rafmacalaba/data-use-mentions-extended huggingface dataset:
An explanation for this is that since NTMs provide legal asymmetric cost advantages (e.g., for firms with access to quotas), they make tariff evasion less attractive as a means to maintain market share in fuel exporting economies.

# 5 Import monopolization and domestic prices

A manifestation of the resource curse is higher prices in commodity exporting economies. Sachs and Warner (2001) showed that commodity export intensive economies had higher price levels relative to the global average in 1979. A question is whether import monopolization can account for this phenomenon. In theory, the relationship between concentration and prices is not obvious. If higher concentration is associated with higher fixed costs but lower marginal costs of importing, it could be associated with lower prices, even if also associated with higher markups (e.g., as in a differentiated products Nash-in-prices game). Alternatively, higher concentration could be associated with higher prices, if the associated markups outweigh any marginal cost savings.

To distinguish between these hypotheses, we relate import market concentration to ICP data on the average local currency domestic prices of 685 narrowly defined goods (i.e., parboiled long-grain rice, basmati rice, brown rice—family pack). From this correlation, we infer whether, on average, marginal cost differences associated with import market concentration outweighs the markups associated with concentration in determining equilibrium prices. The spirit of this exercise is an accounting decomposition, not an attempt to estimate a causal relationship between prices and concentration. The industrial organization literature has long argued that such causal effect is not well-defined, because a variety of economic mechanisms can cause a (positive or negative) correlation between prices and concentration (see Miller, Berry, Scott Morton, Baker, Bresnahan, Gaynor, Gilbert, Hay, Jin, Kobayashi et al., 2022). Our exercise is simply to estimate the correlation between prices and concentration, conditional on product-year fixed effects that account for global supply and demand.

18
quality of work. Another limitation is that the data exclude self-employed individuals and some substantial portion of migrants. Nevertheless, this study suggests that higher wages paid by foreign firms to unskilled women relative to alternative jobs in the informal sector may be among the factors which drew women disproportionately toward foreign job opportunities in Vietnam.

# **Reference**

Almeida, R. (2007), ‘The Labor Market Effects of Foreign Owned Firms,’ _Journal of International Economics_ , **72** , 75-96.

Anderson, J. B. and D. Dimon. (1999), ‘Formal Sector Job Growth and Women’s Labor Sector Participation: the Case of Mexico,’ _The Quarterly Review of Economics and Finance_ , **39** , 169191.

Andrews, M., L. Bellmann, T. Schank and R. Upward. (2009), ‘The Takeover and Selection Effects of Foreign-owned Establishments: an Analysis Using Linked Employer-Employee Data,’ _Weltwirtschaftliches Archiv_ , **145** , 293-317.

Athukorala, P. and T. Q. Tien. (2012), ‘Foreign Direct Investment in Industrial Transition: the Experience of Vietnam,’ _Journal of the Asia Pacific Economy,_ **17** , 3, 446-463 _._ Brassard, C. (2004), ‘Wage and Labour Regulation in Vietnam within the Poverty Reduction Agenda,’ National University of Singapore.

Braunstein, E. and M. Brenner. (2007), ‘Foreign Direct Investment and Gendered Wages in Urban China,’ _Feminist Economics_ , **13** , 3-4, 213-237.

Conyon, M., S. Girma, S. Thompson and P. Wright. (2002), ‘The Productivity and Wage Effects of Foreign Acquisition in the United Kingdom,’ _Journal of Industrial Economics_ , **50** , 85-102.

Fan, C. S. and H. Lui. (2003), ‘Structural Change and the Narrowing Gender Gap in
numeracy skills tend to have considerably lower science achievement than their female counterparts with equivalent early literacy and numeracy skills by grade 4, while science achievement of boys and girls with stronger early literacy and numeracy skills tends to be similar. In other words, boys who begin school at an early learning disadvantage to their peers are further disadvantaged as they progress through the education system and appear to be at more risk of falling behind than girls who begin school with weaker early skills. This can also be seen in students’ reports, in NALO, that boys are more likely to repeat a year in school because of poor academic performance.

It is important that parents are aware that early childhood development lays a foundation for future education, health, well-being, and economic success. Public health and education agencies should promote awareness among parents and provide guidance and resources to encourage greater engagement in early learning in the family. For example, simple activities that can contribute to a child’s early literacy and numeracy development could include reading together, describing a scene in everyday life, counting everyday objects or singing counting songs, and using mathematical and spatial language while playing with shapes or other objects (e.g., “behind”, “above”, “beside”, “straight”, “curved”, “double”). Data from TIMSS 2019 show that parents in Saudi Arabia report engaging in activities of these types less frequently with young boys than young girls. Taking steps to increase the level of support for early childhood learning at home for boys, in particular, would likely lead to a stronger foundation in the future for boys starting school and to greater progress in learning among boys. Cultural and social barriers that contribute to low enrollment of young children in kindergarten—for example, social expectations relating to motherhood and childrearing at home—also need to be considered in this respect.

It is noteworthy that, despite the differences seen across the four sets of models, two variables were found to be significantly associated with
short-lived, they have long-term effects for household food security and welfare. For example, households try to cope with such shocks by lowering calorie intake. Torlesse et al. (2003) show that the percentage of underweight children in Bangladesh is negatively correlated with expenditure on rice (because lower rice expenditure allows more income to be spent on higher quality food). Rice expenditure in turn rises with rice prices. In addition, households sell assets such as livestock and spend less on children‘s education as shown by a recent survey in Bangladesh (Wodon and Zaman 2008). Such coping strategies can lead in the long run to lower labor productivity and less accumulation of human capital, which can prevent households from getting out of poverty again once food prices are back to lower levels. As a result, households that are pushed into poverty because of temporary shocks may become chronically poor.

In order to evaluate the future potential of renewed food crises, the factors that caused the recent price spike have to be understood. The literature suggests that several causes have contributed to the price increases.<sup>16</sup> First, higher fuel prices have led to high production costs in agriculture by raising the cost of inputs such as fertilizers. Second, the depreciation of the dollar vis-à-vis previous years has led to higher prices of US$ priced commodities. Third, diet changes around the world, and especially in China and India, have caused a higher demand for meat and thereby for feed grains. Fourth, increased bio-fuel production in the United States, the European Union and Brazil increased the demand for feedstock and put pressure on the land area used for food production. The European Union and the United States subsidize production while Brazil is the only competitive producer of biofuel. Fifth, weather-related shocks leading to droughts and low harvests have hit countries like Australia and Russia. Finally, financial speculation in the agricultural commodity markets may have contributed to food price increases (von Braun and Torero 2009). Although some of these factors had
.

First, preschools may alleviate one of the constraints that women face when joining the labor market - childcare responsibilities - enabling them to choose between productive and reproductive work based their ability and earning potential. This is likely to improve allocative efficiency of talent across jobs, potentially leading to an increase in productivity. We provide evidence in support of this mechanism by demonstrating that preschool expansion had a positive impact on the share of women hired by manufacturing firms using SI data. We complement this evidence with an analysis based on individual Labor Force Survey data (SAKERNAS), which shows 4
and soil samples were collected.

Table 1 presents the timeline of the intervention. In March 2015, the team collected yield expectations and field activities to date. Farmers were divided into 26 strata based on their location and agro-climatic conditions.<sup>16</sup> Individual randomization was done at the stratum level and announced at the end of the March survey.<sup>17</sup> A first mid-line survey was carried out in August 2015 focusing on labor inputs and agricultural practices to date during the growing season of the intervention. We also collected administrative data on fertilizer purchases from our partner agro-dealer. A second mid-line survey was fielded in October 2015, just before the harvest, to measure yield expectations and to record agricultural activities since the first mid-line. In January 2016, we collected yield data for the 2015 growing season. Finally, we collected further information on grain sales from the 2015 harvest in June 2016. In May 2017, 2 years after the experiment, we went back for a final end-line survey to collect information on practices for the 2017 growing season, which allow us to measure persistence. As a result, we have a panel on agricultural practices and input use for the years 2014, 2015 and 2017 and yield information for 2014 and 2015.

To be considered for the farmers had to cultivate maize in at least one eligible program, hectare of owned or rented land (and in no more than 15 hectares), had to be between 18 and 70 years old and had to sow maize in the 2015 growing season. We have consistent panel data on agricultural practices in 2014, 2015 and 2017 for 678 farmers and they comprise the core sample for the study.<sup>18</sup> Table 2 provides summary statistics for the study sample of 678 farmers and their registered plots. Panel A reports farmer characteristics. Incomes were low by Mexican standards with an average self-reported annual income of 29,414 pesos (US$ 2,200). Panel B of Table
which are very clearly visible for the case of Bolivia.

<!-- Start of picture text --> 1980 1985 1990 1995 2000 2005 2010 2015<br>Raw Data: AGR Adj Data: AGR Raw Data: SER Adj Data: SER<br>80<br>60<br>40<br>Share of Employment (%) 20<br>0<br><!-- End of picture text --> Figure A.1: Employment Shares: Raw vs. Adjusted (Bolivia) The first step involves identifying the LRS. There are two major data sources that are free from any of the limitations. These refer to the official estimates for 1980 – 1988 and the estimates from household/labor surveys for 2004 – 2009. As the official estimates are available for more periods, they serve as the LRS for Bolivia. The maximum annual change in sectoral share within the LRS happened for Services during 1985 – 1986 when Services realized a 3.4 percentage points expansion in its share. To add data points from other sources to the LRS, I begin with the year closest to the LRS which is 1989. The deviation in sectoral share of Agriculture from 1988 to 1989 exceeds 45 percentage points. This is much higher than the maximum annual change of 3.4 percentage points observed in the LRS and hence, the raw data from 1989 is dropped. Similarly, data points from 1989 to 1997 are all excluded as the average annual change for each of them exceed 3.4 percentage points. However, the average annual change for all sectors from 1988 (the nearest data point that has passed the selection criteria) to 1999 is lower than 3.4 percentage points. Therefore, raw data from 1999 is appended to the LRS and serves as a reference point for 2000. Likewise, the process moves forward for the remaining data points.

The hollow circles and squares represent the subset of data points that pass the selection process for Agriculture and Services respectively.
OOBBRRFF h , which requires taking a stand on timing.  Because we are usually interested in evaluating the effect of today’s policy changes in the future ωω (forward looking) we want a measure on human capital of the next generation: which is what the World Bank

> 17 The definition of formal employment varies across countries, but generally refers to the coverage of the worker with respect to benefits like unemployment insurance, pensions, and sick or annual leave.

> 18 The categories here are based on "International Classification by Status in Employment, 1993 (ICSE-93)”, and also exclude workers not classifiable by status.

> 19 For example, if h= 3 then the worker is three times as productive a raw labor.

8
Policy Research Working Paper 5641

# **Abstract**

Although it had a a lower income level than India in 1980, China’s 2006 per capita gross domestic product stands more than twice that of India’s. This paper investigates the role of the business environment in explaining China’s productivity advantage using recent firm-level survey data. The analysis finds that China has better infrastructure, more skilled workers, and more labor-hiring flexibility than India, but a worse access to finance and higher regulatory burden. Infrastructure appears to be a key constraint for India: it lags significantly behind China, yet it has important indirect effects for the effectiveness of labor flexibility. Labor flexibility is also likely a major constraint for India, as evident in the predominance of small firms, the importance of firm size in accounting for India’s disadvantage in productivity, and the complementarity of proxies of labor flexibility with infrastructure and access to finance. Interestingly, regulatory uncertainty has adverse effects in India but not in China. The empirical analysis suggests that it is important to consider countryspecific growth bottlenecks and the indirect effects of policy reforms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at lxu1@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
widespread perception in Nepal is that forests are declining. These perceptions are somewhat validated in government statistics which show that between 1995 and 2005 land area covered with forests declined from 41 percent to 32 percent.

The effect of out-migration and remittances on agriculture has been studied, among others, by Lucas (1987)and Taylor, Rozelle, and De Brauw (2003). However, these studies are confined largely to issues of agricultural investments, farm productivity, yields and income. More importantly, by treating land as a fixed factor, they are largely agnostic about expansion or shrinking of agricultural activity at the extensive margin. Research on forests in developing countries has typically taken place in the context of economic growth and how it interacts with the demand for forest resources. A particular emphasis has been on the investigation of whether there is evidence of an “environmental Kuznets curve” for forests as has been found for air and water (Grossman and Krueger, 1995). Some examples of rigorous micro-econometric work on this front are Bardhan, Baland, Das, Mookherjee, and Sarkar (2010), Foster and Rosenzweig (2003)and Chaudhuri and Pfaff (2002). However, except Foster and Rosenzweig (2003) none of these papers makes an effort to connect the changes in the consumption of resources such as fuelwood, for example, with actual forest area.

It is generally accepted that greater openness - be it through participation in trade, through expansion of economic activity or through migration to labor markets abroad for work - raises 4
“The Economic Hokum of ‘Secular Stagnation.’” Wall Street Journal, January 1.

Terrones, M. E. 2019. “Do Fixers Perform Worse than Non-fixers during Global Recessions and Recoveries?” MPRA Paper 97524, University Library of Munich, Germany.

Teulings, C., and R. Baldwin, eds. 2014. Secular Stagnation: Facts, Causes and Cures. London: CEPR Press.

United Nations. 1959. World Economic Survey 1958. New York: United Nations.

World Bank. 2009. East Asia and Pacific Economic Update: Transforming the Rebound into Recovery. November. Washington, DC: World Bank.

World Bank. 2010a. Middle East and North Africa Economic Update: Recovering from the Crisis. April. Washington, DC: World Bank.

World Bank. 2010b. South Asia Economic Update: Moving Up, Looking East. June. Washington, DC: World Bank.

World Bank. 2016. Global Economic Prospects: Spillovers amid Weak Growth. January. Washington, DC: World Bank.

World Bank. 2020. Global Economic Prospects: Slow Growth, Policy Challenges. January. Washington, DC: World Bank.

Zarnowitz, V. 1992. Business Cycles: Theory, History, Indicators, and Forecasting. Chicago: University of Chicago Press.

29
0.85)_∗∗∗_<br>(1.67)_∗∗_<br>(0.39)_∗∗∗_<br>(0.80)_∗∗∗_<br>-0.555<br>-0.667<br>-0.144<br>-0.510<br>(0.23)_∗∗_<br>(0.27)_∗∗_<br>(0.17)<br>(0.25)_∗∗_<br>-0.126<br>-0.216<br>-0.050<br>-0.118<br>(0.09)<br>(0.17)<br>(0.04)<br>(0.11)<br>-0.058<br>(0.32)<br>0.701<br>(0.39)_∗_<br>3.778_∗∗∗_<br>9.826_∗∗∗_<br>5.750_∗_<br>9.388_∗∗∗_<br>1.687<br>74.473<br>-1.473<br>IV-1<br>IV-2<br>S-GMM<br>IV-1<br>2<br>3<br>82<br>3<br>42<br>75<br>352<br>37<br>lculated from GDP data in real 2000 U.S. dol<br>ta periodicity fve-year averages for 1999–2003<br>ty and autocorrelation-robust standard errors <br>erm (all specifcations) were included in the re<br>ifcance at 5 percent level, and _∗∗∗_indicates si|
|Table 5:
were supposed to receive after October 2018 when books became available in MOGE's central warehouse. When schools reported receiving books for a specific grade before these dates and before DEBS’s reported receiving date, the books were considered as old curriculum books that were delivered late; this event was coded as not receiving new curriculum books. **For the textbook-pupil ratio, the study measured both old and new curriculum books delivered since 2015. This approach was chosen because schools did not keep separate records for old and newly published books.

# **Empirical Analysis Strategy**

In order to answer the first research question, descriptive statistics were analyzed. These include (i) textbook delivery and availability status based on the school and DEBS surveys in the baseline and the end-line; and (ii) regressing outcome variables, at baseline, on possible contributing factors, including measures for information and communication on textbook availability, school-level financial resources (PTA funds and education budget), and location (remoteness) of schools.

The second and third research questions were studied by conducting econometric analysis employing difference-in-difference estimation. Difference-in-difference captures changes in outcomes of the treatment and control groups before and after the treatment using the following equation: i 0 1T T 1 2T T 2 3P 4T T 1 ∙P 5T T 2 ∙P i i YY denotes the outcome variable for each individual school; “= ββ + ββ TT + ββ TT + ββ PPTT+ ββ _i_ TT” for two different periods and “PPTT+ ββ TT PPTT+ εε _t_ ” (Grade 2 textbooks for pre-treatment and Grade 4 textbooks for post-treatment). T T 1 and T T 2 are dummy variables of whether the school belongs to the first treatment group (school incentive group) or to the second YY treatment group (DEBS incentive group). P is post-treatment dummy variable and TT i is an idiosyncratic TT error term. PPTT εε The coefficients of the interaction terms, T T 1 ∙P and T T 2 ∙P ( 4 and 5)
i.e. aggregation bias in the tariff variable should make it easier to find a significant NTB effect for TA notified under the Enabling Clause.

All the challenges discussed above work in favor of finding a positive NTB-effect. Hence, our NTB coefficients should be interpreted as upper bounds.

# **3 Data**

To revisit the trade-creating effects of NTBs, we combine trade data with information on ad valorem tariffs and on the depth of trade agreements. For tariffs, we use the new Global Tariff Database (GTD) put together by Teti (2020) that deals with misreporting of tariffs (details can be found in the corresponding paper). We aggregate the data that are available at the HS6digit level to two broad sectors, i.e. agriculture and manufacturing, and for the disaggregated analysis to 18 sectors, respectively; in the area of services, tariffs are set to zero. For the baseline results, we use simple averages, results using trade-weighted averages are available upon request.

We want to relate our new estimates to the ones the literature has reported in the past and get a better understanding of how severe the bias due to mismeasured tariff data is. To do so, we download the effectively applied tariff from WITS.<sup>5</sup> Using Trains as a primary source, we download the effectively applied tariff for ISIC Rev. 3 at the four-digit level and aggregate it to the sector level. We use the ISIC Rev. 3 classification instead of the HS classification because it involves fewer steps for concording across different classifications and nomenclatures to get to the sectors that are used in the trade data, i.e. ITPDE and WIOD.

We use the International Trade and Production Database for Estimation (ITPDE, Release 2) put together by Borchert et al. (2020) for our baseline results for three reasons: first, to estimate Equation 4 theory-consistent and to be able to include trends for globalization, we need data on international and domestic trade flows (Yotov 2021
del Toro, Chiriqui and Other Panama) defined using household survey data (Table 5).

**Table 5: Panama: Size and Expenditures of Major Household Groups, 2005**

||**Population**<br>**('000)**|**Population**<br>**(percent)**|**SAM**<br>**Income**<br>**($mn)**|**SAM**<br>**Income**<br>**(percent)**|**SAM**<br>**Income /**<br>**Capita ($)**|
|---|---|---|---|---|---|
|Urban Poor – Panama City|209.9|6.9%|371|3.2%|1,770|
|Urban Non-Poor Panama City|1,076.7|35.2%|7,466|64.7%|6,934|
|Urban Poor – Bocas del Toro|19.1|0.6%|31|0.3%|1,601|
|Urban Non-Poor Bocas del Toro|20.2|0.7%|111|1.0%|5,504|
|Urban Poor – Chriqui|40.9|1.3%|84|0.7%|2,059|
|Urban Non-Poor Chiriqui|148.7|4.9%|749|6.5%|5,039|
|Urban Poor – Other Panama|101.0|3.3%|89|0.8%|878|
|Urban Non-Poor Other Panama|238.4|7.8%|615|5.3%|2,579