rafmacalaba/datause-encoder-data huggingface dataset:
norms. For the former, it built on research showing that three factors were important for young men’s attitudes about relationship between men and women: having important relationships to men who modelled more equal gender relations, having a peer group that supported more equal gender relations, and reflecting upon the negative consequences of the norms of _machismo_ . As a result, the project concentrated on offering young men opportunities to interact with men who modelled more gender equality, promoting these attitudes in both small groups of young men, and in the wider community, and on creating activities that promoted reflection on the negative consequences of machismo, for example, by sharing and listening to the experiences of young men who have observed domestic violence in their own homes. The last point illustrates the importance of promoting a discussion of the consequences of the bad norm, thus affecting the perception of a normative requirement to comply. But the project obviously also illustrates other issues discussed earlier in this paper; for example, the importance of the messenger (a woman would not have been as effective).

Program H used two different types of interventions: an education‐focused campaign, and a social marketing campaign. The first was based on small group sessions led by men who could serve as role models, and had as a central aim the discussion and awareness of the effects of machismo norms. The second was focused on what they called a “lifestyle social marketing” process, aimed at promoting a life‐style characterized by more gender equality. This involved working together with the men themselves to identify how they would prefer to receive information, and from where. It was crucial to manage to make it “cool and hip” to have more equal relations with women. Together with SSL International, who makes Durex condoms, PROMUNDO has used these ideas to create a social marketing campaign aimed at changing lifestyle choices (Barker 2003)

# **The effects of attempting to influence social norms**

The team behind the MINDSPACE report on how to influence behavior through policy point out an important ethical issue (Dylan et al 2010). On the one hand, governments influence social norms all the time, so the idea that governments should be neutral towards norms is unrealistic. But governments are usually not aware of how their actions and policies affect social norms, and so the consequences can be the reinforcement or even creation of bad norms. Further, money and effort spent on a democratically decided policy are wasted if the implementation does not take into account existing social norms that prevent a successful implementation. This suggests that governments should pay close attention to social norms, and should attempt to influence them in desirable ways. On the other hand, though, the team points out that this type of subtle manipulation 38
the 1983-2012 period, holding constant average adult and average child TEE, would generate a 4.7% _increase_ in population TEE.

# **3.2 Comparison of TEE with caloric intake, measurement error, and estimates of disease effects**

Table 4 combines several data sources to provide an overall assessment of trends and levels for nutrition for India between 1983 and 2012. Columns 1 and 2 provide an illustration of the basic puzzle raised by (Deaton and Dr`eze 2009). Column 1 shows that real per capita expenditure (deflated to 1993-1994 rupees) has increased, particularly in the most recent years since 2005. Column 2 reports official estimates of caloric intake from the National Sample Survey Organization ((NSSO 2013)) that are almost identical to the numbers reported in (Deaton and Dr`eze 2009) for the 1983-2005 period but incorporate the latest available data (NSS Rounds 66 and 68 conducted in 2009-2010 and 2011-2012). The official numbers show that caloric intake declined further from 2005 to 2010, but then increased considerably from 2010 to 2012, which erased almost half of the decline between 1983 and 2005 measured in (Deaton and Dr`eze 2009).

Columns 3 and 4 of Table 4 provide some insight into measurement error related to recall bias. It is well known that longer recall periods can lead to downward biased estimates of consumption, and the 51st through 54th NSS rounds (1994-1999) randomly allocated a 7-day as well as traditional 30-day recall period for food across households. The results confirmed that consumption estimates are higher for the 7-day recall period, but also that the difference was much larger for food categories like fruits and vegetables, eggs, fish and meat, and beverages and processed foods (over 40%) compared to under 15% for categories like milk and cereals ((National Sample Survey Organization 2000)). These findings led the NSS to use two different recall periods (canvassed for different households) for the food categories with the highest discrepancy between 7-day and 30-day recall periods in 2010 and 2012. For these categories, the magnitude of 7-day/30-day bias was similar to the earlier 1994-1999 period at around 44% for caloric intake ((NSSO 2013) Tables 5A and 5B).<sup>6</sup> 6 Note also that the use of 30-day and 7-day recall period for the _same_ households in the 55th NSS round (1999-2000) is likely to account for some of the unusual pattern in Table 12
and is higher for the poverty gap and squared poverty gap indices, reflecting gains to those living well below the poverty line. Rural poverty measures, that were historically higher than for urban areas, have been converging with urban measures over time, and the (distribution-sensitive) squared poverty gap index for urban India has actually overtaken that for rural India in recent years. There has been a marked urbanization of poverty in India over this period, from about one-in-eight of the poor living in urban areas in the early 1950s to one-in-three today.

Even though a trend decline in poverty emerged around the early 1970s, the year 1991-92 – the benchmark year for economic reforms in India – stands out as the year of the great divide. Markers of a structural break are many, even as an attribution to the reforms remains unclear. There was a significant spurt in economic growth, driven by growth in the tertiary sector and to a lesser extent, secondary sector. The pace of poverty reduction also accelerated, with a 3-4 fold increase in the proportionate rate of decline in the post-1991 period. The acceleration in rural poverty decline was even higher than that for urban poverty. This happened alongside a significant increase in inequality both within and between urban and rural areas, in contrast with a decline in rural inequality and no trend in urban inequality pre-1991. Despite the increase in inequality, we find greater post-1991 responsiveness of poverty to growth in the aggregate, regardless of whether growth is measured based on national accounts or survey-based consumption. Thus, faster growth also appears to have been more pro-poor when the latter is measured by the growth elasticity of poverty reduction.

Even more striking has been the structural break in the relationship between poverty and the composition of growth. Both urban-rural and sectoral (output) decompositions are suggestive of stronger inter-sectoral linkages, whereby growth in one sector transmits its gains elsewhere. Post-1991, urban growth has emerged as the primary driver of poverty reduction. Urban poverty has become significantly more responsive to urban growth, but (even more importantly) urban growth has become significantly more rural poverty reducing since 1991. This is in sharp contrast to the pattern prior to 1991, when urban growth had no impact on rural poverty.

Also striking is that our post-1991 data point to sector-neutrality (by output) in the poverty reducing effect of growth in net domestic product. Unlike the pre-1991 period, when only primary and tertiary sector growth contributed to poverty reduction, after 1991 all three sectors have had a significant impact. The tertiary sector has the highest (absolute) growth 28
_CHAPTER 2. DIRECT ESTIMATION_ 8 Direct estimators based on the sampling design offer several advantages, particularly when applied to areas with large sample sizes. Specifically, they avoid making model assumptions for the study variable and have good properties across all possible samples _sd_ drawn from _Ud_ . As mentioned earlier, the HT estimator is design unbiased for _Y_<sup> ̄</sup> _d_ , and the ratio HT estimator is design consistent as the area sample size _nd_ grows. This means that they are more likely to be close to the true mean _Y_<sup> ̄</sup> _d_ as _nd_ increases. Another important advantage of direct estimators is their use of “all-purpose” expansion weights. This means that the same expansion weights _wdi_ are employed for the estimation of totals or means of any variable of interest, facilitating the automatic production of large amounts of statistical information. However, challenges arise for areas with small sample sizes, where the design variance becomes unacceptable. Note that var _π_ ( _Y_<sup>ˆ ̄</sup> _d_ ) = _O_ ( _n_<sup>_−_</sup> _d_<sup>1),indicatingthatitdecreasesasthe</sup> area-specific sample size _nd_ grows at the same rate as 1 _/nd_ . However, it grows unboundedly as _nd_ decreases, as illustrated clearly for SRS in (2.3).

Generalized Regression (GREG) estimators and more general calibration estimators (Deville and S ̈arndal, 1992; Lehtonen, S ̈arndal and Veijanen, 2003) applied to areas are designed to enhance the efficiency of direct domain estimators, owing to the knowledge of the totals of certain auxiliary variables. These procedures adjust the sampling weights _wdi_ so that the expansion estimates of the known totals of the auxiliary variables, based on the final weights, become equal to the known true totals. The adjusted weights may be similarly used to estimate totals or means of other variables of interest.

Nowadays, expansion weights are typically calibrated using the known totals of certain auxiliary variables and are also adjusted for non-response. However, the resulting expansion estimators may still be inefficient for areas with a small sample size _nd_ , as their design variances are _O_ ( _n_<sup>_−_</sup> _d_<sup>1).</sup> One way to ameliorate the SAE problem at the design stage of the survey,
training emphasizes the role of networks and women working together to seek financing or purchase products together to obtain bulk discounts, which could result in lower costs and higher profits for both treated and untreated.

Implicit in this discussion is the assumption that the production function _f(.)_ is itself unchanged. But business training may encourage firm owners to consider producing new products or offering new services. This offers another means for treated firms to grow without negatively impacting on untreated firms – they can expand the market through increasing variety.

# **4. Follow-up Surveys and Estimation Approach**

# **4.1 Follow-up Surveys**

Four rounds of follow-up surveys were conducted, in order to measure outcomes approximately one year and three years after training occurred (see timeline Appendix 1). Two types of surveys were used. A comprehensive long-form survey collecting data on a wide range of business outcomes was used in rounds 2 and 4. These were supplemented by much shorter surveys in rounds 3 and 5. These short surveys were conducted two or three months after the long surveys, and were intended to provide a second observation on volatile business outcomes like sales and profits, as well as an additional opportunity to gather data from individuals who could not be found at the time of the long survey rounds. Appendix 6 describes how the key outcomes are measured.

Appendix Table 2 details response rates. Overall, we were able to interview 95.0 percent of the sample in at least one of round 2 or 3, and 92.3 percent in at least one of round 4 or 5. In addition, in cases where we were unable to interview someone due to refusal, travel, death, or other reasons, we collected information from other household members or close contacts on whether the individual in our sample was currently operating a business. This enables us to have data on survival status for 99.3 percent of the sample at one year, and 97.2 percent at three years. There is no significant difference in data availability with treatment status at the three-year horizon, although those assigned to treatment are 1 to 2 percentage points more likely to have data available at the one year horizon.

In addition to the survey data, we have two other sources of information that aid in assessing impact. The first are photos of the inventories of the businesses, which were taken at the time of the baseline survey and in rounds 2 and 4. We had two independent field staff value these inventories based on the market prices of the different items, and average these values to get a 10
It is necessary to design indexes that represent the energy policy packages so that theoretical and statistical comparisons between them make economic sense and reflect characteristics like the extent of renewable energy policy reform or the extent to which policy instruments are adopted together. We use three different methods to tally policies or "operationalize" each energy policy package, potentially affecting the regression results.

The first alternative is the “RISE index,” based on the practices ESMAP uses to create the aforementioned aggregate traffic light indicators in institutional publications (Foster et al., 2018). The RISE index weighs policy groups equally, but as shown in Appendix A.3, groups and sub-nested groups can contain different quantities of policies. Therefore, in the RISE index, the weight given to each policy is affected by how many policies are in the group this policy belongs to. For instance, when there are four policies in a group, each is worth 25 percent, and when there are two policies, each is worth 50 percent. The arbitrary aspect of weighing the policies within groups is part of our motivation to seek alternative methods to create other indices that we can use as independent variables.

An alternative approach is to create an index that sums up the enacted policies at each time. Such an index, which we will call the "Summation index," has been tried before (Cubbin & Stern, 2006) and is the simplest alternative to the RISE index for each of the seven energy policy packages shown in Table 2. When applied at the Heading level, the Summation index weighs all policies equally and produces a manageable number of independent variables. Arguably, because all policies are weighted equally within Headings, it is also a proxy for the depth of reform across countries. We use the Summation index as our second option.

We propose a third, "Composite Index," based on correlation analysis for robustness. The Composite index first reduces dimensionality by dropping highly correlated variables so that highly collinear policies are not counted several times over. It then sums the remaining 13
Return on assets*Big0.1 -0.269** 1.380* <u>(0.107) (0.710)</u>

# Panel C: Big0.25 included

|Big0.25|-0.001|0.006|0.011*|0.001|-0.053***|-0.090**|-0.104**|-0.070***|
|---|---|---|---|---|---|---|---|---|
||(0.002)|(0.005)|(0.006)|(0.003)|(0.017)|(0.037)|(0.048)|(0.026)|
|Equity*Big0.25||-0.124||||0.679|||
|||(0.081)||||(0.500)|||
|Liquidity*Big0.25|||-0.170*||||0.914||
||||(0.088)||||(0.650)||
|Return on assets*Big0.25||||-0.220||||2.038|
|Panel D: Big0.5 included||||(0.172)||||(1.491)|
|Big0.5|-0.003|0.007|0.013|-0.002|-0.094***|-0.162***|-0.150**|-0.109***|
||(0.004)|(0.009)|(0.009)|(0.004)|(0.025)|(0.048)|(0.061)|(0.033)|
|Equity*Big0.5||-0.203||||1.385*|||
|||(0.146)||||(0.732)|||
|Liquidity*Big0.5|||-0.269*||||1.039||
||||(0.160)||||(0.969)||
|Return on assets*Big0.5||||-0.189||||2.662|
|||||(0.269)||||(2.249)|
|Panel E: Big1 included<br>Big1|0.011**|0.027**|0.029**|0.012*|-0.098***|-0.209***|-0.194***|-0.119***|
||(0.005)|(0.012)|(0.013)|(0.006)|(0.035)|(0.056)|(0.055)|(0.040)|
|Equity*Big1||-0.380*||||2.583**|||
|||(0.215)||||(1.030)|||
|Liquidity*Big1|||-0.400*||||2.378**||
||||(0.221)||||(0.989)||
|Return on assets*Big1||||-0.264||||4.924|
|||||(0.351)||||(3.168)|

38
Table A3: Guinea - Educational attainment of children and their parents by five-year birth cohort

||Average years|||||
|---|---|---|---|---|---|
||of schooling|No schooling|Primary|Secondary|Tertiary|
|Son||||||
|1933-1937|0.9|87.8|8.2|1.9|2.1|
|1938-1942|1.4|85.1|5.8|5.4|3.8|
|1943-1947|2.6|76.7|6.5|7.5|9.3|
|1948-1952|4.4|61.8|9.5|12.2|16.5|
|1953-1957|5.5|54.0|9.8|14.6|21.7|
|1958-1962|4.7|56.2|10.5|19.0|14.3|
|1963-1967|3.9|57.9|13.1|20.1|8.9|
|1968-1972|4.2|54.7|15.7|20.7|9.0|
|1973-1977|4.8|47.9|17.3|24.7|10.1|
|1978-1982|5.9|36.5|14.2|44.5|4.8|
|Daughter||||||
|1933-1937|0.2|97.2|1.4|1.1|0.3|
|1938-1942|0.2|96.9|1.8|1.1|0.2|
|1943-1947|0.4|94.9|2.7|1.6|0.7|
|1948-1952|1.0|90.2|2.8|4.6|2.4|
|1953-1957|1.6|83.9|4.6|6.3|5.1|
|1958-1962|1.4|84.1|4.7|8.9|2.3|
|1963-1967|1.6|81.7|6.4|9.6|2.4|
|1968-1972|1.6|80.0|9.3|8.3|2.3|
|1973-1977|1.6|78.7|9.3|10.1|1.9|
|1978-1982|2.7|67.0|11.2|20.0|1.9|
|Mother||||||
|1933-1937|0.0|99.4|0.4|0.1|0.0|
|1938-1942|0.0|99.5|0.2|0.4|0.0|
|1943-1947|0.1|98.7|0.9|0.5|0.0|
|1948-1952|0.1|98.8|0.5|0.5|0.2|
|1953-1957|0.2|98.2|0.9|0.8|0.2|
|1958-1962|0.3|96.9|1.5|1.0|0.6|
|1963-1967|0.4|96.0|1.5|1.8|0.7|
|1968-1972|0.7|92.1|3.4|3.1|1.4|
|1973-1977|1.0|89.7|3.5|3.9|2.8|
|1978-1982|1.9|82.1|5.1|7.2|5.6|
|Father||||||
|1933-1937|0.2|97.7|1.1|0.6|0.6|
|1938-1942|0.4|96.4|0.9|1.3|1.4|
|1943-1947|0.5|95.2|2.4|1.1|1.3|
|1948-1952|0.5|95.0|2.3|1.3|1.5|
|1953-1957|0.8|93.0|3.1|1.4|2.6|
|1958-1962|1.2|89.8|3.3|2.4|4.5|
|1963-1967|1.2|89.7|3.4|3.0|4.0|
|1968-1972|1.7|85.5|5.0|3.3|6.3|
|1973-1977|1.9|84.6|4.4|4.0|7.1|
|1978-1982|2.8|76.7|6.2|6.4|10.6|

35
# **Aquatic Salinization and Mangrove Species in a Changing Climate: Impact in the Indian Sundarbans**

Anirban Mukhopadhyay Senior Researcher, School of Oceanographic Studies, Jadavpur University David Wheeler Senior Fellow, World Resources Institute Susmita Dasgupta* Lead Environmental Economist, Development Research Group, World Bank Ajanta Dey Joint Secretary, Nature Environment & Wildlife Society Istiak Sobhan Environment Specialist, World Bank

## **JEL Classification:** Q23; Q54; Q57

**Keywords:** Mangrove, climate change, aquatic salinization, mangrove‐dependent livelihood, Sundarbans

## **This research was conducted under the South Asia Water Initiative ‐ Sundarbans Landscape.**

*Corresponding Author: Susmita Dasgupta. Email: sdasgupta@worldbank.org; Telephone: 1‐202‐473‐2679; Fax: 1‐202‐522‐2714

We would like to extend our special thanks to Dr. Kakoli Sengupta for her help with the data. Our sincere thanks go to Dr. Anamitra Anurag Danda and Dr. Sunando Bandyopadhyay for their expert opinion. We are thankful to Norma Adams for her help with the editing and Polly Means for her help with the graphics.
associated with fiscal rules prior to the onset of the pandemic. This coefficient ranges from -.444 to -.542, implying a 88 to 105 basis point reduction in spreads for a country with rules of mean strength. The spread reduction associated with fiscal rules through the pandemic is then given by the sum of the coefficient estimates for the standalone fiscal rule index and interaction between fiscal rule index and post-pandemic indicator variable. This sum ranges from -.884 to -.958, implying a spread reduction of 186 to 199 basis points through the pandemic.

Similarly, we retest the specification shown in Equation (5) which distinguishes between countries that temporarily suspended their fiscal rules, countries that enacted an escape clause, and those that maintained their rules through the pandemic, using the lagged fiscal rule index in place of binary measurements. As was the case in the initial test outlined in Section 3.3, the results of this test imply a similar spread compression for all countries with fiscal rules, regardless of the degree of enforcement through the pandemic. The results from this test can be seen in Appendix Table A12. Finally, the results of our three main tests outlined in Sections 3.1-3.3 remain robust to tests in which outlier countries are excluded. See the Appendix for a description of the methodology used for these tests and see Table A13 for the full set of coefficient estimates for our baseline estimation with outliers removed.

# **4 Conclusion**

This paper provides empirical evidence that the adoption of fiscal rules can help suppress borrowing costs for emerging markets and developing economies during periods of global financial stress. Using the COVID-19 timeframe as our baseline sample period, we find that the existence of a fiscal rule is associated with lower sovereign spreads, with estimates of the average spread-compressing effect ranging from 319 to 378 basis points. The result is robust after controlling for institutional quality, and to the extent to which enforcement of the rule occurred during the global crisis. Our robustness tests also indicate that the spread-compressing effect is increasing in rule strength, and we therefore interpret the spread-compressing effect estimated in our baseline test as an upper bound. Further, we show that the spread-compressing effect of fiscal rules is robust to other global crisis periods such as the GFC of 2008-09. The spread-compressing effect is estimated to be stronger during the COVID-19 pandemic than during the GFC, a result which we interpret to reflect the gradual improvement of fiscal rules and generation of “second-generation” rules in the decade preceding the pandemic.

Regarding the finding that fiscal rules compressed spreads through the pandemic regardless of the degree of enforcement, we also provide suggestive evidence for the mechanism driving this result by performing an event study empirically estimating the time it takes to return to compliance following an abandonment of a budget 22