuning parameter _λ_ also differs.<sup>3</sup> It is worth noting the similarities and differences with a common, but criticized, approach of deciding which control variables to include in a randomized experiment on the basis of a balance test. Here researchers do a t-test for difference in means, and then include as controls variables which are found to have a significant difference in means between the treatment and control groups. Permutt (1990) notes that this pre-testing can affect the size of subsequent tests, and be worse for power than simply randomly choosing covariates to control for. He instead argues for controlling for the covariates most highly correlated with the outcome. Step 2 of the PDS Lasso method is akin to a penalized balance test, while Step 1 does aim for find the variables most highly predictive of the outcome, irrespective of how balanced they are.

> _<u>G</u>_

> 2To make these comparable, one needs to multiple the standard errors reported in _pdslasso_ by _GN−_ 1 , where _G_ is the ~~�~~ _N −k−_ 1

> number of clusters, _N_ the number of variables, and _k_ the number of variables included in the regression.

> 3Both use a default plug-in _λ_ described by equation (9), except that _dsregress_ uses a version that allows for heteroskedastic errors.

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# **A Financial Social Accounting Matrix for the Integrated Macroeconomic Model for Poverty Analysis**

- **Application to Cameroon with a Fixed Price Multiplier Analysis**<sup>**1**</sup>

By

## Christian A. Emini* and Hippolyte Fofack**

**Abstract** : Cameroon is engaged in the Poverty Reduction Strategy Paper (PRSP) process, which emphasizes increased focus on poverty reduction in the design and implementation of growth and adjustment strategies. The Integrated Macroeconomic Model for Poverty Analysis (IMMPA) framework recently developed at the World Bank provides an analytical structure for supporting the PRSP process and quantifying poverty reduction strategies. Drawing on that framework, this paper provides a detailed financial social accounting matrix for the Cameroonian economy to serve as input into the construction of an IMMPA model for Cameroon. An analysis of this financial SAM shows that the dramatic fall in investment during the crisis period persisted in the post-devaluation growth period in the late 1990s. Continued low investment has implications for ongoing high unemployment rates and poor welfare indicators. This is illustrated by simulations based on fixed-price multiplier analysis, which highlight the potential growth and welfare benefits of increased public investment, following hypothetical debt relief and reduction of external debt servicing within the framework of the Highly Indebted Poor Countries Initiative.

### JEL classification: C68, D31, D57, O11

_Keywords_ : Backward and forward linkages, financial social accounting matrix, fixed-price multipliers, poverty reduction strategy papers, integrated macroeconomic model for poverty analysis (IMMPA)

## World Bank Policy Research Working Paper 3219, February 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 *CEREG - University of Yaoundé II, formerly Université Laval (Quebec, Canada), and ** World Bank. This paper has benefited from discussions with Pierre-Richard Agénor and the authors would like to thank him for his assistance on various aspects of this research. We are grateful to Cadman Atta Mills, Chukwuma Obidegwu, Henning Jensen, Eduardo Haddad and Ernest Bamou for helpful comments on an earlier version of this paper, and Issouf Samaké and Raju Kalidindi for research assistance. The authors gratefully acknowledge the support provided by the World Bank Research Committee.
Policy Research Working Paper 8649

# **Abstract**

This paper assesses the impact of media sentiment on international equity prices using a dataset of more than 4.5 million Reuters articles published across the globe between 1991 and 2015. Media sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. Media sentiment affects mainly foreign— rather than local—investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/research. The authors may be contacted at sfraiberger@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
merit some discussion. First, as a non-weak instrument, the dummy indicating that an owner was active in an activity after 2009 is positive and strongly significant, indicating that all else equal, those taking up activities after stabilization are much more likely to not have an under-contract income in their household. Not surprisingly, those with beyond a secondary education are less likely to have NOWAGE=1, as are those with more employees. Operators with a fixed, but temporary premises (e.g. a market stall) are also less likely to be operating in a situation of few opportunities outside informality.

As mentioned above, the biprobit specification is particularly susceptible to bias from misspecification, including from additional, omitted variables. The rich data from the survey allow for additional co-variates to be included. Table 6 includes several additional controls (in both X’ and therefore Z’), replicating results from Table 5, but including controls for dummies on whether there are children (6 years old or younger) in the owner’s household, a dummy for whether the owner is the primary income earner, a dummy for the marital status of the owner, a dummy if the business uses public services (in the form of use of the electrical grid), and a dummy variable for whether the business was enumerated in an isolated residential area (i.e. no industrial or retail area within 500m).<sup>40</sup> With these additional controls, for NOWAGE, the 2SLS estimates are sufficiently identified with non-weak instruments, and the biprobit estimates are bivariate normal with evidence that a two-stage correction is needed; for NECESSITY, though ρ is significant (and negative), suggesting the appropriateness of the biprobit, the 2SLS fails to reject under-identification (though the instrument is statistically not weak).

> 40 This was done using OpenSourceMaps, which include coded data on land usage, each informal business can be coded according to the near-area surrounding where they were interviewed.  Using a 500m radius, an estimated 90% of informal businesses in Harare operate within 500m of a residential area. Only 27% of businesses are near a commercial or industrial area and a scant 16% are within half a kilometer of retail space, providing further support that the informal and formal sectors operate in segmented areas within Harare’s city limits.

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<!-- Start of picture text --> Figure 12. Electricity consumption per capita<br>6000<br>5000<br>Bulgaria Serbia<br>4000<br>Macedonia<br>3000<br>Croatia<br>Romania<br>2000<br>Bosnia &<br>Herzegovina<br>1000<br>Albania<br>0<br>Electricity consumption per capita (kWh)<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Source: Author’s calculation based on _WDI_ and IAEA _Energy and Environment Data Reference Bank_ .

# **_Quality of services_**

Finally, one remaining important characteristic of the SEE countries is the poor quality of public electricity services. This will complicate the sector’s financial problem, as in many other transition economies. While tariff adjustments are difficult to justify in the absence of reliable power supply, the quality of services cannot be improved without tariff increases. It is worth noting that unlike residential customers, industrial energy users can always choose to install their own captive generators if they are not satisfied with the quality and price of publicly provided energy. In the SEE region, in fact, several large-volume energy consumers, such as a new cement factory in Albania, do not rely on public utilities for energy.

From the empirical perspective, it is generally difficult to measure the quality of public infrastructure. The BEEPS asks individual firms various questions about the quality of infrastructure, such as annual frequency and daily duration of service suspensions. Hence, the information is available on how many days a firm experienced power outages last year. The answer ranges from zero to 365 days. The information is also available on how many hours are required to restore the electricity supply if it is interrupted. It ranges from zero to 24 hours.
characterized by the following joint density function: . The generalization of the Oaxaca-Blinder decomposition considered here requires the marginal distribution of y noted as: . This marginal density function can be obtained by integrating the covariates x out of the joint density. Furthermore, the factorization principle allows one to write the joint density as a product of the distribution of y conditional on x, , and the joint distribution of characteristics, . These are the two factors underpinning the decomposition. Any change in the marginal outcome distribution induced by a variation in the distribution of observed characteristics ( _ceteris paribus_ ) represents the _endowment effect_ , while any change in the distribution associated with a ( _ceteris paribus_ ) variation in the conditional distribution is interpreted as the _price-behavioral effect_ (Bourguignon and Ferreira 2005).

To see clearly what is involved<sup>16</sup> , we express the joint density function as a product of the two underlying functions: . On the basis of this factorization, we can write the marginal density of y in a way that facilitates the expression and interpretation of the decomposition results, that is: . Thus the observed change in the outcome distribution between the two periods can be stated as follows.

We can add to and subtract from the difference defined in (16) the following counterfactual<sup>17</sup> : . This is the marginal density function that would obtain if the conditional distribution were that of period 0, and the joint distribution of characteristics

> 16 This account draws on Essama-Nssah and Bassolé (2010)

> 17 To clarify our notation, we consider the simplest case where x represents a single characteristic.  No loss of generality is involved.  The marginal distribution of y is equal to , where mx stands for the maximum value of x.  Equivalently, .  The counterfactual used in equation (17) is therefore defined as follows: .  This expression can be derived from the marginal outcome distribution in the initial period, , by replacing with . As explained in footnote 11, for this operation to lead to a meaningful counterfactual, two invariance conditions must be met.  The conditional distributions must be invariant with respect to changes in the marginal distribution of observables, .  This would be the case if there are no general equilibrium effects.  The distribution of unobservables must be at least conditionally independent of that of observables.  Ignorability guarantees this.

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95

# Budget Deficit(-)/GDP Other Adjusters

<!-- Start of picture text --> OUSigt tuI.floI*OOP<br>O__________________<br>-4 _4<br>.10 _-1<br>12.4-_r____ _=_________________66<br>IS 67 so so 90 91 02<br>Year<br>-Pmowibns +AotualI<br>Gross Domeutic Investment/GDP<br>Other Adjusters<br>Grow Domesic Imve,m.nt<br>10<br>-<br>o<br>1 807 66 09 90 91 02<br>Your<br>-PpojatIone +AtulM<br><!-- End of picture text --> Real Export of GNFS Growth Rate Other Adjusters <!-- Start of picture text --> Efpott NIFO towth Rsb 1DP<br>-6<br>S'S.S'? S'S so go $11<br>Year<br>-Prlt5sn. 4 AIe,uael<br><!-- End of picture text --> .-

# Current Acoount/GnP Other Adjustors

<!-- Start of picture text --> CUW.nt .sOGP<br>0g _____________uwo<br>=<br>_<br>7 66 6 SI 2<br>Year<br>Propeno +etul F TOTedjthd<br>TOT Trms d Trade<br><!-- End of picture text --> Domestic Savings/GDP Other Adjusters <!-- Start of picture text --> O.g brh OP<br>4<br>3<br>2<br>~~~~~~~~~~~~~~~~~~0<br>so 67 SO so 00 SI 92<br>Year<br>-Projatimne, +Aotuaal<br>Real GDP Growth Rates<br>Other Adjusters<br>Rtibs<br>.<br>6 67 6* O2 U 01<br>Year<br>-0-Prsleam +A*UW*<br><!-- End of picture text -->
_Combined incidence._ While we have emphasized the understanding of the different options in expenditure policy choices, tax policy and cost recovery may also have important implications for poverty reduction. User charges for education and health services, for example, tend to be regressive and may restrict poorer household’s access to critical social services. A strand of the benefit incidence literature has attempted to gauge the _net_ impact of government interventions, combining both the expenditure and tax incidence to estimate the net incidence of fiscal policy.<sup>30</sup> Despite its limitations, net incidence analysis nonetheless represents an important improvement over simple expenditure incidence analysis.<sup>31</sup>

# **V. CONCLUSIONS AND IMPLICATIONS**

- The impact of public spending on common economic goals such as growth, equity and poverty reduction is difficult to assess because of the complex chain of linkages, the time lags involved and the interdependence among the goals. Both initial conditions and institutional capabilities have an important influence on the effectiveness of transmission mechanisms and must be factored into country-specific policy recommendations.

- Nevertheless, understanding these linkages is key to good public expenditure policy. The absence of an appropriate conceptual framework to investigate the link between public spending and its impact on growth and poverty reduction is a weakness that has contributed to ad hoc approaches to public expenditure policy. The recent, donor-led emphasis on identifying and increasing spending on categories of expenditure presumed to be “pro-poor” reflects the lack of such a theoretical framework.

- The main contribution of this paper is to propose a conceptual framework within which questions about broad economic objectives, including growth and poverty reduction, and the appropriate policy instruments (including the appropriate composition of public spending) can be studied. The framework suggests a hierarchy of issues to be addressed sequentially prior to focusing on particular questions regarding the contribution of public spending to poverty reduction.

- For any given objective, the development strategy is a point of reference that is country specific. But it also provides an important point of reference against which to evaluate the consistency of public policies relative to the dynamic (growth and poverty reduction) aspects of the strategy.

- Public economics suggests evaluating the policy framework in terms of the efficiency (market failure) or equity rationale for public policy intervention. While important and necessary, these principles do not provide a sufficient basis to understand and evaluate the dynamic effects of public spending on growth and poverty.

> 30 See for example Toye and Jackson (1996).

> 31 See Whalley (1987) on the improper measurement of consumer surplus. In addition, as typically implemented, net fiscal incidence analysis focuses only on the static incidence and not on net benefits accumulated over time (see, for example, World Bank 1990).

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