rafmacalaba/datause-encoder-data huggingface dataset:
and unobserved productivity differences. These are discussed in turn.

The possible existence of increasing returns to scale in tropical farming cannot be ruled out, and indeed a number of tropical countries have (or have had) large successful commercial farms – including Liberia, which lies in West Africa like The Gambia. These commercial farms, however, rely on technological and organizational innovations that are out of the reach of the predominantly illiterate farmers residing in the study villages. Successful use of animal traction requires a minimum scale of production, but this scale is well within the realm of the smallholder farmers in our study. Using a simple replication argument, one may conclude that traditional farming is characterized by constant returns to scale in land and labor. While this argument is not without merit, it is widely recognized that hired labor is subject to various moral hazard considerations which create a wedge between the productivity of labor on one’s own farm or someone else’s. This particularly true in West Africa because agrarian sharing norms tend to delegitimate efforts to hire large number of agricultural laborers (Platteau et al., 1998). In the context of our study population, decreasing returns to scale thus are a reasonable assumption, substantiated by much existing data on smallholder farming in African and elsewhere (e.g., Deininger and Feder 2000, Ali and Deininger 2015).

To circumvent the possible existence of unobserved productivity differences, the methodology was applied to factor ratios which, if productivity differences are Hicks-neutral, should be equalized to achieve allocative efficiency. Land transfers do not equalize ex ante land-labor ratios, and labor transfers do not equalize ex ante or ex post factor ratios. The conclusion that follows from this evidence is that factors of production are not efficiently allocated so as to match possible Hicks-neutral differences in productivity across farmers.

There remains the possibility that productivity differences across farmers are factor-biased. If some farmers have a higher land productivity than others<sup>28</sup> , they can be expected to have a higher land-labor ratio. The authors of this paper suspect that unobservable productivity differences matter more in modern agriculture, and less in traditional subsistence agriculture which is fairly undifferentiated and follows simple heuristic rules of behavior. In the study area, factor-biased productivity differences would have to be large to explain a Gini coefficient of 0.47 in land available for cultivation – given how thin agricultural labor markets are in the 32
<!-- Start of picture text --> Banking Sector Holdingsof Domestic Public Debt in 2008<br>(6 of total outstanding)<br>90<br>eo<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>; Egypt<br>Jordan Lebanon Morocco Tunisia<br>Source: Country websites; conference presentations<br><!-- End of picture text --> <!-- Start of picture text --> 350 Banking Sector Assets / GDP (%]<br>2008<br>300<br>250<br>& 200<br>0<br>‘S<br>2 150<br>100<br>es (ee ae .<br>So<br>Q<br>fey.x iyOs$e Pe¥4PFoePefeee8 Ssoeé &oveyAo 2fee2<br>oF are) é R =<br>MENA LAC SSA, ECA EAP SAI<br>Source:IMF IFS<br><!-- End of picture text --> <!-- Start of picture text --> 300 Non-performingLoansin 2008<br>(% of total loans)<br>25.0<br>= 20.0<br>4<br>= 150<br>fa<br>2 10.0<br>5.0 in _-__- 3 _- _-- .<br>0.0<br>ae oeRY et Pee feo o oe eee2 ye<br>ESSEMENASASSfeeGLAC es eee55A3 FSETECA EAP SESAR |<br>Note: Algeria and Libya-datafor2007<br>Source: IMF Global Financial Stability Report April2009;/MF Regional Economic Outlook<br>MECA May 2009<br><!-- End of picture text -->

<!-- Start of picture text --> ts Loan to Deposit Ratio<br>‘ 2008<br>2.0<br>18<br>10<br>O58<br>09 a 2 > 2 o a & A<br>= q ss<br>&3PS e 8s ESESROPESESESESJ ~ Sos ¥ vs Ms é oseESSERE* ~<br>is og - ees<br>MENA LAC 554 Ec EAP SAR<br>Source:IMF IFS<br><!-- End of picture text --> <!-- Start of picture text --> Assets held by 3 Largest Banksin 2008<br>(share of total commercial bank assets)<br>1<br>Lo<br>0.8<br>a “= - “= 7 “ss “=<br>o4<br>0.2<br>0.0<br>as¢ SF Pe PS SL CSAS veoeoePoeeS<br>thes wre oes re Clee<br>MENA LAC 554 ECA EAP SAR<br>Note: Libya-datafor 2006<br>Source: World Bank Financial Structure Database 2008v2<br><!-- End of picture text --> <!-- Start of picture text --> State Ownership of Banking System Assets* in 2005<br>10<br>og<br>Og<br>OF<br>o6<br>os<br>04<br>03 -=---8-— w_—_Beeeee eee eee Ke<br>02<br>o1<br>rs&eSoF # FFPP Pd&Pe HeoC Se KEA 2 A eeo Db oo<br>¥ | F ef . - ¥ < we<br>* fraction of banking system assetsin banisthat are 50% or more government owned as of year-end 2005<br>Source: World Bank BankRegulationand Supervision Database2008, Gerard Caprio, Ross Eric Levine James R. Barth<br><!-- End of picture text -->
domestic employers, the share of foreign sector employment in total skill-intensive employment accordingly rose from 35.3 percent in 2000 to 55.7 percent in 2007 suggesting the importance of foreign firms for upgrading Vietnam’s industrial structure. It is also noted that, unlike domestic private and state enterprises, foreign-owned skill-intensive enterprises tend to employ more females than males, becoming another factor contributing to a rise in female intensity in foreign sector employment.<sup>15</sup> Overall, high export-orientation and high female intensity in foreign firms in Vietnam are consistent with the view that expansion of exports has boosted the demand for female labor in manufacturing in developing countries (e.g., Ozler, 2000; Rama, 2003; Wood, 1991).<sup>16</sup>

# **3. Impacts of Foreign Ownership on Wages**

# **3.1. Data and Summary Statistics**

The _Vietnam Household Living Standard Survey_ 2004 (VHLSS 2004) was conducted by the GSO of Vietnam with the technical support of the World Bank and is generally recognized to be of high quality and representative of all of Vietnam. The VHLSS 2004 consists of 9,188 households of which about half were also represented in the sample for VHLSS 2002. Table 2 shows the summary statistics of individuals from 15 to 59 years of age who responded to the survey’s ownership question. The ownership information is based on an individual’s “most timeconsuming job in the last 12 months”. The ownership categories consist of “foreign”, “state”, “private”, “informal wage”, and “self-employment” sectors.<sup>17</sup> Table 2 reveals that the “formal”

> 15 This trend appears to reflect largely the employment expansion in certain export-oriented and female-intensive sectors such as electronics. For instance, in terms of the electrical machinery (VSIC 31), the female intensity among foreign firms registered at 80.5 percent in 2004 as opposed to 23.4 percent and 31.4 percent for private and state enterprises respectively (the _Enterprise Survey_ data).

> 16 Measuring the impact of North-South trade on the female intensity of manufacturing, Wood (1991) finds that developing countries which exported a rising proportion of their manufactured output tended to experience rising female intensity in their manufacturing sectors.

> 17 The classification of ownership is based on the response to a question “for whom has [name] worked?” in the VHLSS 2004: “foreign” is referred to “foreign-invested sector”; “state” sector includes government and SOEs;

8
51. These data confirm Nigeria’s relative cost advantage in cheap labor in the industries identified above.

# **IV. How can growth in the selected value chains be promoted?**

52. In addition to proposing the above methodology to identify target sectors, the GIFF also identifies a number of steps to encourage growth in these targeted sectors. As discussed above, the government can try to identify the obstacles that are preventing these firms from upgrading the quality of their products or the barriers that limit entry to those industries through value chain analysis or the Growth Diagnostic studies suggested by Hausmann, Rodrik and Velasco. In addition, the government can adopt specific measures to encourage firms in the higher-income countries identified in the first step to invest in these industries. Moreover, in developing countries with poor infrastructure and an unfriendly business environment, the government can invest in industrial parks or export processing zones. Such industrial parks or EPZs typically provide conditions that are specifically targeted at certain sectors or industries, e.g., IT or light manufacturing, and are often built around already existing industry clusters. Lastly, the government can also provide limited incentives to domestic pioneer firms or foreign investors that work within the list of industries identified in step 1 in order to compensate for the non-rival public knowledge created by their investments. These steps may include corporate income tax holidays, directed tax credits, or priority access to foreign reserves to import key equipment. In the literature, the former type of intervention is referred to as soft and the latter as hard industrial policy.

53. The section below discusses key constraints in the selected value chains and what specifically could be done about it in the Nigerian context. Available value chain studies provide an analysis of the binding constraints to growth in a number of these value chains.<sup>10</sup> The binding constraints can be broadly categorized in 5 categories: (i) physical infrastructure, in particular lack of power and roads; (ii) business environment (cumbersome procedures); (iii) lack of access to finance; (iv) lack of a technical and vocational education system that corresponds to the needs of the market; and (v) restrictive trade policy. The table below (Exhibit 1) summarizes binding constraints and the measures that could be undertaken to address them in a number of value selected chains.

54. Specific measures to be undertaken for each category of constraints to growth could be as follows:

> 10 A jobs summit that took place in Abuja in August 2010 identified binding constraints to growth for each of the key value chains. How to alleviate these constraints has been agreed upon in a Memorandum of Understanding between the public and the private sector. These measures have subsequently been ratified by the government and are currently being implemented.

26
92(1):1–45.

**URL:** _https://www.sciencedirect.com/science/article/pii/S0304407698000840_

- Courtney, Jocelyn. 2010. “The Civil War That Was Fought by Children: Understanding the Role of Child Combatants in El Salvador’s Civil War, 1980-1992.” <u>Journal</u> of Military History 74(2).

- De Groot, Olaf J, Carlos Bozzoli, Anousheh Alamir and Tilman Br ̈uck. 2021. “The global economic burden of violent conflict.” <u>Journal</u> of Peace Research .

- De Janvry, Alain, Kyle Emerick, Marco Gonzalez-Navarro and Elisabeth Sadoulet. 2015. “Delinking land rights from land use: Certification and migration in Mexico.” American Economic Review 105(10):3125–49.

- Dell, Melissa. 2010. “The persistent effects of Peru’s mining mita.” Econometrica 78(6):1863–1903.

- Dell, Melissa and Benjamin A Olken. 2020. “The development effects of the extractive colonial economy: The dutch cultivation system in java.” The Review of Economic Studies 87(1):164– 203.

- Dell, Melissa, Nathan Lane and Pablo Querubin. 2018. “The historical state, local collective action, and economic development in Vietnam.” Econometrica 86(6):2083–2121.

- Fergusson, Leopoldo, Ana Mar ́ıa Ib ́a ̃nez and Juan Felipe Riano. 2020. “Conflict, educational attainment, and structural transformation: La violencia in colombia.” Economic Development and Cultural Change 69(1):335–371.

- FMLN. 1983. “Sobre el Desarrollo del FMLN.” Comandancia General del FMLN .

- FMLN. 1984. “Situacion Revolucionaria y Escalada Intervencionista en la Guerra Salvadorena.”. Text digitized by Centro de Estudios Marxistas ”Sarbelio Navarrete” (CEM) and Servicio Informativo Ecumenico y Popular (SIEP).

- Foster, Andrew D and Mark R Rosenzweig. 2022. “Are There Too Many Farms in the World? Labor Market Transaction Costs, Machine Capacities, and Optimal Farm Size.” <u>Journal</u> of Political Economy 130(3):636–680.

- Grasse, Donald, Renard Sexton and Austin Wright. 2021. “The Logic and Impacts of Rebel Public Services Provision: Evidence from Taliban Courts in Afghanistan.”.

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# **_Capital-output ratio_**

The consequence of these very different sectoral trends has been that the overall capital-output stock of government assets (schools, hospitals and offices _etc_ .) has only grown slightly faster than GDP, with the ratio of the capital stock to overall GDP remaining low at between 0.2 and 0.3 (Figure 8, Panel A). The increase in the overall capital-output ratio has been driven by the significant rise in the capitaloutput ratio in the infrastructure sector, predominately related to assets for transport and urban development.

The housing sector has been the other source of the rise in the overall capital-output ratio. Prior to the reform of the housing sector at the turn of the century, investment in housing had been neglected. Housing investment picked up markedly a decade ago. Given the rapid increase in GDP after the country’s entry into the WTO, the pickup in growth of housing stock did not, at first, generate an increase in the ratio of the housing stock to GDP. Housing investment rose to a new high in 2007, fueled by expectations that rapid price rises would continue. This coupled with a slackening in the growth of GDP has resulted in a steady increase in the ratio of the housing stock to GDP from 2007 onwards (Figure 8, Panel A). The business sector capital-output ratio has increased somewhat whether measured relative to total GDP (Figure 8, Panel A) or relative to GDP in the business sector (Figure 8, Panel B).

**Figure 8 Capital-output ratio by economic sector**

<!-- Start of picture text --> Constant prices<br> A. Capital stock by sector as ratio of total GDP B. Capital stock by sector as ratio of sectoral GDP<br>3.5 4.0<br>3.0 3.5<br>2.5 3.0<br>2.5<br>2.0<br>2.0<br>1.5<br>1.5<br>1.0<br>1.0<br>0.5 0.5<br>0.0 0.0<br>1980 1985 1990 1995 2000 2005 2010 2015 1991 1996 2001 2006 2011 2016<br>Government Business Infrastructure Housing Business sector Whole economy<br><!-- End of picture text --> _Source:_ Author estimates using NBS data The above estimates include an estimate for the stock of R&D capital. The stock of this type of capital is, however, quite small. Using the revisions to the estimates GFCF published in 2016 and the estimate of the average depreciation rate for R&D expenditures in the United States, which Li and Hall (2017) put at 28% per year, the stock of R&D capital can be estimated for China. Over the past two decades, the stock has risen sharply but still amounts to only slightly below 4% of GDP, accounting for about 21⁄2% of the capital stock in the business and government sectors (Figure 9).

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# VII. **Conclusions**

We find that 18 months after the baseline survey, the proportion of the sample of workfare participants getting a private sector job was 14% for randomly selected voucher recipients versus 9% for the control group. This difference is statistically significant. On dissaggregating the aggregate impact we find that the gain in wage employment was largely confined to women and younger workers. There was a slightly higher impact on employment for those who also took up the offer of training, in addition to the voucher. An impact of training only emerges once one corrects for the endogeneity of take-up amongst those randomly assigned to the treatment. However, we cannot reject the null hypothesis that the training had no impact even after correcting for endogenous compliance.

Clearly, Proempleo did not succeed in achieving a major transition to private sector jobs amongst workfare recipients in the study area. Arguably, without greater labor demand in these company towns, this would have been difficult. However, even in this setting, there appears to be scope for assisting the transition to regular jobs. Given the low take-up of the subsidy, the gain in private sector employment attributed to the voucher was achieved at very little cost to the government. Since the workfare wage was roughly the same as the subsidy paid to firms, the government saved 5% of its expenditure on workfare wages for those receiving the voucher in return for an outlay on wage subsidies that represented only 10% of that saving.

In considering the policy implications of these findings, one must acknowledge the possibility that scaling up could increase the take-up rate amongst firms, and/or reduce the empowerment effect of the voucher. The signal value to employers of the voucher could well be different in a national version of the program. And scaling up might occur at the expense of the 18
advantage in the public sector in the 2000s. In the 2000s, the labor market for young workers appears competitive, with no significant difference in the public and private sectors' pay (α= 1 and η= 0). This finding suggests that public sector rents only benefit older workers.

|TABLE7. Exten|ded Model: In<br>|teraction wit<br>1993<br>|h Experi<br>|ence<br>|2006<br>||
|---|---|---|---|---|---|---|
||Coef.|Std. Err.|P>t|Coef.|Std. Err.|P>t|
|Relative returnsα|0.381|0.271||1.013|0.591|*|
|Publicpremiumηt|−0.288|0.220||−0.029|0.078||
|Publicpremiumηt+1|−0.292|0.227||0.107|0.078||
|Old workers * Returnsγ|0.080|0.206||0.976|0.756||
|Old workers * Publicpremiumδt|0.118|0.083||0.053|0.117||
|Old workers * Publicpremiumδt+1|0.202|0.076|***|−0.026|0.119||
|Time dummy τt|0.118|0.083||1.294|0.037|***|
|Time dummy τt+1|0.202|0.076|***|1.538|0.035|***|
|Sample size||1750|||7786||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# _Returns to skill and gender_

Another interesting method for extending our model is to consider the differences in the labor market earnings between the male and female workers. Confirming the previous findings by Liu (2004), we have seen that in the 1990s, women were paid more favorably in the public than in the private sector. After controlling for the workers' characteristics, the gender gap was not significant in the public sector in the 2000s (table 4). We use the model described by equation (3), replacing Di with the gender dummy Gi = 0 for men and Gi = 1 for women. As before, the estimated returns to the comparative advantage in the public sector are about half of those in the private sector in the 1990s and are not significantly different from each other in the 2000s (see table 8). We find that in the 2000s, the returns to the comparative advantage are much higher for women, with γ being a positive and significant coefficient, whereas the public premium 32
# Nowcasting Prices Using Google Trends: An Application to Central America

Skipper Seabold `American University` Andrea Coppola `The World Bank`<sup>_∗_</sup> JEL codes: E31,C55,C8 Keywords: Macroeconomic modeling and statistics, Inflation, Big Data _∗_ Corresponding author e-mail: jsseabold@gmail.com. This is a preliminary draft.
income similar to the wage they would have earned in an employer-employee relation, based on observable characteristics. We extend the estimates in ILO (2019), using ILOSTAT (2021) data on self-employment shares of the workforce, to all countries since 1991, and B. impute the series backwards to complete it. Details are in the data Appendix

# **3.1.2 Effective tax rates on capital and labor**

We allocate each tax revenue source to labor, capital, or a mix of the two. Specifically, (1) Corporate income taxes, wealth taxes, and property taxes are allocated to capital. (2) Payroll taxes and social security payments are allocated to labor. (3) Personal income taxes are allocated partly to labor and partly to capital (see below), reflecting the fact that personal income is composed of salaries, capital income, and mixed income. (4) Indirect taxes are treated as a separate category (i.e., are assigned neither to labor nor to capital). Table A1 summarizes our allocation.

**Allocation of the personal income tax to factors of production.** The main empirical difficulty in assigning taxes to labor or capital concerns the allocation of the personal income tax (PIT). In most countries, both labor income and capital income are subject to the PIT. As labor income accounts for about 70% of national income and capital income for about 30%, a naive procedure would allocate 70% of the PIT to labor and 30% to capital. In practice, however, not all labor and capital income is subject to personal income taxation, due both to the fact that some individuals are not required to file an income tax return and to legal exemptions for some forms of income. Exemptions for capital (e.g., imputed housing rents, undistributed corporate profits, investment income earned on retirement accounts) are typically larger than for labor (e.g., pension contributions, health insurance contributions); see, e.g., Saez and Zucman (2019b). In the United States, Piketty, Saez, and Zucman (2018) find that 75% of labor income is subject to the individual income tax in 2015, as opposed to only a third of capital income. This would call for allocating about 15% of the personal income tax to capital and about 85% to labor.6 A last difficulty is that

> 6If 75% of labor income is taxable and labor income is 70% of national income (resp. 33% and 30% for capital income), then 75% _×_ 70% _/_ (75% _×_ 70% + 33% _×_ 30%) = 84% of the PIT base is labor income and 16% is capital income.

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same welfare level as smaller households). Again, it is neccesary to assume **that all households** possess the same household utility function. Another consequence of the presence of consumption data at the household rather than the individual level is that one does not know the distribution of welfare within the household; one has little choice but to assume that all household **members enjoy the same** level of welfare.

While one would like to observe the actual utility levels of households, one only observes their levels of consumption. Yet these are monotonically related. Specifically, "money-metric" utility, which is defined as the amount of money required (given a set of prices) to attain a specified level of utility, is equal to observed levels of consumption under the assumption of utility maximization.4/ Given consumption data at the household level, adjustments must be made for household size and differences in prices. Additional household members, particularly children, are less "costly", in the sense of requiring additional consumption to maintain the welfare level of the household, relative to the initial cost of attaining that welfare level in a household composed of a single person or a childless couple. This idea is supported by both common sense and economic reasoning. Clothing and other items can be handed down from older to younger children, durable goods such as radios and refrigerators can be enjoyed by additional members at no extra cost, and even in the case of food children consume less then adults. The method for adjusting for this phenomenon is the estimation of "adult equlivalence scales", 4/ For a thorough presentation see Deaton and Muellbauer (1980).