rafmacalaba/datause-encoder-data huggingface dataset:
reports represent the most relevant antecedents to the novel indicators proposed and tested in this report. First, the World Bank has created a measure of legislative predictability for Romania and Türkiye that focuses on the count of amendments and repeals over time (World Bank 2019, 2021). In the case of Romania, the World Bank relied on the number and frequency of amendments and repeals to show changes per year, trends over time, comparisons between different year intervals, as well as amendment patterns across different sectors. For Türkiye, the World Bank analyzed the number of annual changes and average changes over larger time periods with a further breakdown of these patterns by type of legislation. One of the key insights of these analyses was that they allow for an identification of the sectors where unpredictability was the highest. Second, Brenner & Fazekas (2023) uses legislative data on France, Hungary, Italy and the UK, to develop new indicators of legislative predictability focusing on the timing of first modification and number of total modifications.

While our main innovation is the development of legislative predictability indicators, we also propose a number of variables which are essential for exploring the prevalence, distribution and trends of legislative predictability (Table 1).

**Table 1. Indicators used in the analysis (unit of analysis=individual law)**

|**Name**|**Description**|
|---|---|
|Modification count|Total count of modifications a law/regulation received until 2022|
|First modification date|Date when the law/regulation was first modified|
|Category|Type: law or regulation|
|Date passing|The date a law/regulation was published|
|Title|Title of a law/regulation|
|Text|Full text of a law/regulation|
|Text size|Size of the text (expressed in number of characters)|
|Policy Production|Number of laws/regulations passed in a year (level of country-year)|
|Related laws|Count of laws/regulations related to a given law or regulation|
|Initiator|Name of the initiator of legislation|
|Sector|Classification of legislation into non-economic, multisector, and individual<br>NACE industries (including taxation and budget)|

_Modifications_ . To analyze modifications, we create two variables i) tracking the first modification, and ii) a series of modifications. In both cases, each law is analyzed over time. To this end, the data has to be organized in the following way: Each law is observed for a series of months beginning in the month it was published and ending at the moment of data collection. The first one is initially set to zero and stays zero until the law is first modified. At the time of transition, the law takes on a value of 1 and is dropped from the sample thereafter. The second formulation of the dependent variable equals 1 every month the law is changed, otherwise it is 0. Collecting the first date of modifications and the overall number of changes over time is a tested approach and previous work has applied a similar 7
within each block (Table 1); (ii) GPs selected for female reservation do not differ significantly from non-reserved GPs in a number of village attributes (Table 2, first three columns);<sup>16</sup> (iii) the female-to-total population ratio is uncorrelated with any village characteristics for which we have data (Table S1.1 in the supplemental appendix); and (iv) a t-test of the difference in mean public goods levels in 1991 (before reservation assignments were made) shows that GPs selected for female reservation in 1996 did not differ significantly from unreserved GPs in most of the 11 public goods measures used in this analysis (Table 3). Reserved GPs did have fewer public toilets; however, we do not feel that this affects our main findings. Because we use a DID approach to estimate the effect of women’s GP reservations, even if unobservable factors exist that influence a GP’s reservation status and affect public goods provision, our estimate of the effect of reservation on public goods provision will be unbiased as long as those factors do not change over time. We discuss this further in Sections III and IV.

# **II. Data**

The data for this study come from village government records and village-level surveys conducted in Tamil Nadu in 2005 and 2006, implemented through a joint research project of the International Food Policy Research Institute (IFPRI) and Tamil Nadu Agricultural University (TNAU).<sup>17</sup> Tamil Nadu has 29 district panchayats (DPs), 385 panchayat union councils (PUCs), and 12,618 GPs in total. To select GPs for our survey, we divided the districts of Tamil Nadu into three categories based on their Human Development Index: Developed, Moderately Developed, and Less Developed. We randomly selected one district from each of the three

> 16 There is one exception: the number of Christian households is significantly greater in unreserved villages. However, Christian households comprise less than two percent of total households in our sample, so we are not concerned with this difference.

> 17 We also used election data posted by the Tamil Nadu State Election Commission to verify the reservation status of GPs in our study (http://tnsec.tn.nic.in).

10
National Microfinance Bank, which assumed most of the branch network and the mandate to foster access to financial services. The new National Bank of Commerce’s profitability and portfolio quality improved although credit growth was initially slow. Although finding a buyer for the National Microfinance Bank proved difficult, profitability eventually improved and lending grew, while the share of non-performing loans remained low (Cull and Spreng, 2011).

# **2.4. Enterprise access to finance in international comparison**

As documented above, access to formal financial services is lower in the median African country than in the median non-African developing country (Figure 5). These aggregate indicators are based on Enterprise Surveys conducted across countries. The Enterprise Survey data, however, also allow us to dig deeper and distinguish between firms of different sizes. Figure 5 shows that there is only a small gap across firms of different sizes in the use of formal account services in both African and non-African developing countries. On the loan side, on the other hand, there is a stark difference across small, medium-sized and large enterprises, though the size gap is actually smaller within than outside Africa. However, across all three size groups, enterprises within Africa are less likely to have a loan than outside Africa, with large firms in Africa as likely to have a loan as mid-sized firms outside Africa.

18
challenges, specifically, that interventions can lose fidelity when implemented through cascade training without adequate quality control.

These findings have important implications for program design and policy. Three key lessons emerge. First, adolescent-centered interventions like safe spaces are valuable but insufficient for structural empowerment. Addressing the enabling environment through systematic male engagement and community engagement is critical. Second, the modality of community engagement matters critically. Leaders’ engagement yielded positive impacts only when paired with husbands’ clubs; implementing leaders’ training in isolation risks diluted messaging. When engaging leaders, programs should match ambitious goals with adequate training, ongoing support, and careful attention to message fidelity. Third, multi-sectoral approaches addressing multiple barriers simultaneously appear most effective, though they require substantially more resources and careful coordination. Policymakers must balance potential impact against implementation costs and complexity.

These resource constraints can be substantial. Under the program evaluated in this paper, community safe spaces absorbed the largest share of resources (USD 1.94 million) but were the least costly per participant at about USD 122 per girl. The other components vary considerably in cost: IGA support required around USD 460 per girl, husbands’ clubs USD 190 per man, and leaders’ engagement only USD 6 per girl in treated localities. These figures illustrate a trade-off: safe spaces provide broad but lighter-touch outreach and can serve as an entry point, a platform for broader girls’ programming, while add-on interventions offer more intensive engagement yielding stronger outcomes. However, it is worth noting that the leaders’ engagement intervention was least costly per girl but produced the most inconsistent results. This suggests that cost-effectiveness depends critically on whether intervention design matches implementation capacity.

This study has several limitations. We measure outcomes only one year post-intervention; longer-term effects remain unknown. A 20% attrition rate may affect our results. We cannot fully isolate all mechanisms of change, such as whether husbands’ clubs work primarily through shifting male attitudes, improving couple communication, or reducing household resistance to girls’ activities. Our findings from northern Côte d’Ivoire may not generalize to contexts with different gender norms, economic structures, or baseline female empowerment levels.

In summary, this study provides rigorous evidence that multi-sectoral approaches to adolescent girls’ empowerment—combining direct support to girls with systematic engagement of their male partners and communities—are most effective at addressing multiple barriers faced by adolescent girls in low-income countries. However, successful implementation requires careful attention to message fidelity across intervention components, adequate resources for quality delivery, and recognition that some intervention combinations may produce unintended consequences.

29
specifically, a ten-unit reduction in the cumulative crop water requirement index is shown to reduce maize yields by 0.1 tons per hectare. Figure 3 also illustrates the existence of a positive relationship between yield anomalies (i.e.: deviations from the mean at the provincial level) and WRSI anomalies. Since the WRSI is capped at 100 and is not designed to measure excess water, the points located in the lower right of the graph might be revealing the negative effects of too much rain or flooding. Low values of the WRSI are observed with high frequency in Mozambique, and this may be indicative of recurrent crop failures in the areas in which precipitation levels fall well below the historical mean (Figure 4).

Lower crop yields caused by rainfall shocks may reduce household incomes and consumption, and significantly affect parents’ ability to afford nutritional inputs for their young children. When confronted with floods or droughts, households may be forced to cut basic investments in the nutrition and human capital of their children. Hence, it is plausible that birth-year rainfall affects the early nutritional status of children. The underlying mechanism that causes the effects of rainfall shocks to persist over time is arguably their influence on critical endowments of affected individuals, such as their health during a crucial period of physical development (i.e. the nutritional environment in the womb and in the first year of life). Seeking to investigate this possibility in more detail, we employ the same identification strategy used above to explore the contemporaneous (i.e. short-term) impacts of the floods and droughts on the anthropometrics of children, more specifically on the height-for-age z-score, a strong predictor of height in adulthood. The sample is comprised of children aged 0 to 4 that were measured and weighted in the DHS 2011.

The results show a strong relationship between rainfall anomalies in the first year of life and the height-for-age z-score (Table 3). For instance, shortly after the shocks occur, affected children are about 0.5 to 0.8 standard deviations (based on the preferred specifications that control for district fixed effects) smaller than the control children, whose mean is -1.89 standard deviations below the World Health Organization international reference group. The results are robust across specifications and subsamples, and are statistically significant in a quantitative sense.

In addition, affected children underperform in terms of schooling indicators, and this is possibly mediated by the negative effects of extreme rainfall anomalies on their health as infants.

11
Table 4. Responsiveness of Catch to Price: Including Zero Catch

||Dep. Variab<br>Pichilingue<br>|le: catch<br>||Abreojos<br>|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Log coop price|0.441***|0.441|0.543|1.489***|1.489***|1.500***|
||(0.024)|(0.548)|(0.512)|(0.007)|(0.172)|(0.147)|
|Log mkt Price|-2.566***|-2.566|-0.791|-0.455***|-0.455|0.898|
||(0.091)|(1.568)|(1.855)|(0.020)|(0.553)|(0.664)|
|Obs|9341|9341|4458|60497|60497|41695|
|Fixed effects|Sp-M-FT|Sp-M-FT|Sp-wk-<br>FT|Sp-M-FT|Sp-M-FT|Sp-wk-Ft|
|Clustering|none|Sp-M-FT|Sp-wk-<br>FT|none|Sp-M-FT|Sp-wk-Ft|
|Num. groups|1345|1345|1470|3147|3147|6966|

Note: All specifications use fixed effects Poisson estimation. First three columns use only Pichilingue observations, and next three columns use only Abreojos observations. Columns 1, 2, 4, and 5 include fixed effects at the species-month-fishing team level, while columns 3 and 6 include fixed effects at the species-week-fishing team level. “Num. groups” indicates the number of unique combinations at each level used in estimation. Standard levels clustered at the level indicated in the “Clustering” row.

4
# **Krueger/Schiff/Valdés Revisited: Agricultural Price and Trade Policy Reform in Developing Countries since 1960**

## Kym Anderson

School of Economics University of Adelaide Adelaide, SA 5005, Australia Phone +61 8 8303 4712 <u>kym.anderson@adelaide.edu.au</u> Keywords: Agricultural price distortions, trade policies, developing countries JEL codes: F13, F59, H20, N50, O13, Q18 This article draws on results from a recent multi-country World Bank research project (see <u>www.worldbank.org/agdistortions). Financial assistance from World Bank Trust Funds (particularly those</u> provided by the governments of Japan, the Netherlands and the United Kingdom), and the Australian Research Council is gratefully acknowledged, as are the contributions of the country case study contributors to the Agricultural Distortions project, computational assistance by a team of research assistants led by Ernesto Valenzuela, research collaboration with Johanna Croser and Peter Lloyd, and helpful feedback from commentators on numerous conference and seminar papers over the past year and from referees. Views expressed are the author’s alone and not necessarily those of the World Bank or its Executive Directors, nor the countries they represent, nor of the institutions providing funds for this research project. Forthcoming in _Applied Economic Perspectives and Policy_ 1(2), 2010.
-12-

means of a minimum distance estimator, using the estimated robust variance-covariance of the estimated II as the weight matrix:

# **_Min(Vecdll_** _A) (VecIll-))_

where b is the set of parameters of interest, and n is the robust estimated variancecovariance of the 11 matrix. Chamberlain (1982) shows that this procedure obtains asymptotically efficient estimates.

In order to use this method, we need to make explicit the restrictions that our model imposes on the fI matrix. After removing the time means, our basic model in equation (6) can be written as _zj,_ = ejxi,+ a +ykt, L,-l+eu Afe4Ix Jx.-.,. 7<sup>=O]</sup> _for_ t= 1,...,T By recursive substitution of the z, term in each regression equation, we have
<!-- Start of picture text --> Access time Population density Person - hours<br>per 30 meters per 30 meters per 30 meters<br>1 1 )<br>hours 3E O pop p-h p-h<br>bel<br>x =<br>4 5 30<br>pop p-h p-h<br><!-- End of picture text --> <!-- Start of picture text --> Person - hours Person - hours Access improvement<br>Existing roads e, service s Road r, service s Road r, service s<br>4 1 E 1 1 3<br>p-h p-h & p-h p-h p-h<br>= |nl=<br>5 30 5 30<br>p-h p-h p-h p-h<br><!-- End of picture text -->

<!-- Start of picture text --> Proposed Proposed Proposed<br>Road 1 Road 2 coe Road n<br>Dry Monsoon Dry Monsoon Dry Monsoon<br>i on is oon i on i on i ol is oon<br>Patt a =o CH Pat ia<br>—t— —t— —t— —t+— —t— —t—<br>meet MMos ST OTST Bap ETT OTST Mag OTST Saal tt Sad ETT OTT Saad<br>afin of af oe oe eB ee od Be oe afQat of of of of<br>41'1<br>aa | a a a | | a a a | | a a a a a<br><!-- End of picture text -->

|||Prop<br>Ro|osed<br>ad 1||Prop<br>Roa|osed<br>d 2<br>coe||Prop<br>Roa|osed<br>d n|
|---|---|---|---|---|---|---|---|---|---|
||Dry||M|onsoon|Dry|Monsoon|Dry||Monsoon|
||i on<br>Patt<br>|||isoon<br>a<br>|i on<br>=o<br>|i on<br>CH<br>|i ol<br>Pat<br>||isoon<br>ia<br>|
|me<br>af|t<br>et<br>in|MMos<br>of|ST<br>af|t—<br>OTST<br>Bap<br>oe<br>oe|—t—<br>ETT<br>OTST<br>Mag<br>eB<br>ee<br>od|—t+—<br>OTST<br>Saal<br>Be<br>oe|—t<br>tt<br>afQat|Sad<br>of|—t—<br>ETT<br>OTT<br>Saad<br>of<br>of<br>of|
|||||||||||
||a|||a|a<br>a||<br>|<br>a|a<br>a<br>|||<br>a|a|a<br>a<br>a|
||RoadSe|quence|Name|Season|Service<br>Acce|ssibilityImprovement (p-h)|Budget(NPR|)|P-h/ NPR|
|1108<br>:T10_3|ccc|eeecc|eeeeee|eeeeeeee<br>OM<br>en|eeeeeeeeeeee<br>ble**a**lthFacili**ti**es<br>|ecceecceneeeeeeseeeee<br>495,01|0<br>......62|**00**0000|2**.**407**3**6**E-06**|
|||||iDry|<br><br>‘Fin ncial Ins<br>tutions|<br>5284.9:|<br><br>621|0<br>000|8 510 7|
|M103||||Monsoon<br><br>|{HealthFacilities0<br>|)<br>2869.9!6<br> <br>|210000<br>|00.<br>|3.81623E-06<br>|
|||1||0<br>Monsoon.|<br>0<br>iFinancialInstitutionsj|<br><br>o<br>0804.4!|21000|00|1.73984E-05|
|{T15_1.<br> <br>|1<br> <br>|||<br>iDry<br>|<br>:HealthFacilities<br>|<br><br>8181.7:<br> <br>|527<br> <br>|000000<br> <br>|1.55251E-05<br>|
|TUS <br>1T15_1_|d<br>|cccec|ceseeseeeesee|eee(DIV...|<br>fFinancialinstitutions|fo<br>63262.2!|o<br>527000|<br>00.|0.000119852|
|<br>|<br> <sup>1</sup>|||<br>:Dr|<br>{Municipal headquarter.|<br><br>5574.9:|<br><br>527|<br><br>000000|1.05786E-05|
|{T15_1.<br>|1<br>|||{Monsoon_ <br>|Financial Institutions<br>|116589.6:<br> <br>|527<br>|000000<br>|0.000221233<br>|
|TIS|4|4||Monsoon|jMunicipalheadquarter§|0<br>5725.2E|270000|00.|1.08637E-05|
Turning now to bilateral trade flows (i.e. using the findings from the BTIO approach as discussed), Figures 3 and 4 break down the sources of flows of virtual carbon imported into the EU and the US. In terms of virtual carbon imports, the EU imports large amounts from China, the US, economies in transition and Russia, with smaller flows from India and Japan (Figure 3). The US imports significant flows of virtual carbon from China, the EU, and Canada, with smaller flows from Mexico, Russia and Japan (Figure 4). As seen in Figure 5, the major destinations for Chinese exports of virtual carbon are the EU, the US and Japan, with smaller flows to India, economies in transition, Mexico, Canada and Russia.

The bilateral trade IO analysis also lends itself to analysis of the net flows of virtual carbon between countries, as presented in Table 1. China has large net exports to the EU, the US and Japan, while the EU is a big net importer from other middle income countries, economies in transition and the US. The US is also a major net importer from other middle income countries. These bilateral flows of carbon provide the basis for estimating the effective tariff rate associated with any particular level of a border tax on virtual carbon.

_Estimates of border taxes on virtual carbon_ Table 2 contains our key results regarding the potential size of taxes on virtual carbon trade. It presents the effective tariff rate that exporting countries would face on their goods and services if all importing countries placed a $50/ton CO2 tax on the virtual carbon content of imports. This illustrative carbon price is in line with recent experience, given that emissions permits in the EU Emission Trading Scheme (ETS) traded as high as €35 in 2008.

Reading these results _by column_ tells us about the (effective) tariff burden that the USA, for example, would place on the imports that it receives from its trading partners such as Russia and the European Union. This burden is, in turn, expressed in Table 2 as a percentage of the value of the total imports from that particular country (or region) to the USA. The effective tariff rate on trade in this example would vary from a high of 10.4% and 10.3% on imports from Russia and China respectively through to 8.9% for South Africa, 7.9% for India, to a low of 1.2% for the European Union. The final value in the column for the USA is the average tariff placed on the imports received from all of its trading partners (3.1%).

Reading these results _by row_ , by contrast, tells us about the tariff that a given exporter would face in each of the countries where its goods are destined. In the case of China, for example, its exports are subject to an effective tariff rate (based on the virtual carbon in those exports) of 10.5% for those exports consumed in the European Union, 10.4% for Japan and 10.3% for the US. The final number in the row for China is the average tariff that its exports to its trading partners would face, 10.7%. As a comparison, US exports to other countries would face a trade-weighted average tariff rate of 3.1% and EU exports would face a rate of 1.2%.

14
10 two different samples of PD programs—one sample of _evaluated_ PD programs, those with impact evaluations and student assessment results—and one sample of _at-scale_ , government-funded PD programs. Below we discuss the design of the survey instrument and both data sets. The remainder of this section describes the methodology for each of the three aforementioned research phases.

# **Designing the In-Service Teacher Training Survey Instrument (ITTSI)**

The ITTSI was designed based on (a) the descriptive, impact evaluation, and theoretical literatures characterized in the previous section, and (b) the authors’ prior experience studying inservice teacher training. Drawing on these, we drafted a list of key indicators to capture details about a range of program characteristics falling into four categories: Overarching Aspects, Content, Delivery, and Perceptions (Figure 1).

Taking each of these in turn, the Overarching Aspects section includes items such as the type of organization responsible for the design and implementation of a given teacher training program, to whom the program is targeted, what (if any) complementary materials it provides, the scale of the program, and its cost. Content includes indicators capturing the type of knowledge or skills that a given program aims to build among beneficiary teachers, for example, whether the program focuses on subject content (and if so, which subject), pedagogy, new technology, classroom management, counseling, assessment, or some combination of these.

Delivery focuses on indicators capturing program implementation details, such as whether it is delivered through a cascade model (where the program trains trainers who in turn train the teachers, sometimes with additional layers in between), the profile of the trainers who directly train the teachers, the location of the training, the size of sessions, and the time division between lectures, practice, and other activities. Finally, the Perceptions section includes indicators capturing program implementers’ own perceptions of which elements were responsible for any