rafmacalaba/datause-encoder-data huggingface dataset:
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# Social Karketing of Contraceptives

Social marketing applies a model of using private distributors to market subsidized, nonclinical contraceptives (pills, condoms, injectables or vaginal methods), thus using market inceat es to distribute and retali the contraceptives at belov market prices. Subsidized products are proviaed a nev brand name, slogan end packaging and are sold alongside traditional private sector products. Typically the nev contraceptive brand is developed and marketed intensively through an aggressive and broad advertising campaign. Market research accompanies the product choice, packaging and the advertising campaign so as to ensure community acceptance. It is assumed that the product vill be of greater interest to lover income couples than the commercially priced commodities that are beyond their means. USAID's long standing Social Marketing of Contraceptives (SOMARC) project has pioneered this effort to use the incentives and methods of private marketing to encourage pr4vate vholesaling and retailing of family planning, thereby greatly expanding access points (Sherris et al., 1985).15 SOMARC is adjusting its traditional approaches to take greater advantage of its relationship with the private sector. In Ghana, SOMARC is training traditional chemists and pharmacists to assist potential users in selecting an appropriate contraceptive product and identifying complications. The premise is that an informed seller can and will promote a product vith a sufficient profit margin. Since there are more chemist shops than pharmacies, the approach vould reach a broader audience than relying on trained pharmacists alone. A similar 15. Social marketing campaigns often promote different facets of private sector involvement. Local market research, survey and advertising firms are used for product development and product launch as vell as for monitoring program progress, local packaging operations are often used to do the overpacking of the socially marketed products.

**a.fp:Fauily** PlarmAng **_Paper_ (whole)**
http://siteresources.worldbank.org/INTLSMS/Resources/33589861199367264546/Migration_Data_v14.pdf.

De Brauw, A. and R. Mu (2011), ‘Migration and the overweight and underweight status of children in rural China’, _Food Policy_ , **36** (February), 16–27.

De Brauw, A., V. Mueller and T. Woldehanna (2013), ‘Does internal migration improve overall well-being in Ethiopia?’, Ethiopia Strategy Support Program II (No. 55). Retrieved from http://essp.ifpri.info/2013/06/20/does-internal-migration-improveoverall-well-being-in-ethiopia/.

De Brauw, A., Q. Li, C. Liu, S. Rozelle, and L. Zhang (2008), 'Feminization of Agriculture in China? Debunking Myths Surrounding Women’s Participation in Farming', _China Quarterly_ , **194** :327–334.

De Haan, A. (1999), ‘Livelihoods and poverty: the role of migration – a critical review of the migration literature’, _Journal of Development Studies_ , **36** (2), 1–47. Deaton, A. and M. Grosh (2000), ‘Consumption’, in M. Grosh and P.

Glewwe (eds), _Designing Household Survey Questionnaires for Developing_ _Countries: Lessons from 15 years of the Living Standards Measurement Study Volume 1_ . Washington, DC: The World Bank, pp. 91–134.

Deaton, A. and S. Zaidi (2002), _Guidelines for Constructing Consumption_ _Aggregates for Welfare Analysis_ , Washington, DC: The World Bank.

Docquier, F. and A. Marfouk (2004), ‘Measuring the international mobility of skilled workers (1990–2000) : release 1.0’, Policy Research Working Paper Series 3381, Washington, DC: The World Bank.

Docquier, F. and A. Marfouk (2006), ‘International migration by educational attainment 50
bank lending ( _L_ **)6** using the reduced-form forecasting equations 5 and 6, as shown below, and comparing the estimated coefficients, bi and c; , of each equation.

where Y, _L_ and Mare the same as in equations 2 to 4 and the monthly dummies (D) for the restrictive monetary policy are included in each equation. We first estimated equations 5 and 6 without dummies _(D)_ by OLS and then estimated each equation with dummies _(D)_ by two-stage least squares, instrumenting with the lagged values of the right-hand-side variables<sup>7</sup> and the current and lagged values of a dummy that is equal to one on the focal episode on which we identify shift to restrictive monetary policy. The next step is to show the dynamic responses of industrial production (Y) to money (MI and MCT) or bank lending (L), implied by the OLS and IV estimates of the money/output or lending/output regressions. s We then need to compare the OLS and the two-stage least squares (IV) estimates of equations 5 and 6 to examine the relative strength of the estimated relationships between money (or bank lending) and output. Note that the OLS estimates of equation 5 simply summarize the usual money/output relations, whereas the IV estimates summarize the relationship between movements in output and the average deviation of money from its usual behavior in the focal episodes. Again, analogous comments apply to the difference of the OLS and IV estimates of the lending/output relationship. As just discussed, if we allow for the possibility that monetary policy affects output not only through money but also through bank lending, and if monetary policy is a relatively more important source of output fluctuations in the focal episode than other normal times, it may be the case that the tightened monetary policy in the focal episode would cause the IV estimates to imply a weaker impact of money on output than the OLS estimates. In contrast, we may expect the IV estimates of the lending/output relationships to be stronger than the OLS estimates.

- 6 This data refer to the total loans (averages) of domestic deposit money banks comprising domestic commercial banks (sixteen nationwide commercial banks, ten local banks and fifteen-two foreign banks in Korea) and specialized banks such as the Industrial Bank of Korea.

- 7 A set of instrument variables used here include constant, lagged values (I through 18) of the measured Y and M (or L). It turns out that there were no substantial differences in the results for adding other instrumental variables such as deposits and security holding of banks which presumably do not belong in each equation explicitly but enter only through their influence on the monetary aggregates or bank lending.

- 8 For example, the response of M to Y at period zero is co The period one response amounts to co + (blc0 + cl). The period two response is c0 + (b,c, + c,) + b1(b1co + c,) + b2CO + c2; and so on.

11
one company acquires another for a premium, in the computation of invested capital in equation 2 so that our measure is not distorted by price premiums paid for by acquisitions, allowing for an even comparison of operating performance across companies. Thus, ROIC measures the return that an investment generates for the providers of capital and reflects management’s ability to turn capital into profits. In calculating ROIC we subtract cash stocks in excess of those estimated required for transactions purposes. Following Koller et al. [2017], we treat cash above 2% of sales as excess cash and subtract it from the firm’s invested capital but in section 4.2 we undertake robustness tests allowing for varying percentages. Thus, our principal estimates are not affected by firms’ decisions on whether to stockpile cash in low-tax jurisdictions in order to manage their tax liabilities, as is the case of many large U.S. multinationals.

We define _ROIC star_ as a dummy variable that takes the value 1 if the firm’s ROIC is above the 90<sup>_th_</sup> percentile of ROIC across all firms in the US economy in a particular year and 0 otherwise. To replicate the figure in previous studies such as Furman and Orszag [2015] and Koller et al. [2017], we restrict our sample to large firms and drop firms with negative invested capital. As noted before, Figure 1 shows that there is a large rise in capital returns over the past three decades where the ratio of the 90<sup>_th_</sup> percentile ROIC firm to the median ROIC firm has increased by over 69%. We also see that the divergence of the top decile of firms from the rest of the economy really takes off in the 1990s.<sup>10</sup> These results are qualitatively consistent with Furman and Orszag [2015], Koller et al. [2017] and the Council of Economic Advisors [2016], all of which were produced using a proprietary dataset of US firms from McKinsey & Co.

# **1.1 Role of Human Capital**

Firms differ in the complexity of tasks that they perform and the product market may reward certain capabilities more than others. To address this issue, we construct industry-level indices of the composition of tasks firms perform and assess how they affect the likelihood of a firm from that industry becoming a star firm. In creating these indices, we draw on a large labor market literature in economics. Autor et al. [2003], Costinot et al. [2011] and Acemoglu and Autor [2011],

> 10In an early version of the paper, we find much higher increases in ROIC (over 190%) for the 90th percentile without the sample restrictions to large firms and firms with positive invested capital.

8
7 _Y_ = _A_ ( _h_ ). _F_ ( _K_ , _L_ ) where _h_ is the stock of human capital.

Grossman and Helpman (1991)<sup>5</sup> introduced growth theory into an open-economy setting and argued that goods embody technological know-how and therefore countries can acquire foreign technology through imports. Thus, increases in imports tend to lead to increases in TFP: where _r_ is the level of domestic R&D

- _e_ is the level of education

- _m_ is the quantity of imports from other countries.

Continuing with economic growth in an open-economy setting, Coe and Helpman (1995)<sup>6</sup> found that for a sample of developed countries that both domestic and ‘foreign’ R&D had significant impact on total factor productivity (TFP), and that the latter increased with the general degree of openness of the economy and with openness toward the larger R&D producing countries. As such, their interpretation of the aggregate production function can be represented as: where _r_ represents country’s domestic R&D efforts

- _e_ is the level of education

- _rF_ represents the R&D efforts of the country’s trading partners

- _m_ is the quantity of imports from other countries.

The knowledge economy framework, in agreement with the existing literature, postulates that total factor productivity is affected by the education level of the workforce/population and also the level of innovation that occurs within the economy. However, the framework takes a more holistic approach and further postulates that the economic and institutional regime and the level of information and communications

> 5 Grossman, Gene M., and Elhanan Helpman (1991). _Innovation and Growth in the Global Economy_ . Cambridge, Mass and London: MIT Press.

> 6 Coe, D. T. and E. Helpman (1995).  “International R&D Spillovers.” _European Economic Review_ .  Vol. 39, pp. 859-87.
few differences stand out for smaller transactions, possibly pointing to different behavior between larger or institutional and retail investors. Focusing on Model 5 (and 10), the magnitude of U.S. inflation expectations is about half compared to Table 5 but remains economically very relevant. At the same time, the bitcoin price coefficient is about twice as large, while the ether price results are weaker. Further, the pandemic dummy implies that retail volumes have grown less compared to total volumes: 105 percent and 119 percent for all countries and EMDEs only, respectively. Finally, the factors are able to explain somewhat less of the variation volumes for smaller transactions: the adjusted R-squared is about 0.58-0.65 compared to 0.70-0.75 in Table 5.

# 5.2 Robustness and extensions of the main results

## _Robustness_

This section documents several robustness checks of the main results in Tables 5 and 6. Tables 7 and 8 report the results using alternative measures for three indicators. First, we use the countrylevel change in Broad Money instead of inflation (ρ = 0.69). Note that our sample size drops significantly due to missing observations for this indicator. However, the results are broadly similar: when considering small transactions, Model 1 (and 5) shows that an increase in broad money is also associated with an increase in crypto volumes at the 10-percent level of significance, but it loses significance once global factors are included. Second, we use the U.S. 10-year Breakeven Inflation Rate, the expected average inflation rate over the next 10 years which is also derived from Treasury yields, instead of the U.S. 5-year, 5-year forward Inflation Expectation Rate (ρ = 0.97). The coefficient on the 10-year breakeven rate has a similar sign and magnitude and is also significant at the 1-percent level. Finally, we use the U.S. BBB Corporate Bond Spread as an alternative proxy for risk appetite (higher values indicate higher risk aversion), instead of the VIX (ρ = 0.78). Similar to the VIX, Model 4 (and 9) show that crypto volumes fall when risk aversion increases. A 1-percent increase in the corporate bond spread reduces crypto volumes by about 0.5 percent. However, in Model 5 (and 10) the coefficient changes sign in the presence of inflation expectations, similar to the results in Table 5.

We also undertook various unreported robustness checks. First, as auto-correlation may be present in the dependent variable, in unreported results we add to the specification in Tables 5 and 6 the 1-month lagged independent variable as a separate robustness check and the results continue to 31
Table 5: Mechanism: Sanitation-cognition <u>gradient</u> is steepest for early-life exposure

||(1)<br>1st (age 0)|(2)<br>efect of <br>2nd (age 1)|(3)<br>TSC latrines <br>3rd (age 2)|(4)<br> in child’s yea<br>4th (age 3)|(5)<br>r of life:<br>5th (age 4)|(6)<br>6th (age 5)|
|---|---|---|---|---|---|---|
|Panel A: Reading|||||||
|TSC latrines|0.752*|-0.189|0.329|0.0206|-0.203|-0.142|
|per capita|(0.339)|(0.230)|(0.235)|(0.176)|(0.203)|(0.227)|
|_n_ (six-year-olds)|47,684|47,684|47,684|47,684|47,684|47,684|
|Panel B: Math (nu|mbers 1-9)||||||
|TSC latrines|0.754*|-0.234|-0.00616|-0.236|0.0271|0.0546|
|per capita|(0.359)|(0.209)|(0.255)|(0.180)|(0.189)|(0.219)|
|_n_ (six-year-olds)|47,133|47,133|47,133|47,133|47,133|47,133|
|Panel C: Math (nu|mbers 10-99|)|||||
|TSC latrines|0.897*|0.161|0.138|0.0888|0.199|0.0975|
|per capita|(0.407)|(0.246)|(0.307)|(0.193)|(0.234)|(0.228)|
|_n_ (six-year-olds)|47,133|47,133|47,133|47,133|47,133|47,133|

Standard errors clustered by districts in parentheses. Two-sided _p_ -values: _† p <_ 0 _._ 10; * _p <_ 0 _._ 05; ** *** _p <_ 0 _._ 01; _p <_ 0 _._ 001. The sample is the same set of six-year-olds used in table 3. Within a panel, each column reports a separate regression where the sample and dependent variables are held constant and the TSC administrative data for the dependent variable is moved one year later. Each regression includes district and year fixed effects.

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• **_In terms of the poverty gap it appears that households whose main wage earnings are from the transport and telecommunication sector experiences the largest fall away from both poverty lines_** . See table A.2d of the appendix.

• **_18.7 percent of the poorest households headed by men would fall below the extreme poverty line, compared to 14.2 percent of the poorest households headed by women_** . In absolute terms, the incidence of poverty among the poorest households, i.e those in the first quartile, is higher among female headed households relative to their male counterparts, though this gap narrows, the greater the degree of exchange rate pass-through (Tables A.2a and A.2c of the appendix).

• **_20.7% of male headed households in the 3_**<sup>**_rd_**</sup> **_quartile fall below the national poverty line compared to 18.3% of female headed households._** Male headed households are adversely affected more than female headed households due to the larger budget share of qhat consumption among male headed households across all income groups. On average male headed households’s share of qhat in overall expenditure is 10.9 percent compared to 4.7 percent for female headed households as seen in Table 6. This share reaches the highest among the richest strata of male headed households with nearly 16 percent of the total expenditure devoted to qhat.

**Table 6: Qhat consumption as a share of total expenditure (%)**

|||<br>**Househol**|<br>**ds**|
|---|---|---|---|
|**Income Categories**|**All**|**Male Head**|**Female Head**|
|All|9.5|10.9|4.7|
|poorest|3.5|4.4|1.1|
|2nd quartile|7.5|8.9|3.5|
|3rd quartile|12.2|13.3|8.2|
|richest|14.7|15.9|8.1|

_Source: Authors' calculation based on EDAM 1996_

# **_5.5. Impact on income distribution_**

**_Income inequality as measured by the Gini index rose slightly across all three scenarios which tentatively show that the devaluation exercise will not exacerbate inequality._** Again this appears to be due to the greater budget share of qhat consumption among the richer segment of the society where higher cost due to the devaluation causes a flattening of the post-devaluation income distribution.

# **_5.6. Impact on real wages_**

**_In the absence of wage indexation, nominal exchange rate devaluation will result in falling real public sector wages._** With a pass-through elasticity of 60 percent, a 30 percent nominal devaluation will cause real public sector wages to fall by 12.6 percent. The higher the passthrough elasticity and magnitude of devaluation, the greater the fall in real public sector wages as in shown in the Appendix Tables A.6 to A.8.  However, if the authorities decide to apply a 30 percent wage indexation, as it was the case in the past, it will offset the decline in real wages engendered by the nominal exchange rate devaluation. It may even lead to a net increase in real public sector wages. For example, under the scenario of 30 percent nominal exchange rate

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sector. An example is the Phillippines’ “Return of Talent” program, which over two phases recruited 60 highly qualified Filipinos abroad to fill vacant positions in the public sector (Tornea, 2003).

Perhaps the best-known of these programs is the Return of the Qualified African Nationals (RQAN) program managed by the IOM, which has since evolved to become the Migration for Development in Africa (MIDA) program. The RQAN provided airline tickets for the return migrant and their dependents, shipment of their possessions, purchase of professional equipment needed for their work, settling-in expenses, and assistance finding positions in their home countries (Lowell, 2001). It placed around 2,000 highly skilled persons in 41 African countries between 1974 and 1990 (World Bank, 2006), so the effective placement in any given African country in a particular year was low on average. Existing “evaluations” of this program appear to consist largely of surveys which directly ask those participating whether they feel they are contributing to development in their home country, and whether they felt the program was important in their decision to return (e.g. Pires et al, 1996).

The main purpose of such programs is to generate externalities for others in the developing countries of origin. However, the numbers of people involved in these programs have often been relatively small, which coupled with qualitative evidence on the types of activities these participants have been involved in, suggest that the aggregate externalities are likely rather limited. In many cases the programs may just end up subsidizing the return of individuals who were likely to return anyway (potentially speeding up this return), and as Dumont and Spielvogel (2008) note, these policies can have adverse effects in terms of feeding resentment among nonmigrants<sup>14</sup> , or even potentially encouraging more people to emigrate in order to get these benefits upon return.

# **4.3  Policies intended to make return migrants more productive and reintegration easier**

A final set of return migration policies are intended to make it easier for migrants returning to be productive, earn more, and re-adjust to life in their home countries. One of the most comprehensive of these types of programs is the Overseas Foreign Worker (OFW) reintegration

> 14 For example, an evaluation of the return for qualified nationals program in Bosnia-Herzegovina noted that grants to help returnees set up businesses were criticized by businessmen who had stayed in the country (Sandgren, 2001).

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**As noted above, Mauritania is currently Africa’s second largest exporter of iron ore, and iron accounts for the largest share of wealth in the country’s extractive-resource sector.** Extractive resources as a whole represent roughly 45 percent of Mauritania’s natural capital. The net present value of its iron deposits is estimated at approximately US$6.5 billion, or US$1,800 per capita, and iron accounts for approximately one fifth of all natural capital—the same share as the country’s pastureland. This analysis projects that annual iron production (currently 11 million tons) will increase to 25 million tons by 2020 and reach at least 35 million tons by 2025 and will last for many years. Future prices and production costs are based on the World Bank Development Economics Department’s projections and on estimates by JP Morgan.<sup>39</sup>

# **<u>Gold Ore</u>**

**Gold reserves represent between 3 and 4 percent of Mauritania’s natural capital stock, or US$1.1 billion (US$300 per capita), and gold is the resource most vulnerable to price volatility.** Estimates of the net present value of gold reserves are extremely sensitive to price projections, and the figures presented in this analysis should be viewed with appropriate caution. The analysis estimates that gold prices will remain at an average of US$1500 per ounce over the medium term and then decrease to US$1200 per ounce.<sup>40</sup> Should average world prices fall below US$1300 per ounce before 2020 the rent generated by this sector would be significantly diminished or also negative. The annual depletion of gold reserves is estimated at US$41 million in 2013.

# **<u>Natural Gas</u>**

**Natural gas has only recently been discovered in Mauritania, and the sector currently represents an estimated US$2 billion (US$550 per capita), or approximately 6 percent of total natural capital.** The analysis is based on the assumption that production will start in 2016 and will last for 22 years. The annual depletion of wealth in the natural gas sector is estimated at US$75 million. Once more it is useful to highlight that these figures depend on current assumptions: considering that natural gas is a very recent discovery in Mauritania, this paper adopted a conservative approach, and the resulting estimates are to be regarded as provisional.

# **<u>Crude Oil</u>**

**Finally, petroleum reserves are estimated at just over US$3.3 billion (US$930 per capita) and represent more than 10 percent of Mauritania’s natural capital.** Due to price volatility the analysis is based on an average fixed price of US$100 per barrel over the long term, adjusted for inflation. Data for production costs are

> 39 For more detail on the methodology used see Annex 2.

> 40 Prices expressed in nominal 2013 terms.

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