rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5: Estimated effects of inheritance on log cell per capita consumption with and without controls**

||(1)<br>No controls|(2)<br>Rural location<br>and department<br>dummies|(3)<br>As in (2) + controls for<br>individual and household<br>characteristics|
|---|---|---|---|
|Inherited land|-0.482***|-0.0620|-0.0655|
||(0.0702)|(0.0572)|(0.0585)|
|Inherited house|0.327***|0.0955*|0.0515|
||(0.0606)|(0.0488)|(0.0498)|
|Other inheritance|0.0631|0.167***|0.106**|
||(0.0656)|(0.0521)|(0.0526)|
|Constant|11.45***|12.28***|11.91***|
||(0.0381)|(0.303)|(0.383)|
|Observations<br>|4,326|4,326|3,558|
|R<sup>2</sup>|0.016|0.376|0.499|

29
# App 2ndix: Predictor Variables

# Student. School and Community Back ground

1. Student socioeconomic status (SES) was derived from measures of father's education, mother's education, father's occupation, mother's occupation, and the natural logarithm of the amount of pocket money the child typically brings to school (alpha = .73), as reported by the students.

2. Students' linguistic background ("Dialect") was coded as "central Thai" vs "other," as reported by the students.

3. Student nutrition was measured by students' reports about how often they eat breakfast. An indicator variable was constructed: "Breakfast" (1 = daily, 0 = not daily).

3. Time needed to travel from home to school (in hours) was based on student reports.

4. Children's pre-primary experience was also coded dichotomously (1 = one or more years of pre-primary experience; 0 = no pre-primary experience) based on student reports.

39
# 2. **_Some_** Stylized Facts on the Informal **Sector**

## 2.1 Magnitudes and Other Characteristics

Informal economic activity is a mainstay of the Peruvian economy. Its sustained growth stands in marked contrast to formal activity, which has deteriorated in the last decade at an alarmingly rapid rate. There is extensive evidence that a high proportion of the labor force, especially the Lemale component, is in informal activities. As Glewwe and de Tray (1989) and SuarezBerengue]a (1987) discuss, the majority of the bottom socioeconomic strata in urban areas earns a livelihood from self-employment in the informal sector.

5 Peru's shadow economy has recently attracted worldwide attention as a result of the recent publication of El Otro Sendero: La Revolucibn Informal6 by Hernando de Soto, a businessman and president of Lae Instituto Libertad y Democracia (ILD) in Lima. He concludes that the informal sector is the dominant and most dynamic part of the economy, and believes that removing the burdensome obstacles to legitimacy (such as bureaucratic red tape) would considerably improve Peru's economic malaise.

Several other recent studies corroborate de Soto's view ,hat a sizable portion, perhaps a majority, of the labor force is in the informal sector.7 Surveys by the ILD in 1985 and 1986 show that the informal sector in Lima makes up almost half the labor force, accounts for 61 percent of the hours worked and s See The Economist, February 18 and September 23, 1989; and Leaders, March 1989 (12) 1.

> **6** The English translation is The Other Path: The Invisible Revolution in the Third World. (See de Soto 1989).

> 7See Althaus and Morelli (1980), Kafka (1984), Litan and others (1987), Stelcner and Moock (1988), Moock, Musgrove, and Stelcner (1989), Strassmann (1987), Suarez-Berenguela (1987), Vargas Llosa (1987), Webb (1977), and World Bank (1987).

4
# **Table 5:** Fixed Effects Robust Regressions

# Dependent Variable: STANDARD DEVIATIONS OF GDP, CONSUMPTION, AND INVESTMENT GROWTH

# (Annual Rates)

|||_G_|_DP_|||_C_|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|_BANKt-1_|.426<br>(1.41)|.<br>.|..<br>....|<br>..|-5.38*<br>(1.56)|...|...|...|-8.58*<br>(3.95)|...|...|.. .|
|_LLYt- 1_||-. 844<br>(4.50)|...|...|...|.676<br>(2.93)|...|...|...|-.460<br>(9.83)|...<br>||
|_PRIVATEt-_<br>_1_|...|...|-1.68*<br>(.497)|...|...|...|-1.78*<br>(.512)|...|...|...|62.<br>(.924)|0*<br>..|
|_PRIVYt- 1_|...|...<br>|...<br>__|-2.32<br>(1.70)|...|...<br>_|...|-. 577<br>(1.68)|...|...|...|4.24<br>(4.67)|
|_GROWTHt_|.135*<br>(.063)|.016<br>(.074)|.025<br>(.070)|.122**<br>(.066)|-.047<br>(.082)|-.103**<br>(.056)|.034<br>(.083)|-.062<br>(.075)|-.063<br>(.082)|-.176*<br>(.072)|-.262<br>(.350)|.049<br>(.082)|
|_MEANt- 1_|.060<br>(.120)|.059<br>(.139)|.027<br>(.130)|.107<br>(.123)|.322<br>(.227)|.005<br>(.150)|.125<br>(.223)|.196<br>(.203)|-.098<br>(.495)|-.015<br>(.430)|.265<br>(.349)|.097<br>(.488)|
|_INFMEANt_|.024<br>(.021)|.028<br>(.025)|-.030<br>(.024)|.024<br>(.022)|-.043**<br>(.024)|-.016<br>(.016)|-. 079*<br>(.024)|-.005<br>(.022)|-. 045*<br>(.605)|.105*<br>(.053)|-. 057<br>(.043)|-.046<br>(.062)|
|_INFSTDEVt_|.051*<br>(.021)|.043**<br>(.024)|.078*<br>(.024)|.050*<br>(.022)|.074*<br>(.023)|.078*<br>(.016)|.088*<br>(.024)|.058*<br>(.021)|.179*<br>(.059)|-. 016<br>(.051)|.089*<br>(.043)|.134*<br>(.060)|
|_FXVOLt_|.437<br>(1.71)|2.84<br>(1.97)|6.53*<br>(1.91)|1.90<br>(1.80)|6.37*<br>(1.93)|2.65*<br>(1.29)|13.4*<br>(1.97)|2.20<br>(1.78)|7.13<br>(5.00)|9.31*<br>(4.23)|18.2*<br>(3.60)|10.9*<br>(4.99)|
|_No. of obs._|198|192|197|198|199|192|198|197|198|190|197|197|

Note: Country-specific and time-specific fixed effects estimate. *, ** respectively denote significance at the 5 percent and 10 percent levels. The variables _GOVMEAN, GOVSTDEV, POLITY, OPEN, OPEN * FXVOL_ are included but not shown.
shorter delays at customs (although higher productivity firms do report shorter delays).

These results provide a strong case for a policy focus on the constraints identified by firms as most costly. In the next piece, we examine how the relative importance of different public input constraints reported by firms varies across countries in the sample.

# **8.  Which elements of the business environment matter most for firms and how do they vary across countries?**

In this section, we use the country averages of the costs of public inputs reported by firms, _Rij_ , to assess which elements of the external environment are more problematic for firms across the region and in each country. We also compare the country average evaluations with those in other countries at a similar level of development.

The data that we use in this section pools the surveys of the SAR countries used above with data from the surveys available from the World Bank’s Enterprise Surveys portal. We consider only formal sector firms in the analysis. Altogether, the surveys cover almost 120,000 firms from over 230 different surveys in 126 countries over the period 2000-2010. Of these, about 16,000 firms were from the 8 SAR countries. We use these firm-level data to calculate country average evaluations, i.e., country means.

In order to correct for differences in survey samples when comparing reported constraints across countries, we construct the “conditional country means” for each constraint for a standard firm. The controls included in the estimations are the same as those used above: size (log employment), and dummy variables for whether the firm is newly established, expanding employment, has substantial foreign ownership, is a significant exporter or importer, and is located in a small city.<sup>6</sup> Log employment is centered on ln(30). The estimations are identical to the country regressions reported earlier; the country conditional means are, in fact, the estimated intercepts

> 6 Also as previously, we include a dummy variable in case the importer dummy is missing, in order to increase the sample size and number of surveys included.

26
NLYTICL UMY OF TIV T SWIES

||||C1iAru-|ClialHPATE<br>CALATICI|.....................-<br>MUTAL|INDEET<br>VA<br>.......-<br>SECTOIL Q|RILES<br> <br>...............--<br>RABLE|-.. --...||
|---|---|---|---|---|---|---|---|---|---|
||RNE<br>OFS1?|PWJNCT<br>TYPEAM<br>ISICE|<br>ISTICS OF<br>SPE<br>WEARIES|<br>PERICD<br>AND<br>AMLYSIS<br>PUIOD|<br>_AM_<br>EVIWI<br>L<br>VARIAIES|<br>EXONIC-<br>POICT<br>VAIABLES|EUUTEING<br>PRUUCT<br>UWIN FACRS|IOCIO-<br>POLITICAL|CIASS|
|FRY<br>m1<br>LILIES<br>(19k)|Stu* iquct of<br>anticyclical<br>inary<br>pdicy<br>an dot-w0<br>**ltu-tuu**<br>.carmiC gruth|GDP<br>(INF)|55 dmwlcd<br>and d1cqpIr.<br>inms<br>of Nw<br>with papArs<br>or<br>2 dtllian|1yar<br>w period<br>190-3<br>Amat grath<br>**rat**of**a**<br>binl<br>praijt<br>aworap<br>Pooiru|Si (t<br>**vrimle**<br>rqpritq,<br>irntriltizd<br>cowtrim<br>Si lt<br>v wridite<br>paing<br>oI lrtirw<br>coidtrle<br>Iate of<br>wriatien<br>of<br>oil price<br>Oit<br>,ali_r<br>**ceitrim**<br>Oil-iprptiru<br>comtries<br>GP<br>rate of two<br>Prw~<br>wars<br>*|Avwap of<br>saNt<br>reti<br>of wriation<br>of_mw_<br>ty<br>Std<br>dwiation of<br>antwy<br>Aod<br>iictor<br>Silent<br>**wriable yr**<br>1914-77<br>Awmop<br>lat<br>inftatin<br>**rate**<br>RateofuraAth<br>of law to<br>dos<br>d<br>tic<br>credit ratio<br>Umptay sbcho<br>(runial<br>of<br>vamt rate<br>timteda a<br>fuwctin of<br>**tim**_ud_of<br>dw detye<br>**wriable)**<br>correted_bV_<br>its vuiuie|||R-Wu0.31|
For each firm _f_ and country of destination _j_ , we generate a simple average tariff over all HS6 products exported by that firm to that country in year _t_ . To avoid possible endogeneity issues, we use the simple average of the tariffs faced by each firm in a particular country, instead of using the weighted average. In the last section we present a robustness test with an alternative tariff measure constructed using weighted average and fixing the weights at the beginning of the period. Tariffs for each firm-country-year, _τfjt_ , are computed in the following way: _τfjt_ = <u>�</u> _<u>gN∈fjtG</u>_<sup>_τgjt_</sup> , where _G_ is the set of products exported by firm _f_ and _Nfjt_ is the number of products that firm _f_ exports to country _j_ in year _t_ . Previous empirical works focus on tariff variations across industries where the firm produces. One of the few exceptions is the work of Teshima (2009), who uses plant-level tariffs to study import competition and R&D in Mexico.

The changes in firm-level tariff will then capture market access differences across firms. These differences depend on the type of HS6 products that each firm produces and exports. These tariff changes are most likely exogenous from the perspective of French firms. In fact, Asian’s tariff changes were part of the negotiation process aimed at allowing entrance into WTO. We will thus exploit this exogenous variation in tariffs, to identify how changes in market access affect French firms’ export patterns.

# _Foreign competition measures: at the firm-country level_

Our market access measure varies at the firm-country level. Thus, the foreign competition measure should also vary at the firm-country level. To control for the foreign competition at this level, we build three different measures. These measures, which capture different types of competition, are called: (i) extensive margin competition (N<sup>_◦_</sup> countries), (ii) extensive margin competition (N<sup>_◦_</sup> firms) and (iii) intensive margin competition.<sup>15</sup> The first measure is a product-specific concentration measure, based on the number of countries. We compute this measure using import flows at the HS6 product level and country of origin from BACI for 198 developed and developing countries.This measure captures, product by product, the geographical 12
# **Table A.5b: Sustainability of Reform: Full Difference-n-Difference Estimates** **<u>(Margins Based on 2 Week Lagged World Price of Crude Palm Oil)</u>**

||Trading M|argins|
|---|---|---|
||Wholesale-World Price|Retail-World Price|
|**Announcement Effects**|||
|Announcement Dummy|-3.815***|-3.723***|
||(-4.278)|(-4.530)|
|**Announcement*Palm**|**30.53***|**11.86**|
||(1.782)|(0.473)|
|**Announcement*Palm*World Price**|**-0.284**|**-0.0536**|
||(-1.319)|(-0.170)|
|**Effects of Policy Implementation**|||
|**Effects during first 9 months**|||
|Policy Implemt.|-0.278|-1.149|
||(-0.285)|(-1.300)|
|**Policy*Palm**|**39.57*****|**49.81*****|
||(6.459)|(8.649)|
|**Policy*Palm*World Price**|**-0.446*****|**-0.556*****|
||(-5.620)|(-7.394)|
|**Effects after first 9 months**|||
|Policy Implement.|-1.999|-2.243*|
||(-1.440)|(-1.738)|
|**Policy Implement.*Palm**|**6.769**|**12.49*****|
||(1.113)|(2.734)|
|**Policy*Palm*World Price**|**-0.0332**|**-0.0654**|
||(-0.439)|(-1.142)|
|World Price*palm|0.0748|0.00812|
||(0.866)|(0.0988)|
|World Price|-0.445***|-0.361***|
||(-5.670)|(-4.805)|
|Diesel Price|25.26***|27.31***|
||(6.214)|(7.569)|
|Dummy if World Price was rising (DW)|-0.0719|0.0362|
||(-1.132)|(0.654)|
|DW*palm|0.462|0.279|
||(0.321)|(0.203)|
|Palm|-1.835|-1.234|
||(-0.872)|(-0.666)|
|Relative world prices of palm-soy/wheat-rice|-0.860|-3.606**|
||(-0.522)|(-2.200)|
|Relative world prices of palm/wheat|0.701**|0.816**|
||(2.129)|(2.571)|
|Intercept|<br>19.16***|<br>17.35***|
||(5.682)|(5.890)|
|Observations|1,292|1,292|
|Controls|||
|Year, Quarter, Product Fixed effects|Yes|Yes|
|Ramadan Dummies|Yes|Yes|
# **Evidence gap maps – A tool for promoting evidence-informed policy and prioritizing future research**

Birte Snilstveit, Martina Vojtkova, Ami Bhavsar, Marie Gaarder<sup>1</sup> Keywords: evidence-informed policy, development effectiveness, evaluation, systematic reviews, gap maps, intervention effectiveness, knowledge translation, research prioritisation <mark>JEL classification: I</mark> 15; I38; D04; I28; Q10; Q50; Z18 Sector board: Social Protection

> 1 Snilstveit, Vojtkova and Bhavsar: International Initiative for Impact Evaluation, 3ie,

<u>bsnilstveit@3ieimpact.org, mvojtkova@3ieimpact.org, abhavsar@3ieimpact.org; Gaarder:</u> Independent Evaluation Group (IEG), World Bank, mgaarder@worldbank.org. <mark>Acknowledgements: The authors thank Annette Brown, Claire Allen, Dafni Skalidou, Hugh Waddington, Howard White, Phil Davies and Peter Tugwell for comments on previous drafts of this paper. The authors are also grateful for the inputs from participants at the Symposium on Systematic Reviews in International Development, Dhaka, December 2012. The work was supported by the International Initiative for Impact Evaluation (3ie). All errors are the responsibility of the authors.</mark>
For the three Asian lead firms, on the other hand, only 5.5 percent of suppliers were domestic Chineseowned firms, on average. In two of the three cases, the share was well below 5 percent. Imports also make up a non-negligible share of components, accounting for almost 22 percent on average and even one third of parts in one case. In contrast, for the American and European lead firms interviewed, the share of parts sourced from domestic firms was noticeably higher and imports were lower. For one U.S. lead firm, in particular, sourcing is almost as domestically focused as for Chinese lead firms.

**Table 5: Sourcing by OEMs in China from three possible sources (% of Tier 1 suppliers)**

|**Lead firm**|**Domestic firms**|**Joint-ventures**<br>**Foreign subsidiaries**|**Overseas**<br>**(imported)**|
|---|---|---|---|
|European|14.0|86.0|0.0|
|European|23.8|68.3|7.9|
|U.S.|14.3|57.1|24.5|
|U.S.|39.6|58.5|3.8|
|**Average Western**|**15.4**|**69.7**|**14.5**|
|Asian|2.2|64.4|33.3|
|Asian|4.8|85.5|9.7|
|Asian|9.4|67.9|22.6|
|**Average Asian**|**5.5**|**72.6**|**21.9**|
|Chinese|58.2|41.8|0.0|
|Chinese|63.4|36.6|0.0|
|**Average Chinese**|**60.8**|**39.2**|**0.0**|
|**Average (all)**|**25.5**|**62.9**|**11.3**|

**Source:** Brandt and Van Biesebroeck (2008)

While the two approaches to expansion in China have been apparent for some time, the effect of the crisis has, by and large, meant an acceleration of the observed pre-crisis differences, at least in the short term. For some firms, the aggressive localization strategy has been driven by scarcity of resources – either financial or in terms of management capacity. The greater toll of the crisis on American lead firms, in particular, has further encouraged a very aggressive expansion strategy in China, since sales there accounted for as many vehicle sales as the U.S. market in the first 9 months of 2009. Starved of funds for vehicle development, reliance on global suppliers to tailor vehicles to local tastes has also been deepened in this high growth market. The need for cost savings has intensified the quest to utilize lower costs tier 2 and 3 suppliers in China as well.

On the other hand, the crisis has made firms pursing the cautious localization strategy, in particular the Japanese, even more cautious. A common strategy for firms in a recession is to return to core markets and perceived comparative advantages. During a recession, there is even less incentive to deviate from strategies — centered on efficient production and high quality — that have served Japanese lead firms relatively well in the past.

17
Policy Research Working Paper 10123

# **Abstract**

This paper uses a 4-year panel (2019–2022) of 10,125 village councils in Ukraine to estimate direct and indirect effects of the war started by Russia on area and expected yield of winter crops. Satellite imagery is used to provide information on direct damage to agricultural fields; classify crop cover using machine learning; and compute the Normalized Difference Vegetation Index (NDVI) for winter cereal fields as a proxy for yield. Without conflict, winter crop area would have been 9.14 rather than 8.38 mn. ha, a 0.75 mn. ha reduction, 86% of which is due to economy-wide effects. The estimated conflict-induced drop in NDVI for winter cereal, which is particularly pronounced for small farms, translates into a 15% yield reduction or an output loss of 4.2 million tons. Taking area and yield reduction together suggests a war-induced loss of winter crop output of 20% if the current winter crop can be harvested fully.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Table 11: Heterogeneous impact|s of_Mais Educação_|Test score<br>_by_city GDP per c|Test score<br>apita|
|---|---|---|---|
|Dependent variable|Dropout rates|<br>mathematics|<br>Portuguese|
||(1)|(2)|(3)|
|<br>Panel A: Impa|cts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.703|–4.761***|–2.673*|
||(1.353)|(1.711)|(1.530)|
|Dummy_Mais Educação_* year 2009|0.854|–4.453***|–2.694**|
||(1.119)|(1.477)|(1.368)|
|Dummy_Mais Educação_* GDP per capita|0.199**|0.491*|0.208|
||(0.0914)|(0.265)|(0.221)|
|_R_ <sup>2</sup>|0.667|0.786|0.789|
|Observations|2,988|2,988|2,988|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Impac|ts on ninth-grade va|riables||
|Dummy_Mais Educação_* year 2011|-0.758|-4.376**|-1.834|
||(1.198)|(1.914)|(1.912)|
|Dummy_Mais Educação_* year 2009|-0.313|-5.514***|-2.792*|
||(0.970)|(1.664)|(1.674)|
|Dummy_Mais Educação_* GDP per capita|0.169|0.201|0.423|
||(0.151)|(0.285)|(0.337)|
|_R_ <sup>2</sup>|0.736|0.755|0.742|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. GDP per capita was deflated by IPCA, and is in 2007 prices. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
World Bank (BOOST) to ensure that the instrument is not picking up community-level variation in the supply of public education. Matching both datasets is imperfect because the availability of the budget data was not anticipated at the time of the household survey (see appendix S.1 for a detailed description of the data and matching procedure). We include the school-level executed budget in several expenditure categories as additional explanatory variables.<sup>29</sup> The strong negative effect on bribes remains even after adding the additional controls, which approximately halves the sample size.<sup>30</sup> Schools’ wage bills, which closely correspond to the schoolteachersper-pupil ratio (cf. figure S.4), teaching material and schools’ maintenance funds are not significantly correlated with household educational expenditures (table S.11: columns 1-6). By contrast, schools’ expenditures on utilities and transports, where community leaders often report lacking funds, exhibit signs of substitutability of private and public expenditure. There is also some tentative evidence of substitution between the parental investment of time and the time teachers could allocate to individual children (column 7).<sup>31</sup> The strong correlation between migration and informal payments to teachers is also robust when controlling for an index of infrastructural quality of the school (table 4: column 1). We furthermore tested whether the migration-induced reduction in informal payments is lower in worse-funded schools where informal payments may be less controversial but did not find any robust differences (results available upon request). Sending students to schools with funding for school buses and attending a more distant school, both of which proxy secondary and advanced secondary schools that cover larger areas, correlate positively (although statistically insignificantly) with informal payments. Using school fixed-effect regressions to compare students within schools, we find that better off parents pay more to teachers and buy more tutoring from their children’s teachers (table 4: columns 3 and 4).<sup>32</sup> This underscores the importance of the income effect. The migration coefficient is negative but insignificant, suggesting that much of the variation associated with migration occurs at the school level. This fits well our discussions with Moldovan experts, who stated that the payments to teachers that

> 29. We do not find evidence that they are systematically correlated with migration. 30. The first-stage estimates are reported in column 2 of table S.4.

> 31 Table S.12 provides OLS results when the sample is split by migration status. The negative coefficient on the teacher-pupil ratio (proxied by wages per pupil) is similar for both migrant and non-migrant households, although statistically insignificant for the former.

> 32. Note that migration as a major source of income inequality is not exogenized here due to a lack of a valid within-community IV.

15