rafmacalaba/datause-encoder-data huggingface dataset:
|GPOBA Scale-upfor Bangladesh RERED II<br>Amit Jain|
|---|
|GPOBA Scale-upfor Bangladesh RERED II<br>Amit Jain|
|GPOBA Scale-upfor Bangladesh RERED II<br>Amit Jain|
|GPOBA Scale-upfor Bangladesh RERED II<br>Amit Jain|
|GPOBA Scale-upfor Bangladesh RERED II<br>Amit Jain|
|GPOBA Scale-upfor Bangladesh RERED II<br>Amit Jain|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|College Education Development Project<br>Shiro Nakata|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Bangladesh Regional ConnectivityProject 1<br>Jan Erik Nora|
|Kenya Climate Venture Facility<br>Aun Ali Rahman|
|Kenya Climate Venture Facility<br>Aun Ali Rahman|
|China: GuangdongCompulsoryEducation Project<br>Andrew B. Ragatz|
|China: GuangdongCompulsoryEducation Project<br>Andrew B. Ragatz|
|China: GuangdongCompulsoryEducation Project<br>Andrew B. Ragatz|
|China: GuangdongCompulsoryEducation Project<br>Andrew B. Ragatz|
|China: GuangdongCompulsoryEducation Project<br>Andrew B. Ragatz|
|China: GuangdongCompulsoryEducation Project<br>Andrew B. Ragatz|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|China: Gansu Technical and Vocational Education and TrainingProjeLipingXiao|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Innovative Financingfor Air Pollution Control in Jing-Jin-Ji<br>XiaodongWang|
|Hebei Air Pollution Prevention and Control Program<br>Dafei Huang|
**Table 8: Assessing the Fit of the Models**

||_Maize_|_Wheat_|_Rice_|_Soybeans_|_Palm oil_|
|---|---|---|---|---|---|
|_1960–2012 fitted prices based on the 196_|_0–2012 model_|||||
|Mean Absolute Percent Error|10.97|10.57|18.34|12.11|20.03|
|Theil’s Inequality coefficient (_U_)|0.092|0.075|0.128|0.077|0.116|
|Covariance proportion of_U_|0.767|0.921|0.878|0.839|0.829|
|_1960–2004 fitted prices based on the 196_<br>|_0–2012 model_<br>|||||
|Mean Absolute Percent Error|9.42|9.88|15.14|11.06|19.70|
|Theil’s Inequality coefficient (_U_)|0.070|0.067|0.098|0.069|0.123|
|Covariance proportion of_U_|0.996|0.994|0.976|0.996|0.907|
|<br>_1960–2004 fitted prices based on the 196_<br>|_0–2004 model_<br>|||||
|Mean Absolute Percent Error|7.42|9.34|12.16|10.23|18.57|
|Theil’s Inequality coefficient (_U_)|0.055|0.062|0.081|0.062|0.120|
|Covariance proportion of_U_|0.967|0.985|0.981|0.977|0.817|
|_2005–12 fitted prices based on the 1960–_<br>|_2012 model (in-_|_sample foreca_|_st)_|||
|Mean Absolute Percent Error|19.45|14.32|35.94|17.33|21.64|
|Theil’s Inequality coefficient (_U_)|0.120|0.085|0.168|0.088|0.106|
|Covariance proportion of_U_|0.015|0.358|0.705|0.089|0.179|
|<br>_2005–12 fitted prices based on the 1960–_|_2004 model (out_|_-of-sample fo_|_recast)_|||
|Mean Absolute Percent Error|31.14|22.11|45.44|28.84|30.51|
|Theil’s Inequality coefficient (_U_)|0.257|0.144|0.293|0.208|0.189|
|Covarianceproportion of_U_|0.069|0.364|0.618|0.127|0.163|

**_Source:_** Authors’ estimates based on table 2 and 4 estimates.

**_Note_ :** The Mean Absolute Percent Error (MAPE) is the percentage difference between actual and fitted price. For example, the value of the MAPE statistic for maize in the in-sample forecast panel, 19.45, is the average of the absolute values reported in the third row of the maize panel in table 6 (48 percent, 23 percent, 2 percent, −17 percent, −5 percent, −7 percent, −27 percent, and −26 percent. The Theil’s inequality coefficient ( _U-statistic_ ) shows how well the fitted prices compare with the actual prices.

— 27 —
percentage point disadvantage, for an overall reduction in their likelihood of completing secondary school of 25.3 percentage points (Table 7). The cumulative effect is smaller in Mexico at 10 percentage points, but the absolute differences are as large as they are in Bolivia—only 3 percent of ethnic minority women have completed secondary school, compared to 22 percent of men and 20 percent of women in the reference group, respectively.

**Table 7. Secondary School Completion Regression Results - Ordered Probit**

_Marginal effects, using Male Reference Language (Spanish) Speakers as reference group_

||Bolivia(2001)|Mexico(2010)|Peru(2007)|Senegal(2002)|Sierra Leone(2004)|
|---|---|---|---|---|---|
|Age|-.003 ***|-.004 ***|-.003 ***|.000 ***|-.000 ***|
|Rural resident|-.146 ***|-.076 ***|-.250 ***|-.070 ***|-.034 ***|
|Female|-.042 ***|-.011 ***|-.029 ***|-.029 ***|-.020 ***|
|Minority|-.118 ***|-.060 ***|-.075 ***|-.005 ***|-.007 ***|
|Female * Minority|-.093 ***|-.029 ***|-.080 ***|-.009 ***|.003 ***|
|Obs.|320,603|5,612,740|1,289,699|327,958|171,963|

Note: Regressions include individuals aged 25 and above. ***p<0.01, **p<0.05, *p<0.10.

# **5. Discussion and Policy Implications**

The results in this paper demonstrate the importance of taking the intersectionality of gender and ethnicity into consideration in development research and policy. Using harmonized census data for five developing countries, we find important differences in the absolute and relative roles that gender, ethnicity, and the intersection of gender and ethnicity play on literacy, primary school completion and secondary school completion outcomes.

The intersectionality effect is large and negative in the three Latin American countries in our sample, implying that ethnic minority women face cumulative education disadvantages compared to other groups. On the other hand, gender- and ethnicity-based differences are small in Senegal and Sierra Leone, though they represent large relative losses for women and ethnic minorities given generally low levels of educational attainment in these two countries. We recognize that the findings for these two countries may be conflated by the possibility that a language-based proxy for ethnicity may not overlap with ethnic divisions in Africa in the same 23
# **Fact or Artefact: The Impact of Measurement Errors on the Farm Size - Productivity Relationship**<sup>**1**</sup>

Calogero Carletto<sup>2</sup> Sara Savastano Alberto Zezza

**Keywords: Inverse Farm Size Productivity Relationship, Land Measurement Error, Uganda,**

**JEL Classification: O13, Q12, C81,**

Corresponding Author: Gero Carletto The World Bank 1818 H St. NW Washington DC, 20433 Email: gcarletto@worldbank.org

> 1 The views expressed in this paper are the authors‟ only and should not be attributed to the institutions they are affiliated with. The authors are grateful to Hans Binswanger, Keijiro Otsuka, Franco Peracchi, Christopher Udry, Paul Winters, and participants in the ICAS-V Conference in Kampala, Uganda (October 2010) for their comments on an early version of this work.

> 2 Calogero Carletto and Alberto Zezza are with the Development Research Group of the World Bank.  Sara Savastano is with the University of Rome, Tor Vergata.

1
The identification assumption in this instrumental method is that past migration networks do not influence household outcomes directly other than through their likelihood of having a migrant member. While the validity of instruments is usually argued and not easy to verify, we support the identification strategy in this paper in two ways. First, tests for over-identification in the 2007 analysis fail to reject that excluded instruments are exogenous. P-values of Sargan’s over-identification tests are reported. Second, it is important to consider possible threats to the exclusion restriction. Past migration with remittance inflows might have affected local levels of human and physical capital intensity, possibly affecting labor demand or infrastructure level and local economic development in general. These factors in turn might affect schooling and work decisions now. To gain insights about these possible channels, we investigated the raw and conditional correlations between various measures of local development in 2007 with 1993 and 1997 migration rates. Since correlations exist for some variables of local development but not others, and since they do not tell a coherent story, we control for these variables in our regressions. However, if current migration also results in greater development, such over-controlling might not capture the full impacts of migration. In the end, our analysis checks for robustness with and without these various controls.

# 5.2 Estimating Equations

The base estimating equation is, for each household i at time t = 2000 or 2007

- (1) Y_it = alpha*X_it + beta*M_it + gamma*female_it*M_it + delta* female_it + μ_it

The outcomes of interest, Y_it, include hours worked for remaining household members, household head employment status, children’s school enrollment, and child labor supply. X is a set of household characteristics, which can include household composition, log of per capita expenditure (lagged), asset information (land and house 13
be explained by logistic constraints. This necessitates the development of measures of data reliability that prioritize identifying discrepancies that go beyond structural factors.

Numerous studies have attempted to measure and reconcile the discrepancies observed in UNSD’s Comtrade database. Often, they rely on measures of bilateral asymmetry to capture discrepancy between recorded value and its mirror (Ferrantino & Wang 2008; Chen et al., 2022; Farhad et al., 2023). However, some suggested measures are often linear to the observed discrepancy (Carrère & Grigoriou, 2015), i.e., they change linearly in relation to the unaccounted trade value irrespective of whether the unaccounted trade value is due to the addition of insurance and freight costs for imports or an underreporting of imports in the equivalent amount.

This is inconvenient because the first type of discrepancy is expected and structurally guaranteed by IMTS2010 guidelines, while the latter indicates missing imports, which is not easily explained by logistic factors alone. In addition, some measures are constructed using absolute differences in recorded values (Chen et al., 2022), resulting in indicators that cannot explicitly account for the direction of misreporting, i.e., whether an import was over- or underreported. This is similarly inconvenient because import overreporting is measured as equally severe as import underreporting. While indices of bilateral asymmetry that are non-linear to unaccounted trade value and can account for the direction of discrepancy have been proposed (Ferrantino & Wang 2008), their application in the literature is usually limited to specific countries.

# 3. Methods for measuring discrepancies in trade reporting

The quality of reported trade is usually investigated in relation to its mirror, i.e., the data reported by the trading partner. A comparison between the recorded value and its mirror is usually achieved through a measure of bilateral asymmetry that quantifies the magnitude (and direction) of the observed discrepancy. Many such measures have been proposed to assess the quality of the UN Comtrade database (Carrère & Grigoriou, 2015, Chen et al., 2022; Farhad et al., 2023). These measures have proven especially useful in understanding general patterns in trade reporting, pinpointing uncertain and problematic data, and relating country-specific and bilateral characteristics to misreporting. However, special attention must be paid to both structural and non-structural causes of discrepancies in the measure’s construction and interpretation.

7
(Ohene et. al. 2005, Koller 2004; World Bank 2007) or mental health (Blum and Irelend 2004, Koller 2004).

Young people who engage in one risky behavior often engage in many; i.e. negative behaviors often co-occur (Zweig et. al. 2002). The US data show that most of the risky behavior is being undertaken by a small set of young people who are engaging in multiple risky behaviors (Lindberg et. al. 2000, Bartlett et. al. 2005, Husler et. al. 2005, Brener et. al. 1998, and Zweig et. al. 2001). Similarly, smoking, drug and alcohol use, early sexual initiation, violence, and delinquencies co-occur in the Caribbean (Ohene et. al. 2005) and in Brazil (World Bank 2007). The co-ocurrence is not surprising given the common set of risk and protective factors correlated with different negative behaviors. However, the co-occurrence may also be due to causal factors between behaviors. For example, youth in Peru, the Dominican Republic, Honduras, Brazil, and Argentina report that early school dropout results primarily from early work as well as substance abuse, violence and pregnancy and a myriad of other risk factors. Further, the respondents point to early dropout and early parenthood as underlying causes of their current unemployment or employment in low-quality jobs (IDDI 2006, Dasso 2006, Weiss 2006, World Bank 2006b).

# **III. Methodology**

This paper uses step-wise regression analysis to identify those variables with the highest explanatory power for positive youth behaviors. The regressions are estimated using the forward stepwise method, which is an iterative method in which a set of independent variables is identified a priori. A variable is then randomly selected from that set and included in the regression. If the newly included variable is significant at the 1% level, it is maintained in the regression and another randomly selected variable is added to the regression. If any variable loses significance, it is dropped in the next round. Once all the variables in the initial set have been tested, the regression will only contain those variables that are significant at the 1% level, which can be interpreted as the sub-set of variables that are most correlated with the dependent variable. The regressions were also run using the backward stepwise method as a robustness check, with similar results.

6
# List of TabIes

|Table 1:|Effects of Alternative NEPA Tariffs under 1988 Conditions: Essential Substitutes<br>Model|
|---|---|
|Table 2:|Responses of Selected Firms to the Optimal Tariff in 1988: Essential Substitutes<br>Model|
|Table 3:|Effects of Alternative NEPA Tariffs under Mid-1989 Conditions: Essential<br>Substitutes Model|
|Table 4:|Responses of Selected Firms to the Optimal Tariff in Mid-1989: Essential<br>Substitutes Model|
|Table 5:|Effects of Alternative NEPA Tariffs under 1988 Conditions: Strict Complements<br>Model with Exogenous Reliability|
|Table 6:|Responses of Selected Firms to the Optimal Tariff in 1988: Strict Complements<br>Model with Exogenous Reliability|
|Table 7:|Effects of Alternative NEPA Tariffs under Mid-1989 Conditions:<br>Strict<br>Complements Model with Exogenous Reliability|
|Table 8:|Responses of Selected Firms to the Optimal Tariff in Mid-1989:<br>Strict<br>Complements Model with Exogenous Reliability|
|Table 9:|Effects of Alternative NEPA Tariffs under 1988 Conditions: Strict Complements<br>Model with Endogenous Reliability, Elasticity = 1 %|
|Table 10:|Responses of Selected Firms to the Optimal Tariff in 1988: Strict Complements<br>Model with Endogenous Reliability, Elasticity = 1 %|
|Table l1:|Effects of Alternative NEPA Tariffs under Mid-1989 Conditions:<br>Strict<br>Complements Model with Endogenous Reliability, Elasticity = 1 %|
|Table 12:|Responses of Selected Firms to the Optimal Tariff in Mid-1989:<br>Strict<br>Complements Model with Endogenous Reliability, Elasticity = 1 %|
|Table 13:|Summary of the Aggregate Effects of The Optimal NEPA Tariffs|
|Table 14:|Aggregate Cost Shares of Inputs in Social Cost and in Private Operating Cost:<br>Nigeria Sample, 1988 and Indonesia Sample, 1991|
Wijnbergen, S. van (1986),"Trade Reform, Aggregate Investment and Capital Flight: on Credibility and the Value of Information", Economic Letters.

Wijnbergen, S. van (1990), "External Debt, Inflation and the Public Sector: Towards Fiscal Policy for Sustainable Growth", World Bank Economic Review, 1990 World Bank(2005), How To Do a debt Sustainability Analysis for Low-Income Countries, May 20, 2005, Washington DC.

37
this study uses panel data on retailers and manufacturing firms operating in Romania during the period 1997-2005. The analysis proceeds in two steps. First, we document differences between characteristics of global retail chains and other retailers. We show that Romanian subsidiaries of global retailers are indeed larger in size, more capital intensive and exhibit higher labor productivity than other retailers operating in the country. Second, we use a difference-indifferences method to examine the effects of the entry of global retail chains on the performance of the supplying industries. Our identification strategy relies on the differences in the timing of the entry of global retail chains into Romanian regions and the fact than only some industries within each region should be affected. We use both OLS and instrumental variable approaches. We find that an expansion of global retail chains leads to a significant increase in the total factor productivity in the supplying industries in the region where the expansion took place. Their presence in a region increases TFP of firms in the supplying industries by 15.2 percent and doubling the number of chains leads to a 10.8 percent increase in TFP. Larger manufacturers seem to be affected more than small enterprises.

Our results suggest that opening of the retail sector to FDI may stimulate productivity growth in upstream manufacturing and provide another piece of evidence in favor of services liberalization.

This study is structured as follows. Section 2 reviews the related literature. Section 3 discusses the channels through which presence of global chains may affect supplying industries. Section 4 describes the data. In section 5, we compare the performance of global retail chains to that of other retailers operating in Romania. In section 6, we examine the link between the expansion of global retail chains and the performance of the supplying sectors in Romania. Section 7 concludes the paper.

# **2. Related Literature**

Two strands of the literature are relevant to this study. The first one is research on how inflows of foreign direct investment affect manufacturing industries in a host economy. A large number of studies search for intra-industry effects, postulating that foreign entry may result in knowledge spillovers to local firms as well as in local producers losing part of their market share to foreign entrants. Empirical analyses based on firm-level panel data produce mixed results. While Aitken and Harrison (1999) find that an increase in FDI presence negatively affects the total factor 3
where _n_ is the number of sectors _s_ .

ˆ Table 1 provides summary statistics for _RCAs_ , _t_ based on trade data across ISIC 2- digit sectors in 1990 and 2004 for LA as a whole, the Andean countries (Bolivia, Colombia, Ecuador, Peru, and Venezuela), Central America (Costa Rica, Guatemala, and Nicaragua), Mexico, the Southern Cone (Argentina, Brazil, Chile, and Uruguay), China, and India.

ˆ In 1990 LA’s comparative advantage (when _RCAs_ , _t_<sup>></sup> 0 ) was in commodities: agriculture (ISIC 11), logging (ISIC 12), fishing (ISIC 13), crude petroleum (ISIC 22), ore mining (ISIC 23), food manufacturing (ISIC 31), basic metal industries (ISIC 37), and electricity and gas (ISIC 41).7 In 1990 China’s comparative advantage was relatively similar. It was concentrated in logging (ISIC 12), fishing (ISIC 13), coal mining (ISIC 21), crude petroleum (ISIC 22), other mining (ISIC 29), food manufacturing (ISIC 31), textile and apparel (ISIC 32), non-metal products (ISIC 36), and other manufacturing (ISIC 39). Thus of the eight two-digit sectors in which LA had a comparative advantage in 1990, there were four in which China also had a comparative advantage. The comparative advantage of India in 1990 was also relatively similar to the one observed in China, and to a lesser extent in LA. It was concentrated in agriculture (ISIC 11), logging (ISIC 12), fishing (ISIC 13), ore mining (ISIC 23), food manufacturing (ISIC 31), textiles and apparel (ISIC 32), non-metal products (ISIC 36) and other manufacturing (ISIC 39). Thus, of the eight sectors in which India had a comparative advantage in 1990, there were six in which China also had a comparative advantage and five in which LA also had a comparative advantage.

> 7 LA’s comparative disadvantage was in some commodities such as coal mining (ISIC 21) and other mining (ISIC 29), but mainly in manufacturing: textile and apparel (ISIC 32), wood products (ISIC 33), paper products (ISIC 34), chemicals (ISIC 35), non-metal products (ISIC 36), fabricated metal products (ISIC 38), and other manufacturing (ISIC 39).

6