rafmacalaba/datause-encoder-data huggingface dataset:
_Family Planning Success in Two Cities in Zaire_ _30_ Table 7.

|Use of Contraceptive Methods Among|Women In Union, A|ges 1S-49, In|Kananga|
|---|---|---|---|
||1982|1987|1990|
|CURRENT USE (based on women<br>currently in union)|n=669|n=826|n=968|
|Percent reporting current use||||
|<br>of a modern method|2.6|4.1|17.4|
|Method currently used||||
|<br>Female sterilization|1.3|1.7|3.8|
|Pill|0.6|0.6|6.4|
|Condom|0.3|0.6|4.4|
|Injectable|0.3|1.2|1.4|
|<br>IUD|0.1|-|_0.5_|
|Vaginal tablets|-||0.2|
|NORPLANT||-|0.7|

Figura drawn from reports of Kananga Contraetve Provalencoe Surveys: 1982 t3], 1987 [18], and 1990 i201.

The project concentrated on improvements in service delivery rather than on sociocultural obstacles. The basic premise was that contraceptives had never been easily accessible to Kananga residents, so the first priority was to ensure access to services. Only if contraceptive prevalence rates failed to rise would one be justified in focusing on socio-cultural factors or exploring other barriers. To this end the two main strategies were:

- to use existing service points to deliver contraceptives: clinics, pharmacies, and shops.

- to assure constancy of personnel and supplies.

Clinic supervision was regular and never-ceasing. Moreover, the supervisor did whatever was necessary to assure the availability of services at all field sites. Problems occurred often at the participating health centers. Several of the trained nurses were swamped with all their former duties as heads of maternity wards or outpatient clinics and were given no time to devote to family planning. Others became ill, left on vacation or maternity leave, or moved away. In such a situation, a Project nurse went to every family planning session at the health center for several weeks, providing services herself if necessary, while working out difficulties and training another nurse on the spot. Clinic supervision took much more time and attention than originally envisioned, but it was judged to be essential to making contraceptives accessible.

Finally, Project staff concentrated all efforts on their target area, the city of Kananga. Project nurses received requests for training from outside health centers and distant hospitals; the information team was often asked to travel to schools and churches in other towns and cities. Rather than diluting energy and efforts through such travel, the Project invited nurses and others to come to Kananga to learn by doing, alongside active and enthusiastic team members. Only by
To estimate the poverty headcount, we predict expenditures using these common variables and then apply the food and overall poverty lines developed by the GSO and the World Bank for use with the VLSS surveys (Poverty Working Group, 1999). The lower of these two lines, the food poverty line, corresponds to the expenditure (including the value of home production and adjusted regional and seasonal price differences) required to purchase 2100 kilocalories per person per day. The upper overall poverty line also incorporates a modest allowance for nonfood expenditures.<sup>3</sup> The Ministry of Labor, Invalids, and Social Assistance (MOLISA) estimates provincial poverty rates based on a system of administrative reporting that uses different welfare indicators (rice equivalent income), different poverty lines, and a different unit of analysis (households). Nonetheless, the results are fairly similar to those obtained in this study.

# 2.2 **Estimating poverty with a household survey**

As mentioned above, the first step in implementing this approach is to estimate poverty or household welfare as a function of household characteristics. In this study, we use per capita consumption expenditure as the measure of household welfare. The explanatory variables must be useful in "predicting" household welfare and they must exist in both the household survey and the census. Economic theory provides no guidance on the functional form, but often a log-linear function is used: ln(y1) = X' i3 + **E()** where y1 is the per capita consumption expenditure of household i, X's is a kxl vector of household characteristics of household i, D is a kxl vector of coefficients, and **Ej** is a random

> 3 In 1998, the food poverty line was VND 1286,833 and the overall poverty line was VND1,789,871 per person per year. See Annex 2 of Poverty Working Group (1999) for further details concerning the estimation of these poverty lines.

7
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**Table** **<u>3: Estimates of the farm size-productivity relationship, 1982/99 and 1999/2008</u>**

||**198**|**2-99**|**1999-**|**2008**|
|---|---|---|---|---|
||**FE**<br>**(1)**|**FE-IV**<sup>**a**</sup><br>**(2)**|**FE**<br>**(3)**|**FE-IV**<sup>**a**</sup><br>**(4)**|
|Log of land|0.781***|0.734***|0.816***|0.946***|
||(15.329)|(9.380)|(21.452)|(21.538)|
|Share of irrigated land|0.248***|0.254***|0.191***|0.166**|
||(4.093)|(4.192)|(2.912)|(2.575)|
|Share of leased land|0.103|0.121|0.016|-0.016|
||(1.217)|(1.387)|(0.240)|(-0.246)|
|Log of assets|0.048***|0.052***|0.022***|0.013|
||(3.882)|(4.045)|(2.687)|(1.496)|
|Head is female|-0.027|-0.025|-0.036|-0.042|
||(-0.398)|(-0.359)|(-0.728)|(-0.850)|
|Head’s age|<br>-0.197|<br>-0.399|<br>-0.346|<br>0.177|
||(-0.202)|(-0.383)|(-0.298)|(0.155)|
|Head’s age squared|0.022|0.051|0.061|-0.014|
||(0.169)|(0.367)|(0.408)|(-0.093)|
|Head’s education|<br>-0.077***|<br>-0.074***|<br>0.027|<br>0.029|
||(-2.642)|(-2.599)|(1.373)|(1.464)|
|No. of males 14-64 years old|0.004|0.014|0.063**|0.039|
||(0.109)|(0.348)|(2.135)|(1.374)|
|No. of females 14-64 years old|0.017|0.025|0.033|0.016|
||(0.436)|(0.701)|(1.364)|(0.636)|
|No. of males > 64 years old|0.117|0.115|0.073|0.020|
||(0.356)|(0.341)|(0.477)|(0.118)|
|No. of females > 64 years old|0.496**|0.520**|0.136|0.106|
||(2.017)|(2.097)|(0.766)|(0.599)|
|R<sup>2</sup>|<br>0.546|<br>0.545|<br>0.580|<br>0.568|
|No. observation|7,146|7,146|6,389|6,389|
|No. farmers|4,145|3,001|3,805|2,584|
|Hansen J statistic (overid test)||8.849||9.783|
|Chi-sq P-value||0.1822||0.2806|
|Anderson-Rubin Wald test||21.68||25.69|
|Stock-Wright LM S statistic||82.57||93.87|

Source: Authors’ computation from 1982, 1999, and 2008 ARIS-REDS surveys.

Note: The dependent variable is the output value per acre in logs. All specifications are for the first difference. Standard errors adjusted for clustering effect at the village level, and asymptotic t-ratios are given in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1).<sup>a</sup> As discussed in the text, we instrument cultivated area using as instruments inherited land and assets, the number of male and female claimants, and the variance of their schooling, as well as the number of co-resident brothers of the head. FE=fixed effects; IV=instrumental variable.

18
Topalova, P. (2005). “Trade Liberalization, Poverty, and Inequality: Evidence from Indian Districts,” NBER Working Papers 11614, National Bureau of Economic Research.

U.S. International Trade Commission (2001). “Industry & Trade Summary. Fresh or Frozen Fish,” USITC Publication 3463, October.

U.S. International Trade Commission (2003). “Certain Frozen Fish Fillets From Vietnam,” Investigation No. 731-TA-1012, Publication 3617, August.

Zanardi, M. (2004). “Anti-dumping: What are the Numbers to Discuss at Doha?,” _World Economy_ , Volume 27, Issue 3, pp. 403-433.

World Bank (2005). “Vietnam: Fisheries and Aquaculture Sector Study,” Rural Development and Natural Resources, East Asia and Pacific Region, Washington DC.

36
do not differ across levels of educational attainment. Because mortality is ignored, our estimates of both the educational capital stock and the average number of years of schooling completed per person of working age are likely to be biased upward.' In the following tables we present the average years of schooling completed per person in the labor force, for primary, secondary, and total education, by region. The ratio of completed years of primary education to completed years of secondary education is also presented in Table 4.

'There are undoubtedly many other ways of estimating the annual enrollment figures for the pre-1960 period. It is our belief that short of attempting to collect actual school enrollment data on a country-bycountry basis it is unlikely that there will be an unequivocally better method.

12
12 maximum maturity difference for the Mexican Brady bonds is slightly more than 5 years (with the last Brady, Ecuador) over a period of 24 years. The effect of a different maturity is important for short maturities and it is a much less relevant factor over such a long time horizon.

# **4. Mexico and Poland**

It is our opinion that the case of Mexico and Poland is the most suitable for the analysis of spillover effects in the Brady bond market: Mexico at the time of the crisis was a relatively stable country; Poland was stabilizing and had a relatively short record of economic reforms. The December 94 shock was substantial, but truly originating from the Mexican economy and in this sense it was exogenous to the Poland economy. This can be thought as a good approximation of an experiment: the Mexican fundamentals were dramatically changed while Poland's were not.

We will concentrate on the discount bonds: discount bonds are 30 years maturity floating market interest rate, issued at a discount to the original face value of the previously rescheduled loans. Because they are floating market interest rate, discount bonds do not involve market risk (the risk of a change in the interest rate), so that the measure of country risk given by the stripped spread on discount bonds is more accurate than the one given by the stripped spread on the Par bonds.

# 4.1 **Univariate time series description**

In this section we report the time series analysis on the variable MEXDIS (the stripped spread on the Mexican discount bonds (series A,B,C,D)), and on the variable POLDIS (the stripped spread on the Poland discount bonds). The goal is to have a preliminary understanding of the behavior of the stripped spread, that is the measure that traders use for the risk premium on a sovereign country bond. The main finding is that we cannot reject the null hypothesis of unit root for both variables, even if also a different (and stationary) model fits well the data.

# _i) MEXDIS_ (the sample goes from 5/18/90 to 7/12/94)

1) The first model is an AutoRegressive with four lags - AR(4) - with one unit. The Augmented Dickey-Fuller Test on the whole sample is -1.134891, so we do not reject the null of unit root even at a 10% confidence level.'<sup>3</sup> An AR(4) with one unit root behaves like a random walk in the sense that the shocks have a permanent effect; in the case of the AR(4), the shocks are translated permanently into the variable after 4 periods. To decide on the model specification for the Augmented Dickey-Fuller (ADF) test for unit root, we performed an F test - the values are i3 We reject the null of autocorrelation of the residuals based on the Breusch-Godfrey Serial Correlation LM Test.
29 J.E. Anderson Tariff Index Theory the opposite direction. Appendix 2 presents four such simulations for the Cobb-Douglas case. Thus, the trade restrictiveness index should be used to replace tariff averages or coefficients of variation, while recognizing the limits of any such measure due to having to identify or assume a substitution effects structure. This problem is met with severe substitution structure assumptions in Computable General Equilibrium (CGE) models, and sensitivity analysis on substitution parameters. A similar procedure in calculation of the trade restrictiveness index should be usable in principle. Here, a potentially great advantage is that the TRI can feasibly aggregate consistently from the fine structure of protection, a task which is currently not possible with CGE models.

Future work should experiment with various substitution effects assumptions and approximative vs. numerical integration, in order to see how much difference the refinements make, and how the trade restrictiveness index compares with the standard indices. A current World Bank project is attempting to implement the index in a pilot study of a dozen developing countries. Early results<sup>15</sup> indicate that (i) the trade restrictiveness index often gives results opposite to the standard methods, and (ii) the trade restrictiveness index is not very sensitive to the substitution elasticity values.

15See Anderson and Bannister (1991) and Anderson (1991).
# **Table 8. Trade Partners whose Exports to Belarus Are Likely to Drop the Most Because of Belarus Adopting the CET**

|**Partner**|**Change in Import Value**<br>**(US$, millions)**|**Percentage of Total 2008 Belarus**<br>**Imports from Partner**|**Percentage of Total**<br>**Decrease in Belarus Import Values**<br>**Explained by Changes in**<br>**Imports from Partner**|
|---|---|---|---|
|EU-27|-45.4|-0.6|-19.7|
|_Of which:_||||
|Germany|-17.10|-0.6|-7.4|
|France|-15.95|-2.8|-6.9|
|Slovak Republic|-6.40|-3.6|-2.8|
|United Kingdom|-3.54|-1.3|-1.5|
|Netherlands|-2.87|-0.8|-1.3|
|Czech Republic|-2.07|-0.6|-0.9|
|Spain|-2.05|-1.0|-0.9|
|Japan|-21.95|-6.7|-9.5|
|Russia|-11.8|-0.1|-5.1|
|Brazil|-6.95|-4.5|-3.0|
|Korea, Rep.|-4.00|-2.1|-1.7|
|Ukraine|-3.51|-0.2|-1.5|
|Georgia|-2.51|-22.3|-1.1|
|Mexico|-1.98|-8.4|-0.9|
|United States|-1.96|-0.4|-0.9|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 35
In contrast to the rising trend in technical change with farm size, the estimated mean technical efficiency falls with farm size (Figure 3). Notably, technical efficiency losses over the study period appear to have been relatively greater for larger farms. This is more clearly demonstrated in Table 4, which shows mean technical _Figure 3: Change in Technical Efficiency by Farm size_ efficiency by farm size quartiles. LOWESS and Scatter Plots of Technicial efficiencies (Extended Specifications), 2000-2008. Technical efficiency first rose marginally in 2004 and then declined in 2008. With no measurable technical change in 2000-2004, technical efficiency improved as the average farmer moved closer to the frontier. But with a shift in the frontier, indicated by the positive technical 0 1 2 Farm Size (Ha)3 4 5 change by 2008, the distance to the 2000:lowess 2000:scatter 2004:lowess 2004:scatter frontier for the average farmer rose, or 2008:lowess 2008:scatter the average efficiency fell (albeit _Source_ : Authors estimates from the 62-village panel survey. marginally). A larger decline in the average efficiency for the larger farmers (highest land quartile) reflects the same dynamic; the positive relationship between farm size and technical change suggests that large farmers (as a group) gained relatively more in terms of technical progress, but the average farmer within the largest farm size quartile failed to keep up with the change, operating at a point further from the (new) frontier.<sup>17</sup> 17 A comparison of the estimated technical efficiency from this study with the findings of other studies shows significant differences. These differences may be due to the methodology applied, the scope of analysis (whole farm versus single crop – rice), or time of study. Rahman (2003) found much lower levels of efficiency (ranging from 0.48 to 0.56) with a profit frontier but using a two-step procedure (calculating efficiency scores based on a first step SPF without parameterizing the inefficiency function, and then regressing the efficiency scores in a second step regression to explain the differences in inefficiency). Salim and Hossain (2006) similarly obtained lower estimates of average efficiency (though rising over time from 0.56 in 1977 to 0.60 in 1984 and 0.64 in 1997), again using a two-step procedure. Alam et al. (2014) use the first three rounds (1988, 2000 and 2004) of the same panel survey as used in this study, but use a two-step procedure applied to only rice production, to find a falling level of efficiency from 1988 to 2000 to 2004 (0.85, 0.76 and 0.63, respectively). The study by Rahman and Rahman (2008) is notable as it simultaneously estimates the frontier and inefficiency functions of the SPF. They use data from a single cross21
empirical CDFs of weather anomalies in 2018 and 2019 and test for the equality in these distributions.<sup>18</sup> The horizontal black line at the bottom of the graphs shows the range where the CDF equality is rejected at a familywise error rate (FWER) of 5 percent.

First, note that the distribution of rainfall shocks is extremely tight in both years (Figure 5a). Rainfall z-scores exceed 1 in 10 percent of the clusters and 4 in 3 percent of the clusters, respectively, implying that a very small sample of clusters received more than usual rainfall variation in the country in 2018. The remaining clusters show almost no rainfall deviations from the national long-run distribution in both years. This finding is not surprising since most of the country receives very little rainfall even in an average year, given the arid conditions.<sup>19</sup> With little variations in the measure over time, rainfall shocks do not capture the full intensity of droughts experienced in the country in 2018 and, thus, are not appropriate to measure drought-like conditions.

Although the correlation between 2018 and 2019 NDVI and temperature anomalies is high, the distribution of NDVI and temperature z-scores is left- and right-skewed, respectively, in 2018 compared to 2019 (Figures 5b and 5c, respectively). In Figure 6b, we also reject the test of equality of distributions between 2018 and 2019 for most ranges. The growing season NDVI z-score in 2018 was lower than the 2019 z-score in 93 percent clusters. NDVI anomalies in 2018 are reflective of drought-like conditions in the country. Similarly, in Figure 6c, we reject equality in distributions of temperature z-scores when z-scores is between 0 and 2. In this range, temperatures were higher in 2018 than in 2019.<sup>20</sup> In sum, NDVI and temperature z-scores exhibit spatial and temporal variation in the country. Extreme drought conditions in 2018 are most apparent in the more negative NDVI anomalies and, to an extent, are also captured by positive temperature anomalies. On the other hand, the distribution of rainfall z-scores is highly concentrated at zero and does not capture the intensity of the drought experienced in 2018. Therefore, in this paper, we focus on NDVI and temperature anomalies as measures of the 2018 drought in Afghanistan.

> 18We use the method proposed in Goldman and Kaplan (2018) which assesses equality of the distributions point by point and performs slightly better in the tails over the Kolgomorov-Smirnov test.

> 19We also observe little variation in absolute precipitation levels in 2018 and 2019 similar to this relative z-score measure presented here.

> 20Temperatures z-scores were higher in 64 percent of the clusters in 2018 compared to 2019.

11
labor, stable income or employment; (4) school age family members receive lower secondary school education; (5) have access to clean water and stable sources of energy; (6) have access to primary public health services. Decree suggests that implementing ministries should divulgate its provisions to local governments. The extent to which village heads adhere to these criteria when selecting beneficiaries in practice depends on several factors, such as success in disseminating the decree and village heads training on it, potential differences in interpretation of some of these criteria (e.g. stable employment) and village heads’ access to information about these criteria for all households in the village. Lastly, village heads may still select on a different criterion, as in Basurto, Dupas and Robinson (2019), especially given that there are no formal checks of accuracy in place.

We study the outcomes of village heads selection process in 85 villages across 7 provinces in northern and southern Lao PDR where the RMG program was implemented.<sup>8</sup>

# 3. Data

To understand how village heads determined household eligibility and to assess the quality of their decisions in terms of targeting the poor, RMG baseline data collection included two targeting assessment components. First, in addition to collecting data on RMG and WL households that form the treatment and control groups for the impact evaluation, we administered the same survey to a Random Sample (RS) of village households with a woman of eligible age to participate in the RMG program (limited for simplicity in the random sample to ages 18-55). Households selected into the random sample could be replaced if they did not have a female household member of eligible age, if they had moved out of the village, if they could not be located after multiple attempts spanning at least two days, if they refused to participate, or if they were already part of the RMG or WL sample.<sup>9</sup> This sampling strategy enables comparison of household characteristics between the RMG-eligible and RS groups to establish whether the households granted entry to the lucky draw were truly among the villages’ poorer households. Second, we administered the village heads a targeting survey to probe the key factors influencing their selection of welfare beneficiaries and to understand their underlying perceptions regarding what constitutes poverty.

> 8 Although the RMG program was implemented in 86 villages, one village was dropped from the analysis because recruitment of beneficiaries there deviated from the model described above.

> 9 There was a total of 274 replacements: 151 households were replaced because they were already in the RMG/WL sample. The remaining replacements were due to: absence of women of eligible age in the household (36); households were absent due to harvesting season (20); households were no longer in the village (33); refusal (14) and other reasons, such as mental illness or temporary absence (20).

6