rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Co-movement between Cotton and Agricultural Commodity Prices**

||**————— Eq**|**. (1) —————**|**— Eq. 2 (D1=1**|**, 1960-2001) —**|**— Eq. 2 (D1=**|**1, 1960-2002) —**|
|---|---|---|---|---|---|---|
||**1960-2009**|**1960-2010**|**1960-2009**|**1960-2010**|**1960-2009**|**1960-2010**|
|**_μ_**|-0.23|-0.23|||||
||(1.14)|(1.14)|||||
|**_D1_**|||-0.15|0.02|-0.02|-0.12|
||||(0.57)|(0.05)|(0.07)|(0.43)|
|**_D2_**|||2.80***|1.49|2.30**|1.69*|
||||(2.73)|(1.63)|(2.53)|(1.64)|
|**_1_**|0.61***|0.66***|||||
||(5.97)|(6.34)|||||
|**_11D1_**|||0.85***|0.89***|0.87***|0.87***|
||||(7.53)|(7.34)|(7.62)|(7.13)|
|**_12D2_**|||0.23|0.55***|0.36**|0.48***|
||||(1.29)|(3.88)|(2.37)|(2.85)|
|**_2_**|0.67***|0.60***|0.33**|0.22|0.26|0.30*|
||(5.16)|(4.49)|(2.16)|(1.31)|(1.63)|(1.81)|
|**_100*_****_3_**|-2.29***|-2.11***|-1.30***|-0.86|-0.98*|-1.19*|
||(7.31)|(6.29)|(2.69)|(1.52)|(1.85)|(2.30)|
|**_Adj-R_**<sup>**_2_**</sup>|0.91|0.91|0.93|0.92|0.93|0.92|
|**_ADF_**|-6.03***|-6.21***|-7.17***|-7.00***|-7.34***|-6.86***|

**Source:** Author’s estimates based on World Bank price data.

**Notes** _:_ The dependent variable is the logarithm of cotton price. The numbers in parentheses denote absolute _t-values_ while asterisks denote parameter estimates significant at 10 percent (*), 5 percent (**) and 1 percent (***) levels, respectively. ADF is the Augmented Dickey-Fuller (Dickey and Fuller 1979) statistic for unit root and corresponds to the MacKinnon one-sided _p-value_ . The lag length of the corresponding ADF equations was determined by minimizing the Schwarz-loss function. The standard errors and covariance matrix have been estimated in a heteroskedasticityconsistent manner using White’s method.

17
21 Table 11(b) also shows the same tests based on the IV estimations. In this table none of the differences in marginal budget behavior between the various groups of households are significant. Since these IV results differ from the OLS results, it is important to know if the instrumentation that we have used generates a difference in the coefficients (i.e. whether the problems solved by the IV really matter). The Hausman tests shown in Table 10 (third to last row) indicate that instrumentation does make a difference at the 5% level for four categories of expenditure: food, consumer goods/durables, housing and education.

This means that the significant differences observed with the OLS estimations in Table 11 (b) for differences in marginal budget shares for food, consumer goods/durables and education are all spurious. Consequently, our results indicate that at the margin all households in Ghana spend similarly, regardless of whether they receive remittances or not. In other words, our results suggest that income (expenditure) levels and household characteristics determine spending patterns in Ghana not the source of the income. Households in Ghana treat remittances just like any other source of income.

# 7.  Conclusion

This paper has used a new, nationally representative household survey from Ghana to analyze how the receipt of internal remittances (from within Ghana) and international remittances (from African or other countries) affects the marginal spending behavior of households on a broad range of consumption and investment goods, including food, education and housing. One principal finding emerges. Contrary to other studies, which find that remittances are either spent disproportionately on consumption (food and consumer goods/durables) or investment goods (education and housing), we find that households receiving remittances spend their remittance income just like any other source of income. When we
35 framework59 and the switch in financial regulation towards a functional, as opposed to an institutional, perspective<sup>60</sup> . The CAD is an example of functional regulation at a broad level because it regulates similar activities between securities firms and banks, though there are questions about the wisdom of doing that (see Section 111.3.1 .). However, at a narrower level, it ignores the market risk found in the banking book and, with the exception of forex risk, it focuses exclusively on the trading book.

The building bloc approach is problematic because it is inflexible, and thereby inefficient in accurately measuring risk, and static. By focusing on the classification of **61** instruments rather than risk types , it distorts different instruments' attractiveness, it ignores portfolio principles of diversification and correlation hedging, and it encourages substantial gaming. It has to be admitted though that, based on the experience of the credit standards introduced in 1988, the willingness of countries to implement risk standards may be positively related to their relative simplicity, notwhistanding the unrealistic assumptions they may involve.

The internal model approach is an improvement to the BBA, since it is aligned to bank industry best practice, the use of internal VAR models. However, the attempt by regulators to create consistent estimates of VAR across different institutions' models, as well as their conservative parameters, have reduced the approach's appeal. In addition, at least in the near future, its application will be limited to a relatively small number of institutions (about 25 in the USA, according to the Federal Register) that have both material trading activities and sophisticated, comprehensive VAR models.

> 59 Current accounting principles (more than practices) do a good job at valuation, inherently a static measure of financial conditions. However, they are totally inadequate to deal with risk allocation and exposure, which is of critical concern nowadays.

60 See Merton (April 1995). He argues that regulation organized along functions, as opposed to institutional categories of products or asset types, would be operationally more effective and comprehensive. The reason is that it will be increasinigly more difficult and distortionary to regulate along traditional institutional lines because the flexibility in contract designs would by-pass those regulations.

61 As seen in Section 1.2., in order to calculate the true market risk of a particular institution under some confidence interval, it is necessary to disaggregate income down from individual transactions to its fundamental elements, and then aggregate them up again while taking into account any correlations between different risks.
Policy Research Working Paper 9152

# **Abstract**

Accurate estimates of men’s and women’s employment are at the heart of understanding sources of productivity and economic growth and designing well-targeted, gender-sensitive labor policies. How respondent selection in household and labor force surveys affects these estimates is a key question, for which experimental evidence outside of high-income settings is limited. Leveraging two concurrent, national surveys in Malawi that differed in their approach to respondent selection, the analysis shows that compared to the best practice of privately interviewing adults about their employment outcomes, the common “business-as-usual” approach that permits the use of proxy respondents and non-private/group interviews leads to significant underreporting of employment across a range of wage- and self-employment activities, with stronger effects for women and for a longer (12-month) recall period. Under the business-asusual approach, the main factors linked to under-reporting include household wealth, proxy reporting, and potential difficulties associated with interpreting/answering on questions regarding household non-farm enterprises.

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/prwp. The authors may be contacted at tkilic@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
|Tables:|
|---|
|Table 1: Number of countries by weight in key emerging market indices................................... 13|
|Table 2: Selected factors influencing the investability decision................................................... 25|
|Figures:|
|Figure 1: Hypothetical Risk / Reward Tradeoff ........................................................................... 10|
|Boxes:|
|Box 1: CalPERS investment guidelines.......................................................................................... 7|
|Box 2: International Sanctions...................................................................................................... 14|
|Box 3: “Socially Responsible” Investment................................................................................... 16|
|Box 4: The Russell 20-20 Association.......................................................................................... 21|
|Appendixes:|
|Appendix 1 CALPERS Permissible Country List: Country and Market Factors......................... 31|

iii
I **POLICY** RESEARCHI WORKING **PAI'EI1** 3089

# Abstract

Stifel and Alderman evaluate the Vaso de Leche (VL) feeding program in Peru. They pose the question that if a community-based multistage targeting scheme such as that of the VL program is progressive, is it possible that the program can achileve its nutritional objectives? The authors address this by linking VL public expenditure data with hiousehold survey data to assess the targeting, and then to model the determinants of nutritionial outcomes of children to see if VL program interventions have an impact on nutrition. They confirm that the VL program is well targeted to poor households and to those with low nutritional status. While the bulk of the coverage of the poor is attributed to targeting of poor districts, the fact that the poor receive larger in-kind transfers is attributed to intradistrict targeting. But the impact of these food subsidies beyond their value as income transfers is limited by the degree to which the commodity transfers are inframiiarginal. The authors find that transfers of milk and milk substitutes from the VL program are inframarginal for approximately half of the households that receive them. So, it is not entirely surprising that they fail to find econometric evidenice of the nutritionial objectives of the VL program being achieved. In moclels of child standardized heights, the authors find no impact of the VL program expenditures on1 the nutritional outcomes of young children-the group to whom the program is targeted.

Thils paper-a product of Public Services, Development Research Group-is part of a larger effort in the group to understand the distribution of public services. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hedy Sladovich, mail stop MC3-311, telephone 202-473-7698, fax 202-5221154, email address hsladoviclh@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contactedatdavid.stifel@cornell.edu or halderman@worldbank.org.June 2003. _(35_ pages) _-T/e_ **_Policy_** _Researcb_ **_Worktng_** _Pape Series_ **_drssemicdates_** _the_ **_fiditgs_** _of_ **_tyork_** _Bin progress_ **_to encourage the excbange_** _of Ideas_ **_about Ivuelopmne)t Issties. Ani_** _objiective of the_ **_series is_** _to_ **_get_** _the_ **_foitdutigs outt qutsckly, eveni_** _if tbe_ **_presentattonts are_** _less_ **_thant ftily_** _polished. TFhe papers carty the_ **_namtes_** _of the_ **_aitthors_** _and sbould b)e cited_ **_accordingly._** _The_ **_findutgs intterpretationts_** _and_ **_cottclitsionis_** _expressed_ **_in_** _this paper_ **_are enittrely_** _those of the_ **_atuthors_** _17hey do_ **_niot niecessarnly represcent_** _the_ **_vteiv_** _of the WVorld Bank,_ **_its Execit ttve Directors, or_** _the_ **_coutnitres_** _they_ **_represenit_** Produced by Partniershalps, Capacity Buildinig, aind Outreach
as the World Bank are increasingly paying attention to data and analytics on refugees. However, this process has started only recently and will require time to bear fruits in terms of improved research on refugees.

For obvious reasons, the quality of refugee data is also very different depending on whether refugees are in camps or outside camps. In camps, refugees are assisted with shelter, education, health and any other requirement for conducting a decent life. Outside camps, refugees would normally receive cash and food assistance but they are on their own to find shelter and services. Refugees outside camps have an incentive to register because registration provides access to protection and assistance but contacts with UNHCR staff are more difficult and less frequent than in camps, whichs result in information that may be less accurate or up to date. In some countries, like Jordan and Lebanon over 90 percent of refugees are outside camps.

# IDPs

Data collection on IDPs is more scarce and fragmented than the one illustrated for refugees. By definition, IDPs remain in their nation of origin and data collection should, in principle, be easier. In reality, IDPs derive from an internal shock such as a civil conflict where a large number of people move quickly within a country. These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short‐term. In most cases, new surveys are suspended or carried out under the pre‐crisis frameworks and, in either case, information on IDPs is not collected or poorly collected.

This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN‐OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or 17
In most of the nine countries, multi-segment firms are less profitable than their single-segment counterparts. As in Table 2, multi-segment firms in seven of the nine countries exhibit lower mean (median) EPM and the differences in means (medians) are statistically significant in four (seven) countries. Multi-segment firms in Indonesia and the Philippines appear to have higher EPM, but the mean difference is not statistically significant, while the median difference is only significant for the Philippines.

# _4.2 Relatedniess_

We use two variables to capture the degree of relatedness (vertical and complementary) between the primary and secondary segments of a firm. The vertical relatedness variable measures the degree to which a firm integrates forward and/or backward into its secondary segment(s), given its primary segment. For example, if a car manufacturer takes over a car upholstery business, this would reflect a high backward vertical relatedness. If, in contrast, an electricity generation company takes over an electricity distribution business, this would reflect a high forward vertical relatedness. The complementarity variable measure the degree to which the primary and the secondary segments complement each other (forward) in marketing and distribution and/or (backward) in procurement. An example of forward complementarity is a peanut butter producer taking over (or expanding into) strawberry jelly production - the two products do not use the same inputs, and are not vertically related, but that use the same distributors and marketing agents. An example of backward complementarity would be merging together a gin producer and an aspirin producer, as they both use glass containers for their products. To ensure consistency across countries and to provide a common benchmark, the two variables are constructed from information in the US input-output tables.<sup>9</sup> The procedure for constructing the variables is detailed in Fan, Lang and Stulz (1998). It entails three steps. First, we create four matrices of inter-industry relatedness coefficients. This involves computing the coefficients between each pair of over 400 industries defined in the input-output tables. We follow an approach similar to Lemelin (1982) in measuring inter-industry relatedness. The building block of this approach is the _Use Table of the 1992 Benchmark U.S. Input-Output Accounts._<sup>_1 0_</sup> The _Use Table_ reports

> 9 We are unable to obtain input-outpu **t** ables for individual Asian countries.

> '° The Accounts reports commodity flows between pairs of over 400 non-governnent and non-household industries. See Lawson (1997) for details.

9
> - 72 -

status will reduce the size of the subsidy. For Malaysian firms it turns out that pioneer status eliminates the bias of the tax system in favor of marginal inves,tments financed by debt rather than equity, and provides a net additional subsidy only to investments with sufficiently low debt-equity ratios.

Table 2 also reports effective corporate tax rates under the post-1989 tax structure. In addition to the reductioa in u, the ability of pioneer firms to defer initial and annual depreciation allowances until the end of the tax holiday period was removed. As can be seen from Table 2, pioneer firms now face a large positive t. as compared with the net subsidy received under the previous system. Not only has the sign of the distortion been changed in this case, but its absolute size has been increased considerably.

Debt-financed marginal investments continue to be treated more favorably for non-pioneer than for pioneer firms after the tax reform. In the case of equity-financed investment, machinery is now slightly better treated for non-pioneer firms. This is also for the reason mentioned above, that the loss of depreciation tax savings during the pioneer period is more important for machinery. The tax reforms also have the effect of reducing the differences in distortions between pioneer and non-pioneer firms.

# **_3. Tax Loss RFms_**

Table 3 presents estimates of _tc_ for firms in a temporary loss position for five years. We take 1983 as the presumed initial year of the firm's operations. As in the case of a pioneer firm, we calculate t during the final four years of the loss period and the steady state for both the pre-1989 and post-1989 tax regimes.

Under the pre-1989 tax rules, debt-financed investments receive a small subsidy during the tax loss period, but it is considerably less than that received for a fully taxpaying firm. The size of the subsidy increases over time (during the tax-loss period). In the case of equity-financed investments, tc is positive. It declines over time, but remains more than double that facing tax-paying firms on the same investments. For firms financing investments through a combination of debt and equity, the tax system discourages investment for loss firms, while subsidizing investment for tax-paying firms.

The 1989 tax reforms do not change any of the qualitative patterns of investment distortions facing tax loss firms, but they have the uniform effect of reducing the magnitude of all the positive and negative tax incentives. This is the same thing that was observed earlier in the case of taxpaying firms.

To summarize, the corporate tax system plays an important role in determining the relative profitability of different types of investments. For some investments, it provides a considerable net subsidy. But many other investments are faced with sizeable tax penalties. The overall pattern of
**Table 3: Heterogeneity Across Urban vs. Rural and Sons vs. Daughters**

||**CH**|**INA **|**IN **|**DIA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
||YrsSch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|
||||**Ur**|**ban**|||
|Father’s Edu|0.356***<br>(0.013)|**-0.548*****<br>**(0.053)**|0.563***<br>(0.007)|**-0.772*****<br>**(0.034)**|0.468***<br>(0.016)|**-0.707*****<br>**(0.065)**|
|Constant|7.564***<br>(0.147)|**17.449*****<br>**(0.586)**|5.498***<br>(0.074)|**21.478*****<br>**(0.250)**|7.225***<br>(0.171)|**16.935*****<br>**(0.598)**|
|Observations|35,308|35,308|31,216|31,216|5919|5919|
|R-squared|0.158|0.013|0.316|0.029|0.236|0.027|
|Mean ofY|9.376|14.66|8.446|17.44|10.835|11.475|
||||**Ru**|<br>**ral**|||
|Father’s Edu|0.321***<br>(0.014)|**-0.524*****<br>**(0.044)**|0.589***<br>(0.007)|**-0.022**<br>**(0.040)**|0.467***<br>(0.012)|**-0.294*****<br>**(0.069)**|
|Constant|5.271***<br>(0.165)|**17.799*****<br>**(0.342)**|3.798***<br>(0.048)|**18.702*****<br>**(0.177)**|6.331***<br>(0.115)|**15.967*****<br>**(0.516)**|
|Observations|58,851|58,851|55,532|55,532|12437|12437|
|R-squared|0.097|0.013|0.226|0.000|0.161|0.003|
|Mean ofY|6.408|15.94|5.408|18.64|8.899|14.351|
||||**So**|**ns**|||
|Father’s Edu|0.329***<br>(0.014)|**-0.476*****<br>**(0.048)**|0.595***<br>(0.006)|**-0.927*****<br>**(0.025)**|0.461***<br>(0.013)|**-0.522*****<br>**(0.066)**|
|Constant|6.824***<br>(0.142)|**17.033*****<br>**(0.547)**|5.361***<br>(0.049)|**20.974*****<br>**(0.165)**|6.971***<br>(0.133)|**16.259*****<br>**(0.516)**|
|Observations|46,791|46,791|46,701|46,701|8558|8558|
|R-squared|0.121|0.009|0.290|0.038|0.194|0.012|
|Mean ofY|8.169|15.09|7.557|17.55|9.821|13.03|
||||**Daug**|**hters**|||
|Father’s Edu|0.438***<br>(0.015)|**-0.364*****<br>**(0.052)**|0.643***<br>(0.006)|**0.334*****<br>**(0.040)**|0.528***<br>(0.013)|**-0.483*****<br>**(0.061)**|
|Constant|5.057***<br>(0.174)|**19.099*****<br>**(0.347)**|2.974***<br>(0.048)|<br>**16.306*****<br>**(0.200)**|5.964***<br>(0.123)|**16.892*****<br>**(0.499)**|
|Observations|47,368|47,368|40,047|40,047|9798|9798|
|R-squared|0.172|0.006|0.332|0.004|0.231|0.010|
|Mean ofY|6.880|17.58|5.269|17.50|9.263|13.875|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given category.

27