rafmacalaba/datause-encoder-data huggingface dataset:
POLICY **RESEARCH WORKING PAPER** 2964

# Abstract

Acevedo identifies the determinants of wages and productivity in Mexico over time using national representative linked employer-employee databases from the manufacturing sector. She shows that both employers and employees are benefiting from investments **in** education, training, work experience, foreign research and development, and openness after the North American Free Trade Agreement (NAFTA). Additional years of schooling have a higher impact on wages and productivity after NAFTA than before. Endogenous training effects are larger for productivity than for wages, suggesting that the employers share the costs and returns to training. The author also finds that investment in human capital magnifies technology-driven productivity gains. By comparing four regions of Mexico-north, center, south, and Mexico City-regional wage and productivity gaps are found to have increased over time.

This paper-a product of the Economic Policy Sector Unit, Latin America and the Caribbean Region-is<sup>a background paper</sup> for the region's 2002 Flagship Report "Knowledge in Latin America and the Caribbean: Reconsidering<sup>Education, Training,</sup> and Technology Policies." Copies of this paper are available free from the World Bank, 1818 H Street<sup>NW, Washington,</sup> DC 20433. Please contact Michael Geller, room 14-046, telephone 202-458-5155, fax 202-522-2112,<sup>emailaddress</sup> mgeller@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.<sup>The</sup> author may be contacted at gacevedo@worldbank.org. January 2003. (36 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to_<sup>_encourage the exchange of ideas about_</sup> _development issues An objective of the series is to get the findings_ **_out_** _quickly, even if the presentations_<sup>_are less than fully polished The_</sup> _papers_ **_carry_** _the names of the authors and should_ **_be cited_** _accordingly. The findings, interpretations,_<sup>_and conclusions expressed in this_</sup> _paper are entirely those of the authors. They do not necessarily represent the view of the World Bank,_<sup>**_its_**</sup><sup>_Executive Directors, or the_</sup> _countries they represent._

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4. Quantifying Vertical Trade among the B&R Economies <mark>FVA in the foreign value-added in bilateral sectoral exports. The foreign value-added can again be present in</mark> intermediate goods exports (FVA_int) or final goods exports (FVA_fin). Each of these sources of value-added can come from the direct partner or from third countries.

Finally, PDC are purely double counted items that can come from two sources. FDC and DDC.

The vertical specialization index from the importing side (VS, backward linkages) denotes the foreign content of exports. It is defined as the share of value-added in gross exports that comes from foreign sources plus the double counted terms.

## VS= FVA+ FDC

Each of the terms of VS indicates a country-sector position into global value chains (Wang et al. 2014).

From the exporting side, we define vertical specialization (VS1, forward linkages) as the domestic value-added that gets re-exported to third countries. In addition, we also separate the domestic value-added first exported and then returned home (VS1*=RDV).

**Source:** Author’s elaboration based on Koopman et Al. (2014) and Wang et Al. (2014)

# **_4.2 Backward Linkages_**

Backward linkages represent vertical specialization from the importing side. They refer to the use of foreign inputs (foreign value-added) embedded in a country’s gross exports. Using the decomposition of Wang et al. (2013), we can calculate backward linkages at the bilateral-sector level.<sup>8</sup> Therefore, we are able to map backward value-added linkages existing in exports of the selected group of B&R Economies.

We use the vertical specialization index (VS) discussed in Box 2 to map the foreign content of exports by the B&R Economies. The foreign content of exports refers to the remuneration of foreign activities embedded in a country’s gross exports. A large foreign content of exports implies stronger linkages from an inputs’ perspective to map the foreign content of exports by the B&R Economies. The foreign content of exports refers to the remuneration of foreign activities embedded in a country’s gross exports. A large foreign content of exports implies stronger linkages from an inputs’ perspective.

We notice in Figure 20 that the share of foreign content of exports of B&R Economies increased to 29 % in 2005 and started decreasing to reach 24 % in 2010. This is an indication that total exports are using more domestic inputs and rely a bit less in foreign intermediate goods for production. The pattern is similar for both exports to B&R and non-B&R Economies.

> 8 Trade in value-added at the bilateral-sectoral level is not properly defined using the pure Leontief decomposition.

20
|_gdp_growth_|115|2.081|1.523|-0.188|1.192|1.832|2.846|5.542|
|---|---|---|---|---|---|---|---|---|
|_inflation_|115|1.940|1.557|-1.617|1.127|2.096|3.147|4.115|
|_deposits_|115|111.919|29.242|55.669|89.988|114.566|132.786|156.988|
|_bonds_|110|38.521|28.391|3.377|19.518|30.017|48.117|109.896|
|_stock_market_cap_|113|86.145|47.252|37.917|47.348|74.083|110.685|204.916|
|_stock_market_turnover_|115|72.685|39.067|12.040|40.853|74.934|102.785|165.470|
|**Panel E. Other variables**|||||||||
|_investor_|115|6.374|1.880|3|5|6|8|10|
|_common law_|115|0.348|0.478|0|0|0|1|1|
|_gov38_|40,019|0.6|0.114|0.235|0.516|0.595|0.684|0.946|
|_nom_comm_|40,019|3.409|1.757|1|1|4|5|5|
|_comp_comm_|40,019|4.073|1.438|1|4|5|5|5|
|_audit_comm_|40,019|4.44|1.047|1|4|5|5|5|

Panel B of Table 2 provides information on the distribution of governance variables. _gov44_ attains a minimum value of 29.7% and a maximum value of 95.3%. The mean value of this variable in our sample is 60.7%, meaning that an average firm satisfies 60% of the minimal governance attributes that are listed in Table A1. The distribution of the _gov_board_ variable is more dispersed, with a mean value of 55%. Thus, an average firm satisfies about half of the minimal governance criteria that are related to boards. The _gov_indep_ index variable has a mean value of 0.645. With respect to board size ( _size_eff_ ), our sample firms have, on average, slightly smaller or larger board sizes compared to the optimum size of 9-12 members. Finally, the mean value for the _ceo_chair_ variable is 2.12, meaning that the CEO and Chairman positions are not separated for our sample firms on average.

Panel C of Table 2 shows a summary of the firm-level control variables. The average asset tangibility and asset turnover figures in our sample are 22.5% and 91.6%, respectively. The mean value of return on assets (-4.6%) is significantly lower than its median value (2.2%), which is driven by the fact that _roa_ has a negatively skewed distribution with large negative values. The distribution of the market-to-book ratio is positively skewed with mean and median values of 1.968 and 1.374, respectively.

Panel D provides information for the country-level control variables. The average GDP growth rate for our sample countries is 2.8% which is consistent with the historical economic growth patterns. The slowest growing country during our sample period is Portugal in 2004 (- 1.3%) whose economy had shown a volatile trend between 2003 and 2007. The fastest growing country during the sample period is Singapore in 2005 (8.18%). _inflation_ has a mean value of 1.94% during our sample period, while _deposits_ , which shows the share of banking firm assets in 16
**Table 8.  Financial Cost of the Gas Price Increase on Households**

|**Quintile**|**Gas Consumers**_(in million_<br>_drams per year)_|**Percent of 2009 GDP**|
|---|---|---|
|**Armenia**|||
|_Poorest_<br>|2,005|0.06|
|_2_<br>|3,781|0.12|
|_3_<br>|4,626|0.15|
|_4_|5,364|0.17|
|_Richest_|6,767|0.22|
|_All_|22,543|0.73|
|**Yerevan**|||
|_Poorest_|741|0.02|
|_2_<br>|1,446|0.05|
|_3_|1,588|0.05|
|_4_|1,871|0.06|
|_Richest_|2,189|0.07|
|_All_|7,835|0.25|
|**Other Urban**|||
|_Poorest_|676|0.02|
|_2_|1,275|0.04|
|_3_|1,617|0.05|
|_4_|1,895|0.06|
|_Richest_|2,470|0.08|
|_All_|7,933|0.26|
|**Rural**|||
|_Poorest_|563|0.02|
|_2_|1,020|0.03|
|_3_|1,351|0.04|
|_4_|1,787|0.06|
|_Richest_|2,026|0.07|
|_All_|6,748|0.22|

_Source:_ Author’s calculations based on 2008 ILCS and 2009 GDP data The lifeline tariffs benefit scheme had several inadequacies as a measure to provide compensation against the impact of the gas tariff increase. First, the amount of lifeline tariffs benefit was only about 15% of the cost of gas price hike to the bottom poorest 20% of households and less than 5% of the total cost to households of the gas price hike (see Table 8). Second, while this compensatory measure provides some relief for the poor and low gas 18
Conversely, the study of Uganda by Reinikka and Svenson (2002) illustrates well how inadequate public infrastructure may adversely affect private investment. A survey of 243 manufacturing firms conducted in 1998 in that country showed that the lack of adequate electricity sources was ranked as the most important constraint to investment. Firms on average did not receive electricity from the public grid for 89 operating days on average, which led to 77 percent of large firms (in addition to 44 percent of medium and 16 percent of small firms) purchasing generators, representing 25 percent of their total investment in equipment and machinery in 1997. The same survey showed that for a firm without a privately-owned generator, a one percent increase in the number of days without power results in a 0.45 percent reduction in investment.

In the short run, public capital in infrastructure may also affect private capital formation indirectly, through changes in output and relative prices. As noted earlier, public capital in infrastructure may raise the marginal productivity of all factor inputs (capital and labor), thereby lowering marginal production costs and increasing the level of private production. In turn, this scale effect on output may lead, through the standard accelerator effect, to higher private investment—thereby raising production capacity over time and making the growth effect more persistent.

Another indirect channel is through the effect of public infrastructure on the price of domestic consumption goods relative to the price of imported goods, that is, the (consumption-based) real exchange rate. An increase, for instance, in public investment in infrastructure would raise aggregate demand and put pressure on domestic prices. If the nominal exchange rate does not depreciate fully to offset the increase in domestic prices, the domestic-currency price of imported consumption goods will fall in relative terms (that is, the real exchange rate will appreciate), thereby stimulating demand for these goods. The net effect on domestic output may be positive or negative, depending on the intra-temporal elasticity of substitution between domestic and imported goods. If this elasticity is low (as one would expect in the short run), the net effect may well be positive. Again, through the accelerator 9
# **5 Concluding Remarks**

With the decline of traditional trade barriers, differences in product requirements across market destinations are increasingly viewed as impediments for trade. Yet despite the growing evidence of the negative impact of product standard heterogeneity, there is little understanding of the gains of harmonization at the micro level. This paper provides, to the best of my knowledge, the first firm-level evidence of the gains from product standards harmonization and decomposes them into the intensive and extensive margins of trade.

In this paper, I develop a heterogeneous firms model featuring the role of product standards differences across market destinations as country-specific fixed costs to export. The model is a three-country version of the Cheney (2008) and Helpman et al. (2008) frameworks in which I assume that two countries have different but equally costly product standards (countries _H_ and _F_ ) while the third country has less stringent product requirements (country _R_ ). After setting out the base case scenario without harmonization, I modify the model to allow harmonization between countries _F_ and _R_ and characterize the trade impact in country _H_ . The theory provides three testable results: First, harmonization increases the number of _H_<sup>_′_</sup> _s_ exporting firms to market _F_ , the extensive margin of trade. Second, new entrants are mainly drawn from the most productive set of _H_ ’s firms exporting to country _R_ , the extensive margin of trade composition. These firms are characterized by being smaller and less productive than the incumbent exporters to country _F_ . Third, harmonization decreases export sales of _H_<sup>_′_</sup> _s_ existing exporters to country _F_ , the intensive margin of trade.

To examine the impact of product standards harmonization on the margins of trade, I use the European agreement to harmonize product standards to international norms as a natural experiment. This agreement was achieved thanks to the cooperation between CENELEC —the European Committee for Electrotechnical Standardization— and the IEC —the International Electrotechnical Commission— laid down in the Lugano and Dresden agreements in 1991 and 1996. I combine this information with U.S. linked/longitudinal firm trade data and the U.S. Censuses of Manufactures to empirically confront the results of my theoretical model for the Electronic sector.

An important contribution of my analysis is the creation of a new set of industry level measures of EU product standards for the Electronic sector. Since there is currently no official mapping between the classification of standards and any industry or product classification, I develop a method to obtain a concordance between five-digit ICS codes and the four-digit standard industrial The empirical findings largely confirm the theoretical results. First, I confirm that industries with relatively high harmonization and high reduction in tariff rates exhibit relatively high U.S. export value to the EU. Furthermore, I show that product standards harmonization increases the 18
# **<u>Table 6b.</u>** <u>Multivariate Analysis of Tree Cash Crops</u>

|||_Adoption Anal_|_ysis (Probit)_|_Dete_|_rminants of sha_|_re of farmland with trees_|
|---|---|---|---|---|---|---|
||_Dep. Vari_|_able: Tree cash_|_crops on farm (yes=1)_|_Dep. Vari_|_able: Share of f_<br>_ca_|_arm area with presence of tree_<br>_sh crops_|
||(I)|(II)|Shapley Value|(III)|(IV)|Shapley Value|
|**_Household Controls_**|||**0.007 (2.70%)**|||**0.006 (4.29%)**|
|Household Size|0.005|0.010**||-0.003|0.002||
||[0.006]|[0.005]||[0.013]|[0.012]||
|Number of Children (<14 years old)|-0.001|-0.003||-0.012|-0.015||
||[0.007]|[0.006]||[0.009]|[0.010]||
|Head's Age (years)|0.001*|0.001||0.002*|0.001||
||[0.001]|[0.001]||[0.001]|[0.001]||
|Head Female (yes=1)|-0.052***|-0.048***||-0.055**|-0.052**||
||[0.014]|[0.015]||[0.025]|[0.023]||
|Head education (years)|0.002|0.003*||0.008**|0.009**||
||[0.002]|[0.002]||[0.004]|[0.004]||
|**_Assets and land_**|||**0.001 (0.37%)**|||**0.061 (44.08%)**|
|Tropical Livestock Units (TLU)|-0.002|-0.002||-0.001|-0.001||
||[0.002]|[0.002]||[0.001]|[0.001]||
|Land Owned (area - ha)|-0.002|0.000||0.111**|0.112**||
|<br>|[0.003]|[0.003]||[0.043]|[0.044]||
|**_Geo- and climate variables_**<br>|||**0.084 (30.35%)**|||**0.021 (15.24%)**|
|Log Population Density around 20km (people/ km<sup>2</sup>) (2010)|-0.002|-0.002||-0.000|0.001||
||[0.001]|[0.001]||[0.001]|[0.001]||
|Tree Cover % around 20km (mean) (2010)|0.007***|0.008***||0.007***|0.006***||
||[0.002]|[0.001]||[0.002]|[0.002]||
|Fertile Soil % around 20 km (mean) (2010)|0.012*|0.008||0.001|-0.003||
||[0.007]|[0.006]||[0.005]|[0.004]||
|Log. Annual Mean Temperature ( C )|0.019**|0.020**||0.021|0.014||
||[0.009]|[0.009]||[0.015]|[0.014]||
|Log. Annual Precipitation (mm)|-0.000|0.000||-0.000**|-0.000||
||[0.000]|[0.000]||[0.000]|[0.000]||
|**_Country Fixed Effects (Baseline Country: Ethiopia)_**|||**0.11 (42.02%)**|||**0.015 (10.85%)**|
|<br>Malawi|||||||
|Nigeria|-0.287***|-0.295***||-0.133|-0.037||
||[0.067]|[0.060]||[0.112]|[0.109]||
|Tanzania|-0.198***|-0.205***||-0.074|-0.005||
||[0.043]|[0.028]||[0.101]|[0.083]||
|Uganda|<br>-0.189***|<br>-0.191***||<br>0.185|<br>0.246||
||[0.041]|[0.025]||[0.183]|[0.168]||
|(pseudo) R-Square|0.192|0.263||0.122|0.141||
|Observations|8994|8975||8994|8994||
|District/Zone Fixed Effect|No|Yes||No|Yes||

_Note:_ This table presents the multivariate results for tree cash crops on farms. Robust standard errors in brackets, clustered at strata level. Sampling weights used for all regressions. * p<0.10 ** p<0.05 *** p<0.01.

30
understand the opportunity costs. The Marginal WTA values are shown in Table 5a and Table 5b for the main effects specifications for both the conditional logit models and the MMNL model for both groups of respondents. We see from Table 5a and Table 5b that if the annual REDD+ payment per household increases by on average 29 Nepali Rupees (about $0.30) the proportion of payments to community can be decreased by 1%. We find that if households are required to reduce their firewood use by 1% from the status quo, they would require the payment amount to increase by on average 150 Nepali rupees (about $1.50). Comparing the values in table 5a and Table5b for the opportunity cost of reduction in firewood use we see that the opportunity cost is higher for communities that currently have community managed forests (on average 185 Rs vs 132 Rs for non-CF communities). This may be due to communities with community controlled forests already having restrictions on firewood use. For the non-CF communities the opportunity cost of grazing restrictions is on average 2325 Rs (about $23.00).

# **7. Conclusions and Policy Implications**

In this paper we present results from a choice experiment survey conducted in Nepal in 2013 as part of a collaborative effort to analyze the preference for REDD+ contracts in Nepal. In both CF and non-CF communities we find, for example, that households prefer higher REDD+ payments and would rather not take on REDD+ obligations without adequate compensation. For example, CF and non-CF respondents generally are not likely to choose options with high levels of firewood reductions and low REDD+ payments. Non-CF households also have the option to reduce grazing in exchange for payments. We find that those respondents are less likely to choose options with grazing restrictions than options without such restrictions. Such results are consistent with individuals making choices that are in their own interests.

24
|**T**|**able 7. Domestic**<br>(1)<br>|**Violence** <br>(2)<br>|(3)<br>|(4)<br>|
|---|---|---|---|---|
||**Experienced**<br>**emotional**<br>**violence**|**Experienced**<br>**less severe**<br>**violence**|**Experienced**<br>**severe**<br>**violence**|**Experienced**<br>**sexual**<br>**violence**|
|Wife gives herself more power than|0.031***|0.025***|0.010**|-0.000|
|husband [A]|(0.01)|(0.01)|(0.00)|(0.00)|
|Husband gives herself more power than|0.015**|0.013**|0.004|0.008|
|herself [B]|(0.01)|(0.01)|(0.00)|(0.00)|
|Wife and husband agree she is main dm|-0.031***|-0.038***|-0.024***|-0.032***|
|or dm is joint [C]|(0.01)|(0.01)|(0.00)|(0.01)|
|Constant|0.153***|0.155***|0.022***|0.024***|
||(0.01)|(0.01)|(0.01)|(0.01)|
|_Controls_|||||
|Individual/Household-level|||||
|characteristics|Yes|Yes|Yes|Yes|
|Country Fixed Effects|Yes|Yes|Yes|Yes|
|Observations|31,264|31,267|31,240|31,257|
|Adjusted R-squared|0.03|0.06|0.03|0.05|
|Mean Control|0.19|0.20|0.05|0.08|
|P-value A=B|0.02|0.13|0.21|0.11|
|P-value A=C|0.00|0.00|0.00|0.00|
|P-value B=C|0.00|0.00|0.00|0.00|

It is important to note that the direction of causality has not been established for the relationships presented above. In addition, three further points are worth mentioning. First, the risk of reverse causality is mitigated given the specific way in which we conceptualize and construct power in our data. The decision-making question we use i) refers to _usual_ patterns, which may be less malleable to our outcomes (say, girls’ stunting) than specific time-bound occurrences (such as women’s savings levels) and ii) is constructed using _discrepancy in cross-reporting_ while controlling for agreement on women’s decision-making, a very specific facet for the outcomes to affect. While reverse causality cannot be ruled out, the probability of it driving the results is likely 27
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**30 |** P a g e