rafmacalaba/datause-encoder-data huggingface dataset:
The government’s reluctance to drastically reduce tariffs could be explained by the fact that a significant portion of government revenue originated from tariffs. However, two uncorrelated events occurring in 1991 led to a complete overhaul of the tariffs structure and significant reductions in tariffs. On the one hand, conditionality of the SAP enabled World Bank and IMF to enforce tariff reductions.<sup>7</sup> On the other hand, the launching of the Economic Community of West African States (ECOWAS) trade liberalization scheme in 1991 also forced Ghana to embark on tariff reductions.

Although Ghana had been a member of GATT since 1957, it took the conditionalities imposed by the SAP and ECOWAS free trade scheme in 1991 to compel the government to significantly reduce tariffs. Figure 1 shows the trend in simple average tariffs for threedigit ISIC sectors between 1991 and 2001.<sup>8</sup> With the exception of beverages, all other three-digit sectors saw a reduction in tariff levels between 1991 and 1993. In 1993, the maximum applied tariff rate was approximately 25 percent. However, a fall in government revenue prompted an upward revision of tariff rates.

<!-- Start of picture text --> 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Year<br>Food Processing Other Food Products<br>Beverages Textiles<br>Wearing Apparel Wood Processing<br>Furniture Machinery<br>Fabricated Metal Products Transport Equipment<br>50<br>40<br>30<br>20<br>Tariff Rate<br>10<br>0<br><!-- End of picture text --> Figure 1: Trend in Sector Level Average Output Tariff Rates

**_Source:_** Author’s analysis based on data from _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII). **_Note:_** Tariffs data is based on revision 2 of the International Standard Industrial Classification (ISIC) 3-digit industry level. Reference line indicates a year after the formation of World Trade Organization (WTO).

After the SAP and ECOWAS enforced tariff reductions in 1991, the next major change occurred with the founding of the World Trade Organisation (WTO) in 1995. The intro5
<!-- Start of picture text --> 1960 1980 2000 2020 2040<br>year<br>GDP per capita growth<br>Fitted GDP per capita growth<br>Scenario 1<br>Scenario 2<br>Scenario 3<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br><!-- End of picture text -->

**Figure 5** – Actual and fitted GDP per capita growth rates (1970-2009) and predictions under different scenarios (2010-2034)

For the projections we assume that the fixed effect specific to Ghana is converging towards the world mean of fixed effects until 2050, with our projections ending in 2030. This assumption reflects a slow equalization of fixed effects across countries over time. If Ghana follows this trend, it reaches a fixed effect of -0.015 by 2030, which would correspond to the fixed effect estimated for Ecuador, South Africa, Egypt or Azerbaijan in the estimation for the period 1970-2009.

**Scenario 1:** In a first scenario, we assume that all covariates remain at the level of the last observation, which corresponds to 2005-09, and only the initial income level is updated (and with it the interaction term of education and initial GDP). This implies that the values of investment, government consumption, openness, education, inflation, democracy and natural resource rents do not improve as compared to the period 2005-09 and the impact of oil production is not considered. In particular, the covariates remain at the values displayed in Table 5.

Figure 5 shows how growth rates could develop under these assumptions (Scenario 1). As the initial income level increases, growth rates are predicted to slow down, but for the upcoming decade growth rates above 4% are predicted.

15
<!-- Start of picture text --> A oS)<br>2<br>2<br>s<br>a ?<br>&<br>o<br>a<br>Fa<br>6<br>2 8<br>2<br>=]<br>-30 -20 -10 O 10 20 30 40 50 60 70 80 90 100 110 120<br>Days since workplace closing<br>——_ Workplace ——__ Transit<br><!-- End of picture text --> <!-- Start of picture text --> &<br>® °<br>3<br>2<br>o<br>8 §<br>2<br>Da<br>=<br>= ¢<br>e<br>=<br>om<br>=o<br>= f=]<br>*<br>30 06-20 :«6-1006-600C6 100 0 0s 00—-sB—ias—CsCiBSC‘i‘SCdsCisd400''s:«12200<br>Days since workplace closing<br>———_ Retail& recreation ———_ Groce& pha r macyy<br><!-- End of picture text -->
located the center of the assigned block, they had to identify the majhee who was in charge of the majority of households surrounding the point and list all households under said majhee.

UNHCR provides a publicly available camp navigation application that is primarily used by site management and NGOs operating inside camps. A customized version of the tool, containing the NPM R12 majhee mapping system and outlining the CBPS sample blocks, was shared with the team in the second half of the camp listing period. After the tool was received, the teams used the application to locate and list within demarcated sample blocks.

# _Ground truthing and developing the listing methodology for hosts_

The host mauza segmentation was conducted digitally using an algorithm that divided each host mauza into segments of approximately 100-150 households based on population counts from the Bangladesh Population Census 2011 and the number of buildings within mauza boundaries visible on OSM. The proposed listing methodology instructed field teams to locate the selected segments using maps generated on Google Maps and do a full listing within those segments. However, owing to the issues faced during the camp listing, the suboptimal quality of the imagery, and heterogeneous settlement patterns across host communities, it was judged necessary to carry out a ground validation exercise before the actual listing could start. Non-sample mauzas were selected for a two-day validation exercise which highlighted that the actual number of households in some of the selected segments was significantly higher or significantly lower than the expected 100-150 households. For these segments, a number of field protocols were developed in addition to the core listing guidelines:

- _For segments with fewer than the expected number of households (that is, fewer than 100 households)_ , if the border of the segment cut through a settlement cluster belonging to another nonsample segment, the field team was instructed to continue listing along the border of the segment until they had listed approximately 100 households.

- _For segments with fewer than 100 households and with no avenue for expanding_ listing into clusters along the segment borders, field teams were instructed to complete listing among the households inside the border. In the case where the selected segment had fewer than 10 households, the field team was provided with a replacement segment to list.

- _For segments with more than 150 household_ s, a deviation of 50-60 households was accepted, and field teams were instructed to list the full segment. However, for segments with significantly more than 150 households, a fanning protocol was followed, where GPS coordinates were provided for the settlement clusters inside the segment, and each field team was instructed to list at every one of those clusters, starting from the coordinates provided and fanning out in all directions. In total, the field team was expected to list 150 households in a segment, combining all the marked clusters.

19
business equipment, house, large and small consumer durables, cell phones, nonagricultural land and means of transport. In the baseline surveys, these questions included on productive capital: 1) Does anyone in your household currently have any [item]?, 2) How many of [each item] does your household currently have?, 3) Who owns most of [each item]?, 4) Who can decide whether to sell [each item] most of the time?, 5) Who can decide whether to give [each item] away most of the time?, 6) Who can decide to mortgage or rent out [each item] most of the time?, 7) Who contributes most to decisions regarding a new purchase of [each item]?

The focus is on understanding who owns most of the assets, which serves as a proxy for bargaining power within the household. Thus, a woman who lives in a household with three parcels of land and owns one of them, would not be identified as a landowner. Similarly, if she can decide whether to sell only one parcel, but not most, the instrument will not identify her as having these land rights. It does not provide data to calculate how each asset is owned (jointly or individually and by whom) or the incidence of asset ownership by individuals, although one can impute the extent to which groups of assets (in predefined categories) are solely or jointly owned. Note that the WEAI is designed to be administered together with a household survey that collects asset information, so the WEAI should not be considered as a stand-alone module.

Instead of using household member identification numbers, the WEAI uses very detailed response codes. For the questions regarding who owns or control specific assets, the response codes usually include: (1) self (2) partner/spouse (3) self and partner/spouse jointly (4) other household member (5) self and other household member(s), (6) partner/spouse and other household member(s), (7) someone (or group of people) outside the household), (8) self and other outside people, (9) partner/spouse and other outside people, and (10) self, partner/spouse and other outside people. Thus, it provides information on whether most of the assets in each type are owned individually or jointly. Because the WEAI is administered together with a household questionnaire that includes a household roster, it is possible to map some of the codes in the WEAI back to the household roster.

The more recent Abbreviated WEAI (A-WEAI), a shorter version of the original WEAI asks, “Does anyone in your household currently have any [item]?” and “Do you own any of the item?” Respondents can reply that they own an item solely or jointly or that they do not own any of an item. This facilitates calculation of the incidence of ownership by men and women within the sample.

13
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One objective for the design of mNAIS insurance products is that the value of insurance products for the same crop in the same district should not vary significantly between insurance units.<sup>4</sup> If the value of products does vary considerably between insurance units in the same district the insurer would be exposed to the threat of adverse selection, and insurance purchase in the district may become dominated by farmers in the insurance units for which the products were particularly good value.

One technically feasible solution would be for farmer and government premium rates to be fixed by government at a crop or crop/state level, and threshold yields to be calculated on an actuarial basis according to the principles of risk classification. Such an approach would have no actuarial disadvantages compared to risk classification through premium rates, but could yield significant political economy advantages by allowing fixed premium rates, and therefore fixed premium subsidies, across a state in addition to a low variation in threshold yields for a given crop across the state.

However, if rates under mNAIS are to be set at the crop/district level and the threshold yield in each insurance unit must be based on a seven year moving average yield, at the very least these moving average yields should be smoothed within the district to improve predictive stability. This second best solution is described and motivated in the suggested calculation of probable yields.

A rule for calculating indemnity limits is also suggested. The calculation rule for threshold yields affects the equity of mNAIS; if the variation in commercial premium rates is high within a state then state and central government subsidies may be unevenly spread between farmers, with farmers from insurance units with high commercial premium rates capturing a larger share of subsidies than other farmers. The indemnity limit rule is intended to reduce the variation in commercial premium rates within a state.

# _Probable Yields_

# <u>Description of step</u>

First, the yield data to be used in calculating the probable yield must be determined according to the rules of mNAIS. For the worked example of cotton in Gujarat, it is assumed that the probable yield is to be calculated using the seven years of yield data between 2004 and 2007 inclusive, after detrending by 50 kg/ha/year (columns [2] to [8] of Table 4).

Second, calculate the average detrended yield for each insurance unit (column [9] of Table 4).

> 4 There are much broader questions of political economy as to the degree to which the value of a mNAIS insurance product should be the same for different farmers growing different crops in different states. These broader questions are not addressed by this objective.  Instead, the focus is on the technical issue of how products could be designed and priced so that, for example, the mNAIS cotton product sold in one insurance unit in one district offers the same expected claim payment to farmers as the cotton product sold in another insurance unit in the same district.

11
|sigma||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||11.718***||||9.146***||||12.966***|
|R Adjsq.|||129.76||||84.7||||98.3|
|N|0.408|0.425|0.428|0.568|0.57|0.578||0.164|0.206|0.216||
||8663|8661|8661<br>8661|3695|3695|3695|3695|4968|4966|4966|4966|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

**_Table 11b – Ukrainian Language, Mean Score_**

|||To|tal|||Ur|ban|||Ru|ral||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|# students|0.009***|0.009***|0.009***|0.007***|0.008***|0.008***|0.016***|0.008***|0|-0.002|0.010***|0.009***|
||18.93|19.08|7.05|5.41|15.76|16.08|9.49|4.94|-0.11|-1.32|2.81|2.61|
|(# students)<sup>2</sup>|||0|0|||-0.000***|-0.000**|||-0.000***|-0.000***|
||||-1.36|-0.3|||-5.28|-1.98|||-3.5|-3.38|
|Student/Teacher|-0.169***|-0.198***|-0.052|-0.114|-0.095|-0.154**|-0.09|-0.186|0.097|0.057|0.175|0.112|
||-3.77|-4.57|-0.42|-0.96|-1.49|-2.5|-0.46|-1.02|1.37|0.84|0.87|0.54|
|(Stud./Teacher)<sup>2</sup>|||-0.005|-0.002|||-0.002|0.003|||-0.015|-0.011|
||||-1.03|-0.37|||-0.36|0.43|||-1.48|-1.04|
|Class Size|-0.102***|-0.083***|-0.556***|-0.495***|0.027|0.068*|-1.062***|-0.811***|-0.083***|-0.089***|-0.087|-0.092|
||-5.04|-4.15|-6.29|-5.72|0.77|1.85|-4.44|-3.79|-3.18|-3.58|-0.72|-0.77|
|(Class Size)<sup>2</sup>|||0.013***|0.012***|||0.026***|0.020***|||-0.001|-0.001|
||||5.57|5.23|||4.89|4.15|||-0.32|-0.18|
|Urban|1.670***|2.099***|2.061***|1.296***|||||||||
||7.08|9.04|8.4|5.54|||||||||
|Gender Comp.||-0.120***|-0.121***|-0.144***||-0.136***|-0.135***|-0.151***||-0.122***|-0.122***|-0.131***|
|||-15.46|-15.53|-18.53||-8.31|-8.27|-10.44||-14.12|-14.09|-14.88|
|Unemployment<br>rate 2009||0.029|0.036|0.02||0.003|0.02|-0.032||0.004|0.016|0.011|
|||0.67|0.84|0.47||0.03|0.24|-0.39||0.09|0.32|0.22|
|Average Wage||0|0|0||-0.001|-0.001|-0.001||0|0|0|

52
incentives stimulate private R&D investments. Government grants and contracts, and government spending on basic research do not displace private R&D funding except when R&D inputs have inelastic supply. The outcome depends on market demand and supply conditions, which are unobserved most of the time. About two-thirds of studies surveyed by David et al. (2000) conclude that public funding is complementary to private financing, while one-third point to a substitution between the two sources.

However, there was a high degree of heterogeneity in the surveyed studies (conducted over the previous three decades), in terms of the data used, the level of analysis (micro/macro; industry/firm) and the econometric strategies. In addition, most of these studies were subject to a potential selection bias and other serious methodological limitations that disqualify the predominantly positive evidence and poses more questions than concrete answers about the relationship between private investment and government support to R&D. The relationship between the two also depends on the level of aggregation of reported studies and on the country studied. Studies based on a lower level of aggregation (line of business and firm data) tend to report substitution almost as often as 'complementarity' [47% of all studies, and 58% of US studies respectively (the US studies represent two-thirds of all surveyed studies)]. The authors note that the tendency of aggregate studies showing a complementary relationship could be result of: i) Positive covariation of public and private components and inter-industry differences in technological opportunity; and/or ii) The effect of government funding of R&D raising the cost of R&D inputs to private R&D activity.

This paper contrasts and combines the results from 37 papers published during the 2004-11 period in order to identify patterns among study results, sources of disagreement among their results, or other interesting relationships that may come to light in the context of R&D interventions. We used Meta-Analysis techniques that aim to combine studies with similar research questions to increase precision and assess the generalizability of results. The regressions ran this precision value against the standardized degree of correlation between papers. The precision variable contains the standardized measure of the impact estimators of R&D additionality reported in each paper. This systematic review aims to obtain a better understanding of the impact of these interventions. The analysis suggests that based on the surveyed studies there is indication of positive R&D impacts. Furthermore, public funds do not 3
- Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., and Sulaiman, M. (2020). Women’s empowerment in action: evidence from a randomized control trial in Africa. _American Economic Journal: Applied Economics_ , 12(1):210–259.

- Baranov, V., Bhalotra, S., Biroli, P., and Maselko, J. (2020). Maternal depression, women’s empowerment, and parental investment: Evidence from a randomized controlled trial. _American Economic Review_ , 110(3):824–59.

- Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. _Journal of the Royal Statistical Society: Series B (Methodological)_ , 57(1):289–300.

- Bergstrom, K. and Ozler, B. (2023). Improving the well-being of adolescent girls in developing<sup> ̈</sup> countries. _The World Bank Research Observer_ , 38(2):179–212.

- Bhat, B., de Quidt, J., Haushofer, J., Patel, V. H., Rao, G., Schilbach, F., and Vautrey, P.L. P. (2022). The long-run effects of psychotherapy on depression, beliefs, and economic outcomes. Working Paper 30011, National Bureau of Economic Research.

- Blanshe, M. and Dahir, A. L. (2022). Uganda reopens schools after world’s longest covid shutdown. _The New York Times, January 10, 2022._

- Blattman, C., Chaskel, S., Jamison, J. C., and Sheridan, M. (2023). Cognitive behavioral therapy reduces crime and violence over ten years: Experimental evidence. _American Economic Review: Insights_ , 5(4):527–45.

- Bolton, P., Bass, J., Neugebauer, R., Verdeli, H., Clougherty, K. F., Wickramaratne, P., Speelman, L., Ndogoni, L., and Weissman, M. (2003). Group interpersonal psychotherapy for depression in rural Uganda: a randomized controlled trial. _JAMA: Journal of the American Medical Association_ , 289(23):3117–3124.

- Bossuroy, T., Goldstein, M., Karimou, B., Karlan, D., Kazianga, H., Parient ́e, W., Premand, P., Thomas, C. C., Udry, C., Vaillant, J., and Wright, K. A. (2022). Tackling psychosocial and capital constraints to alleviate poverty. _Nature_ , 605:291–297.

- Bowlby, J. (1969). _Attachment and Loss_ . London: Hogarth.

- Bowlby, J. (1973). _Attachment and Loss. Volume II. Separation, Anxiety and Anger_ . London: Penguin Books.

21
Calzonetti and Walker [1991], McCann [1998], Deichmann et. al [2005], Hanushek and Song [1978], Webber [1984]). These include

- (a) “ _Business environment_ ”, which includes access to inputs (quality and cost of labor and capital); provision of basic infrastructure; local regulations; institutional environment; and industry-specific subsidies or tax breaks;

- (b) “ _Agglomeration economies_ ” from industry clustering, which include external economies provided by localization and urbanization.

In this section, we discuss both sets of factors, with particular emphasis on their relative performance across Indian cities and states. In the next section, we provide the estimation strategy to evaluate the relative importance of these factors in influencing firm location decisions.

# A. <u>Business Environment</u>

The importance of the business environment or investment climate, as it is alternatively called, has been brought into prominence in a recent World Bank report [World Bank, 2005].<sup>3</sup> Based on a survey of approximately 30,000 firms across 53 developing countries this report documents the effects on economic performance from deficiencies in a country’s investment climate. We complement this work by examining the impacts of deficiencies in various aspects of the business environment<sup>4</sup> on distribution of economic activities across cities and regions within a large country. Although the term business environment includes a wide range of policies affecting the economic setting of firms, we

> 3 The report is entitled: _World Development Report 2005: A Better Investment Climate for Everyone_ .

> 4 We use the terms business environment and investment climate interchangeably.

5
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