rafmacalaba/datause-encoder-data huggingface dataset:
- 19 -

# B. Female Education. Desired Family Size and Contraceptive Prevalence

4.8 Female education affects fertility in several ways, but we consider two ways:<sup>it reduces</sup> the desired family size, and it promotes contraceptive use. There are important<sup>empirical problems in</sup> mapping out these effects. For example, desired fertility could fall for<sup>a variety of reasons of which</sup> female education is one. Similarly, contraceptive prevalence could increase due to many factors. There are (unknown) time lags in each effect. There are, of course, serious<sup>data and measurement problems,</sup> as discussed in para 2.6 above. For example, desired family size is available from WFS<sup>surveys only</sup> for 37 countries, may not be exactly comparable across countries, and reflects subjective<sup>judgments.</sup> These limitations must be taken into consideration when interpreting<sup>the results.</sup> 4.9 Data from World Fertility Survey **s** hown in Figure 4 depict the total (actual) fertility<sup>and</sup> desired fertility for a sample of 37 countries in 1988, along with the country's<sup>female secondary</sup> enrollment rate in 1975. Above a female secondary enrollment of about 40<sup>percent, actual and desired</sup> fertility rates begin to converge, indicating that women with more education are better able to<sup>reach their</sup> desired fertility. In Figure **_5_** contraceptive prevalence, total fertility and female secondary<sup>school</sup> enrollments are plotted. As expcted. contraceptive prevalence increases and total fertility decreases with higher levels of female education.

4.10 The results on DFS and CP were obtained from two regressions with desired family size (DFS) and with contraceptive prevalence (CP) as the dependent variables; the estimated equations are presented in Tables 2 and 3 and show the role of female secondary education relative to other factors.
# **DLefiitions of Variab-. used in the USAL4KTS Model**

**The data used** in **the** USAGMKTS model can be divided into two broad groups -- annual and quarterly. The annual data is used in the estimation of the annual crop supply equations and the quarterly data is used in the estimation of zrop demand, livestock supply and demand, and the exchange rate equations. Data were collected from government publications and contacts and data used in th- FAIRMODEL of the macroeconomy (to facilitate linkage) with remaining variables computed as transformations of these data.

The definitions of basic variables with aninual data used in the econometric work (in alphabetical order along with sources) are as follows:

**AC -** Acreage of corn in million acres **(Feed:** Outlook _&_ Situation **_Report)_**

- ACGS **-** Acreage of corn and grain sorghum (computed from AC and AGS)

- ACGSN **-** Corn and grain sorghum acreage on noncomplying farms (computed from ACGS, BACGS, COMPFGA, and DRFG)

- **AGS -** Acreage of grain sorghum in million acres (Feed: Outlook _&_ Situation Report)

- AS **-** Acreage of soybeans in million acres _(Oil Crops: Outlook & Situation Report)_

- BAC = 3ase acreage of corn in million acres (Feed: Outlook _S_ Situation Report, text of _various issues)_

- BACGS = Base acreage for corn and grain sorghum (computed from BAC and BAGS)

- BAGS = Base acreage of grain sorghum in million acres (Feed: Outlook _&_ Situation Report, text of various issues)

- BVDPC - Real inducement for additional voluntary diversion (computed from VDPC, VDFG, BACGS, and GNPD)

- COMPFGA - Program participation rate for corn & grain sorghum in percent of acreage (U.S. Agricultural Stabilization and Conservation Service)

- COSTC = Variable costs per acre of corn in dollars (includes seed, chemicals and labor) (USDA, ESCS, Paul Gallagher, _"Costs of Producing Selected Crops in_ the _U.S.")_

- COSTCGS = Variable costs per acre for corn and grain sorghum in dollars per acre (computed from COSTC, AC, COST,S, AGS, and ACGS)

- COSTGS = Variable costs per acre of sorghum in dollars (includes seed, chemicals **&** labor) (USDA, ESCS, Paul Gallagher, _"Costs of Producing Selected Crops in the U.S.")_

- COSTS - Variable costs per acre of soybeans in dollars (includes seed, chemicals & labor) (USDA, ESCS, Paul Gallagher, _"Costs of Producing Selected Crops in_ the _U.S.")_

- D70 - **Dumuny variable, 1** if 1970, 0 if not

- D71 - **Dummy** variable, 1 if 1971, 0 if not

- D72 - Dummy variable, 1 if 1972, 0 if not D73 **- Dummy** variable, 1 if 1973, 0 if not

10
- Concern Universal. (2012). _A study of the economic and social impact of three irrigation schemes in dedza and ntcheu districts_ (Report). Retrieved from https://cepa.rmportal .net/Library/climate-change/A%20Study%20of%20the%20Economic% 20and%20Social%20Impact%20of%20Three%20Irrigation%20Schemes% 20in%20Dedza%20and%20Ntcheu%20Districts.pdf/view

- Connor, D., Comas, J., Macpherson, H.-G., & Mateos, L. (2008). Impact of small-holder irrigation on the agricultural production, food supply and economic prosperity of a representative village beside the senegal river, mauritania. _Agricultural Systems_ , _96_ (1-3), 1-15. Retrieved from https://EconPapers.repec.org/RePEc:eee: agisys:v:96:y:2008:i:1-3:p:1-15

- Dauda, T. O., Asiribo, O. E., Oladele, S. A., Saka, J. O., & Salahu, B. (2009, 05). An assessment of the roles of irrigation farming in the millennium development goals. _African Journal of Agricultural Research_ , _4_ .

- Development Data Group. (2021). _Monthly food price estimates by product and market._ World Bank, Development Data Group. Retrieved from https://microdata .worldbank.org/index.php/catalog/study/WLD ~~2~~ 021 ~~R~~ TFP ~~v~~ 02 ~~M~~ doi: 10.48529/2ZH0-JF55

- Dillon, A. (2008). _Access to irrigation and the escape from poverty: evidence from northern Mali_ (IWMI Working Papers No. H043847). International Water Management Institute. Retrieved from https://ideas.repec.org/p/iwt/worppr/h043847.html

- Dillon, A. (2011). The Effect of Irrigation on Poverty Reduction, Asset Accumulation, and Informal Insurance: Evidence from Northern Mali. _World Development_ , _39_ (12), 2165-2175. Retrieved from https://ideas.repec.org/a/eee/wdevel/ v39y2011i12p2165-2175.html doi: 10.1016/j.worlddev.2011.0

- Duflo, E., & Pande, R. (2007). Dams. _The Quarterly Journal of Economics_ , _122_ (2), 601646. Retrieved from https://EconPapers.repec.org/RePEc:oup:qjecon: v:122:y:2007:i:2:p:601-646.

- Dyer, J., & Shapiro, J. (2023). Pumps, prosperity and household power: Experimental evidence on irrigation pumps and smallholder farmers in kenya. _Journal of Development Economics_ , _163_ , 103034. Retrieved from https://www.sciencedirect.com/ science/article/pii/S0304387822001766 doi: https://doi.org/10.1016/ j.jdeveco.2022.103034

- Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C., & Taneja, J. (2021). Annual time series of global viirs nighttime lights derived from monthly averages: 2012 to 2019. _Remote Sensing_ , _13_ (5). Retrieved from https://www.mdpi.com/2072-4292/13/5/922 doi: 10.3390/rs13050922

- Ersado, L. (2005, January). _Small-scale irrigation dams, agricultural production, and health - theory and evidence from Ethiopia_ (Policy Research Working Paper Series No. 3494). The World Bank. Retrieved from https://ideas.repec.org/p/wbk/wbrwps/ 3494.html

- FAO and IIASA. (2020). _Global agro ecological zones version 4 (gaez v4)._ Retrieved 2023-03-01,

21
||||||||Root-m|ean-s|quare|error||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.08|<br>1.35|0.20|5.87|0.14|5.89|0.08|0.30|0.04|0.15|0.03|0.09|
|0.1|0.3|0.14|1.09|0.25|8.04|0.17|21.61|0.10|0.47|0.06|0.23|0.03|0.13|
|0.1|0.5|0.15|0.92|0.26|10.12|0.18|18.59|0.11|0.49|0.06|0.24|0.03|0.14|
|0.1|0.7|0.12|0.77|0.27|11.15|0.18|34.14|0.10|0.48|0.05|0.23|0.03|0.14|
|0.1|0.9|0.05|0.56|0.21|3.06|0.18|19.59|0.09|0.31|0.04|0.16|0.02|0.10|
|0.3|0.1|0.07|1.13|0.18|3.19|0.13|0.39|0.08|0.14|0.05|0.07|0.03|0.04|
|0.3|0.3|0.13|1.07|0.24|8.31|0.18|1.04|0.11|0.22|0.06|0.11|0.03|0.06|
|0.3|0.5|0.15|1.01|0.25|25.13|0.17|0.98|0.10|0.23|0.06|0.12|0.03|0.07|
|0.3|0.7|0.12|0.93|0.23|42.05|0.15|0.47|0.09|0.23|0.05|0.11|0.03|0.07|
|0.3|0.9|0.06|0.79|0.18|39.50|0.10|0.30|0.05|0.15|0.03|0.08|0.02|0.05|
|0.5|0.1|0.06|0.97|0.18|4.46|0.12|0.93|0.07|0.12|0.04|0.07|0.02|0.04|
|0.5|0.3|0.13|1.01|0.26|18.72|0.18|1.64|0.11|0.19|0.06|0.10|0.04|0.05|
|0.5|0.5|0.15|1.02|0.28|45.66|0.20|1.82|0.12|0.20|0.07|0.10|0.04|0.06|
|0.5|0.7|0.13|1.01|0.25|12.23|0.19|1.24|0.11|0.19|0.06|0.10|0.04|0.06|
|0.5|0.9|0.06|0.96|0.17|10.47|0.11|0.85|0.07|0.13|0.04|0.07|0.02|0.04|
|0.7|0.1|0.06|0.80|0.19|24.85|0.11|0.82|0.06|0.14|0.03|0.08|0.02|0.04|
|0.7|0.3|0.12|0.93|0.23|36.60|0.15|1.09|0.08|0.22|0.05|0.11|0.03|0.06|
|0.7|0.5|0.15|1.02|0.26|49.65|0.18|1.09|0.10|0.23|0.05|0.12|0.03|0.07|
|0.7|0.7|0.13|1.07|0.25|56.82|0.18|0.83|0.11|0.22|0.06|0.11|0.03|0.07|
|0.7|0.9|0.07|1.13|0.18|41.57|0.13|0.90|0.08|0.15|0.05|0.08|0.03|0.05|
|0.9|0.1|0.05|0.58|0.23|4.47|0.20|1.85|0.11|0.31|0.04|0.15|0.02|0.09|
|0.9|0.3|0.12|0.77|0.27|7.58|0.17|2.96|0.10|0.47|0.05|0.24|0.03|0.13|
|0.9|0.5|0.15|0.94|0.26|10.60|0.18|2.96|0.10|0.51|0.06|0.24|0.03|0.14|
|0.9|0.7|0.14|1.11|0.25|9.90|0.18|2.62|0.11|0.47|0.06|0.23|0.03|0.14|
|0.9|0.9|0.08|1.36|0.21|7.01|0.14|2.21|0.08|0.31|0.04|0.15|0.02|0.09|

Table 6: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with covariate X and ρ = 0.

29
|(deg. C)||||||
|---|---|---|---|---|---|
|Relative|61.3|22.0|5|100|0.0|
|Humidity (%)||||||
|Wind speed|4.7|5.8|0|62|0.02|
|(kmph)||||||
|Rainfall (mm)|0.07|1.0|0|56|0.0|

Source: Authors’ analysis based on data provided by the Central Pollution Control Board, CPCB and the India Meteorological Department.

1. Missing Obs. refers to percentage of missing observations in the corresponding hourly series.

2. Data for PM2.5 was only recorded at ITO and only from November 2004 onwards.

# **Table 3. Window Length (in Weeks) of Good Quality Data around Each**

# **Extension**

Each included week has no more than 20% missing observations At least five such weeks (symmetric window around extension date)

||NO2|CO|PM2.5|
|---|---|---|---|
|ITO||||
|Yellow Line (first extension), Jul<br>03, 2005|19|41|—|
|Blue Line (introduction), Dec 31,<br>2005|13|5|5|
|Blue Line (second extension),|13|13|7|
|Nov 11, 2006||||
|Siri Fort||||

43
Finally, emerging results from the testing of this methodology in pilot countries (Tanzania, Kenya Rwanda and Uganda) are already challenging the conventional wisdom on Africa public infrastructure spending. After adding up on-budget and nonbudgetary spending data, it is clear that most Sub-Saharan countries might, in fact, be allocating large amounts of resources to infrastructure, relative to the size of their economies. These sometimes enormous spending efforts are, however, challenged by these countries‘ own purchasing power of large-scale infrastructure, high costs, under-developed construction capacity as much as by their institutional ability to efficiently and effectively spend scarce resources.

# Content

This guide includes concepts, definitions, and classifications with the aim of providing a comprehensive analytical framework for the measurement of infrastructure expenditures. It organizes the relevant work in three stages, namely: (i) pre-field, (ii) field, and (iii) back office. A schematic overview of the entire data collection process is provided in the table below.

For the purpose of the SPI exercise, infrastructure is broadly understood as six sectors: irrigation, energy (primarily power), transport, communication, wastewater management, and water supply. Detailed definitions are provided in section 3 of this Guide.

## **Overview table 1 Data collection matrix**

|**Sector**|**Institution**|**Agent**<br>**responsible**|**Qualitative**<br>**templates**<br>**to be**<br>**completed**|**Quantitativ**<br>**e templates**<br>**to be**<br>**completed**|**Additional**<br>**information**<br>**to request**|
|---|---|---|---|---|---|
|**Pre-field /**<br>**Desktop**|||Template 1: Jurisdictional<br>responsibilities in<br>infrastructure service<br>delivery<br>Template 2: Special funds<br>financing infrastructure<br>service delivery|Template 3: Private<br>participation in<br>infrastructure<br>Template 4: ODA in<br>infrastructure service<br>delivery|_Data available_<br>_online:_<br> _http://ppi.worldba_<br>_nk.org_<br> _http://www.oecd._<br>_dac.org_|
|**Overall**|Ministry(ies)<br>responsible for<br>finance or<br>planning|Department<br>responsible<br>for national<br>budget|Template 5: Basic<br>budgetary institutions,<br>national level<br>Template 6: Budgetary<br>cycle diagram, national<br>level|Template 8:<br>Macroeconomic<br>parameters for<br>normalization<br>Template 9:<br>Macroeconomic<br>parameters for<br>budgetary context of<br>infrastructure spending<br>Template 10: Functional<br>and economic<br>classification of<br>government expenses<br>(filled for the central<br>government)|Copies of<br>government<br>budgets for the<br>past five years<br>Copy of budget<br>cycle diagram<br>Data for<br>Template 8<br>available at<br>World Bank open<br>data<br>_http://data.worldb_<br>_ank.org/_<br>Copy of public<br>investment<br>programme|
||Ministry(ies)<br>responsible for<br>local<br>governments|Department<br>responsible<br>for local<br>government|n.a.|Template 10: Functional<br>and economic<br>classification of<br>government expenses|Copies of<br>government<br>budgets for the<br>past fiveyears|

9
capita food consumption is less than the food poverty line established for the region.<sup>15</sup> Extreme or hard-core poverty is on the other hand usually characterized by a situation when a household, with combined expenditure on food and nonfood, cannot match the food poverty line, let alone the total poverty line. So, extreme poverty indicates a dire economic situation, much worse than food poverty.<sup>16</sup> Table 2 reports the distribution of moderate, food and extreme poverty for rural households, disaggregated by region and season, for 2000 and 2005. There are four basic trends observed from these poverty figures. First, the poverty situation is worse in the Rangpur region than in the rest of the country. Second, the poverty situation is worse during the _monga_ period than during the non- _monga_ periods. Thirdly, the poverty situation improves from 2000 to 2005. Finally, the gap in poverty status between the _monga_ and non- _monga_ periods is larger in the Rangpur region than in rest of the country, which is consistent with our earlier finding that consumption seasonality in the Rangpur region is higher than that in other regions. For example, the gap is about 12 percentage points for food poverty in the Rangpur region in 2005, whereas it was 7 percentage points in the rest of the country.

> 15 Food poverty line is calculated by estimating the cost of a food basket needed to maintain the per capita daily caloric requirement (2112 calories) recommended by FAO (Food and Agricultural Organization). Since, household’s food consumption calculated from HIES data refers to the consumption during the prior month’s interview, the calculated food poverty is monthly and, hence, seasonal.

> 16 The question may arise whether poverty figures represent any role of seasonal dimension.  Note that consumption aggregates, the basis for poverty calculation, include both food and non-food.  However,, more than 80 percent of total expenses comprise of food expenses.  The information on food and some non-food expenses is drawn weekly and monthly data and hence, displays seasonality.  But some non-food expenses are drawn from yearly data.  However, to the extent that the non-food expenses do not vary much across seasons, the poverty (either moderate or extreme) figures would display seasonality primarily driven by seasonality in food consumption.

11
Table 15. Di¤erence-in-di¤erence estimates on equipment at the health facility.

|Dependent variable|Bicycles<br>|Examination<br>beds<br>|Blood pressure<br>equipment<br>|Weighing<br>scale<br>|Microscope<br>|
|---|---|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|(4)|(5)|
|Program impact|0.04|0.20|-0.08|0.08|0.22|
|(Treatment*2005)|(0.19)|(0.25)|(0.19)|(0.11)|(0.15)|
|2005|0.40***|0.20|0.36**|0.12*|-0.001|
||(0.14)|(0.11)|(0.14)|(0.06)|(0.001)|
|Mean control group 2005|2.92|2|2.04|2.6|0.44|
|Facility ...xed e¤ects|Yes|Yes|Yes|Yes|Yes|
|Observations<br>|100|100|100|100|100|
|R<sup>2</sup>|0.97|0.84|0.92|0.99|0.99|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level.

b. Dependent variable is the number of each equipment available at the health facility.

c. Robust standard errors in parenthesis. .

60
sector and encourages seed companies to develop a profitable portfolio of products—maize hybrids with different traits such as yield, time to maturity, and resistance to biotic and abiotic stresses—and then market them directly to farmers.

A related explanation for the differences in crop-specific outcomes stems from the historical evolution of DSM in Ethiopia. Even before the introduction of DSM, there was considerable accumulated experience in maize seed production and marketing, along with clear evidence of farmers’ preference for high-quality traits and seeds. While similar experience in wheat seed production also existed in Ethiopia alongside farmer demand for similarly high-quality traits and seeds, willingness to pay for such attributes may have been significantly lower due to farmers’ ability to save seed across multiple years without significant genetic deterioration, their access to traditional farmer-to-farmer exchange mechanisms, and other such factors. This has an intrinsic influence on how private firms view the opportunities in seed markets beyond hybrid maize and may explain the differences in our findings.

In fact, data on the DSM approach indicate that the initial emphasis was placed exclusively on maize beginning in 2011, with the expansion to wheat following in 2013 (Figure 4). The resulting supply response for maize occurred more rapidly than for wheat: whereas maize seed production increased steadily across the nine years for which we have data, wheat seed supply remained low until an inflection point emerged in 2018. Several factors might explain these different supply responses.

This delay may be attributable to the adjustment time required for private firms—most of which were likely more experienced with the production and marketing of maize than with wheat seed, or had fixed investments in land, capital, and technical expertise more suited to maize than wheat— to assess and respond to increased demand and changes in demand for specific traits. Adjustment times in seed production are particularly challenging because seed multiplication and bulking processes can take several years for a new variety and depend on the availability and quality of early-generation seed. This challenge is especially pronounced in Ethiopia, where the limited availability and use of irrigation for off-season (dry season) seed production restrict the ability to accelerate supply responses (Alemu, Rashid, and Tripp 2010). The delay may also be attributable to the time required to establish new distribution channels, marketing strategies, and logistics operations to directly reach farmers with wheat seed.

23
periencing at least one deprivation.<sup>23</sup> Consistent with the much higher shares of the population that are deprived in non-monetary indicators, the majority of deprivations experienced by the population are due to lack of access to basic services. Figure 3a demonstrates that 63 percent of deprivations on average are contributed by the access to basic services dimension, 21 percent of deprivations are contributed by the education dimension, and only 16 percent of deprivations are contributed by the monetary poverty dimension.

Figure 4 investigates these patterns by reporting the share of the population deprived in each indicator in the World Bank’s MPM by imputed expenditure deciles, where higher deciles are associated with higher expenditure. Further corroborating the validity of imputed expenditure, there is a decline in deprivations for higher levels of expenditure. However, the figures illustrate very significant deprivations even among the richest households in the population. The average deprivation among the highest expenditure decile is 33.1 percent, with the highest deprivations in access to basic services.

To better benchmark these levels of deprivation of the richest, Table 4 compares the average deprivation for the top expenditure decile in PNG to the average of the population in the rest of the world for each indicator in the basic MPM aside from monetary poverty. Strikingly, the deprivations among the top expenditure decile of the population are still often comparable to the average deprivations of the entire population in some of the poorest countries in the world. The education indicators compare the best, where the top income decile in PNG ranks in the 44th and 31st percentiles globally; and the rest of the MPM indicators range between the 21st and the 7th percentiles globally.

The deprivation extends to the entire country and the least deprived provinces in Papua New Guinea have deprivation levels similar to many of the most deprived countries in the world. Figure 5 reports the global ranking of each province in the share of the population that is multidimensionally poor. Even the National Capital District (NCD), with a substantially lower multidimensional poverty rate, still ranks only 85th out of the 122 countries. The countries that have the most similar international rank to the NCD 23The share of the population that experiences at least one deprivation is 92.8 percent of the population.

13
over a decade due to continuous problems and with other PCBs showing healthy balance sheets and growth.<sup>17</sup> The smallest component of the banking system is made up by 10 foreign-owned banks that are present in the form of branches, account for 9% of total banking system assets and have an aggregate gross NPL ratio of only 1.5%.

# **4. Three pillars of an effective and stable financial system**<sup>**18**</sup>

This section discusses three building blocks for a sound and effective financial system and tries to benchmark Bangladesh. We will show that Bangladesh has made great progress in macroeconomic stability, but still has a long way to go in building the contractual and informational framework in spite of some promising reforms.

Financial contracts consist of an exchange of money today for the promise of money tomorrow, thus an enormous leap of faith into an uncertain future. The intertemporal nature of the contract and several market frictions make financial contracts critically different from other contracts in a market-based economy. First, there is uncertainty whether today’s contracted repayment (such as interest or insurance payout) or expected repayment (such as dividends or capital gains in the case of equity claims) will be the same in terms of tomorrow’s consumption units due to inflation. Second, information asymmetries between borrowers and lenders make the assessment of viable investment projects difficult and lead to principal-agent problems and moral hazard risks, i.e. the possibility that the borrower will not act in the best interest of the lender.

> 17 There were several waves of PCBs licensed at different points in time, referred to as generations.  The first generation of PCBs was licensed during 1972 and 1989, the second during 1991 and 1996 and the third between 1999 and 2001.

> 18 This and the following section draw heavily on Beck (2006).

12