rafmacalaba/datause-encoder-data huggingface dataset:
has been publishing for 149 years, since one of his papers is identified in Google Scholar as having been published in 1862; that actually makes him seem young compared to Wagstaff, who has been publishing since 1803 according to POP! This certainly brings down both authors’ statistic for citations per year in POP, although other bibliometric measures for authors (discussed later) are robust to such errors. The messiness of some of the Google Scholar data meant we had to do some careful manual checks of citation numbers for some titles.

The paper reports on our efforts to tap into these various bibliometric data sources. We make comparisons between the Bank and other institutions, including some of the world’s top universities for economics, though we recognize that these institutions have different roles. There are special features of the Bank’s work that need to be noted. Naturally that work is focused mainly on the problems of economic development, and the main field is development economics. Most academic economists are unconstrained in their choice of research topics, but that is not the case in institutions such as the World Bank, where the staff members are constrained to work within specific fields, related to the institution’s overall objectives. The research topics at the Bank must be relevant to development and Bank researchers are typically specialists working at the intersection of development with another field, such as agricultural economics, inequality, finance, health, education, environmental economics, macroeconomics or international trade. While intellectual freedom is encouraged, the choice of topics is more constrained than is the case for a typical academic. Policy relevance is emphasized, and this does not necessarily lead to papers of interest to academic readers. If we assume that research is subject to diminishing returns, in the sense that new discoveries become harder to make the more research effort is applied in a specific field, we can also expect there to be diminishing returns to citations in a given field (i.e., the first paper gets the most attention than the second and so on). It follows that, for any given number of publications, a research portfolio that is constrained to be more specialized will tend to have a lower level of total citations (and of course lower average citations per paper). This difference between the research output of the World Bank and comparable universities reflects the underlying difference in the roles and objectives of these institutions.

There are also arguments suggesting that mainstream academic economics has been somewhat biased against field specialists, including in development economics; Bardhan (2003) provides a carefully argued case for this view. The 1980s and ‘90s saw a marked decline in the 4
the preferred estimator.<sup>13</sup> In our case, it also turns out to produce the most plausible coefficient estimates. For example, although the PPML produces an estimate of the Special Rule coefficient that is closest to the one estimated by the trimmed LAD method, some of the other coefficient estimates do not have expected signs and/or reasonable magnitudes. For all these reasons, the results in column 5 with the trimmed LAD estimator are retained as the preferred set of estimates. Additional estimates in table 3 are based on this estimator.

# **5. Results**

# **5.1. Main Results**

Table 2 presents the results from estimating (1) with the last two rows reporting the estimated change of exports to the triple transformation at an average preferential margin of 11%, and to the introduction of the single rule at an average preferential margin of 10.5% for each estimator. Column 1 reports the truncated OLS method. Not all coefficients have the expected sign: the coefficient of VC is negative and non-significant. Switching from the triple to the simple transformation rule is estimated to boost apparel exports from 66% to 158%.

Table 2 here: Elasticity of Exports to Changes in RoO Columns 2 and 3 report estimates for the “standard” Tobit (with aV=1) and the ET-Tobit , which account more appropriately than OLS for corner solution outcomes of the dependent variable.<sup>14</sup> The overall fit for the models summarized in the likelihood-ratio values and the R<sup>2</sup> values (at the bottom of the table) are reasonably good. All coefficients now have the expected sign and are significant, but the estimated values of β1 and β2, which are very sensitive to the

> 13 In their simulations,  SS-T(2006 and 2011) do not assess the performance of the PPML either in a panel context or in the presence of omitted variable bias and/or measurement error.  In addition, the Trimmed LAD estimator for Tobit models used here is not considered as a contender to the PPML estimator in the simulations reported by SS-T.

> 14 We estimate pooled Tobit models. Their maintained hypothesis is that the structure of the error-term is uniform across exporters and years.  This assumption is defensible insofar as African exporters in our sample have arguably a similar structure.  Moreover, as discussed by Woodridge (2002), the Tobit is flexible and it can accommodate many categories of independent variables, such as time dummies, interactions of time dummies with time-constant or time-varying variables, or lagged dependent variables.

14
The actual cotton lint production in Guntur district for 1989/90 was 383 thousand bales. If better management practices had been instituted there would have been an increased availability of 114,900 bales valued at Rs. 57.4 crores and cottonseed worth Rs. 8.8 crores1l. The combined loss works out to Rs.66.2 crores and forms about 5% of the agricultural income of the district.

Finally, because of the need for scouting and increased harvesting there are increased labour costs. These work out to about Rs 11.5 crores12.

The net savings (due to increased cotton output and reduced pesticide input and increased labour costs) of Rs. 63.7 crores (about $38 m), represents an enormous return to better crop management techniques if adopted on a districtwide scale. (The potential problems in adoption of IPM techniques will be discussed in Section XI). Additionally, it must be pointed out that, primarily due to the need for scouting and increased yields, the IPM techniques create a net additional demand for labour.

11 Cotton lint price has been worked out as an average of H-4 and MCU-5 prices, weighted in the ratio 40:60. This works out to Rs.2938 per quintal. The data on prices has been drawn from the, "Ha;dbook of Statistics on the Cotton Textile Industry", 22nd edition.

Cottonseed prices have been put at Rs.450 per quintal, which represents about a 5% markup on the 1988/89 prices.

> 12 It has been estimated that yields will increase on average by 4.7 quintals per hectare (see Table 9). This requires additional harvesting costs of Rs. 425. Furthermore, scouting costs are Rs. 300 per hecvare. With 158000 hectares under cultivation, the total labour costs go up by Rs. 11.46 crores.

72
**_Figure 3 ‐ Innovation rates by country in ES, follow‐up module and clean follow‐up module_**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>BGD DRC GHA IND KEN MWI NAM NGA NPL PAK SDN SSD TZA UGA ZMB<br>ES Innovation follow‐up Innovation follow‐up clean<br>% of firms<br><!-- End of picture text --> _Source: Enterprise Surveys and Innovation Enterprise Surveys_

**_Table 1: Decomposition of innovation responses in the short, long, and long‐clean questionnaire_**

||**no>no**|**yes>no**|**y>y>y**|**y>y>n**|**n>y>y**|**n>y>n**|**% verified**|**Correct in**<br>**short**<br>**questionnaire**|**Differences**<br>**short and**<br>**long**|**Long**<br>**questionnaire**<br>**reclassified**|
|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|
||||||||**(3) (4) (5)**<br>**and(6)**|**(1) (3) and (6)**|**(2) (5) and**<br>**(6)**|**(4) and (6)**|
|BGD|1.01|1.52|53.84|12.42|24.04|7.17|97.47|62.02|32.73|19.59|
|DRC|33.07|17.06|30.18|6.04|11.55|2.1|49.87|65.35|30.71|8.14|
|GHA|28.13|25.18|31.8|11.58|2.39|0.92|46.69|60.85|28.49|12.5|
|IND|2.18|3.12|71.63|12.96|8.66|1.46|94.71|75.27|13.24|14.42|
|KEN|11.19|33.39|43.67|7.89|3.3|0.55|55.41|55.41|37.24|8.44|
|MWI|12.35|26.34|42.8|7.00|9.47|2.06|61.33|57.21|37.87|9.06|
|NAM|10.37|32.18|45.21|4.52|6.12|1.6|57.45|57.18|39.9|6.12|
|NGA|23.85|25.46|37.44|5.18|5.53|2.53|50.68|63.82|33.52|7.71|
|NPL|17.23|45.32|26.38|4.68|4.47|1.91|37.44|45.52|51.7|6.59|
|PAK|52.02|19.73|10.31|14.2|1.35|2.39|28.25|64.72|23.47|16.59|
|SDN|29.1|32.34|4.23|29.85|0.75|3.73|38.56|37.06|36.82|33.58|
|SSD|14.6|8.5|63.96|8.87|3.88|0.18|76.89|78.74|12.56|9.05|
|TZA|21.67|33.15|27.96|7.22|6.85|3.15|45.18|52.78|43.15|10.37|
|UGA|9.87|18.39|45.07|10.31|12.56|3.81|71.75|58.75|34.76|14.12|
|ZMB|18.84|0|66.04|8.77|5.22|1.12|81.15|86|6.34|9.89|
|**Total**|**14.39**|**15.36**|**49.09**|**10.87**|**8.01**|**2.28**|**70.25**|**65.76**|**25.65**|**13.15**|

_Source: Enterprise Surveys and Innovation Enterprise Surveys_ Note: no weights are used in the table 27
and fuel. The Ilahi and Grimard (2000) study investigates the latter using community and household level data on water infrastructure to estimate the reduced form equations of a model specifying a production function for water of the form in (23). Households that must spend more time collecting water will face a higher implicit price per unit of output and will, ceteris paribus, be worse off.

It is the limitations of the standard household model in analyzing policy issues associated with this kind of heterogeneity that lead us to the view that the more serious deficiency of the model is its treatment of household production. Much of the recent literature on the economics of the household focuses on its limitations as a “black box” in regard to the intra-household distribution of income by gender. But this would seem to be of second order importance when intra-household gender differences are the result of government policies that can only be justified by a model that allows the contribution of household production to family living standards to be ignored.

In fact, the focus on intra-household household outcomes may be misdirected. A finding widely considered from a policy point of view to be one of the most important to emerge from intra-household studies is that additional income in the hands of mothers is more likely to be spent on the children. For example, the Lundberg et al. (1997) analysis of a change in the UK child benefit system finds that the transfer of resources from husbands to wives that followed the change led to an increase in household expenditure on women’s and children’s clothing. Results of this kind are taken as evidence of the advantage of targeting welfare programs to women.<sup>36</sup> However, if tax and welfare policies are structured to lower the net income of mothers as second earners, especially in households on relatively low wage rates, then why not recommend the introduction of a gender neutral tax-transfer system rather than a complex system of gender targeted transfers?

> 36 See the discussion in Alderman, Haddad and Haddinott (1997a)

32
bt = Pt<sup>I</sup> <u>B</u><sup>X</sup> <u>t</u><sup>tNtarerealbonds:pK</sup> t<sup>=P</sup> Pt<sup>K</sup> t<sup>I,pL</sup> t<sup>=</sup> PPt<sup>I</sup> <u>t</u><sup>XLt,pC</sup> i;t<sup>=</sup> PPi<sup>C</sup> t<sup>I</sup> <u>;t</u> denote, respectively, the user cost of capital, the wage per hour of e¤ective labor, and the price of consumption goods. (1 + rt) is the real interest rate (again in terms of new capital goods) and �t = Pt<sup>I</sup> <u>�</u><sup>X</sup> <u>t</u><sup>tNtdenotespro...ts.</sup> Log linearizing around the non stochastic steady state, intertemporal household utility can be written (to a ...rst order approximation) as: where v is the steady state value of utility, xb = log xt � log x denote log deviation from the steady state. In obtaining this result we have used the FOC of the household maximization problem: and the log linear approximation of the budget constraint around the steady state: Equation (4) says that intertemporal utility (in log deviation from the steady state) equals the expected present discounted value of terms that represent the sum of the components of ...nal demand (in log deviation from the steady state), weighted by their steady state contribution to demand, minus primary inputs (in log deviation from the steady state) times their respective steady state factor prices.

# 2.2 Connecting with the productivity residual

We are now close to relating utility to a modi...ed version of the Solow residual. There are two options here. The ...rst one is to obtain a ...rst order approximation for the log level of utility in terms of the log level productivity residual. Simple manipulations allow us to rewrite log level utility as a function of expected future Solow residuals plus an initial (log) level productivity residual. The second one focuses instead on approximating the log change in utility over time.

8
microfinance organizations, and have sufficient resources and capacity for these additional functions. There is also a risk that promoter agency’s attention is diverted to developing the new functions before core functions of the SHGs and the federations are sufficiently established. Hence, it is advisable that SHG federations establish their competency as microfinance organizations before diversifying into other activities.

# **7.  Issues and Challenges: External**

## **Capacity of Promoter Agencies**

Limited organizational capacity of promoter agencies is perhaps the most crucial constraint in developing sustainable SHG federations. Capacity of the promoter agencies primarily determines the quality of SHG federations. Promotion of federations requires institution-building skills beyond that required in promoting SHGs. Very few promoter agencies –both NGOs and government agencies possess skills.

NABARD should identify SHG federation models that add value to the SHGs and take initiatives to build the capacity of SHG promoter agencies in promoting such federations. As in the case of SHG promotion, these initiatives could include exposure visits to such federations and structured training programs. Promoter agencies that have promoted such federations could be used as lead trainers. Additionally, new organizations that focus exclusively on capacity building of promoter agencies and SHG federations also need to be promoted in states with significant number of SHGs. Donor agencies could support the establishment of such organizations. An effort in this direction is the creation of Andhra Pradesh Mahila Abhivrudhi Society (APMAS with the support of DFID and CARE-India. APMAS works on quality assessment and enhancement of SHGs, SHG federations, and SHG promoter organizations.

## **Inadequate Knowledge Base on SHG Federations**

No estimates are available on the number of federations in existence, their outreach in terms of SHGs supported, the extent of the services provided by them, or the amount of financial resources handled by them. NABARD should play a leading role in improving the knowledge base. A database of SHG federations needs to be created. Assessments and impact studies of the different federation models, and development of performance standards and rating indices need to be carried out. APMAS is in the process of developing a rating index. NABARD should also facilitate of mutual learning among the promoter agencies that have promoted SHG federations and the federations themselves.

Some aspects of SHG federations that need further study include: (a) the effect of federating on transaction costs and delinquency rates; (b) the effect of federating on SHG promotional costs, and outreach of the SHG-Bank Linkage program; (c) cost-benefit analysis of federating; (d) analysis of SHG federations compared to other federated models in microfinance – village banking and credit unions; and (e) impact of SHG federations on community social capital.

- 25 -
- 42 -

- Sachs, Jeffrey, Andrew Warner. 1997. Sources of slow growth in African economies. _Journal of African Economies_ , Vol. 6, pp. 335-376.

- Seifert, Stefan, Karl-Martin Ehrhart. 2005. Design of the 3G spectrum auctions in the UK and Germany: an experimental investigation. _German Economic Review_ , Vol. 6, pp. 229-248.

- van Damme, Eric. 2002. The European UMTS-auctions. _European Economic Review_ , Vol. 46, pp. 846-858.

- World Bank. 1994. _World Development Report 1994: Infrastructure for Development_ . The World Bank.

- World Bank. 2005. _Infrastructure and the World Bank: A Progress Report_ . The World Bank.

- World Bank. 2006. _Seven Findings for the Nexus of Infrastructure, Agriculture, and the Environment: Evaluation Briefs_ . Independent Evaluation Group, The World Bank.

- World Bank. 2007. Implementation plan for strengthening World Bank Group engagement on governance and anticorruption. The World Bank.
Table 5: Heterogeneity by age with age-specific leave-out wage ratios

||Hom<br>|icides<br>|Hospital<br>|izatio n s<br>|Rep<br>|orts<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|a. Ages 15_–_29|||||||
|Leave-out F-M wage ratio - [15–29]|−.098|−.01|−1.9*|−1.8**|−2.9*|−2.2|
||(.79)|(.8)|(.98)|(.82)|(1.6)|(1.5)|
|Mean of dependent variable (natural log)|−0.09|−0.09|−0.10|−0.10|3.20|3.20|
|Mean rate (per 100,000 women)|7.80|7.80|12.02|12.02|107.54|107.54|
|b. Ages 30_–_49|||||||
|Leave-out F-M wage ratio - [30–49]|−.17|−.36|.63|.78|.45|.18|
||(1.2)|(1.2)|(.9)|(.85)|(1.2)|(1.1)|
|Mean of dependent variable (natural log)|0.01|0.01|−0.09|−0.09|2.58|2.58|
|Mean rate (per 100,000 women)|5.82|5.82|10.37|10.37|68.34|68.34|
|Controls|No|Yes|No|Yes|No|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of municipalities|841|841|841|841|841|841|

_Note:_ The outcome variables are in natural logs; 0.01 is added to each value. The gender wage ratio is calculated using only the age-group of the analysis. The control variables are the natural log of the population of women ages 15–49, the natural logs of male homicides, hospitalizations and reports, the number of households receiving cash transfers, divided by the population of women ages 15–49, the municipal per capita value of cash transfers, the ratio of females to males in secondary education completion, the share of the population ages 15–49 that is female, the per capita number of health clinics, and the per capita number of hospital beds. Regressions are weighted by the average number of women ages 15–49 in 2011–16. FE = fixed effect. F-M = female to male. Significance levels are reported as * p < .10 ** p < .05 *** p < .01.

33
# **Table 7: Difference in means between widows and non-widows at different ages**

||BMI(|x100)|Share of wo<br>BMI above|men with<br>1900|Share of w<br>dependent|omen with<br>children|Share of w<br>children livin|omen with<br>g elsewhere|Weight for h<br>of ch|eight percentiles<br>ildren 0-5.|
|---|---|---|---|---|---|---|---|---|---|---|
|Woman‘s|Ever w|idowed|Ever wi|dowed|Ever wi|dowed|Remarrie|d widow|Ever|widowed|
|Age|Rural|Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|Urban|
|15|61.83|-41.74|-0.01|0.26|0.07|0.18|0.10|-0.60|-1.22|2.96|
||(0.74)|(-0.36)|(-0.09)|(3.53)|(0.51)|(0.85)|(0.67)|(-3.40)|(-0.32)|(0.44)|
|20|-8.50|-15.51|-0.06|0.13|0.10|0.17|0.23|-0.23|-6.57|6.18|
||(-0.18)|(-0.25)|(-0.82)|(2.90)|(1.23)|(1.35)|(2.41)|(-2.30)|(-1.18)|(0.57)|
|25|-50.41|4.09|-0.09|0.04|0.12|0.16|0.29|0.04|-3.24|0.42|
||(-1.74)|(0.08)|(-2.23))|(1.29)|(2.53)|(2.16)|(5.68)|(0.61)|(-1.15)|(0.07)|
|30|-63.92|17.06|-0.10|-0.01|0.14|0.15|0.31|0.20|-1.58|-4.18|
||(-2.33)|(0.30)|(-3.11)|(-0.33)|(3.50)|(2.60)|(7.80)|(3.11)|(-0.72)|(-1.12)|
|35|-49.01|23.38|-0.08|-0.03|0.15|0.15|0.26|0.25|-1.60|-7.62|
||(-1.80)|(0.40)|(-2.80)|(-0.88)|(4.09)|(2.61)|(6.95)|(4.13)|(0.67)|(-2.11)|
|40|-5.69|23.08|-0.05|-.004|0.17|0.14|0.17|0.19|-3.30|-9.90|
||(-0.23)|(0.46)|(-1.93)|(-0.18)|(5.27)|(2.87)|(5.09)|(4.02)|(-1.35)|(-3.21)|
|45|66.04|16.13|0.008|0.05|0.17|0.14|0.01|0.03|-6.68|-11.00|
||(1.91)|(0.30)|(0.30)|(2.18)|(4.68)|(2.50)|(0.42)|(0.48)|(-1.91)|(-2.75)|
|50|200.14|-7.34|0.11|0.20|0.17|0.14|-0.26|-0.39|-13.91|-8.75|
||(2.53)|(-0.06)|(1.54)|(3.73)|(1.70)|(1.09)|(-2.81)|(-2.27)|(-1.74)|(-0.76)|
|Obs.|9052|4792|9052|4792|9026|4774|9052|4792|7309|3098|

Note: The table gives the mean for widows less that for non-widows. Ever-widowed includes currently widowed as well as previously widowed, currently married women. t-stats in parentheses. DHS multiplies BMI by 100. Source: 2006 Mali DHS.

37
SEDUC estimates a coverage rate of 97%, which has improved in the two subsequent years for which data are available.

Given the recent implementation of the database, it contains limited information. Besides individual enrollment status, the data include some demographics (gender and age), attendance rates, whether the child attends a public or private institution, and current grade. Unlike in Guatemala, more detailed socioeconomic information for students or their household is unavailable. While recent data exist on exam scores, they cannot be matched to our sample because they use different individual identifiers, an issue that has only recently been addressed by SEDUC.

We therefore augment SEDUC’s student data with municipal-level indicators from several sources. First, we add a set of school supply and quality indicators. The number of schools per municipality is included to measure educational supply at the extensive margin. These indicators are weighted by their target population using data from the National Statistics Institute. We first calculate the number of children in each municipality of primary (6-11) and secondary (12-18) age. Then, the number of primary and secondary schools is divided by the target population (in thousands). To measure intensive-margin educational supply, we take the number of available teaching positions in 2015 and weigh them by the target population in each municipality.<sup>7</sup> Quality indicators correspond to municipality-level infrastructure indices from the 2013 school census collected for the School Infrastructure Master Plan ( _Plan Maestro de Infraestructura Educativa_ ). These indices are on a scale from zero to one, and we include a summary measure that is a weighted average of six dimensions: furnishings, basic services, natural disasters, social threats, hydrosanitary issues, and physical infrastructure.<sup>8</sup> Second, we add a set of municipal-level socioeconomic indicators. Honduras is classified as one of the most violent countries in the world (World Bank 2011), but there remains limited evidence

> 7 Available teaching positions do not necessarily correspond to filled vacancies. For instance, a municipality may have 10 available positions, but only 8 employed teachers. The data do not allow us to make this distinction.

> 8 Each index is comprised of multiple indicators (Secretary of Education, 2014). The furnishings index captures the number of desks and blackboards. The basic services index is an average of access to electricity, running water, and sewers. The natural disaster index measures how unlikely it is that a school is affected by floods, landslides strong winds, and earthquakes. The social threat index measures how difficult it is to access and consume alcohol, cigarettes, and illicit drugs. The hydro-sanitary index captures the state of sinks, toilets, and urinals. The physical infrastructure index evaluates how well the building, classrooms, and environments are suited for students to receive lessons.

11
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