rafmacalaba/datause-encoder-data huggingface dataset:
unrationed good α<sup>u</sup> 1<sup>.If estimates ofαu</sup> 1<sup>for unrationed goods are relatively similar, we may hope</sup> that — should good r be unrationed — its α<sup>r</sup> 1<sup>would fall within the same range.With these strong</sup> assumptions, we can ‘bracket’ λ r and, by extension, Ckm<sup>r.</sup>

# 3. The data

The data we use come from the Nepalese Living Standard Measurement Survey (LSMS) of 1995/96. The survey drew a nationally representative sample of 3373 urban and rural households spread among 274 villages or ‘wards’. Between 16 and 20 households were interviewed in each ward. As with other LSMS surveys, data coverage of NLSS 1995/96 is quite comprehensive.

The survey includes a series of questions on the adequacy of consumption level enjoyed by the household. The household head was fi rst asked the following question: "Concerning [your family’s food consumption over the past one month], which of the following is true? It was less than adequate for your family’s needs [1], it was just adequate for your family’s needs [2], it was more than adequate for your family’ needs [3]." The household head was then asked fi ve other similar questions in which the part in brackets is replaced by: [your family’s housing], [your family’s clothing], [the health care your family gets], [your children’s schooling], and [your family’s total income over the past one month], respectively.

Responses to these questions are summarized in Table 1. The overall dissatisfaction of household heads is quite striking. About 69 percent of household heads feel they have less than adequate income. Even for food consumption, which receives the best adequacy rating of the six questions, 47 percent of the household heads report it to be inadequate relative to needs. Only a small proportion of households report their income or consumption to be more than adequate. Although disturbing, these fi gures are consistent with more objectively measured welfare: at the time of the LSMS survey, 42% of the Nepalese population was estimated to be below the 11
# Contents

|I. Overview of Family Planning in Zaire ............................<br>,.|<br>1|
|---|---|
|A. Brief Overview of Zaire ..................................|1|
|B. Family Planning Programs in Zaire ...........................|1|
|1. Background .......................................|1|
|2. Administrative and Logistic Problems .......................|4|
|3. Socio-cultural Barriers to Family Planning in Zaire ...............|_5_|
|H. Matadi: Clinical Services and Community-based Distribution.|7|
|A. Characteristics of the City and its Population.|7|
|B. 1981-83: the PRODEF Project.|8|
|1. Project Design of PRODEF, Phase I.|8|
|2. Results of the 1983 Followup Study.|9|
|C. 1984-89: Expanded Family Planning Activities in Matadi.|9|
|1. PRODEF Project, Phase II.|9|
|<br>2. PSND/AZBEF clinics .11||
|3. Tubal ligation services .13||
|4. Pharmacies .13||
|D. Indicators of Success in Matadi: Three Contraceptive Prevalence Surveys ...|<br>13|
|1. Knowledge of Contraceptive Methods .13||
|<br>2. Ever Use of a Modem Contraceptive Method .13||
|3. Current Use of a Modem Contraceptive Method .14||
|4. Source of Contraceptives .15||
|5. Knowledge of and Contact with PRODEF distributors .17||
|6. Observations Regarding Contraceptive Prevalence in Matadi as of 1989 .|.<br>17|
|E. Reasons for Success in Matadi .18||
|1. Iitial Focus on a Single Goal or Service .18||
|<br>2. Sequential Diversification of Goals and Phased Program Implementation ..|<br>19|
|3. Organizational Autonomy .19||
|<br>4. Use of Network Structures .20||
|5. Flexible Selection and Training Processes .21||
|<br>6. Other Aspects Contributing to Matadi Success .22||
|F. Implications of the Matadi Success .23||
|1. Sustainability .23||
|2. Ways The Program Could Have Been More Successful .23||
# **Table 3:  Data sources and models used in the estimation of the number of accounts in commercial banks, cooperatives & credit unions, specialized state financial institutions and microfinance institutions.**

# **Commercial Banks**

||**# countries**|**% sample**|**% final count**<br>**2008**|**% final count**<br>**2009**|
|---|---|---|---|---|
|Actuals|110|58%|59.4%|59.0%|
|Model (1)|22|12%|11.1%|11.3%|
|Model (2)|7|4%|26.0%|26.3%|
|Model (3)|1|1%|0.3%|0.3%|
|Model (4)|1|1%|0.1%|0.1%|
|Model (5)|25|13%|1.8%|1.8%|
|Model(6)|25|13%|1.3%|1.3%|
|Totals|191|100%|100%|100%|

# **Cooperatives & Credit Unions**

||**# countries**|**% sample**|**% final count**<br>**2008**|**% final count**<br>**2009**|
|---|---|---|---|---|
|Actuals|65|58%|54.6%|54.7%|
|WOCCU|38|34%|28.7%|28.7%|
|EACB|9|8%|16.6%|16.6%|
|Totals|112|100%|100%|100%|

Notes: WOCCU is World Council of Credit Unions; ECBA is European Cooperative Banking Association.

# **Specialized State Financial Institutions**

||**# countries**|**% sample**|**% final count**<br>**2008**|**% final count**<br>**2009**|
|---|---|---|---|---|
|Actuals|79|59%|61.5%|61.4%|
|CGAP "Big Numbers" *|55|41%|38.5%|38.6%|
|Totals|134|100%|100%|100%|

Notes: CGAP "Big Numbers" refers to data gathered for CGAP, Occasional Paper No. 8, 2004.

# **Microfinance Institutions**

||**# countries**|**% sample**|**% final count**<br>**2008**|**% final count**<br>**2009**|
|---|---|---|---|---|
|Actuals|95|75%|88.7%|89.4%|
|MicrofinanceExchange (MIX)|31|25%|11.3%|10.6%|
|Totals|126|100%|100%|100%|

Note: MIX numbers are values voluntarily reported by MFIs and aggregated at the country level.

23
|The World|Senior business|‘How problematic is|1998, 2000|582 (1998)|
|---|---|---|---|---|
|Economic Forum|leaders; domestic|corruption? Irregular,|in 20-26|1800 (2000)|
|(African|and international|additional|countries||
|Competitiveness|companies|Payments are required and|||
|Report)||large in amount’|||
|Political Risk|Expert staff|‘Assessment of "corruption|2000 in 140|NA (expert|
|Service|assessment|in government"’|countries|Assessment)|

_Source: Transparency International, 2012_

# **(ii) Government Effectiveness & Control of Corruption**

Both of these measures are developed by the same institution (the World Bank) and rely on a common methodology. As such, it is possible to summarize the coding and some of the issues associated with each indicator jointly. It is also important to note that these two indicators form part of a larger set of ‘Worldwide Governance Indicators’ which also include: (1) Voice and Accountability, (2) Political Stability, (3) the Absence of Violence, (4) Regulatory Quality, and (5) the Rule of Law (World Bank, 2011). The World Bank uses 30 existing data sources to develop each of these indicators. The sources are selected to include the views of citizens, business owners, academics and experts drawn from the public, private, and NGO sectors from across the globe, and the standard methodology is used (World Bank 2011).

**Coding.** For all six indicators, the World Bank uses the same approach in order to develop an interval measure of the governance dimension of substantive interest. This entails standardizing the variables and then using an ‘Unobserved Components Model’ (UCM) to develop each indicator. This process therefore enables the development of the control of corruption and government effectiveness indicators that ranges from -2.5 (most corrupt/least effective) to 2.5 (least corrupt/most effective) (World Bank, 2011, online).

13
Table 8: General Insurance by Line of 1Busmess, 1997-2001

||1997|1998|1999|2000|2001|
|---|---|---|---|---|---|
|GrossPremiums||(percentof|totalprem|iums)||
|FirePremiums|26.2|22.8|22.6|20.2|20.4|
|Motor|37.9|41.4|41.9|43.6|44.5|
|PersonalAccident|8.8|7.6|7.9|7.5|7.8|
|Transport|9.4|9.4|8.3|7.8|9.3|
|Miscellaneous|17.7|18.8|19.5|20.8|18.0|
|Total|100.0|100.0|100.0|100.0|100.0|
|ReinsuranceRatio||(percentof|grossprem|iums)||
|Fire|79|80|84|85|85|
|Motor|15|13|14|14|13|
|PersonalAccident|57|65|68|68|70|
|Transport|62|61|61|60|62|
|Miscellaneous|65|68|67|76|70|
|Total|49|47|48|49|47|
|Gross ClaimsRatio||(percentof|grossprem|iums)||
|Fire|33|33|242|30|24|
|Motor|64|78|81|86|74|
|PersonalAccident|41|42|39|32|32|
|Transport|20|36|38|21|24|
|Miscellaneous|47|55|94|79|62|
|Total|47|57|113|59|53|
|Net ClaimsRatio||(percentof|netpremiu|ms)||
|Fire|29|41|100|36|34|
|Motor|66|71|77|74|65|
|Personal Accident|49|38|48|32|43|
|Transport|28|39|34|23|31|
|Miscellaneous|45|58|75|68|62|
|Total|56|63|75|66|59|

This table follows the classification used by the FSC. Reinsurance treaties are placed according to fire (including flood and cyclone); engineering (including contractors all risk and machinery breakdown); marine (including cargo, hull and pleasure crafts); miscellaneous accident (including personal accident); motor; and liabilities.

# Source: FSC

10
|Period|Total Tourists<br>(year averages)|Expenditure per<br>Tourist Per Day<br>(USD)|Length of Stay<br>(in days)|RER (ratio of<br>CPI/Nom.<br>Exch.Rate)|
|---|---|---|---|---|
|1990-1994|300466|20.7135|10.666|0.284609|
|1995-1999|426811|26.1087|11.66|0.1316595|
|2000-2004|364756|37.7579|10.968|0.0446322|
|2005-2011|519335|45.5672|11.4486|0.1232776|

Source: Nepal Tourism Board and World Development Indicators (WDI).

Figure 2plots the inflow of tourists and the income per tourist per day (in thousands of dollars), over the period 1990-2011. It reveals, first, that tourist inflows fell toward the peak of the turmoil in the country (around 2000), while they picked up toward the end of the conflict (around 2006), and second, that income per tourist per day has fluctuated substantially during the 2000s.

**Figure 2: Tourists Inflows and Expenditure (Income) per Day**

<!-- Start of picture text --> 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>Year<br>Total Tourist Inflow Income per Tourist per Day<br>800000 .06<br>600000 .05<br>.04<br>Total Tourists<br>400000<br>.03<br>Income Per Tourist Per Day (In thousand dollars)<br>.02<br>200000<br><!-- End of picture text --> Source: Nepal Tourism Board.

The composition of tourist inflows by origin has changed over the last decade, reflecting the international changes in the distribution of income per capita. While inflows from high-income countries represented about 55 percent of the total in the early 2000s, they accounted for 35 percent of total inflows in 2011. During the same period, Chinese inflows increased from 2.4 percent to 8.4 percent, Indian inflows increased from 17.8 to 20.3 percent, and inflows from Sri Lanka (mainly related to pilgrimage) increased in importance from 2.7 to 8.1 percent (Figure 3).

Figure 3: Distribution of Tourist Inflows by Origin 6
our financial liberalization classification is to look at the components of our financial liberalization index. Of particular interest is impact of the reduction of credit control, in particular the removal of directed credit systems. Of all six measures, this measure is thought to have the most significant direct impact on the financing constraints of firms, especially if access to preferential credit differed among different types of finns. We therefore construct a dummy variable (CRE) that indicates whether the country has made substantial progress in abolishing its directed credit system, and reduced the adverse effects of credit control in general. The OLS estimates are presented in Table 10 (Model 5). We find similar, if not stronger, results as with our basic specification. The removal of directed credit systems has had a positive impact on the financing constraints of small firms only.

Since our classification of financial liberalization may suffer from the problem to time liberalization dates we also simply compare financing constraints of firms during the first half and the second half of our sample period. Since financial liberalization has been a gradual and progressive process, the two sub-periods are comparable across countries, in the sense that the financial sectors of all countries in our sample were more liberalized in the second half than in the first half of the period. If financial liberalization does have a positive effect on the financing constraints of (some) firms, we should find this by comparing the two periods, even though the initial level of financial liberalization may differ across countries, so that the magnitude of the effect may differ across countries. We split our sample in the period 1989-94 versus 1995-98. Table 2 shows that most countries in our sample had made substantial progress with liberalizing their banking sectors in the year 1994. The OLS estimates of these two additional specifications are presented in Table 10 (Model 6). Our basic results that use our index of financial liberalization are confirmed. Small firms became less financially constrained during the period 1989-98, a period that has been characterized by substantial progress in financial liberalization in our sample of countries.

# **8 Conclusions**

We have estimated a dynamic investment model using panel data on 394 firms in 13 developing countries for the years 1988-98. Using different specifications of the 28
Table A11: Consumption patterns do not differ by treatment

||Pane|l A||
|---|---|---|---|
||(1)<br>Luxury|(2)<br>Necessary|(3)<br>Total Cons.|
|Work|0.28|3.10|3.91|
||(0.36)|(1.89)|(2.13)|
|Cash|-0.20|2.69|2.85|
||(0.42)|(1.95)|(2.25)|
|Shrp. q-val Work|0.181|0.181|0.181|
|Test Work=Cash|0.189|0.815|0.604|
|Shrp. q-val Work=Cash|1.000|1.000|1.000|
|Mean in Control|4.19|21.41|25.70|
|Observations|726|726|726|
||Pane|l B||
||(1)<br>Savings|(2)<br>Borrowing|(3)<br>Lending|
|Work|2.47|-8.48|0.55|
||(0.63)|(2.22)|(0.18)|
|Cash|1.64|-9.04|0.38|
||(0.73)|(2.61)|(0.22)|
|Shrp. q-val Work|0.001|0.001|0.001|
|Test Work=Cash|0.264|0.793|0.474|
|Shrp. q-val Work=Cash|1.000|1.000|1.000|
|Mean in Control|1.19|23.93|0.10|
|Observations|726|726|726|

**Notes:** This table reports the treatment effect of receiving employment or cash on consumption relative to the control group. The columns show OLS estimates of a regression of the outcome on a dummy for assignment to treatment, as well as the baseline measure of the dependent variable, camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure Belloni, Chernozhukov, and Hansen (2014).

All outcomes are measured in USD spent (converted from Bangladeshi taka with 2020 market exchange rate of 83 BDT to 1 USD). ‘Luxury’ is made up of the following consumption categories: meat or fish, paan or cigarettes, tea, and electronics. ‘Necessary’ is made up of the following consumption categories: fruits or vegetables, health, education, household supplies, and clothing. Quantities reported are total amount spent in given category during the previous two weeks. ‘Savings’ is the total savings reported at endline; ‘Borrowing’ is the total amount in loans respondent has at endline. ‘Lending’ is the total amount lent in the previous two weeks.

Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” reflects p-values, referred to as ‘sharpened q-values,’ adjusted for multiple hypothesis testing using Anderson (2008) for all outcomes in this table.

55
# **Table S2: How the Study Countries Compare with Sub-Saharan Africa as a Whole**

||**GDP per capita,**<br>**PPP (current**<br>**international $)**|**Life expectancy**<br>**at birth (years)**|**Maternal**<br>**mortality per**<br>**100,000 live births**<br>**(modeled**<br>**estimate)**|**Infant mortality**<br>**rate per 1,000**<br>**live births**|**Incidence of**<br>**malaria per**<br>**1,000 population**<br>**at risk**|
|---|---|---|---|---|---|
|Kenya|3,468|66|342|31|71|
|Madagascar|1,891|66|335|38|91|
|Mozambique|1,460|59|289|54|338|
|Niger|1,063|62|509|48|359|
|Nigeria|5,991|54|917|76|281|
|Senegal|3,783|67|315|32|65|
|Sierra Leone|1,602|54|1,120|79|380|
|Tanzania|3,227|64|524|38|113|
|Togo|1,774|60|396|47|371|
|Uganda|2,038|63|375|34|201|
|Average of ten<br>study countries|3,999|59|632|55|221|
|Average of Sub-<br>Saharan Africa|3,988|61|534|53|210|

Source: World Development Indicators 2019.<sup>1</sup> 45
Regardless of the underlying reason for _why_ youths heavily discount future payouts, the implication is that jobs with lower starting wages but high future wage growth are relatively less attractive.<sup>30</sup> Table 7 tests how discount rates are correlated with participants’ reservation wages. For the non-incentivized measure, we indeed find a strong negative relationship. Participants who discount the future by 10 percentage points more (i.e. their discount factor is lower) have reservation wages that are about 4.2 percent higher (Column 2). However, this correlation is small and not significant in the smaller sample for which we elicited incentivized discount factors (Column 3).

# **5.4 Beliefs about wage growth**

The job search framework posits that beliefs about wage growth affect job seekers’ reservation wages and search effort. We elicit these beliefs in an incentive compatible way in the endline survey. Specifically, we present participants with a scenario of a person starting a formal job that pays 5,500 pesos per month. We ask them to estimate how much that person is earning in six months if she is still employed separately for jobs in the formal and informal sector. For each guess that is within 100 pesos of the correct answer, they receive a bonus of 30 pesos.

Figure 8a shows the cumulative distribution of wage growth beliefs for _formal_ employment. The dotted line marks the actual wage growth of about 25% based on social security data. We find that the majority of participants substantially underestimate formal sector wage growth: 20% believe that there is no wage growth and the median belief is less than 10%. Only about one third either has correct beliefs or overestimate wage growth. Beliefs about _informal_ sector wage growth appear to be more accurate. Figure 8b shows that more than 40% believe there is no wage growth and two thirds of youths believe that informal wages grow by less than 10%. While we do not have administrative data for informal wages, ENOE survey data suggests that the actual increase is between zero and 10%.<sup>31</sup>

> 30A related question is whether treatment effects vary across people’s discount rates. Results (not reported) show that the interaction of the treatment indicator and people’s discount factor is positive, but modest in magnitude and not statistically significant. There are at least two factors that make this test inconclusive: first, we only elicit discount rates at the endline (although discount rates are not significantly correlated with the treatment assignment). Second, we only collect discount rates for a subset of participants, which limits the statistical power of subgroup analyses.

> 31The hourly wage for informal work remains flat over the first half year while the monthly income increases by 10% suggesting that workers increase the number of hours over time. In our endline survey, respondents working informally are also less than half as likely to report that they received a wage increase compared to those working in the formal sector.

33
- Sellers PJ. 1985. Canopy reflectance, photosynthesis, and transpiration. _International Journal of Remote Sensing_ . **6** :1335-1372.

- <mark>Sikor T, Stahl J, Enters T, Ribot JC, Singh N, Sunderlin WD, Wollenberg L. 2010. REDD-plus, forest people's rights and nested climate governance.</mark> _<mark>Global Environmental Change</mark>_ <mark>, 20: 423–425.</mark>

- Springate-Baginski O and Blaikie P. 2007. (Eds.) Forests, People and Power: The Political Ecology of Reform in South Asia. EarthScan Forestry Library. London and Sterling, Earthscan.

- Stern, T. 2013. “The Shape of a New International Climate Agreement,” Speech delivered on 22 October 2013 to the confereence titled _Delivering Concrete Climate Change Action: Towards 2015,_ Chatham House. Transcript accessed at http://www.chathamhouse.org/sites/default/files/public/Meetings/Meeting%20Transcripts /221013stern.pdf

- Subedi, B, S. Pandey, A. Pandey, E. Rana, S. Bhattarai, T. R. Banskota, R. Chrmakar, R. Tamrakar. 2010. “Forest carbon stock measurement guidelines formeasuring carbon stocks in community-managed forests in Nepal.” Kathmandu: ANSAB/FECOFUN/ICIMOD/NORAD.

- <mark>UNFCCC 2011. Fact Sheet: Reducing Emissions from the Developing countries. Available at</mark> <u><mark>http://unfccc.int/files/press/backgrounders/application/pdf/fact_sheet_reducing_emissi ons_from_deforestation.pdf</mark></u>

- Yadav NP, Dev OP, Springate-Baginski O and Susan J. 2003. Forest Management and Utilization under Community Forestry. _Journal of Forest and Livelihood_ . 3(1):37-50.

# **Appendix 1**

<u>Community Forests and Chosen Non-CF Matches</u>

||**CF **||**NCF **|
|---|---|---|---|
||**H**|**ill**||
|**CF district**|**CF Name**|**NCF district**|**NCF name**|
|Accham|Raniban cf|Accham|Kokila ncf|
||Kalikaban cf|||
||Jauchhare cf||Furket Salla ncf|
||Majhkatera cf|||
|Baglung|Dudeba Chaur|Baglung||
||Naulo cf|||
||Mauribhircf|||
|Bajhang|Chiuri Bhandar|Bajhang|Nauli nabodayancf|
|Bhojpur|Kaijale cf|Bhojpur|Sallencf|
|Dailekh|Kalika cf|Dailekh||
|Darchula|Sewakendra cf|Darchula||
||Betinicf||DumlaFulkharka|
|Dhading|Takmare cf|Dhading||
||Dharmashala cf||saj botncf|
|Dhankuta|Chetmala cf|Dhankuta||
|Dolakha|Dimalcf|Dolakha|Patiko ban ncf|

**44 |** P a g e
39 poverty gap, nor would low income alone be a sufficient condition for assistance.

The two approaches differ in their attitude toward some of the currently existing benefits which are reasonably well targeted. For example, under the "movement to MIG", there is no reason to retain family allowance. It can be discontinued altogether (or kept at a very low level), because everybody who is really poor should be helped via the guaranteed minimum income. But, if the second alternative is selected, family allowances would continue to play an important role. Their role would be to “pre-screen” many of the families—"vault" them over the poverty line—so that they do not come applying for social assistance.” Before we move to a comparison of MIG and ITS systems, we shall look at some current experience with targeting using the 1993 Polish data.

# Targeting in practice: an example from Poland

## Targeting mistakes are of three types.

- © Spillover. Payments to the poor in excess of the amount they need to reach the poverty

line.

- ® Inclusion error. Payments to the non-poor who should not receive social assistance. @ Exclusion error. Non-payments to the poor.

The first two are also called "Excess" or E-mistakes (Stewart and Cornia, 1994); the third is also called "Failure" or F-mistake.

Excess” and failure mistakes were analyzed on the individual 1993 Polish data using the Heckman-type procedure. Equation (2) is the "screening" equation, explaining the process of "selection into poverty". Equation (3) explains the determinants of the error of exclusion and inclusion. It controls for the selection bias by using the reverse Mills-ratio calculated from (2).

> The focus of family allowances can be improved by some rough income-testing (e.g., discontinuing payments to families with income or wage three or more times higher than the average). "Excess" mistake in this case does not include spillover.

> In order to exactly identify the two equations, we drop large cities from equation (3), and number of adults from equations (2).