rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5490

# **Abstract**

In a seminal contribution, Yi (2003) has shown that vertically specialized trade should be more sensitive to changes in trade costs than regular trade. Yet empirical evidence of this remains remarkably scant. This paper uses data from China’s processing trade regime to analyze the role of trade costs on trade within global production networks (GPNs). Under this regime, firms are granted duty exemptions on imported inputs as long as they are used solely for export purposes. As a result, the data provide information on trade between three sequential nodes of a global supply chain: the location of input production, the location of processing (in China) and the location of further consumption. This makes it possible to examine the role of both trade costs related to the import of inputs _(upstream trade costs)_ and trade costs related to the export of final goods _(downstream trade costs)_ on intra-GPN trade. The authors show that intraGPN trade differs from regular trade in that it not only depends on downstream trade costs, but also on upstream trade costs and the interaction of both. Moreover, intraGPN trade is more sensitive to oil price movements and business cycle movements than regular trade. Finally, the paper analyzes three channels through which intra-GPN trade have amplified the trade collapse during the recent Global Recession.

This paper—a product of the International Trade Department, Poverty Reduction and Economic Management Network— is part of a larger effort to analyze the role of trade costs within global production networks. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ari.van-assche@hec.ca and maa@sandiego.edu.

_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
Policy Research Working Paper 10934

# **Abstract**

Minimum wages are an essential component of a country’s social protection system, aiming to protect vulnerable workers and reduce poverty and wage inequality. Yet, there are risks associated with poor minimum wage design. Higher minimum wages may result in higher earnings for affected workers but fewer job opportunities for others, including the demographic groups they are intended to help, such as those with very low wages and skills and youth, so ex-ante evaluation of potential employment, wage, and distributional impacts is needed. Over the past decade, Romania experienced significant real growth in the minimum wage and a rising minimum-to-median wage ratio. However, when looking at minimum living standards, the analysis shows that the statutory minimum wage is higher than the living wage needed to cover a consumption food basket estimated by the European Commission under the European Reference budget network, but not enough to include non-food components. The microsimulation results using administrative tax data show that tying minimum wage to inflation or the living wage could lead to a slight short-term wage increase for some workers but may cause job loss in the long run, especially for younger workers. The minimum wage increase could have varying impacts across regions and sectors, with the accommodation and food services sector and those living in the Suceava region, which has the highest proportion of affected employees. Moreover, male employees tend to be more affected than their female counterparts.

This paper is a product of the Poverty and Equity Global Practice. 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 mrobayo@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

_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
where the CPI varies by municipality and month and the subscript m stand for months. The base month for the CPI is June 2002.

Table 12 presents the results for the price regression, for two di¤erent time spans. Both regressions include the same set of municipalities, which are only the municipalities that are in the ENE sample. Column 1 of Table 12 corresponds to the time span for which I have ENE data. As predicted by the model above, the coe¢ cient on the reform dummy is negative. In the regression in Column 2, the time period covered goes up to May 2006, which is the last month for which I have the CPI data. The negative coe¢ cient on the reform dummy is similar in magnitude and is statistically signi...cant in this larger sample. The decrease in the consumer price level after the reform con...rms the argument that lowering barriers to entry bene...ts consumers.

The coe¢ cients in Table 12 indicate that the reform decreased the log price level by approximately 0.6 percent. To get a sense of the magnitude of the e¤ect, I compare it to the average in‡ation rate from 2000 to 2001. This average in‡ation rate, calculated as the increase in the CPI from each month in 2000 to the same month in 2001, was 5.7 percent. The decrease in the log CPI of approximately 0.6 percent due to the reform is a decrease in in‡ation of approximately 0.6 percent. A back-of-the-envelope calculation thus suggests that the reform decreased in‡ation to about 5.1 percent from 5.7 percent.

If the decline in prices is indeed due to the reform, then the decline should be present only in industries that are eligible for the reform (low-risk industries). Moreover, the measured decline in prices should come entirely from the non-tradables sector. This is because the estimation strategy compares prices across municipalities. The prices of non-tradables may decrease locally, but the price of tradables should not be a¤ected by an increase in the supply in some municipalities only<sup>19</sup> . Table 13 breaks down the CPI into tradables and non-tradables, by low-risk and high-risk industries. The results indeed show that prices declined signi...cantly only for non-tradables in low-risk industries.

# 6.4 Income

The regression results for the last ENE outcome variable, monthly income, are reported in Table 14. This table displays the e¤ect on log income by past occupation group. The model predicts that the reform should decrease the income of incumbent registered business owners. The results in Column 1 indeed show that nominal income decreased for past registered business owners, by 3.2 percent. Given that the reform led to a decrease in the price level, it is also important to examine the impact of the reform on real income, in order to check whether the

> 19 Even if these counties were big enough to cause the price of tradables to change, no-arbitrage would imply that there should not be any relative price di¤erence for tradables across counties.

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# **4. Economic context and data**

The study of poverty in Morocco has seen major developments during the last two decades thanks to the increased availability of micro-data and thanks to the increased capacity on the part of the government to analyze poverty. The High Commission for the Plan (HCP), for example, has published several studies during the last decade covering a range of topics such as pro-poor growth, geographical targeting, poverty maps and poverty dynamics. All these studies relied on expenditure as a measure of welfare, took a unidimensional approach to the study of poverty and found consistent results in that poverty has declined in both urban and rural areas between 2001 and 2007.

This positive result is consistent with the general macroeconomic framework and key reforms implemented by the government in recent years. Starting from the 1990s, Morocco has embarked in a process of liberalization of the economy that led to increased foreign investments and trade. The country has also implemented a large program of public investments throughout the decade and has been able to improve significantly on other fronts such as health and education. Average GDP growth has increased to 4-5% a year with peaks of 7%. During the second half of the last decade, the government has also launched a large human development program (Initiative for National Human Development – INDH) that covers all poor areas of the country and finances local development activities from private entrepreneurship to public recreational centers. The INDH program has now completed its first phase and is currently launching a second phase. The evaluation of the first phase is extremely complex given the nature of the program but stakeholders agree that the program had a visible impact on local communities.

A multi-dimensional approach to the study of poverty can complement uni-dimensional studies and also offer a better sense of whether recent reforms and improvements in welfare have concerned most dimensions of welfare or only a few dimensions. We therefore rely on the same data used by other studies including the National Survey on Consumption and Expenditure carried out between 2000 and 2001 and including 14,243 households (henceforth NSCE 2000/01) and the National Survey on Living Standards carried out between 2006 and 2007 and including 7,062 households (henceforth NSLS 2006/07). Both surveys are representative at the national, urban-rural and regional levels and they are fully comparable across all variables used in this paper.

Table 1 lists all variables used in the first step of the MCA exercise where we use a large set of variables to test how these variables polarize around the different axes. These variables are grouped in eight broad areas including employment, education, health, housing, utilities, durables, nutrition and expenditure. The choice of these initial variables is largely normative but based on decades of research on poverty in 8
change in environmental quality. The methods based on hypothetical markets are referred to _stated preferences methods_ .

The stated preference methods are based on individual preferences to infer monetary value to nonmarket good. In particular, the approach lies on constructed markets to analyze how individuals would hypothetically value changes in the provision of non-market goods. Two main valuation techniques considered in this section are the contingent valuation method (CVM) and choice modeling (CM). While CVM asks individuals about their WTP for a change in the provision of a given good or services (e.g., to protect endangered species), CM presents a menu of alternatives to be chosen by the individuals to further refine the WTP. CVM aims at valuing the total value while CM looks at the unit value for each attribute of the good. CM has the advantage of assessing a set of policy measures that changes the attributes simultaneously. See _T._ Hett et al (2003) for an example using these methods to investigate the environmental and financial sustainability of the Machu Picchu Sanctuary<sup>12</sup> .

The CVM and CM are applicable to all non-market goods and can capture current and future benefits stemming from the extractive and consumption use to the non-use values. The main concerns relates to the reliability and validity of the results found under those methodologies. As the methods are based on elicited WTP, the way in which the information is gathered and in particular the design of the questionnaire is subject to bias. In spite of those controversies, the stated preference methods are largely used in practice mainly due their wide range of application, e.g. the valuation of water and air quality, species and biodiversity preservation, health impacts, environmental risk reductions and waste management to list only a few examples.

# **3.1.3 Choice of Valuation Techniques**

The previous section has introduced the various valuation techniques used to estimate the shadow prices. Each of these techniques shows advantages and shortcomings in terms of data requirement and scope of analysis so much that no method particularly dominates the others. Understanding the context in which each of these techniques can be applied allows choosing the appropriate instruments that match the purpose of analysis (e.g. assessing a policy measure, performing a cost-benefit analysis or impact evaluation, etc.). This section provides a discussion on the choice of valuation techniques.

Table 1, based on Bolt and others (2005), Hanley and Barbier (2009), and Pagiola and others (2009), summarizes the scope of application, the theoretical framework, the advantages and shortcomings, and the data requirements for each technique.

> 12 The study investigates the optimal entrance fee level that maximizes the revenue from the visits to the Machu Picchu Sanctuary using the contingent valuation method to elicit visitors’ willingness to pay and derive the demand curve. The corresponding number of visits is then computed and help assessing the environmental damages made to the site.

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|**Selected Characteristic**|**(1)**<br>**Full sample**|**(2)**<br>**T1**|<br>**(3)**<br>**T2**|<br>**(4)**<br>**T3**|<br>**(5)**<br>**All treated**|<br>**(6)**<br>**Control**|
|---|---|---|---|---|---|---|
|Electricity outrages > 2 days per week|77.6|75.70|77.36|77.57|76.88|78.95|
||(41.74)|(4.17)|(4.08)|(4.05)|(2.36)|(3.13)|
|Owns English Textbook|38.29|34.58|36.79|41.12|37.50|39.77|
||(48.66)|(4.62)|(4.71)|(4.78)|(2.71)|(3.75)|
|Used English Textbook at least twice in past seven days|0.74|0.70|0.72|0.78|0.73|0.75|
||(0.44)|(0.04)|(0.04)|(0.04)|(0.02)|(0.03)|
|Owns Math Textbook|0.34|0.32|0.35|0.36|0.34|0.33|
||(0.47)|(0.05)|(0.05)|(0.05)|(0.03)|(0.04)|
|Used Math Textbook at least twice in past seven days|71.43*|68.52|65.09|74.77|69.47|75.00|
||(45.22)|(4.49)|(4.65)|(4.22)|(2.57)|(3.27)|
|Attending special exam prep sessions in Math|72.84**|69.44|74.53|80.37|74.77|69.32|
||(44.52)|(4.45)|(4.25)|(3.86)|(2.43)|(3.49)|
|Attending special exam prep sessions in English|73.04*|72.22|73.58|79.44|75.08|69.32|
||(44.42)|(4.33)|(4.30)|(3.93)|(2.42)|(3.49)|
|Baseline Reading Comprehension, % correct|27.51|28.10|26.47|28.26|27.62|27.32|
||(9.55)|(0.91)|(0.88)|(0.95)|(0.53)|(0.74)|
|Baseline Reading Score (School Mean)|27.52|27.83|27.43|27.54|27.60|27.37|
||(6.00)|(0.56)|(0.59)|(0.63)|(0.34)|(0.44)|

T1, eReader with library; T2, eReader with library and curriculum books; T3, eReader with library, curriculum books and supplementary material. Standard errors are reported in parentheses.

*, **, and *** indicate that treatment arms differed at the 10, 5 and 1 percent levels, respectively.

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vis the UNPS 2015/16 sampling weights. The base weights are those defined in Section 3 at the household level. The UNPS sampling weights are those defined at the individual level and are currently used for the survey estimates by UBOS (2011). They are obtained according to a complex process, based on performing preliminary adjustments for non-response based on noresponse propensity scores. Specifically, the final individual-level weights are obtained through the following six methodological steps: i) definition of the base weights as the reciprocal of the probability of inclusion, ii) calculation of the fair share correction factor, iii) pooling of the weights defined in step 3, iv) computation of the correction factors for attrition, v) trimming of the weights defined up to step 4, and vi) post-stratification.

Table 6.1. Population projection by age and gender (2015)

|Age group|Male|Female|
|---|---|---|
|0-4|3,220,300|3,028,800|
|5-9|2,870,800|2,730,500|
|10-14|2,528,000|2,500,100|
|15-19|2,008,700|2,097,500|
|20-24|1,509,200|1,790,100|
|25-29|1,181,200|1,403,800|
|30-34|938,700|1,082,800|
|35-39|743,900|836,500|
|40-44|630,600|677,500|
|45-49|470,800|486,800|
|50-54|382,300|443,200|
|55-59|241,700|277,900|
|60-64|194,600|241,600|
|65-69|140,400|171,100|
|70-74|114,900|158,600|
|75-79|71,500|90,700|
|80+|96,400|140,600|
|Total|17,344,000|18,158,100|

- Note: UBOS official projections for 2015 and based on the population census of 2014 available at <u>https://www.ubos.org/wp content/uploads/statistics/Population_Projections_2018.xlsx, downloaded June 28, 2021.</u>

We use published data and reports from the National Population and Housing Census (NPHC) 2014 (UBOS 2016; UBOS 2017) and the Uganda National Household Survey (UNHS) 2016/17 as main sources of official statistics. The former is conducted by UBOS about every 10 years with the aim of collecting benchmark demographic and socio-economic data of the Uganda population. The latter is the sixth follow-up survey of the UNHS, a cross-sectional survey implemented by UBOS starting in 1999/20, which aims to collect data on demographic and socioeconomic characteristics, with a sample of 15,636 households. The UNPS is a follow-up to its 2005/06 survey and largely implements the same methodology.

The NPHC 2014 and UNHS 2016/17 are the official source of data with the closest collection period to the UNPS 2015/16 we found. The reference period for the UNPS 2015/16 is March 26
**Figure 7 – Land Cover Classes and Distance to City Center ‐ Dar es Salaam**

<!-- Start of picture text --> Dar es Salaam Land cover c. 2011 Dar es Salaam Land cover c. 2011<br>(Percent) (Total)<br>100% 250<br>80% 200<br>60% 150<br>40% 100<br>20% 50<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (kilometers) Distance from CBD (kilometers)<br>Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Percent Area in sq kms Total Area in sq kms<br><!-- End of picture text --> Source: These charts have been calculated using land cover classification layers derived from high resolution satellite imagery (<1m) circa 2011/2012.

# **Nairobi**

The landlocked city of Nairobi is home to more than 3.14 million people, making it the second largest city by population in the African Great Lakes Region, after Dar es Salaam.<sup>35</sup> This centrally located capital is a major hub for commerce, transport and regional development, generating roughly 45% of the nation’s GDP and employing 25% of the nation’s work force. Despite its economic strength, inequality among its inhabitants has grown at a much higher rate than the rest of the country. The UN‐Habitat estimates that roughly 60% of its population lives in slum households, and 78% of those slum household have no running water.<sup>36</sup> Nairobi is also home to the largest urban slum in Africa, Kibera, which houses a rough estimate of 700,000 people living in extreme poverty in a 225ha area. This slum is strategically situated close to the CBD in order to provide cheap and local labor. The high proportion of unplanned and crowded housing units creates massive drainage and sanitation issues in the area.<sup>37</sup> The imagery derived land cover classification of Nairobi, shown in Figure 8, reveals the distribution of residential (irregular), residential (regular) and commercial/industrial land throughout the city. The city center (marked as a black circle in figure 8) is dominated by land covered with commercial, industrial and transportation structures. Irregular neighborhoods are predominately found around 3‐8 kilometers from the city center, with the closest identified near Kariokor in the north east (Pumwani, Mjengo estate

> 35 Kenya Central Bureau of Statistics. 2009. “The 2009 Kenya Population and Housing Census. Volume IC, Population Distribution by Age, Sex and Administrative Units.

> 36 UN Habitat. 2006. Nairobi Urban Sector Profile: Rapid Urban Sector Profiling for Sustainability (RUSPS).

> 37 http://mirror.unhabitat.org/content.asp?typeid=19&catid=548&cid=4962.

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children, missed changes in labor supply owing to migration are not likely to be significantly distorting our results for the population means.

In this paper we relate changes in the price of rice to changes in the labor supply of children, young adults, and adults by gender. Let us first overview the rice price changes that occurred between 1993 and 1998. The sixth row in table 1 reports the consumer price of a kilogram of ordinary rice in 1993 and 1998 from the community-level price survey.<sup>9</sup> We deflate the price of rice with the monthly consumer price index so that all prices are in thousands of 1998 (January) Dong. One U.S. dollar corresponds to approximately 14,000 Dong in 1998.<sup>10</sup> The average domestic price of rice increased by 28 percent relative to the rise in the consumer price index (Table 1).<sup>11</sup> Figure 1 plots the real rice price data for the 147 panel communes that are revisited in both rounds of the survey. The horizontal axis is the real price of rice in a commune in 1993, and the vertical axis is the real price of rice in a commune in 1998. The 45degree line is also pictured. 95 percent of the panel communes in the VLSS experienced an increase in the real price of rice between 1993 and 1998. The variation in rice price changes across communities is substantial. Our study focuses on the 115 rural, panel communes which are a subset of the panel communes pictured in Figure 1. As in figure 1, there is considerable

> 9 6 communes do not report the price in at least one of the survey rounds.  We impute the rice price in these communes.  Based on the unit value of rice purchased by households reported in the VLSS, we calculate the mean unit value of a kg of rice for a commune in a given survey year.  We regress the price of rice reported in the price survey on the third order polynomial of the mean unit value of rice in a commune.  We replace the missing price data with the predicted value of commune price based on this regression.

> 10 The price deflator does not vary by region, because we do not want the deflator to drive the variation in rice prices.

> 11 We have also compared the prices reported in the commune questionnaire with the average commune price based on unit values of purchased rice from the household survey.  The correlation between prices collected in the commune-level survey and average commune price based on unit prices from the household survey in levels is .68. A third available estimate of aggregate rice price movements is the National Statistical Office's rice price index computed in the context of the overall consumer price index.  The sampling frame of the consumer price index differs from the VLSS, and the comparability of the two estimates is not well understood.  Nevertheless, between 1992 and 1998, real rice prices increase by 27 percent according to the official rice price index.

11