rafmacalaba/datause-encoder-data huggingface dataset:
professional and administrative occupations so that there are still white-collar occupations.<sup>9</sup> For low-educated women, employment has shifted from agriculture into food and tobacco, textiles, construction, and domestic services.<sup>10</sup> The changing industrial distribution of workers is consistent with Boserup’s (1970) description of white-collar jobs becoming increasingly scarce when education levels grow rapidly. The distribution of male workers (not shown) confirms that the share of white-collar services employment has declined not just for women but for the entire labor force, while employment growth has been concentrated largely in construction and retail and wholesale trade. At the same time, educational attainment has indeed grown rapidly. The share of women with at least secondary education grew from 21 percent in 1987 to 45 percent in 2011. Among men, this share increased from 38 to 56 percent over the same period (for more detail, see Fig. A1.2 in appendix 1). Besides rising incomes, this growing supply of highly educated workers combined with employment shifting toward less skill-intensive sectors could be an important reason why participation rates among highly educated women have declined.

# **III. Estimating the Determinants of Women’s Labor Force Participation**

Using the NSS survey data, we test how the different factors discussed above have contributed to the stagnation of FLFP in urban India. We first estimate the effect of education, income, and other variables on women’s labor force participation in a reduced form labor supply model. In the next section, a decomposition analysis is used to show how changes in the explanatory variables

> 9 Due to changes in the occupational classification used in the NSS data, we cannot construct a consistent definition of white-collar occupations over time. We do find that in all sectors, highly educated women are more likely to work in professional and administrative occupations than women with less than secondary education.

> 10 Domestic services could be considered appropriate for women, but they are typically not covered by existing legislation and are relatively easy victims of exploitation due to their invisibility, lack of education and, often, migration background (NCEUS 2007).

15
These results suggest that part of the remaining association between informal budget shares and household expenditure in the last column of Table 2 could be due to richer households valuing high-quality goods more and such goods being sold mainly in formal stores. This is in line with results in Faber and Fally (2017) who show that richer households spend more on larger brands in the United States, and Atkin et al. (2018b) who find that richer Mexican households spend more on high-quality products sold by foreign retailers. This explanation could imply that formal varieties of a given good should be more expensive than informal varieties, reflecting quality differences. We test this hypothesis in the 20 core sample countries which report unit values for each purchase. We use unit values to proxy for non-quality adjusted prices. We study price differences between formal and informal stores within the most narrow good classification available, location and measurement units, leading us to interpret price differences as reflecting quality differences (similar to Atkin et al. (2018b)). We limit our analysis to food since this measurement issues and because food items are products mitigates typically exempt from consumption taxes, so that any price difference between formal and informal varieties cannot be due to taxes. Formally, we estimate the price premium in the formal sector in each country separately as follows: where _ln_ ( _unit value_ ) _igmu_ is the unit value reported by household _i_ , for good _g_ , in location _m_ , in units _u_ , and _Formaligmu_ equals one if the good is purchased in a formal store. We add fixed effects at the good * location * unit of measurement level.

Table A5 shows that on food unit values are 6.7% in formal than average, higher informal stores. This formal store premium result is robust to outliers, excluding selfproduction, and controlling for household characteristics. These results are consistent with the hypothesis that formal stores offer high quality varieties at a higher price.<sup>25</sup> Formal stores might of course differ in other ways reflected in prices, such as a higher productivity, which would make it less likely to find positive price differences.

# **3.3 Food Engel Curves**

In this sub-section, we document the shape of the food Engel curve in developing countries. The shape of the IEC determines how much redistribution can be achieved by de facto exemption of informal consumption; the shape of the food Engel curve similarly

> 25Consistent with a quality-gradient in size, we also find that within formal retailers, the larger stores (category 5) charge higher prices than smaller specialized stores formal category (category 4).

14
<!-- Start of picture text --> Syria: Real Credit Libya: Real Credit<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term cycle Real credit growth (y/y) Medium-term cycle Real credit growth (y/y)<br>40 60<br>20 40<br>0 20<br>-20 0<br>-40<br>-20<br><!-- End of picture text -->

# **C- Western countries and Japan**

<!-- Start of picture text --> Norway: Real Credit Netherlands: Real Credit<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term cycle Real credit growth (y/y) Medium-term cycle Real credit growth (y/y)<br>Japan: Real Credit Spain: Real Credit<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term cycle Real credit growth (y/y) Medium-term cycle Real credit growth (y/y)<br>60<br>20<br>40<br>10<br>20<br>0<br>0<br>-20 -10<br>40 30<br>20<br>20<br>10<br>0<br>0<br>-20 -10<br><!-- End of picture text --> 36
North Atlantic Treaty Organization (NATO).<sup>6</sup> The final sample covers EU and US investment between January 2015 and December 2022 to 156 destination markets.

A first inspection of US investment data in Figure 1 shows no indication of permanent shifts in nearshoring or friend-shoring following the trade tension with China (2018-19), the Covid-19 pandemic (2020-21), and the Russian invasion of Ukraine (2022-23).<sup>7</sup> The share of greenfield foreign direct investment in neighboring and NATO countries declined by a few percentage points when comparing the 2015-17 and 2022-23 periods (Figure 1). However, reshoring activity by US multinationals intensified following the Covid-19 pandemic, with the share of greenfield investment in the US increasing from 46 percent to 56 percent, between the 2015-17 and 2020-21 periods. This evidence corroborates the anecdotal evidence of US manufacturers starting to bring back factories to the US.<sup>8</sup> These investments are likely to have an impact on trade flows once they become operational.

> 6 List of NATO countries: Belgium (1949), Canada (1949), Denmark (1949), France (1949) Iceland (1949), Italy (1949), Luxembourg (1949), Netherlands (1949), Norway (1949), Portugal (1949), United Kingdom (1949), United states (1949), Greece (1952), Türkiye (1952), Germany (1955), Spain (1982), Czechia (1999), Hungary (1999), Poland (1999), Bulgaria (2004), Estonia (2004), Latvia (2004), Lithuania (2004), Romania (2004), Slovak Republic (2004), Slovenia (2004), Albania (2009), Croatia (2009), Montenegro (2017), and North Macedonia (2020). We assume that the EU is a NATO member even if some of the EU countries are not part of the alliance.

> 7 These results are similar to Aiyar et al. (2023) that show no impact of geopolitical distance for advanced economies.

> 8 See for instance Bloomberg “US Manufacturers ‘Pumped Up’ About Supply-Chain Reshoring Trend” (November 2, 2022) <u>https://www.bloomberg.com/news/articles/2022-11-02/us-manufacturers-pumped-upabout-supply-chain-reshoring-trend</u> and the Wall Street Journal “U.S. Companies on Pace to Bring Home Record Number of Overseas Jobs” (August 23, 2022) <u>https://www.wsj.com/articles/u-s-companies-on-paceto-bring-home-record-number-of-overseas-jobs-11660968061.</u>

9
# **2. Review of Previous Research of Child Labor and Schooling**

Previous research on child labor has focussed almost exclusively on empirical work. An important exception, however, is Basu and Van (1998). They develop a model of an economy in which child labor is a potentially important component. The economy exhibits multiple equilibria. Whether child labor exists in equilibrium depends on the general level of productivity of the economy. If the economy is very unproductive, child labor exists in equilibrium, while if very productive, it does not. Two assumptions are identified as essential to this result. First, their "Luxury Axiom" states that the children are sent to work only if the household's income from other sources than child labor is very low. Secondly, their "Substitution Axiom", assumes that - from the viewpoint of firms - child labor is a substitute for adult labor.

While these two crucial assumptions are related to the micro-behavior of households and firms, Swinnerton and Rogers (1999) show that in addition to these micro-level assumptions there also exists an essential assumption linked to the macrobehavior. They term this the "Distribution Axiom", which states that income or wealth from non-labor sources must be sufficiently concentrated within only a few of the agents. In particular, Swinnerton and Rogers show that with sufficient equality in the distribution of non-labor income, a market equilibrium with child labor cannot exist in the Basu and Van model. This view is now increasingly held by international organizations, e.g., the World Bank (see Fallon and Tzannatos, 1998). While, thus, some progress has been made in establishing important characteristics and determinants of child labor in developing economies in the theoretical literature, the main focus has been on empirical studies.

The empirical literature has mainly been occupied with the schooling decision, merely viewing child labor as the lack of schooling. Chao and Alper (1998) analyze the access to basic education in Ghana for children between 10-14 years of age. Two supplyside factors that reduce participation are identified, namely (1) distance to primary school, and (2) pupil-teacher ratio at the primary level. Furthermore, demand-side constraints affecting enrollment and drop-out rates are access to drinking water and roads. Lastly, household income, demand for child labor, and parental education are found to be key factors in determining the likelihood of children attending primary school.

4
Table 1: Descriptive statistics

||Observations|mean|s.d.|min|max|
|---|---|---|---|---|---|
|**WBES and WDI:**||||||
|Innovation|79,608|.529|.499|0|1|
|New product|79,379|.392|.488|0|1|
|New process|77,742|.434|.496|0|1|
|loan|125,992|.349|.477|0|1|
|share retained earnings in WC|114,174|.718|.339|0|1|
|share bank fnance in WC|114,174|.126|.240|0|1|
|share other fnance in WC|114,174|.149|.255|0|1|
|share of frms for which retained earnings|114,174|.461|0|.461|.461|
|is the only source of WC fnance||||||
|share of frms for which retained earnings|114,174|.775|0|.775|.775|
|exceed half of their WC fnance||||||
|ln(inv)|48,660|13.8|3.32|0|29.0|
|ln(empl)|130,089|3.25|1.39|0|11.1|
|ln(age)|129,046|3.01|.570|0|5.89|
|subsidiary|127,496|.178|.383|0|1|
|infation|129,309|7.02%|68.3|-2%|100%|
|ln(real GDP pc)|133,141|7.93|1.05|5.25|10.9|

# **4.2 Empirical specification**

The data allow us to examine the key relationships in the mechanism linking inflation to technology choice and to test the specific predictions in Proposition 1. In detail, we test the two key components of the model mechanism: First, that firms reduce their liquidity holdings _<u>∂l</u>_<sup>_j_</sup> in periods of higher inflation, _∂_ (1+ _R_ )<sup>_<_0;andsecond,thatfirmsreducetheirinvestmentin</sup> innovation activities in periods of higher inflation (part (i) in Proposition 1).

We consider empirical specifications of the form where _yjsct_ is either given by the innovation dummies or by the share of working capital financing through retained earnings, bank credit, and other sources for firm _j_ in sector _s_ , country _c_ , and year _t_ . _inflct_ measures the level of inflation firm _j_ faces in country _c_ and year _t_ . _Xjsct_ is the matrix of firm-level control variables, _Xct_ the vector of macro-level control variables (real GDP per capita), _Is_ is a vector of two-digit sector fixed effects, _Ic_ is a vector of country fixed effects, and _εjsct_ an independent and identically distributed error.<sup>19</sup> We always cluster the error

> 19Note that we cannot include establishment fixed effects since the panel dimension of the WBES data is not representative at the aggregate level. But the cross-section of establishments is representative for the formal

18
Thus for a program targeted perfectly to the poor, the targeting coefficient will take on a value of 1 since the average transfer value received by the nonpoor is zero (i.e., _VL_ p _=_ 0). A program that distributes the benefits perfectly randomly will result in equal values going to the poor and nonpoor and a targeting coefficient equal to zero (i.e., _VL_ p _= VLp_ ). Finally, the targeting coefficient takes on a value of negative infinity when the program is targeted perfectly to the nonpoor (i.e., _VLp =_ 0 ).

# _B. Computing Targeting according to Wealth Rankings_

As reported above, of all the data sets we are using, only the ENNIV 1997 has information on both household expenditures/incomes and values of VL benefits received. While this implies that we can only assess targeting across expenditure groups with the ENNIV dataset, we can still indicate the degree of targeting according to household wealth using the PETS data. However, to do this, we also have to consider the nature of the PETS sample. This data set has information on value of transfers at the household level conditional upon participation in the VL program. No information was collected from nonparticipating households. Further, the PETS data do not include household expenditures or incomes. Nonetheless, we can indicate the share of the total program that is received by different wealth quintiles using the asset data in the PETS (and expenditure data in the ENNIV 1997). In order to do this for the PETS data, we create a wealth ranking following a methodology that has been regularly applied to DHS data sets (Sahn and Stifel 2001). In particular, we construct a wealth index from households' asset information. This index is the outcome of a factor analysis of various assets about which the survey asks: household characteristics (water source, toilet facilities, and construction material) and durables (ownership of radio, television, refrigerator, bicycle, motorcycle and/or car), as well as education of the household head. We assume that there is a common factor, "wealth," that explains the variance in the ownership of these assets, and allow the factor analysis to define that factor as a weighted sum of the individual assets.<sup>2</sup> By using the weights derived from the 2000 DHS to construct a wealth index for the PETS sample, we are able to determine how the households sampled in the latter survey rank relative to the overall national population. Although the PETS used a sample frame of recipients, we have sample weights and municipal populations and therefore can derive the proportion of VL recipients in each jurisdiction. Since our unit of analysis is the household, the assumption necessary to derive the proportion of VL households in each jurisdiction is that the share of beneficiary households for a particular committee to all households is the same as the share of beneficiaries in the committee to all beneficiaries.

> 2 See Sahn and Stifel (2001) for a detailed discussion of the factor analysis methodology, and for an evaluation of this type of asset index. Hammer (1998) and Filmer and Pritchett (2001) have employed a similar methodology.

_8_
lag. In both equations we anticipate the coefficients on all explanatory variables to be positive.

Note that this first specification does not distinguish between university and nonuniversity patenting activity because data from the U.S. Patent and Trademark Office did not make this distinction for patent applications in early years of the sample. However, patent grants are broken out in this way. Thus, a second series of equations distinguishes between patents awarded to universities and patents awarded to other organizations: IPAt+5 = α 1 + λ F1FORTGRt + λ G1TOTGRt + λ I1IMCUMt + λ S1SKt + λ R1RDt + φ 1TOTPATSTOCKt + δ B1BD + _θ_ 1t TIMEt + η 1t UIPGt+7 = α 2 + λ F2FORTGRt + λ G2TOTGRt + λ I2IMCUMt + λ S2SKt + λ R2URDt + φ U2 UPATSTOCKt + φ O2 OPATSTOCKt + δ B2BD + _θ_ 2t TIMEt + η 2t OIPGt+7 = α 3 + λ F3FORTGRt + λ G3TOTGRt + λ I3IMCUMt + λ S3SKt + λ R3ORDt + φ U3 UPATSTOCKt + φ O3 OPATSTOCKt + + δ B3BD + _θ_ 3t TIMEt + η 3t The first equation is the same as the initial equation in the pair listed above. The second equation captures patents granted to universities (UIPG) after a seven-year lag. It employs the same variables except it incorporates university real R&D expenditures, cumulative university and non-university patent stocks during the past seven years (UPATSTOCK and OPATSTOCK), all divided by labor force, and the dummy variable for the Bayh-Dole act. The third equation captures non-university patents awarded (OIPG) after a seven-year lag. It incorporates non-university real R&D (ORD) 15
**Table 1: Reported changes between January – June 2009 and the year before**

||%<br>|firms reporti<br>|ng<br>|Mean<br>|Number of<br>|
|---|---|---|---|---|---|
||Decrease|No change|Increase|change<br>reported (%)|observations|
|**Performance**<br>||||||
|Total sales|85.4|9.2|5.4|-30.7|242|
|Domestic sales (if any)|85.3|9.1|5.6|-30.5|208|
|Exports (if any)|70.2|13.7|16.1|-14.5|65|
|Profits|88.5|4.8|6.7|-30.1|269|
|**Prices**||||||
|Unitprice of mainproduct|38.6|47.4|14.0|-5.9|270|
|Unitprice of main input|26.0|38.4|35.5|1.5|269|
|**Permanent employment**||||||
|Total|16.6|68.6|14.7|-0.8|242|
|Management|5.3|90.2|4.5|-0.8|270|
|Professionals|11.0|74.6|14.4|+1.4|235|
|Skilledproduction workers|21.3|62.9|15.9|+3.3|84|
|Unskilledproduction workers|24.6|61.9|13.5|-3.0|71|
|Non-production/service workers|17.9|72.9|9.2|-4.2|251|
|**Wages and compensation (permane**|**nt workers)**|||||
|Total|8.0|58.8|33.2|1.8|242|
|Management|4.9|70.3|24.8|2.2|270|
|Professionals|7.0|58.8|34.2|3.6|234|
|Skilledproduction workers|7.2|46.8|46.0|9.6|83|
|Unskilledproduction workers|4.6|72.8|22.6|2.7|70|
|Non-production/service workers|7.3|60.0|32.7|3.2|250|

Note: all figures are weighted. Source: CRBS 2009.

average. Wages actually increased over the period and also increased in real terms (the inflation rate was - 3.9% between July 2008 and July 2009 - World Bank 2009).

Although the financial crisis affected firms widely, the impact shows clear variation across firm characteristics. Table 2 reports descriptive OLS regressions to identify which firms are more likely to report larger changes in total sales and profits. We include controls for firm size, location, sector, labor productivity, export status and use of finance. Labor productivity is measured by value added per worker and medium and high productivity firms are those firms which have labor productivity levels in the second and third tercile of the productivity distribution. Use of finance indicates the source(s) of working capital used by the firm. Because the variables for export status and use of finance are only available in the ICS 2007/2008 survey, the regression is limited to the panel firms.

6
- 38 -

3.6 Some forest services use log size to differentiate their raw material, for instance the size of sawlogs. Table 3.2 shows an example of this practice used in Malawi. Differentiating by size recognizes the .act that the larger the diameter of a log, the greater the percentage<sup>of</sup> sawnwood or charcoal that can be recovered from it, and the greater the unit price that can be obtained by selling it for poles, etc. However, like the fees in Zambia$ no account is taken of distance from the market.

Table 3.2: MALAWI FOREST DEPARTMENT SALE PRICE OF STEM WOOD EX ROADSIDE a/ b/ (Kwacha per m3 sold)

|Details|**Mk/m**<br>**3** _cI_|
|---|---|
|Under<br>2.5mlong<br>and under<br>20 cm butt diameters|<br>2.9|
|Greater<br>than 2.5 m and up to 3.0_m_In length||
|Butt diamete**r**ange<br>cms.||
|20.0- 23.9|7.9|
|24.0<br>- 27.9|10,2|
|28.0- 31.9|12.5|
|32.0- 35.9|14.9|
|36.0 - 39.9|17.2|
|40,0- 43.9|19.5|
|44.0- 47.9|21.9|
|48.0<br>- 51.9|**24.2**|
|52.0- 55.9|26.6|
|56.0- 59.9|28.9|
|60.0 - 63.9|31.2|
|64.0- 67.9|33.5|
|68.0 and above|34.4|
|Greater<br>than 3.0mand up to 5.0mIn length||
|Butt diameter<br>range<br>cms||
|20.0 - 23.9|14.2|
|24.0- 27.9|18.4|
|28.0 - 31.9|22.6|
|32.0 - 35.9|26.8|
|36.0 - 39.9|31.0|
|40.0 and above|34.4|
|Greater<br>than 5.0**a**and above In length||
|Butt diamete**r**ange<br>coms.||
|20.0 - 23.9|15.7|
|24.0- 27.9|20,4|
|28.0- 31.9|27.6|
|32.0 and above|34.4|

a/ These fees date from 1985. A rovlslon was due In June 1988. b/ No specles are gIvon. c/ Current exchange rate USSI **u** 2.641 M. Kwacha. Source: Malawi Forestry Department.
Policy Research Working Paper 5720

# **Abstract**

This paper incorporates morality—defined as lower utility from consuming goods obtained through appropriative rather than productive activities—into a simple static general equilibrium model in which agents choose whether to be producers or appropriators. The authors analyze the relationship between the _correlation_ between morality and human capital on the one hand, and aggregate economic performance on the other. They show that there is a main effect that tends to cause this relationship to be positive, and that there can be secondary effects that can either rein-force or oppose (or even overbalance) the main effect. They test the theory using the World Val-ues Survey as a source of proxies for morality. Using their preferred proxy, they find evidence that higher within-country correlation between morality and ability, holding constant the levels of morality and ability, increases per-capita income levels. Under the preferred specification, a one-standard-deviation increase in the correlation between morality and ability raises the log of per-capita income by about one-fourth of a standard deviation, equal to approximately $3,600 for the median income country in the sample. The results are robust to correcting for endogeneity and to changes in sample and specification. The results are mixed when the analysis uses alternative morality proxies, but the coefficient on the morality-ability correlation is still usually positive and statistically significant.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sknack@worldbank.org or dbalan@ftc.gov.

_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