rafmacalaba/datause-encoder-data huggingface dataset:
# III. LSMS Methodology

# A. Objective of LSNS Surveys

The main objective of LSMS 3urveys is to provide household level data for evaluating the effect of various government policies on the living conditions of the population. Accordingly, LSMS surveys collect data on all major aspects of household well-being. Tn this sense they are multi-topic surveys, gathering data on income, consumption, savings, employment and -nemplcyment, health, education, fertility and contraceptive prevalence, nutrition, housing and migration. Collecting data on these topics from the same households has the added advantage of allowing for the enalysis of the relationship between these different aspects of the quality of life. Examples of this include studies of the impact of education on nutrition, of the effect of health on employment, and the relationship between income and fertility.

# B. Consumption as an Indicator of Household Welfare

Empirical research on the effect .f government policies on households often requires a broad indicator of household welfare. In most LSMS research on poverty, household ielfare is measured by consumption.21 When one selects a certain level of consumption as the minimum amount necessary, one has a poverty line.

> 2/ Of course, the LSMS data are rich enough to allow for the use of otner indicators of household welfare (cf. Glewwe and van der Gaag, 1988). Household consumption is used in mos; LSMS studies because of its intuitive appeal and rigorous theoretical framework.
Given constant expenditure shares, the sectoral Törnqvist price index reduces to a geometrically weighted average of the construction and equipment indices using the 2010 weights. In the two decades ending in 2016, housing investment rose in price by 0.9% per year relative to all investment goods as a result of consisting almost entirely of structures (Figure 5). At the other extreme, business sector prices fell in relative terms by 0.5% per year due to the high share of expenditures on equipment. The relative prices of investment in the infrastructure and government sectors rose by 0.7% and 0.5% per year, respectively.

There is thus a substantial gain in accuracy from using sector specific price indices rather than using the same price index to deflate the investment of each sector. For the period prior to 1981 when the price indices for equipment and construction are not available, sectoral deflators have been calculated by assuming that the price of each sector follows the same trend relative to the published deflator for GFCF as did the movement of sectoral price indices relative to the overall price index of fixed asset investment in the period 1995 to 2016.

# **Figure 5 Sectoral price indices for fixed asset investment**

<!-- Start of picture text --> Estimated sectoral price relative to total price index for FAI<br>1.25<br>1.20<br>1.15<br>1.10<br>1.05<br>1.00<br>0.95<br>0.90<br>0.85<br>Housing Infrastructure Government Business<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> _Source:_ Author estimates based on NBS data.

# **Results**

# **_Investment flows_**

The estimates of sectoral investment flows quantify the marked re-orientation of the structure of capital formation in China in the past decade. There has been a shift away from investment in the business sector towards investment in housing and infrastructure (Figure 6, Panel A). A number of factors have driven this change. In the business sector, the rate of investment depends crucially on the expected growth of the economy. A fall in the expected growth rate of the economy will result in business sector investment dropping until it reaches a level that brings the growth of the capital stock to be once again in line with the lower expected growth of the economy. Indeed, in the period 2008 to 2016, economic growth slackened, from around 9.6% to about 6.7%. With growth slackening, enterprises lowered their expected need for capital and this resulted in a sharp drop in business sector investment from an estimated 20% of GDP in 2008 to 16% in 2016 (Figure 6, Panel B).

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(GWP) average subjective well-being (SWB) Cantril’s Ladder scores circa 2000,<sup>9</sup> at the country level, to obtain the initial level of well-being u0(r) and separately identify the fundamental amenities a�(r). Subsequently, the model is simulated one period forward; the estimated migration frictions m2(r) are the values such that the model-implied population distribution for the subsequent year matches the one observed in the data. To convert the ordinal SWB scores into cardinal values, Desmet et al. (2018) show that for a given time period t, the following relationship holds: , where ψ is ii ii ψψS SStt(rr) the inverse of the estimated coefficient on the log of real income in a cross-country regression of SWB tt uu (rr) = ee of an individual _i_ residing in location _r_ on the log of their real income (Deaton and Stone, 2013; Kahneman and Deaton, 2010).<sup>10</sup> Table 1 presents the values of all internally and externally calibrated parameters along with their sources.

# **3. Migration frictions: Adjusting for spatial variation in initial utility in Latin America**

When calibrating the migration frictions, Desmet et al. (2018) assume that the initial level of wellbeing u0(r) is the same in all geolocations within a country. This assumption might be appropriate in the case of advanced economies, but not necessarily in the case of developing countries (Burger et al., 2021; Bryan and Morten, 2019; Chauvin, Glaeser, Ma, and Tobio, 2017). Hence, we relax the assumption of uniform initial level of utility in Latin America. Using geo-coded data on subjective well-being from several survey rounds of the Gallup World Poll (GWP) and Latinobarómetro, we first compute the average subjective well-being scores for different subnational (level-1) administrative regions in each LAC country.<sup>11</sup> We then estimate a set of adjusted fundamental amenities and migration frictions in locations across Latin America. In the next section, we provide details on the quantification methodology for the baseline period of 2000.

> 9 The Cantril’s Ladder is an ordinal scale indicating how an individual rates their own life. The scores vary from 0 for their worst possible life to 10 for their best possible life. Although the benchmarks for worst and/or best possible life might vary across individuals, regions, and countries, Desmet et al. (2018) abstract from such potential differences in the benchmarks based on the finding by Deaton and Stone (2013) and Stevenson and Wolfers (2013) of a relationship between subjective well-being (SWB) and the log of real income that is similar within the U.S. as well as across countries.

> 10 The inclusion of a location fixed effect in the estimation of the linear relationship between SWB and log of real income addresses endogeneity concerns that a location with a higher utility attracts more people and affects the amenity levels in the location.

> 11 The details of this procedure, as well as its geographical coverage, are provided in the Data Appendix.

11
# **Figure 5. Movement of Emission Intensities Gap between Annex I and Non-Annex Countries along with Annex I Countries’ Carbon Tax Rates**

<!-- Start of picture text --> Non Annex I Countries Annex I Countries<br>NAI-10<br>39100 AI-BAU<br>3575.6 AI-10<br>NAI-50<br>NAI-BAU NAI-100 39000<br>3575.1 38900 AI-50<br>38800<br>3574.6 AI-100<br>38700<br>38600<br>3574.1<br>38500<br>3573.6<br>38400 AI-250<br>NAI-250 38300<br>3573.1<br>6 7 8 9 10 11 12 13 14<br>3.65 3.67 3.69 3.71 3.73 3.75<br>Emission intensity (tCO2 per capita)<br>Emission intensity (tCO2 per capita)<br>GDP Per Capita (Thousand 2004 US$) GDP per Capita (Thousand 2004 US$)<br><!-- End of picture text --> 11
improving the human development outcomes for poor children remain a formidable challenge for Bangladesh. While there has been significant progress in reducing the incidence of underweight children below 5 years (from 60 percent in 1990 to 36 percent in 2011), progress in reducing wasting and stunting<sup>3</sup> has been less encouraging. The rate of reduction in stunting from 2004 to 2011 was only 1.3 percentage points per year while the prevalence of wasting remained virtually the same. The incidence of low birth weight among newborn babies in Bangladesh is among the highest in the world, ranging between 20 to 22 percent, and maternal under nutrition is at about 24 percent (NIPORT, 2013). Poor dietary diversity and insufficient minerals in the average diet cause major diseases. Although the problem of under nutrition affects the whole population, the poor primarily bear its burden: improvements in childhood underweight and wasting outcomes show a strong negative wealth gradient (World Bank, 2013a).

While Bangladesh has been able to make much better progress in improving outcomes in primary education, some gaps between the poor and non-poor still remain. Inequity in primary enrollment because of household poverty has been reduced by half between 2000 and 2010. However, while more children of primary school age were attending primary school in 2010 compared to 2000, repetition and dropout rates still remain high. About 5 million children are still out of the school system and most of them are from poor households (World Bank, 2013b). The poorest children (coming from the poorest 20 percent of families) are 12 percent more likely to be out of school, and this difference is mostly due to them having a lower chance of ever enrolling in or completing grade one, compared to the richest 20 percent. For poor children in Bangladesh, the opportunity cost of attending school can be high<sup>4</sup> . Further increases in completion rates will thus need to be supported by interventions that help to increase both student enrollment and retention among poor children. Moreover, children living in slums are particularly vulnerable for

> 3 Underweight children are too thin for their age, wasted children are too thin for their high, and stunted children are too short for their age. See detailed discussion on underweight, stunting and wasting on page 27.

> 4 In fact recent analysis (Ito and Shonchoy, 2014) finds statistically significant impacts of seasonal labor demand on the school dropout rate in Bangladesh among children from agricultural households.

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|Marine Traffic .............................................................................................................................................. 37|
|---|

# **List of tables**

|Table 1: Events in the UPU database ...................................................................................................... 5|
|---|
|Table 2: KPI derived from tracking data in the LPI 2023 ....................................................................... 14|
|Table 3: Sample container voyage ........................................................................................................ 16|
|Table 4: Rules that consistent events must follow (TradeLens dataset) .............................................. 17|
|Table 5: Number of observations by event type (TradeLens dataset) ................................................. 23|
|Table 6: Data provisioning requirements by role TradeLens event categories .................................... 25|
|Table 7: TradeLens event categories .................................................................................................... 25|
|Table 8: List of timestamps (TradeLens dataset) .................................................................................. 27|
|Table 9: EDI message names and definitions ........................................................................................ 30|
|**List of figures**|
|Figure 1: UPU coverage by region and income group ............................................................................ 6|
|Figure 2: Cargo iQ events sequence........................................................................................................ 7|
|Figure 3: Country coverage of Cargo iQ dataset by WB region and income group ................................ 8|
|Figure 4: TradeLens supply chain framework ....................................................................................... 11|
|Figure 5: TradeLens dataset: Number of observations by week .......................................................... 22|
|Figure 6: Count of consignments by number of events (TradeLens) .................................................... 24|
|Figure 7: High-level network structure of Tradelens dataset ............................................................... 29|
|Figure 8: Share of null values in 2019 parcel postal records of destination countries ......................... 32|
|Figure 9: Network representation of the UPU dataset ......................................................................... 33|
|Figure 10: Sequence of data events (Cargo iQ dataset) ....................................................................... 34|
|Figure 11: Maritime dataset country coverage by WB region and income group ............................... 37|

iii
markets. Second, we examine the characteristics of firms and the environments in which they operate that determine the relative importance of each type of labour cost adjustment mechanism. Finally, we show how the use of these adjustment practices can be related to firms’ experience regarding nominal wage rigidity, as well as to the extent of wage indexation operating in the firm.

In order to address these questions, we use a novel firm-level survey that contains detailed qualitative information for a large number of firms in 12 EU countries. The survey was carried out within the framework of the Wage Dynamics Network, a research network sponsored by a consortium of Central Banks of the EU and coordinated by the European Central Bank. The most important advantage of using qualitative information from a firm survey refers to the possibility of addressing a broad set of adjustment practices, most of which are typically not observable even in the richest matched employer-employee datasets and are therefore new to the literature.

Our survey shows that firms fairly commonly use strategies to reduce labour costs other than reducing base wages – 63% of the firms’ managers said they had used at least one other margin of adjustment in the recent past, and 58% had used at least one of the six margins explicitly identified in the survey. The use of each margin is related to several firm characteristics such as the relative size or skills distribution, as well as several indicators of the economic environment in which they operate. Firms in more competitive environments tend to use some of these strategies more heavily. Similarly, the degree and characteristics of union involvement in the wage setting process shape the need and ability of firms to use different margins.

The paper is organised as follows. Section 2 describes the main characteristics of the survey and the sample used in the paper. Section 3 describes various compensation channels - other than base wages - that firms may use to reduce labour costs and the frequency with which they are used in different countries and sectors. Section 4 relates the choice of cost reduction methods to firm characteristics and attributes of the economic environment in which they operate. Section 5 looks at the relationship between these alternative margins of cost-cutting strategies and the recent firm experience of nominal wage rigidity and indexation mechanisms. Section 6 concludes.

5
domestic work is extremely low, either migrants or not (Figure 18). Furthermore, most women who migrated work as salaried workers compared to those who did not migrate (71.1 against 66.0 percent). A lower proportion of them work as self-employed (18.6 against 22.7 percent). The same holds for men, although the differences across migration status are more significant (Figure 19).

Figure 18. Distribution of women and men (15+) who did and did not migrate by labor force status <!-- Start of picture text --> 100<br>7.6<br>80 2.7 9.6 Not working<br>37.3<br>3.1 41.2<br>Unable to work<br>60<br>8.2 Unpaid housework<br>1.4 9.4<br>40 80.6 72.1 0.9 Studying<br>20 46.8 40.2 Retired / Pensioner<br>Unemployed<br>0<br>Men who Men who did not Women who Women who did Employed<br>migrated migrate migrated not migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Figure 19. Distribution of employed women and men (15+) who did and did not migrate by type of employment <!-- Start of picture text --> 100<br>14.1<br>21.0 18.6 22.7<br>80<br>4.4<br>Unpaid worker<br>7.2<br>60 Self-employed<br>Employer<br>40<br>73.1 71.1 Paid trainee<br>66.0<br>60.0<br>Daily or farm workers<br>20<br>Salaried<br>0<br>Men who Men who did not Women who Women who did<br>migrated migrate migrated not migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

**Unconditional earning gains from internal migration are higher for men than for women.** On average, women who migrated earn 17.1 percent more than those who did not migrate before controlling for other factors. The unconditional gains from migration are higher for men. Men who migrated make 27.0 percent more than their counterparts (Figure 20). When comparing only internal migrants who moved for economic reasons versus those who stayed, the unconditional earnings gains from migration are higher (39.2 percent for men and 33.5 for women), and the migration earnings gains gap between men and women narrows from almost 10 percentage points to 5.7 percentage points.

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Table 4 presents the relative scale of these rates by expressing each group’s share of net employment growth, gross job creation, and job contraction. Using categories defined by initial employment, SMEs account for nearly all net employment growth, at a rate well above 90%. Using the more conservative categories based on average employment, SMEs under 100 employees account for more than three quarters of net job creation; 90 percent of net employment creation in these economies is accounted for by establishments with fewer than 250 employees, as shown in panel a. Table 4, Panel b presents the relative share of establishments in gross job creation. Again, SMEs dominate. SMEs with fewer than 100 employees account for 76% of gross job creation; establishments smaller than 250 employees account for roughly 90% of gross job creation. In terms of job contraction (panel c), however, SMEs (those with fewer than 100 employees) and large establishments constitute roughly equal shares. Using the SME250 threshold, however, SMEs account for more than 70% of job contraction. This movement is only exaggerated by using the average establishment size.

Generally then, while SMEs account for the vast majority of job creation, they also constitute the majority of job contraction. The relative share of these establishments to large establishments is such that on net SMEs still contribute positively to the majority of net job growth. Older establishments additionally account for the majority of job contraction and in fact it is this high relative contraction among older establishments that accounts for the low relative share of these establishments to net job creation. This finding is undergirded by several others in the literature in line with vested older establishments such as Jovanovic (1982) That is, older establishments are more likely to remain in the market (thus captured in the ES data) and contract, while younger establishments are predicted to exit the market altogether (which is not captured by the ES data).<sup>28</sup>

**Table 4: Job Growth Shares**

# (a) Net Employment Growth Shares

||Based o|n Initial|||Based on|Average|||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|92.6|7.4|97.8|2.2|75.9|24.1|90.2|9.8|56.0|21.6|22.4|

# (b) Gross Job Creation Shares

||Based o|n Initial|||Based on|Average|||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 emp|. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|75.8|24.2|87.2|12.8|68.6|31.4|84.3|15.7|39.5|20.3|40.2|

> 28 Though this finding does in fact hold up in analysis based on ES panels, such as in Aga and Francis (2015).

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WHEN IS THE GOVERNMENT _TRANSFER_ MULTIPLIER LARGE?

26 the Zero Lower Bound, but can also happen when the fiscal shock is not very persistent (if monetary policy follows a Taylor rule). From the RHS of Figure 3.1 one can see that when the demand curve inverts, higher inflation _boosts_ aggregate demand by increasing the disposable income of the HtM HH by more than it reduces the consumption of the Ricardian HH via higher real interest rates. This means that the increase in supply from a government purchases shock actually reduces the multiplier relative to a comparable targeted transfer.<sup>32</sup> 3.5. **The sensitivity of the transfer multiplier to the central bank’s response to inflation and alternative monetary policy rules.** The larger size of the transfer multiplier at ZLB is because the transfer multiplier is generally more sensitive than the purchase multiplier to the monetary policy response to inflation (Proposition 5). Under a Taylor rule, _φπ_ appears in the numerator and denominator of the expression for the purchases multiplier, but only in the denominator of the expression for the transfer multiplier. As discussed above, transfers tend to produce more inflation than purchases, and so an increase in _φπ_ will lead to a larger increase in real rates for transfers than purchases (as _φπ >_ 1), leading to a greater fall in the multiplier.<sup>33</sup>

**Proposition 5.** _Assume A1. A more aggressive monetary policy response to inflation leads to a larger proportional fall in the transfer multiplier than the purchasers multiplier, i.e. ∂lnMTr/∂φπ < ∂lnMG/∂φπ where ∂lnMTr/∂φπ <_ 0 _and ∂lnMG/∂φπ <_ 0 _._

_Proof._ Differentiating the expressions for the multipliers in Proposition 2 with respect to _φπ_ yields:

> 32This result is related to Eggertsson and Krugman (2012) and Eggertsson (2010a, 2012), who argue that the “paradox of toil” means that increase in aggregate supply can reduce actual output. However in those papers this only occurs at the ZLB, whereas here is can also occur with a Taylor rule (due to the presence of HtM HHs). In the latter case, a _reduction_ in price flexibility ( _↓ κ_ ) increases transfer multiplier relative to the purchase multiplier here, whereas _increased_ flexibility makes restrictive labour market practices more stimulatory in Eggertsson (2012).

> 33If the central bank responds to output in the Taylor rule, Equation 3.9 becomes _Y_ ˆ _t_ = _−_ 1<sup>�</sup> _α_ + _αφY_<sup>_<u>φ</u>_</sup><sup><u>)</u></sup> _G_ ˆ _t_ + _Tr_ ˆ _t_ + Γ<sup>_κ_ˆ</sup><sup>_Gt_</sup> , which does not affect the relative size of the trans� (1 _− ρ_ )<sup>+ Γ</sup><sup>_κ_</sup> 1<sup><u>(</u></sup> _−_<sup>1 +</sup> _ρβ_ � 1 _− ρβ_ � fer and purchase multipliers (though it reduces their absolute size).