rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5 – Multiple Correspondence Analysis (Final weights, Urban and Rural areas)**

||||**Urba**<br>**n**|**Rura**<br>**l**|
|---|---|---|---|---|
||||**Dim1**|**Dim1**|
|Employmen<br>t|Head of household unemployed|yes|-0.48<br>|--|
|||no|0.01|--|
||Youth unemployment|active|-0.63<br>|-0.08<br>|
|||unemployed|0.11|0.04|
|Education|Head of household alphabetized|yes|0.8|1.13|
|||no|-0.13|-0.56|
||Household sharewith no education|less than 1/3|0.91|1.85|
|||between 1/3and 1/2|-0.44|0.71|
|||between 1/2 and3/4|-1.49|-0.36|
|||more than3/4|-2.32|-1.4|
||Household share of alphabetized|less than 1/3|-2|-0.99|
|||between 1/3 and 1/2|-0.35|0.5|
|||between 1/2 and 2/3|0.32|1.53|
|||more than 2/3|1.18|2.38|
|Health|Household medical coverage|yes|0.15|0.44|
|||no|-0.13|-0.26|
||Household share of medical<br>coverage|at least half household|1.75|3.16|
|||less than half household|1.2|2.22|
|||no coverage|-0.79|-0.21|
|Housing|Type of accommodation|villa|2.47<br>|--|
|||apartment|1.81|--|
|||moroccan house|-0.1|--|
|||basic dwelling|-3.73|--|
||Type of accommodation|villa, apartment, Moroccan<br>house|--|2.36|
|||rural house|--|0.81|
|||basic dwelling|--|-0.8|
||Cohabitation|one household|0.18|--|
|||two households|-1.49|--|
|||three households|-2.11|--|
|||four households an dmore|-2.56|--|
||Cohabitation|one household|--|0.03|
|||two households and more|--|-0.76|
||Buthtub/shower|yes|1.62|3.5|
|||no|-1.31|-0.27|
||Kitchen|yes|0.35|0.38|

28
<u>Table 1b: Sample means and balance for MDAT participants</u>

||Cash Only<br>(mean)|Mother Leaders|Mother Leaders<br>and Affirmation|Mother Leaders<br>and Planning|
|---|---|---|---|---|
|Male (%)|0.48<br>(0.50)|0.00<br>(0.03)|-0.01<br>(0.03)|-0.02<br>(0.03)|
|Age of child (months)|48.00|-0.79|-0.13|-0.38|
||(12.23)|(0.67)|(0.66)|(0.67)|
|Household size|6.82|-0.05|-0.17|-0.05|
||(2.41)|(0.13)|(0.13)|(0.13)|
|Distance to nearest<br>|1.00|-0.04|-0.09|-0.09|
|school(Km)|(1.60)|(0.08)|(0.09)|(0.09)|
|School (ever attended) by<br>|0.64|-0.04|-0.02|-0.02|
|HH head|(0.48)|(0.03)|(0.03)|(0.03)|
|N<br>**N**|695|677|680|705|

_Note: The coefficients for the Cash-only represent the mean for the group. The figures for all treatment groups represent the added difference from the control mean. ‘*’ denotes significance at p≤0.1; ‘**’ at p≤0.05._

**<u>Table 2: Sample Attrition</u>**

||Cash Only<br>(Mean)|Mother Leaders|Mother Leaders<br>and Affirmation|Mother Leaders<br>and Planning|
|---|---|---|---|---|
|Households at baseline not|0.11|0.01|-0.01|0.01|
|found at midline (%)|(0.01)|(.01)|(0.01)|(0.01)|
|N|963|945|1005|970|

_Note: The coefficients for the Cash-only represent the mean for the group. The figures for all treatment groups represent the difference from the control mean. Standard errors are in parentheses. ‘*’ denotes significance at p≤0.1; ‘**’ at p≤0.05._ 22
Kojima, M. 2013. “Petroleum Product Pricing and Complementary Policies.” World Bank Policy Research Working paper 6396, World Bank, Washington, DC.

Kouame, W., and S. J.-A. Tapsoba. “Structural Reforms and Firms’ Productivity: Evidence from Developing Countries”. Policy Research Working Paper 8308, World Bank, Washington, DC.

Kovasic, W. E. 1995. “Designing and Implementing Competition and Consumer Protection Reforms in Transitional Economies: Perspectives from Mongolia, Nepal, Ukraine and Zimbabwe.” DePaul Law Review 44 (4): 1197-1224.

Laborde, D., C. Lakatos, and W. Martin. 2019. “Poverty Impact of Food Price Shocks and Policies.” Policy Research Working Paper 8724. World Bank, Washington, DC.

Ma, C., and F. Valencia. 2018. “Welfare Gains from Market insurance: The Case of Mexican Oil Price Risk.” IMF Working Paper 18/35, International Monetary Fund, Washington, DC.

Maimbo, S. M. and C. A. Henriquez Gallegos. 2014. “Interest Rate Caps around the World Still Popular, but a Blunt Instrument.” Policy Research Working Paper 7070, World Bank, Washington, DC.

Morton, F. M. S. 2001. “The Problems of Price Controls.” Regulation 24(1): 50-54.

Mpapalika, J., and C. Malikane. 2019. “The Determinants of Sovereign Risk Premium in African Countries.” Journal of Risk and Financial Management 12 (1): 29-49.

Mundaca, G. 2017. “Energy Subsidies, Public Investment and Endogenous Growth.” Energy Policy 110 (C): 693-709.

Munoz, S. 2006. “Suppressed Inflation and Money Demand in Zimbabwe.” IMF Working Paper 06/15, International Monetary Fund, Washington, DC.

Murphy, F., A. Pierru, and Y. Smeers. 2019. “Measuring the Effects of Price Controls Using Mixed Complementarity Models.” European Journal of Operational Research 275 (2): 666-676.

Newfarmer, R., and M. D. Pierola. 2015. Trade in Zimbabwe Changing Incentives to Enhance Competitiveness. Washington DC: World Bank.

Newbery, D. M. G., and J. E. Stiglitz. 1982. “Risk Aversion, Supply Response, and the Optimality of Random Prices: A Diagrammatic Analysis.” The Quarterly Journal of Economics 97 (1): 1-26.

Nguyen, A. D. M., J. Dridi, F. Unsal, and O. H. Williams. 2017. “On the Drivers of Inflation in Sub-Saharan Africa.” International Economics 151 (October): 71-84.

Ore, M. A. H., L. A. Sanchez, L. D. Sousa, and L. Tornarolli, eds. 2018. Fiscal and Welfare Impacts of Electricity Subsidies in Central America. Washington DC: World Bank.

15
C14d_contractunit2

**[Specify unit in which length of contract or agreement is given]**

1. days 2. weeks 3. months 4. years 888. Don’t know 999. Refused

**Go to** à **C15a**

C14e_temp2 What was the main reason that you do not have a permanent contract or permanent verbal agreement in this previous MAIN job? **[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT ALL THAT APPLY.]**

1. This employer did not offer me a job with a permanent contract

2. I did not want to have a job with a permanent contract 3. I was unable to do continuous work 4. I was not able to find any job that offered a permanent contract or verbal agreement 5. Other (specify) 888. Don’t know 999. Refused C15a_sick2 How many days of sick leave did your employer provide PER YEAR in this previous MAIN job?

**[If “As needed” then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”]**

C15b_casual2 How many days of PAID earned or casual leave did your employer provide PER YEAR in this previous MAIN job?

**[If “As needed” then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C15c_holiday2 How many days of holiday did your employer provide EACH WEEK in this previous MAIN job?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

# **[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]**

C16a_maternityactual2 Did you take maternity leave from this previous MAIN job?

1. Yes

2. No à **Go to C17** 888. Don’t know à **Go to C17** 999. Refused à **Go to C17** 4
17

# **Table** 4. True Cost-of-Living Comparisons

||All urban|areas|Jakar|ta<br>only|
|---|---|---|---|---|
|Reference<br>rural income<br>(Rp/person/<br>month)|Equivalent<br>urban income<br> <br>(Rp/person<br>month)|Cost-of-livin<br>differential<br>/<br>(Z)|g Equivalent<br>urban income<br>(Rp/person<br>month)|Cost-of-living<br>differential<br>/<br>_(Z)_|
|3000|3254|8.47|3633|21.1|
|5000|5488|9.76|6143|22.9|
|7000|7743|10.6|8682|24.0|
|9000|10013|11.3|11243|24.9|
|12000|13441|12.0|15113|25.9|
|15000|16888|12.6|19011|26.7|
|20000|22667|13.3|25556|27.8|
|25000|28481|13.9|32148|28.6|
|35000|40185|14.8|45438|29.8|
|50000|57884|15.8|65572|31.1|

Note: Cost-of-living differential , 100(COL-1).
# **2.3 Almost no South-South collaboration to-date**

The benefits of green technologies spill across national boundaries, so a higher level of international collaboration in green innovation would be the expected norm. But as indicated by the incidence of patents with co-inventors from both developing ( ̳South‘) and high income ( ̳North‘) countries, the extent of North-South collaboration is almost identical for green patents as for all patents, with both having increased over time.<sup>8</sup> Across all technology areas, 42% of patents with an inventor from the South also had a co-inventor from the North in 2010 (Figure 4, Panel A). Just for green patents, an almost identical 43% had North collaborators (Panel B). The corresponding figures for 1996 were 35% South-North collaborations across all patents, and 17% collaborations for green patents. Interestingly, these data indicate almost no South-South collaboration: among all green patents granted between 1995 and 2010, there is only one instance of South-South collaboration.<sup>9</sup> Thus, there may be scope for policy to increase international collaboration in green technologies, particularly among developing countries. And even if the benefits from South-South collaboration on frontier innovations are limited, there is a strong case for more collaboration on catch-up innovations when adapted to relatively similar local environments.

<!-- Start of picture text -->
-<br>Figure 4: Cross country Collaboration in Patents Granted to Developing Country Inventors<br>A. All Patents  B. Green Patents<br>50<br>7500<br>6500 Indigenous<br>40<br>5500<br>4500 Indigenous+  30<br>North-South<br>3500<br>Indigenous +  20<br>2500<br>North-South +<br>1500 South-South<br>10<br>500<br>1995 1998 2001 2004 2007 2010<br>0<br>1995 1998 2001 2004 2007 2010<br>Source: USPTO grants in PATSTAT<br><!-- End of picture text -->

# **2.4 Potential for expanding green production and trade**

The patent data suggest that there is little capacity for _frontier_ green innovation in most developing countries. However, there could be enormous capacity for _catch-up_ green -up innovation through new-tothe-firm adoption and adaptation of existing green technologies, and through indigenous base-of-pyramid

> 8 Guellec and Potterie (2001) use patent co-invention to measure international collaboration. Details of our estimation are given in the data annex.

> 9 The incidence of South-South collaboration among _all_ patents granted to developing country inventors is 0.3%.

7
Table A10: Effect of Extended Provider Training on Match Between Patient Prior and Prescription by Accuracy of Pre-Consultation Malaria Beliefs

||(1)|(2)|(3)|
|---|---|---|---|
||Full Sample|Has Valid Hom|e-Based RDT|
||Prescription<br>Matches<br>Malaria Prior|Prescription<br>Matches<br>Malaria Prior|Prescription<br>Matches<br>Malaria Prior|
|Extended Training|-0.123|-0.156<sup>_∗_</sup>|-0.129<sup>_∗∗_</sup>|
||(0.0841)|(0.0880)|(0.0590)|
|Extended Training _×_ Prior-Risk Match|0.222<br>(0.163)|0.249<br>(0.171)|0.183<sup>_∗_</sup><br>(0.0942)|
|Match: Malaria Prior and Malaria Risk|0.615<sup>_∗∗∗_</sup><br>(0.0836)|0.526<sup>_∗∗∗_</sup><br>(0.0808)|0.0902<sup>_∗_</sup><br>(0.0533)|
|P-value: ET + ET _×_ Prior Match = 0|0.245|0.318|0.304|
|Prior Match Measure|Expected<br>Prior Match|Expected<br>Prior Match|Actual Prior<br>Match<br>(Home RDT)|
|Mean (Basic Training, No Pat. Info.)|0.63|0.66|0.66|
|N|1971|1093|1093|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. Standard errors for regressions that include a derivative of predicted malaria risk in the set of independent variables are bootstrapped using 500 replications to account for the fact that predicted malaria risk is a generated regressor. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the potential covariate set and set of pairwise interactions. Outcome is equal to one for patients who, pre-consultation, believe they have malaria and are prescribed an antimalarial _or_ for patients who believe they do not have malaria and are not prescribed an antimalarial. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

A8
creation rate in Moldova is unlikely to be caused by the strict employment protection legislation. This leads us to continue the search for the elements of the business environment which could account for low job creation.

# **What renders the business environment unfriendly in Moldova?**

When Moldavian employers are asked this question they point to high (monetary and procedural) costs of obtaining permits, certificates and licenses, to complicated custom procedures and to high costs associated with various inspections. These impediments are perceived as severe: on a scale from 0 to 10 they get scores in the range of 7 to 10. That is, they represent a substantial constraint on business activity.<sup>29</sup> The perception that doing business in Moldova is costly and difficult is well grounded in reality. The results of the _Regulatory Cost Assessment Survey_ for 2000-2002 clearly show that business regulations in Moldova impose a huge burden on management time and firm resources (ProEra, 2003). They dramatically reduce returns to economic activity by both raising costs and lowering benefits. Low expected returns discourage new firm formation, and high costs associated with regulations limit the scale of production. This translates into less jobs, lower employment and ultimately higher poverty. The welfare costs of the unfriendly business environment are substantial.

The number of required permits, certificates and licenses is huge, they need to be obtained frequently, it takes a long time to obtain them and associated monetary payments – formal and informal – are high. For example, it takes almost half a year to obtain a construction permit at a cost exceeding USD 1,000. Permits are also required to renovate premises, change their profile and to utilize them. Of course, there are many other permits, such as export permits, import permits, equipment permits, hygienic permits, etc. Obtaining certificates for goods and services is an equally costly process. On average a business needs to go through certification procedure for goods 18 times per year, and each certification takes on average 11 days and costs USD 152. A separate procedures exist for the certification of production lines (ProEra, 2003).

> 29 MAEWS Focus Group, Chisinau, Moldova, December, 2002.

38
21 family planning, Nepal. In no case is the higher estimate of costs below family planning. The low estimate for pensions is below the costs of a birth averted through education in Bangladesh, Indonesia, Jordan, Nepal and Sri Lanka. In no case is the higher estimate of costs from pensions below that for education.

The implications of these findings are that in most countries family planning is the most cost effective means of reducing fertility. In a few countries that have very costly programs per effect, education, infant mortality reduction, or pension schemes may be more cost effective. The optimal program ignoring interactions would be designed by estimating the benefits per birth averted and then using programs until their marginal costs exceeded the benefits.

These results also emphasize the country specific nature of the preferred policy. It is recommended that for policy analysis in each country, updated and refined estimates of the costs of births averted be calculated. For comparative analysis, it is necessary to :(1) develop better understanding of the externalities or the community level effects of mortality reduction and education on fertility, and (2) better understand the interaction between different policies. For example,(a) how does increased access to family planning affect educational differentials?, (b) how does improved family planning access alter the relationship between mortality and fertility?, and (c) how does improved mortality and education reduce the pension motive's effect on fertility?

Acknowledgements: I would like to thank Susan Stout, Dirk Prevoo and Ella Hornsby of the World Bank for their assistance at various stages in the preparation of this paper.
13 Table 3. Ambient pollution levels in selected cities

|Pollutant|Cities|Trend in 1973-1986<br>inX|Concentration Level<br>1986|
|---|---|---|---|
|SOx a)|**New**York|- 6|50|
||London|-<br>7|55|
||Tokyo|-<br>6|35|
||Bangkok<br>||18<br>|
||**Kuala**Lumpur||22<br>|
||Lisbon<br>||25<br>|
||Bombay||30|
||NowDelhi|+20|40<br>|
||Hong Kong|+8|45|
||<br>Shanghai<br>||S0|
||Santiago<br>|0|60 c)<br>|
||**Manila**||65 c)<br>|
||Beijing<br>||75c)<br>|
||Sao Paulo|-<br>10|90 c)|
||Riode Janeiro||100 c)|
||Seoul||105 c)|
|**Particulate**|New York|-<br>3|81|
|Matter<br>a)|Tokyo|-<br>1|6o<br>|
||Accra||100 d)|
||Riode Janeiro<br>||<br>101d)|
||**Kuala**Lumpur<br>||105 d)<br>|
||Bombay||110 d)|
||Bangkok<br>|+13|110d)<br>|
||Shanghai||111d)|
||<br>**Jakarta**|-<br>S|115 d)|
||Calcutta|0|130 d)|
||Beijing||130 d)|
||<br>**New**Delhi|-<br>3|<br>131 d)|
|**NO2**<br>**a)**|New York<br>|+**2.6**|65|
||London<br>|+7<br>|e1<br>|
||Bombay<br>|+3|20<br>|
||Bogota||25|
||New**Delhi**|+2|32|
||Hong Kong||43|
||Singapore|-<br>4|46|
||Lisbon<br>|+4<br>|47|
||SaoPaulo|+1|76|
|co<br>b)|NowYork||12e)|
||Los Angeles<br>||13 e)<br>|
||**Sao**Paulo||19e)|
||Bangkok||5|
|Leada)|Stockholm||1.1f)|
||Amsterdam||0.3|
||Frankfurt||0.4|
||Hong Kong||0.14<br>|
||Sao Paulo||0.22<br>|
||Bangkok||0.3|
||<br>Singspore.||0.9|

- **a)** annual average concentration level in ug/cu. m for 1980-1984

- b) maximum 8-hourly coneontration **level** in mg/cu. m for 1980-84

- c) exceeds WH0 guidelines of 40 to 60 ug/cu. m

- d) exeeeds WHO guidelines

- of 60 to 90 ug/cu. m

- *) **exceeds** WHO guidelines of 10 mg/cu. e

f) **exceeds** WHO guidelines of 0.5 to 1.0 ug/cu. a Source: [UNEP A WHO 1984, UNEP A WHO 1988]
C20b_gratuity3 Did your employer offer gratuity (lump sum) in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C20c_pension3 Did your employer offer a pension in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C21a_actualtermination3 Were you terminated from this previous MAIN job?

1. Yes

2. No à **Go to C21d** 888. Don’t know à **Go to C21d** 999. Refused à **Go to C21d** C21b_actualtermlength2 How many days of notice were you given?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C21c_actualtermpay3 How many days of pay did you receive on termination?

**[If don’t know then put “Don’t Know”] [If refused then put “Refused”]**

> à **Go to C23**

C21d_termination3 If you had been terminated from this previous MAIN job, would you have been given notice?

1. Yes

2. No à **Go to C23** 888. Don’t know à **Go to C23** 999. Refused à **Go to C23** C21e_termlength3 How many days of notice would you have been given?

**[If don’t know then put “Don’t Know”] [If refused then put “Refused”]**

C21f_termpay3 How many days, weeks or months of pay would you have received on termination?

**[If don’t know then put “Don’t Know” and go to C23] [If refused then put “Refused” and go to C23]**

6
- ii -

Between July 1981 and February 1987, public employment and pay issues in central governments figured prominently in 45 Bank lending operations in 24 countries. West Africa accounted for more than one-third of these operations; Latin America and the Caribbean had sligntly more than one-fourth; and the rest were distributed fairly evenly among East Asia, South Asia, EMENA, and East Africa.

Despite a heterogenous sample of countries and cases examined, four principal types of problems were identified as characteristic of the group as a whole: excessive public sector wage bills; surplus numbers of civil servants; erosion of public service salaries; and, wage compression. The principal instruments used by the Bank to address these problems were structural adjustment loans, technical assistance projects, and country economic and sector work. A variety of operational approaches were utilized. The most sweeping approach has been to call for reduction of the wage bill tuLrough salary restraint. Freezing wages is usually at best a temporary solution and tends to reinforce inequities or wage compression within salary structures. Employment reforms which aimed at cutting numbers of civil servants included: efforts to eliminate phantom workers or ghosts; elimination of officially sanctioned posts; retrenchment of temporary workers; enforcement of retirement rules; a freeze of initial recruitment and/or prevention of guaranteed entry into the civil service of university graduates; and voluntary retirement of surplus workers. The most difficult political step taken by governments is the actual dismissal of civil servants. The political impact of this action is sometimes mitigated by public education programs or competency testing. Although the Bank has, in some cases, encouraged civil servant reduction, such as the design of severance benefits or redeployment and retraining schemes. These factors may significantly influence the outcome of reforms.

Where low public sector salaries and wage compression posed a problem in attracting qualified staff to central government, the Bank sometimes recommend selective upward wage adjustments, with varying degrees of success. Other mechanisms for attracting higher level personnel were also explored. These included senior executive services, the use of local consultants, secondment from public enterprise3, and compensation through non-wage allowance and benefits systems.

Some of the operational techniques financed by the Bank to carry out the above reforms included improvements in the data collection capacity of personnel management systems, civil service censuses, functional reviews of whole governments or selected ministries, diagnostic studies of various aspects of pay and employment issues in the civil service.

The institutional arrangements for pay and employment reform have varied, but in general, reform efforts located in more autonomous institutions with greater access to higher levels of government have a better chance of succeeding.