rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 21:** **<u>Cargo Dwell Time, by the Main Factors in Selecting C&F Agents, 2011</u>**

<!-- Start of picture text --> 12 10.75<br>9.50<br>10 9.10<br>8.67<br>8<br>6<br>4<br>2<br>0<br>long term relationship/no cost reliability and other factors<br>will to change agent professionalism<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

**Figure 22: Cargo Dwell Time in Select African Countries, by the Main Factors in Selecting C&F Agents, 2011**

<!-- Start of picture text --> long term relationship/no will to change agent cost<br>reliability and professionalism other factors<br>20<br>15<br>10<br>5<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br>Source: Authors based on firm surveys.<br><!-- End of picture text -->

# **Analysis by Product Category**

The overall distribution of imports by category, shown in Table 2, indicates that machinery and electrical products are the largest category (27 percent), followed by chemicals and allied industries (14 percent) and transportation (13 percent).<sup>11</sup> Machinery and electrical is the largest category in Nigeria, South Africa, and Uganda and the smallest in Zambia. While the distribution of most product categories is rather balanced among countries, important differences are evident for transportation, which accounts for 30 percent of imports in Zambia, but only five percent in South Africa. The differences are also important for textiles, which account for only zero to four percent of imports in all countries, except South Africa (16 percent).

> 11 We use 11 categories based on the 15 standard categories (using two-digit Harmonized System codes). We aggregate some of the categories because they are too small, but some still account for less than 5 percent of the total volume of imports. This might explain the problem of selection bias.

29
**Table 3: Base OLS Regression Results Underlying the Mean Decomposition**

Dependent Variable: Log <u>[Self-Reported Productivity (Birr/HA)]</u>

||_Pooled Sample_|_Male Manager_|_Female Manager_|
|---|---|---|---|
|**_Manager Characteristics_**<br>Female †|-0.1348|.|.|
||(0.0898)|||
|Age (years)|<br>0.0016|0.0004|0.0008|
||(0.0023)<br>|(0.0024)<br>|(0.0055)<br>|
|Years of Schooling|0.0094|0.0062|0.0886*|
||(0.0139)|(0.0140)|(0.0451)|
|Manager Disability †|0.0299|0.0066|-0.1010|
||<br>(0.0977)|<br>(0.1278)|<br>(0.1449)|
|Hours per Week for Agriculture Activities|0.0036**|0.0043**|-0.0030|
||(0.0017)|(0.0018)|(0.0035)|
|Access to Extension Program †|0.0701|0.1275|-0.2868*|
||(0.0828)|(0.0875)|(0.1728)|
|Access to Credit Services †|-0.0403<br>|-0.0348<br>|-0.0167<br>|
||(0.0746)|(0.0788)|(0.1642)|
|**_Manager Land Tenancy_**||||
|Log [HA]|-0.2194***|-0.2395***|-0.0249|
||(0.0630)|(0.0733)|(0.0890)|
|Number of Fields Managed|-0.0227**<br>|-0.0193*<br>|-0.0517***<br>|
||(0.0093)|(0.0100)|(0.0138)|
|Total Number of Crops Produced|<br>0.0265|<br>0.0192|<br>0.0863***|
||(0.0180)|(0.0195)|(0.0271)|
|Fields for which HH has a Certificate|<br>0.0069|<br>0.0509|<br>-0.2148|
||(0.0802)|(0.0841)|(0.1403)|
|Manager's Plot Occupation: Rented (% of parcels)|0.6368***|0.6082***|0.7741|
||(01736)|(01820)|(05030)|
|**_Manager's Plot Characteristics_**|.|.|.|
|Intercropping (% of fields)|0.0709|0.0744|0.0490|
||(0.1783)|(0.1911)|(0.2441)|
|Slope|0.0031|0.0040|-0.0035|
||(0.0054)|(0.0055)|(0.0108)|
|Distance to Household|-0.0032***|-0.0027**|-0.0175**|
||(00011)|(00011)|(00078)|
|**_Manager's Agricultural Non-Labor Input Use (for Season)_**|.|.|.|
|<br>Fields that Use (% of Total)||||
|<br>Irrigation|-0.6392***|-0.7571***|0.1814|
||(0.2316)|(0.2321)|(0.3085)|
|Fertilizer|-0.1009|0.0442|-1.2988***|
||(0.1739)|(0.1729)|(0.4236)|
|Organic Fertilizer|00861|00236|08066**|
||.<br>(0.1829)|.<br>(0.1954)|.<br>(0.3318)|
|Pesticide, Herbicide or Fungicide|0.5199***<br>|0.4669**<br>|1.2564***<br>|
||(0.1825)|(0.1975)|(0.4219)|
|Improved Seeds|<br>-0.4844|<br>-0.6947*|<br>-0.0428|
||(0.2937)|(0.3542)|(0.4811)|
|Chemical Fertilizer Used per Hectare (KG/HA)|<br>0.0025***|<br>0.0021***|<br>0.0057***|
||(00004)|(00004)|(00014)|
|Oxen per Hectare|.<br>-0.0066|.<br>0.0090|.<br>-0.1397***|
||(00154)|(00146)|(00374)|
|Agricultural Implement Access Index|.<br>0.0346|.<br>0.0340|.<br>0.0323|
||(0.0241)|(0.0262)|(0.0447)|
|**_Manager's Agricultural Labor Input Use (for Season)_**||||
|<br>Hhld Ml Lb U ('000 H/HA)|00193|00181|00184|
|ouseo ae aor se ours|.<br>(0.0178)|.<br>(0.0169)|-.<br>(0.0322)|
|Household Female Labor Use ('000 Hours /HA)|<br>0.0350|<br>0.0253|<br>0.0673|
||(0.0445)|(0.0685)|(0.0449)|
|Household Child Labor Use ('000 Hours /HA)|<br>-0.1317|<br>-0.4035**|<br>0.2718*|
||(01259)|(01952)|(01439)|
|Total Hired Labor Use ('000 Days/HA)|.<br>0.0789|.<br>0.0109|.<br>-0.4347|
||(0.2047)|(0.1942)|(0.6889)|
|Total Exchange Labor Use ('000 Days/HA)|0.2493|0.8957|-0.1911|
||(0.3717)|(0.8486)|(0.1487)|

24
_Fareed M. A. Hassan, ECSPE_ _4_ estimated to remain at that level in 1997. With the large contraction of output shown in Table 1, the decline of revenue in real terms was even larger than suggested by falling ratios to GDP. Revenue losses from taxes on corporate income accounted for the larger share of the decline of revenue. Profits tax revenue, once the key source of government revenue, has become far less important, falling from 17.3 percent of GDP in 1991 to 2.2 percent in 1993, before recovering to 4.5 percent in 1996. These, together with social security contributions, were the main sources of government revenues under the command economy. Table 1 also shows that the relative importance of personal income tax is increasing, particularly when compared with the dwindling revenue performance of profits tax associatecl with the erosion of the corporate income tax base (Bogetic and Hillman, 1994). Indeed, the revenue productivity of personal income tax has not only been rising but has even surpassed profits tax revenue performance since 1993.

Thoujgh the personal income tax has increased in relative importance as a source of revenue, its contribution to GDP is still lagging behind market economies and several Eastern European countries. For instance in most OECD countries, income taxes, excluding social security contribution, amount to between 10 and 15 percent of GDP (McLure 1990). Also, in Romania, a country at a similar stage in the transition process, the share of personal income tax revenue in GDP was 8 percent in 1992 (Shome, Haindl, and Schenone, 1993). This indicates that the revenue potential of this tax instrument is yet to be fully exploited in Bulgaria.

The overall high tax burden (nearly 30 percent of GDP) is not surprising, given the prevalence of state participation in many economic activities and provision by state of many services (e.g., social security expenditures accounted for nearly 10 percent of GDP in 1996). Despite the relatively high tax burden, the fiscal deficit was large, ranging from 6 to 13 percent of GDP during 1991-1996. Thus the need for revenue reflects the level of government expenditure.
# Table V.6

# Use and Usefulness of Financial Support for Export and Overseas Investment

|Source||=|Number of|Users|=|Usefulness'|
|---|---|---|---|---|---|---|
|A. Individual<br>Sources|Woven<br>Textiles|Auto<br>Parts|Electronic<br>Parts|Facuory<br>Automation|Total|4.74|
|1. Trade Finance|27|2|10|7|46|4.7|
|2. Fund for export<br>industry facilities|16|3|5|7|31|4.5|
|3. Tradebill|3|3|6|3|15|4.2|
|4. E;xport loan fund|7|0|3|1|11|4.4|
|5. Overseas investment<br>fund|0|1|2<br>_|0|3|4.3|
|6. Fund for penetrating<br>newoverseasmarkets|0|0|I<br>_|1<br>_|2|3.0|
|7. Fund for SME<br>export marketing within<br>fund for industrial<br>technology _|1|0|2|2<br>_5||4.2|
|8. Circular Loan (SME<br>Bank) for exporting<br>SMEs|2<br>I|2|0|0|4<br>.|3.7|
|9. Overseas factoring|0|0|0|0|0<br>O||
|10. Fund for the<br>overseas transfers of<br>excess capacity within<br>SME Restructure Fund|1|0-|0|0|1|5.0|
|1. Parent Fund|0|0|0|1|1|4.0|
|Total|57|11|29|22|119||
|Number of Sample<br>SMEs_____|42|20|20|40|122||
|B.Sources Aggregated<br>Into Classes|||||||
|1. Commerical Sources<br>1+3|30|5|16|10|61|4.6|
|2. SpecialGovernment<br>led funds 2+(4-10)|27|6|13<br>_|11|57<br>_|4.4|
|3. Parent Firm|0|O|O||I 1|<br>4.0|

**Average uscuihncss on a scale** of **1** (low) **wo S (high).**

73
# **METHODOLOGY REFERENCES**

Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. _Indian Agriculture in Brief._ Annual.

Food and Agricultural Organization of the United Nations (FAO), _Trade Yearbooks._ Various issues.

Government of India, Ministry of Finance. _Economic Survey._ Annual. Various issues.

Gulati, Ashok and Garry Pursell. 1996. _Trade Policy and Incentives in Indian Agriculture._ Book manuscript. Mimeo.

Gulati, Ashok with James Hanson and Garry Pursell. 1990. _Effective Incentives and India's Agriculture. Cotton, Groundnuts Wheat and Rice._ Policy, Planning and Research Working Paper No. 332. The World Bank. January.

Gulati, Ashok. 1990. "Fertiliser Subsidy: is the Cultivator 'Net Subsidised"'. _Indian Journal of Agricultural Economics, XLV_ (1). January - March.

Kalra, G.D. and Ashok Gulati. 1992. _Protection to Tractors in India - a Firm Level Study._ Mimeo. February.

Pursell, Garry and Neil Roger. 1985. _Manualfor Incentive and Comparative Advantage Studies._ World Bank, mimeo. December.

Schiff, Maurice and Alberto Valdes. 1992. _The Political Economy ofAgricultural Pricing Policy. Volume 4. A Synthesis of the Economics in Developing Countries._ Johns Hopkins University Press for the World Bank.

Sharma, Pradeep K. 1991. _Effective Incentives in India's Agriculture: Role of Transport and Marketing Costs._ Mimeo. March.

Subbarao, K. _Rice Marketing System and Compulsory Levies in Andhra Pradesh: a Study of Public Intervention in Foodgrain Marketing._ Allied Publishers, Bombay. 1978.

United States Department of Agriculture, Economic Research Service, Commercial Agricultural Division. 1994. _Estimates of Producer and Consumer Subsidy Equivalents. Government Intervention in Agriculture, 1982-92._ Statistical Bulletin no. 913. December.

xxxiii
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This lack of substance to negotiate on is reflected in average tariffs of 1.3% for the Davos group. Only four countries have average tariffs above 1% on the APEC list and among the OECD countries only the Republic of Korea and New Zealand would have significant reductions to carry out if tariffs were to be eliminated. Using the TRI measure of protection which gives the average uniform tariff that would give the same welfare loss as the current structure with dispersion, at most (for the WTO list), the tariff protection for the group is equivalent to a uniform tariff of 3.4%, hardly a high estimate.

What about expanding the list along the lines suggested in the Davos communiqué (“APEC [list] has given us a good start, and we are committed to exploring a broad range of additional products”)? As can be seen from column 3, average reductions would be greater in the expanded list because countries have omitted from their submission lists products with tariff peaks. Yet, average protection is only 2.2%, equivalent to a uniform tariff of 3.4%. On the other hand, expanding the list of countries while keeping the APEC list would probably encounter greater resistance since, with the exception of China which has a higher protection than the average for the UMIC group, the Davos countries are the countries with low average protection in EGs in their respective income group.

Second, China and Korea stand out with average tariffs at least twice as high as for others. Comparing EGs tariffs for the Davos participants with the averages for their income category, China, Korea and New-Zealand have above-average tariffs. Korea would probably be interested in pushing for an extension towards the WTO list, as average tariffs on that list are lower than on the APEC list. The disparity between China and Korea and the rest of the Davos members hints at a possible scenario in which these two countries might insist on a less than full elimination of tariffs, say a 50% cut across the board.

Third, even though the country-level tariff-equivalent estimates of NTBs are dated, incomplete (see notes in table 1), and difficult to interpret in terms of welfare, the values are generally high, although they are close to the averages for their respective groups discussed earlier. In conclusion, since there are very few tariff peaks in the Davos group (see Annex, Table 3), as it now stands, the Davos initiative will do little to help moving towards global free-trade.

# **3.3 Import response estimates to a lowering of trade barriers**

Even though the Davos group accounts for 74% percent of trade (intra-EU trade excluded) in EGs using the APEC list, increased country participation is essential to reach free-trade in green goods. So far, all but HIC countries have been adamant to submit lists of goods for tariff-reduction negotiations. This is because of a fear of being inundated by imports from industrialized countries. Taking the same format as table 1, table 2 gives first-order estimates of import response to a 50% and total reduction in tariffs for individual Davos 14
|VARIABLES|Log chang<br>|e in regiona<br>200|l supporting<br>7-2010|employment|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Bartik|0.182|0.181|0.186|0.189|
||[0.250]|[0.238]|[0.260]|[0.245]|
|% White, 2007||0.000||0.001|
|||[0.002]||[0.002]|
|Log of median household income, 2007||-0.024||0.094|
|||[0.188]||[0.227]|
|Log of median homevalue, 2007||0.015||-0.026|
|||[0.051]||[0.078]|
|% Owner occupied, 2007||-0.003||-0.004|
|||[0.002]||[0.002]|
|% With less than high school diploma, 2007||-0.002||-0.003|
|||[0.003]||[0.003]|
|% With only high school diploma, 2007||0.002||0.003|
|||[0.003]||[0.003]|
|Unemployment rate, 2007||0.005||0.004|
|||[0.008]||[0.009]|
|Poverty rate, 2007||0.002||0.003|
|||[0.007]||[0.008]|
|% Urban, 2007||-0.001||-0.001|
|||[0.001]||[0.001]|
|Leverage, 2006|||0.003|0.020|
||||[0.017]|[0.031]|
|Constant|0.036|-0.268|0.031|-0.526|
||[0.043]|[1.878]|[0.050]|[2.018]|
|Observations|2,582|2,582|2,134|2,134|
|R-squared|0.000|0.004|0.001|0.005|

Robust standard errors in brackets (*** p<0.01, ** p<0.05, * p<0.1).

Table 6.1d: Impacts on regional supporting sector employment 24
prevalence distribution. Recall from Table 1 that mean years of schooling is 7.3, and the median is 7. Both the OLS and 2SLS results show clearly that migrants becomes progressively more negatively selected in terms of education as network size grows, with this result stronger after instrumenting for migration networks.

The 2SLS results in Figure 2 show that in communities with small migration networks, where the costs of migrating are thus likely to be high, the probability of migration is increasing in completed years of schooling up to nine or ten years of schooling, around the 75<sup>th</sup> percentile of the education distribution. That is, first-time male migrants from communities with small networks are likely to be positively selected. However, as the migrant network grows, lowering the costs of migration and reducing credit constraints, we find a reversal in this pattern. An individual in a community at the 70<sup>th</sup> percentile of migration networks, where 25 percent of households in the community have someone who has ever migrated to the U.S., has the highest probability of migrating if he has two to five years of schooling.

Thus we find that in communities with low migration networks, migrants tend to be selected from the upper-middle of the education distribution, which concurs with Chiquiar and Hanson’s (2005) description of selectivity. However, in high network communities, where the cost of migrating is less binding, we find negative selection, which is what would be predicted by Borjas (1987) based on wage differentials. As a result, over time, as origin communities accumulate migration experience, one should expect to see a gradual worsening in the relative skill level of migrants. This finding is consistent with more aggregate evidence provided by Feliciano (2001), who finds a decline in the relative skill level of Mexican immigrants over the course of the 20<sup>th</sup> century.

# **6. Robustness**

# **6.1 Robustness to Undercounting of Migrants**

As discussed in Section 3, one concern with the ENADID and other Mexico-based migration surveys is that they do not capture migrants who move with their whole families, who are more educated on average than those who have family members remaining. The omission of such individuals is thus likely to bias against finding positive selection, since some highly educated migrants are not included in the regressions.

15
are equicorrelated at the block level and estimate all equations in feasible GLS (FGLS) to improve efficiency (Cameron and Miller, 2015).<sup>30</sup> To guard against misspecification of the error structure, I additionally calculate Eicker-White standard errors clustered at the sub-district (block) level or district level depending on the level of aggregation of the regressors.

Since the number of clusters is fairly small, these standard errors are likely to be downward biased. Inference on the main hypothesis is therefore based on p-values obtained from performing a wild cluster-bootstrap with Rademacher weights as suggested by Cameron, Gelbach, and Miller (2008).<sup>31</sup> In a more recent paper, Cameron and Miller (2015) suggest the use of Webb’s (2014) weights if the number of clusters is smaller than ten, which seems reasonable when using a district level treatment variable. P-values of the respective treatment variable using both versions of the bootstrap with 4,999 replications are reported in the results section.<sup>32</sup>

# **5 Results**

This section starts by presenting estimates for an agricultural production function. It proceeds by assessing the extent to which the NREGS can actually support households in this sample in coping with shocks, which is the precondition for expecting any insurance effect. This section then analyzes the effects of the NREGS on households’ crop choices and presents a number of robustness checks.

## **5.1 Identifying profitable production strategies**

To understand inhowfar households’ crop choice can improve their income from agricultural production, I estimate an agricultural production function, linking the total value of

> 30Estimation if FGLS is more efficient than OLS as it specifies a model for the error variances.

> 31This approach was applied, inter alia, by Adrianzen (2014) to data clustered in 26 villages and by Akosa Antwi, Moriya, and Simon (2013) to 28 quarter-year groups.

> 32The wild cluster-bootstrap calculates t-statistics for each bootstrap sample and estimates rejection rates based on the resulting distribution of t-statistics. Because this method does not calculate standard errors, I report clustered standard errors throughout the text. Implementation of the bootstrap in Stata is done based on the do-file written by Douglas Miller, which can be accessed online: http://www.econ.ucdavis.edu/faculty/dlmiller/statafiles/.

19
- **WaterAid.** 2008. _Feeling the Pulse: A Study of the Total Sanitation Campaign in Five States_ , Delhi: WaterAid India.

- **Watson, Tara.** 2006. “Public health investments and the infant mortality gap: Evidence from federal sanitation interventions on U.S. Indian reservations.” _Journal of Public Eco-_ –

- _nomics_ , 90(8-9): 1537 1560.

34
CONTENTS

|1.|Introduction|1|
|---|---|---|
|1.1|Development Strategies|1|
|1.2|Environmenital Problems|2|
|1.3|Scope|3|
|1.4|organization|5|
|2.|Industrial Pollution Estimates|7|
|2.1|Methodology|7|
|2.2|Sources of Information|10|
|2.3|Results|11|
|3.|Foreign-Trade<br>Policies and Industrial Development:||
||Conseauences<br>for the Environment|14|
|3.1|Import Substitution Period (1945-1976)|14|
|3.2|Petroleum-Led Growth (1977-1982)|17|
|3.3|Macroeconomic<br>Stabilization and Trade||
||Liberalization<br>(1983-1988)|21|
|3.4|Growth Recovery and North-American<br>Free||
||Trade Agreement<br>(1989-present)|24|
|3.5|Foreign-Trade<br>Policies and the Environment|26|
|4.|Energy Pricing and Energv Intensity in||
||Mexican Industry|28|
|4.1|Introductory Remarks|28|
|4.2|Pricing Policies in General|30|
|4.3|Energy Pricing|31|
|4.4|Implicit Subsidization|34|
|4.5|Energy Intensity of Mexico's Industrial Sector|36|
|4.6|Energy Intensity of Mexican Industry||
||in an International Perspective|40|
|5.|Conclusions|43|
|Refer|ences|47|
|Table|s||
This is because a larger difference between the income and hours worked elasticities and larger income effects both imply that high income individuals have higher preferences for consumption, so that redistributing away from them is less desirable than in the Mirrleesian benchmark. The takeaway is that labor supply elasticities not only impact the efficiency costs of taxation, but also encode important information about the equity benefits of taxation through the information they contain about the drivers of income inequality.

The rest of the paper proceeds as follows: Section 2 discusses related literature, Section 3 illustrates how labor supply elasticities can be used to recover preferences in the context of a labor supply model in which productivity is equivalent to the hourly wage and individuals only choose hours worked, Section 4 extends our analysis when productivity is not equal to the hourly wage (i.e., individuals choose effort per hour as well as hours worked), Section 5 discusses our empirical implementation, Section 6 discusses how our findings impact optimal taxation, and Section 7 concludes.

# **2 Related Literature**

This paper is related to four different strands of literature: (1) decomposing income inequality empirically into wages and hours, (2) survey evidence on the determinants of income inequality, (3) the relationship between income inequality determinants and redistribution, and (4) the invertibility of economic systems.

First, this paper is related to an empirical literature focused on statistically decomposing income inequality into wage heterogeneity vs. hours worked heterogeneity. Haider (2001), for example, decomposes the variance of income into wage and hours worked, finding that most of income variance is due to wage variance, although a non-negligible amount of income variance is due to hours variance (and the covariance between hours and wages). Doiron and Barrett (1996) also find that income variance is driven more by wage heterogeneity for males; however, they find that the opposite is true for women. Gottschalk and Danziger (2005) and Blundell et al. (2018) have similar findings. Our paper contributes to this literature by going a step further and decomposing income inequality into productivity and preferences, which is the more relevant decomposition for welfare analysis; our decomposition recovers preferences from hours worked net of substitution effects and recognizes that productivity may not be equivalent to the hourly wage if individuals exert differential effort per hour.

Second, while, to the best of our knowledge, this is the first paper that attempts to decompose income inequality into productivity heterogeneity vs. preference heterogeneity, there is a large body of work that investigates individuals’ _beliefs_ over the determinants of income inequality and how these beliefs relate to views on the merits of redistribution. Data from the World Values Survey shows Americans are about twice as likely 5