rafmacalaba/datause-encoder-data huggingface dataset:
- _15 -_

A **further** question is whether socioeconomic development, in the long- term, might be sufficient to lower maternal mortality rates. The discussion concerned the impact of long-term development on health. **The** author noted that the actual reduction of maternal mortality in developed countries has been associated with the specific practice of hospital versus home childbirth deliveries -- a practice that permitted access to specialized care for the complications of childbirth. While socioeconomic development hds favorably influenced this process, the change has been brought about through specific obstetrical expertise and facilities. Based on the decline of maternal mortality rates in Europe and North America, such Safe Motherhood components as family planning, nutrition, education, and improved economic levels are thought to be necessary but insufficient factors if developing countries are to lower maternal mortality to the levels prevalent in developed countries.

# Estimated Financial Flows to Developint Countries S millions

||1986|1987|1988|1989<br>(estimates)|
|---|---|---|---|---|
|ODA|829.8|926.9|890.8|953.5|
|Health|||||
|Bilateral|27.0_(15)_|28.2 (7.9)|28.0 (7.9)||
|Multilateral|21.7|30.4|34.0|38.8|
|WHO|6.9 (3.2)|10.0 (3.4)|11.0 (3.6)||
|UNFPA|7.2 (3.6)|10.0_(5.0)_|11.2 (5.6)|12.4 (6.2)|
|UNICEF|7.6 (1.7)|10.4 (2.5)|11.8 (2.8)|15.4 (3.4)|
|Non-UN|||||
|IPPF|2.6 (2.6)|3.1 (3.1)|3.5 (3.5)|4.16 (4.16)|
|NGOs|||||
|(All sectors)|5.6 (1.8)|16.0 (4.0)|16.0 (4.0)||
|Total<br>(at an exchange rate<br>of DK6.85 = S1.00)|56.0 (26.2)|77.7 (25.9)|81.5 (27.4)||

Danish Department of International Development Cooperation (DANIDA) Ministry of Foreign Affairs Asiatisk Plads 2 DK-1448 Copenhagen

- Note: Brackets indicate estimated Safe Motherhood component. DANIDA staff note that identifying Safe Motherhood components from current statistical . cords is difficult and these estimates are tentative.

- Source: Denmark's Development Assistance 1987/88, Ministry of Foreign Affairs 1988; Memo on Danish Health Aid for 1987; a series of memoranda providing projections for UN organizations.
and schooling in India.<sup>18</sup> Lanjouw and Ravallion also show in a theoretical model of the political economy of targeted programs that for programs with relatively large start-up costs, early capture by the nonpoor may be the only politically feasible option (especially when the start-up costs must be financed domestically). So this feature of MGNREGS is possibly not surprising.

Targeting by social groups (castes and tribes) is another dimension of interest. Qualitative studies have suggested that scheduled castes (SC), scheduled tribes (ST) and women—groups that have traditionally been excluded—have benefitted disproportionately from the scheme.<sup>19</sup> We shall return to discuss participation by women in section 5. Here we focus on the scheme‘s performance in reaching ST, SC and other backward caste (OBC) households.

Table 5 gives the participation rates by these groupings. Nationally, 42% and 34% of rural ST and SC households respectively participated. Participation was lower for OBCs at 21% and lowest for all others, at 16%. But there is a wide range across states. For STs, the range is from 6% of households participating in Maharashtra to 82% in Rajasthan, while for SC households it is from 2% to 65%, and for the same states. Figure 6 plots the participation rates against the overall participation rates across states. Similarly to targeting by poverty status, we see that the participation rates for ST, SC and OBCs rise faster as the overall participation rates rise, suggesting that the targeting of disadvantaged castes improves with program expansion.

Table 5 also gives the targeting differential for ST, SC and OBCs together, defined as their (weighted) average participation rate less the participation rate for ―others.‖ This ―caste TD‖ varies from -0.02 in Assam to 0.29 in Chhattisgarh, with a national mean of 0.12, almost identical to the national ―poverty TD‖ in Table 4. Similarly to the poverty TD, the ―caste TD‖ is positively correlated with the overall participation rates (r=0.723).

# **4. Wages and rationing on MGNREGS**

There have been a number of concerns about the stipulated wage rates for the program. On the one hand, it is argued that setting scheme wages below the state-mandated rates under the Minimum Wages Act is a violation of the law and tantamount to ―forced labor,‖ a stand that has

> 18 Lanjouw and Ravallion (1999) study public works programs, the Integrated Rural Development Program, the Public Distribution System and school enrolments. They used data for the 1990s. 19 See Drèze and Khera (2009).

13
-34-

trading partners is one of the determinants of a country's likely export growth. The other determinant is the elasticity of demand for the borrowing country's exports with respect to income in the countries to which it exports.

Empirical analysisll suggests that the income elasticity of demand for Turkey's exports is high: 1.6 with respect to the OECD and 4 with respect to the oil-exporting countries in the Gulf region. This results in a weighted value of 2. Thus, if the weighted output in Turkey's trading partners<sup>12</sup> grows by 4 percent, Turkey's exports are likely to grow by 8 percent. The real exchange rate has no impact on the amount of feasible borrowing, as a consequence of its construction.

The results are presented in Table 3. The table gives the maximum increase in foreign debt that will avoid a rising debt-resource ratio, for different growth rates at home and abroad. The table lists increases in debt and hence gives the feasible current account deficit. The table lists on its vertical axis various alternative growth rates for Turkey, ranging from 3 to 7 percent. On the horizontal, top axis it lists potential growth rates for Turkey's trading partners, aggregated using their respective shares in Turkey's exports. The numbers indicate, as expected, that lower growth rates, whether at home or abroad, allow for less debt accumulation. In fact for zero growth rate at home and abroad, the formula indicates that no further borrowing is possible (this possibility is outside the range of the table). Raising the domestic output growth rate by 4 percentage points allows an extra current account deficit of 1.5 percent of GNP for given foreign output growth 11/ See Anand and van Wijnbergen (1988) for documentation.

12/ Weighted by their share in Turkey's exports.

ChhibberDisk/chhibber-Turkey Paper/ac:07-28-88: 1c
The results in column (1) suggest that China’s supply (or competition shocks) had a negative impact on the multilateral exports of B&R economies: the coefficient of interest is negative and statistically significant at the 1% level. In other words, the rise of China’s exports in sectors where exports of B&R economies were initially relatively more exposed to China had a negative impact on export growth in these countries. By contrast, demand shocks associated with the rise of China’s imports impacted positively the overall growth of their exports. The magnitude of the coefficient on demand shocks is larger than that on supply shocks, suggesting that the overall net impact of China trade shocks on the exports of B&R economies during the period 1995-2015 was significantly positive.

**Table 6: Average impacts of China shocks on exports of B&R economies, 2005-2015**

||(1)<br>OLS|(2)|(3)<br>2SLS|(4)|
|---|---|---|---|---|
|||1st|stage|2nd stage|
||Δ logXjit|Δ Supplycjit|Δ Demandcjit|Δ logXjit|
|Δ Supplycjit|-43.83***|||-52.82***|
||(12.80)|||(8.309)|
|Δ Demandcjit|37.63**<br>(16.80)|||44.95***<br>(16.93)|
|Δ Supplycjit(top 10 destinations)||0.817***<br>(0.0193)|0.0218<br>(0.0177)||
|Δ Demandcjit(top 10 sources)||-0.0272**<br>(0.0113)|0.565***<br>(0.0317)||
|country-industry effects|Yes|Yes|Yes|Yes|
|period effects|Yes|Yes|Yes|Yes|
|observations|21,303|21,303|21,303|21,302|
|R-squared|0.339|0.977|0.929|0.015|
|F-statistic<br>Robust standard errors clustered by c|ountry-industry i|1801.99<br>n parenthesis, ***|359.65<br>p<0.01, ** p<0.05|, * p<0.1|

These findings remain very similar when using changes in Chinese exports to top 10 destinations (or imports from top 10 sources) interacted with the initial similarity index as instruments for the change in China’s multilateral exports (also interacted with the initial similarity index). The results in columns (2) and (3) reveal that these instruments are a strongly correlated with the overall supply 19
run. This is done by setting 750 pesos as a maximum transfer per household (consistent with the rules of _PROGRESA_ ), using the same poverty line as in Skoufias and Di Maro (2008)<sup>31</sup> and taking into account individual level decisions through the BFL model. The proportion of poor households in the _baseline treatment_ village sample is estimated to be 61 percent, which is the same as in Skoufias and Di Maro. Applying the DID estimator on poverty measures, Skoufias and Di Maro find that between November 1997 and November 1999, _PROGRESA_ was responsible for a 16.5, 24 and 27 percent reduction in the poverty headcount rate, poverty gap and squared poverty gap, respectively, in treatment villages. In comparison to their results, our _ex ante_ simulations suggest a similar impact of _PROGRESA_ on poverty headcount rate (13 percent reduction), but a somewhat larger impact for poverty gap (27 percent reduction) and squared poverty gap (33 percent reduction).<sup>32</sup> Table 4 below shows these results.

**Table 4: Poverty Index: Observed (** **_ex post_ ) and Simulated (** **_ex ante_ )**

||_FGT(_|_0)_|_FGT(_|_1)_|_FGT(_|_2)_|
|---|---|---|---|---|---|---|
||_%_|_σ_|_%_|_σ_|_%_|_σ_|
|Treatment Communities (baseline)|60.9%|1.1%|30.8%|1.1%|21.5%|1.1%|
|Change in poverty_ex post_: Skoufias and Di<br>|||||||
|Maro (2008)<sup>1</sup>|-16.5%|1.6%|-24.3%|1.5%|-29.2%|1.4%|
|Change inpoverty _ex ante_: Simulated<sup>2</sup>|-13.1%||-26.6%||-33.1%||

_Source_ : Baseline Survey 1997 and Rounds 1-4; 1: Skoufias and Di Maro (2008), Table 4; 2: authors' calculation using BFL model _Notes_ : Prices of November 1999 (pesos); poverty line: mean of Nov 98 consumption per capita <u>(taken from Skoufias and Di Maro, 2008)</u> The final step in our analysis is the simulation of the cost of the program based on the BFL model. Table 5 shows that 4,558 out of 7,110 (or 64 percent of) poor families in treatment villages would enroll in the program and accept the condition of school attendance. The average cash transfer for these families in treatment villages is estimated at 303 pesos/month, which is very close to the average transfer of 300 pesos in January 2002 for 1.6 million families observed from official program data. Based on an analytical exercise of scaling up the cost of the program

> 31  The poverty line is equivalent to 176.17 pesos per month (for details, see Skoufias and Di Maro, 2008)

> 32 The _ex ante_ results presented in Table 4 remain the same when the simulations were done _without_ incorporating the conditionality of schooling. This suggests that in terms of meeting the poverty reduction objective of safety net programs, conditionality does not necessarily imply better results. In other words, it suggests that if the main objective of a CCT program is poverty reduction, the transfers are important but the conditions are not – which makes perfect intuitive sense.

25
"Voice" of SMEs within Agencies. We found the "voice", or participation of SMEs within the support agencies, very limited. Only three of the nine support providers have SME representation on their boards. In the absence of direct representation, the support providers interviewed use weaker and more indirect mechanisms to reflect SME voice. The SME Bank, KTA, KTC, and KAITECH undertake regular surveys to identify the needs of their client SMEs. SMIPC and KCGF have their own individual systems of monitoring the "voices" of their actual and potential clients.

Market Tests. Support agencies vary in the :xtent to which they charge fees for their services, but in alnost all cases, the fees charged by collective support agencies in Korea are nominal. It therefore appears that fees do not constitute a barrier to use of support agency services for SMEs.

The three sample financial institutions charge no fees for their mgement, legal, tax, and other consulting services, and only direct costs for their training programs. Among the export marketing support agencies, KOTRA charges fees for subscriptions to its periodicals, but these account for only a smal fraction of its budget. KTA, a membership association, charges an annual membership fee of 110,000 won. By contrast, KrC operates more like a private firm and charges actual costs plus margin. Among technical support agencies, KAITECH charges its clients only direct costs for its services, and this income accounts for just a small portion of its budget. SMIPC charges only a percentage of its direct costs to clients.

Competition Among Support Agencies. Although collective support agencies have some advantages over private ones, such as the ability to offer services at below-market prices, there appear to be very few restrictions on private sector participation in the provision of financial, marketing, and technical support. As a result, the collective support agencies in our sample perceive significant competition from other institutions and private firms.

In the area of financial services, the SME Bank and CNB are theoretically in competition with private banks and non-bank financial institutions in offering services to SMEs. Yet in practice, the WME Bank and CNB receive very litme competition in handling the highly desirable low-interest rate "strategic funds" that have grown in relative importance in recent years. In the case of marketing, collective support providers face more substantial competition from the private 41
online appendix production of other textiles, and into the production of masks, potentially due to input adjustment frictions. Lastly, we fail to find evidence of impacts of licensing on firm scale for any other business outcomes, including total turnover, remuneration, and employment. This lack of impacts on firm scale can be explained by garment manufacturers making competitive entry decisions into mask manufacturing.

Table A4: Licensing causes firms to shift into mask production without firm growth

|||_Depende_|_nt variable:_||||
|---|---|---|---|---|---|---|
||Mask Turnover Share|Non Mask Turnover|Turnover|Turnover|Labor Pay|Employment|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel I: Unmatched**|||||||
||-0.038|-0.306|-0.323|0.022|0.206|0.181|
|Treat x Q4 (u)|(0.036)|(0.317)|(0.313)|(0.139)|(0.176)|(0.204)|
||[0.298]|[0.334]|[0.302]|[0.871]|[0.240]|[0.375]|
|Treat x Q1 (u)|-|-|-|-|-|-|
||0.420|-0.422|0.080|0.137|0.358|-0.003|
|Treat x Q2 (u)|(0.098)|(0.238)|(0.258)|(0.209)|(0.291)|(0.193)|
||[0.000]|[0.076]|[0.756]|[0.513]|[0.219]|[0.989]|
|# observations (u)|134|150|153|234|117|117|
|Controls x Quarter FE (u)|||||||
|**Panel II: Matched**|||||||
||-0.008|0.281|0.146|-0.168|0.081|0.473|
|Treat x Q4 (m)|(0.039)|(0.338)|(0.318)|(0.171)|(0.340)|(0.502)|
||[0.846]|[0.406]|[0.646]|[0.327]|[0.812]|[0.346]|
|Treat x Q1 (m)|-|-|-|-|-|-|
||0.428|-0.423|-0.210|0.273|0.052|-0.013|
|Treat x Q2 (m)|(0.124)|(0.305)|(0.256)|(0.192)|(0.454)|(0.306)|
||[0.001]|[0.166]|[0.411]|[0.155]|[0.909]|[0.965]|
|# observations (m)|96|108|111|165|90|90|
|Controls x Quarter FE (m)|X|X|X|X|X|X|
|Data|EBM|EBM|EBM|VAT|PAYE|PAYE|
|Estimation method|OLS|Poisson|Poisson|Poisson|Poisson|Poisson|
|Firm FE|X|X|X|X|X|X|
|Quarter FE|X|X|X|X|X|X|

# **E Additional placebo checks**

## **E.1 Impacts of manufactured mask exposure on turnover in VAT**

As discussed in Appendix A.1, our primary outcomes in our analysis of the impacts of mask manufacturing exposure in Section 3 are only observable in EBM II, which A21
**Table 5. OLS regression results: Do differences in reported time use between time diary and stylized estimates vary according to respondent, enumerator, or interview** **<u>characteristics?</u>**

|||Banglades<br>|h<br>||Uganda<br>||
|---|---|---|---|---|---|---|
|Variable|Model 2A|Model 3A|Model 4A|Model 2B|Model 3B|Model 4B|
|Female|-0.264|2.605|2.620|-11.472<sup>***</sup>|-12.531<sup>***</sup>|-12.694<sup>***</sup>|
||(2.918)|(4.197)|(4.188)|(4.181)|(4.181)|(4.246)|
|Age|0.346|0.465|0.462|-0.623|-0.613|-0.601|
||(0.636)|(0.677)|(0.676)|(0.534)|(0.536)|(0.537)|
|Age-squared|-0.001|-0.003|-0.003|0.009<sup>*</sup>|0.010<sup>*</sup>|0.009<sup>*</sup>|
||(0.006)|(0.007)|(0.007)|(0.005)|(0.005)|(0.005)|
|Literate|-8.567|-8.109|-8.203|-5.237|-4.579|-4.500|
||(6.073)|(6.205)|(6.254)|(5.419)|(5.185)|(5.180)|
|Completed less than primary education|6.428|3.885|3.963|8.713|10.419<sup>*</sup>|10.401<sup>*</sup>|
||(7.433)|(7.507)|(7.516)|(6.431)|(6.130)|(6.139)|
|Completed secondary education or above|2.384|-0.012|0.129|15.518<sup>**</sup>|13.341<sup>**</sup>|13.181<sup>**</sup>|
||(6.733)|(7.168)|(7.234)|(6.240)|(6.162)|(6.185)|
|Interviewed alone|–|–|-1.270|–|–|-3.950|
||||(3.199)|||(11.769)|
|Constant|-17.975|-4.086|-2.838|9.068|20.673|24.315|
||(17.956)|(15.625)|(15.172)|(13.296)|(16.260)|(19.866)|
|Activity effects?|Yes|Yes|Yes|Yes|Yes|Yes|
|Enumerator effects?|No|Yes|Yes|No|Yes|Yes|
|Adjusted R-squared|0.007|0.011|0.010|0.071|0.077|0.077|
|_Tests of joint significance:_|||||||
|Activity effects|8.203***|8.150***|8.148***|24.286***|24.162***|24.154***|
|Respondent effects|1.859*|2.029*|2.024*|6.694***|6.624***|6.543***|
|Enumerator effects|–|57.850***|25.977***|–|2.903***|2.901***|
|Observations|3,591|3,591|3,591|2,952|2,952|2,952|

**Source:** Authors’ calculations based on WEAI 2.0 pilot data. **Notes:** Dependent variable is the difference between time diary and stylized estimates. Household-level cluster-robust standard errors in parentheses. ***, **, and * denote statistical significance levels at 1%, 5%, and 10%, respectively.

29
analysis and entropy class measures are used together to examine the changes in inequality and social welfare for Ghana as a whole (section 3.a), for different localities (section 3.b) and for different socio-economic groups in each locality (section 3.c).

# _3. a) The changes in inequality and social welfare in Ghana_

Table 1 presents the entropy class of measures for inequality in 1988, 1989 and 1992. The comparisons between GLSS1 and GLSS3 and GLSS2 and GLSS3 show very similar patterns. By any measurement, inequality has improved in Ghana. Gini shows an improvement less than that of entropy class of measures, which give a more complete picture about what happened to each part of the distribution. E(O) has the highest percentage change, which indicates that the improvement of inequality mainly comes from the lower end of income distribution. Little change occurred at the higher end of income distribution between GLSS1 and GLSS3, as indicated by the small percent change (-1 percent) in E(2) between the two years. Using adjusted data of GLSS2 and GLSS3, however, a significant improvement in inequality also shows at the upper end of distribution.<sup>2</sup> Table 1. Ghana: Expenditure Per Capita and Inequality, GLSS1(1988), GLSS2(1989) and GLSS3(1992)

|_Measure_|_1988_|_1992_|_Percent_<br>_change_|_1989_<br>_With adju_|_1992_<br>_sted data'_|_Percent_<br>_change_|
|---|---|---|---|---|---|---|
|Mean Expenditure per|||||||
|capita (1992 Cedis)|198345|214992|8.4|147193|172553|17.2|
|Gini coefficient|35.2|33.8|-4.00|37.4|35.3|-5.6|
|E(0)|20.9|18.7|-10.5|23.7|21.1|-11.0|
|E(1)|22.0|20.7|-5.91|25.0|22.7|-9.2|
|E(2)|30.3|29.9|-1.32|36.3|33.2|-8.5|

1 Adjusted data was provided by Demery and Mehra, see their paper for details.

The above analysis shows that inequality has been reduced in Ghana, and one could therefore say that social welfare has improved given a social welfare function that equality is desired. An alternative way to monitor welfare changes is through the first order stochastic dominance analysis, which plots the cumulative distribution functions of the income distributions. If distribution function A lies nowhere above and somewhere below B, then A displays first-order dominance over B; that is A has a higher level of social welfare than B, given a social welfare function that is individualistic and increasing in income regardless of its distributional judgments.<sup>3</sup> reproduced in Appendix B of this paper.

> 2 Uncommon food items were excluded from GLSS3 to make the expenditure level comparable between GLSS2 and 3. However, excluding uncommon food items, which are mainly consumed by the better off households, is least likely to affect inequality at the lower end of distribution in 1992. This is evident in Table 1.

The theorem is established by Saposnik(198 1).

3
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- _Macaohub_ . 2010. ―Provincial Capitals of Angola Linked by Fibre Optic Cable in 2011.‖ November 17. www.macauhub.com.mo/en/news.php?ID=10506.

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

Narain, Divya. 2009. ―Mundo Startel Expands WiMAX Services in Angola.‖ _TMCnet_ , August 4. <u>http://africa.tmcnet.com/topics/othercountries/articles/61312-mundo-startel-expands-wimaxservices-angola.htm.</u>

- _TelecomPaper_ . 2009. ―Govt, Private Operators Launch Angola Cable Joint Venture.‖ June 5. www.telecompaper.com/news/govt-private-operators-launch-angola-cable-joint-venture.

- WFN Strategies. 2009. ―WFN Strategies to Supervise Maintenance of the ADONES Cable System.‖ Press Release, June 29. www.wfnstrategies.com/pressroom.php.

# Irrigation

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- EIU (Economic Intelligence Unit). 2010. _Niger Country Report._ November 2010 Report, Economic Intelligence Unit, London.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- GTZ (Deutsche Gesellschaft für Internationale Zusammenarbeit). 2009. _International Fuel Prices 2009_ . 6th ed. Eschborn, Germany: GTZ.

45