rafmacalaba/datause-encoder-data huggingface dataset:
On the expenditure side, domestic transfers and foreign interest payments account for respectively 24 and 9 percent of the budget, whereas consumption and savings for investment purposes amount to respectively 40 and 27 percent of the budget. Overall, the government relies heavily on indirect taxes for revenue collection (as is common in developing countries), while at at the same time allocating a significant fraction of its resources to investment.

# 4.2 Behavioral Parameters

Consider now the behavioral parameters of the model. In agriculture the Cobb-Douglas (share) parameter in the production of value added equals 0.8, whereas the CES substitution elasticity between rural labor and public capital equals 2/3. Public capital is aggregated from infrastructure and health capital using a substitution elasticity of 1/2. The informal sector only has one factor of production, unskilled labor, and the Cobb-Douglas (share) parameter equals also 0.8. In contrast, the private formal sector has a threelevel nested production structure, with a bottom level substitution elasticity of 2/3 between private capital and skilled labor, a middle level substitution elasticity of 7/6 between the bottom level composite factor and unskilled labor, and a top level substitution elasticity of 5/6 between the middle level composite factor and public capital. Finally, public sector value added is derived using CES aggregation of skilled and unskilled labor with a substitution elasticity of unity.

Turning to the factor market and the wage bargaining equation for private sector skilled wages, elasticities with respect to skilled labor unemployment and public sector wages are set respectively at -2.0 and 2.0, whereas the wage elasticity of private skilled labor demand is -1.0. The parameter measuring the trade union’s bargaining power is set at 0.7. Rural-urban and international migration elasticities with respect to relative expected wages are set respectively at 0.4 and 0.6, whereas persistence parameters for rural-urban and international migration are set respectively at 0.1 and 0.3. The formalinformal sector migration elasticity with respect to relative expected wages is 0.4. the elasticity of substitution between publicly-employed teachers and public education capital in the skills-upgrading CES production function is 1/3, whereas the elasticity of teachers effort with respect to relative wages is 0.8.

The Armington elasticities for rural agricultural and urban formal sector goods imports are set respectively at 2/3 and 1.5. Similarly, the CET trans24
the difference in log lights per capita over 2006-2010 against the difference in log lights, controlling for local conflict. The slope is again positive, indicating that improvements in market access are associated with output growth. In Sections 5 and 6 we make an effort to establish causality.

Figure 12: Nighttime Lights (NTL) vs. market access <!-- Start of picture text -->
-1 -.5 0 .5 1<br>ln Market Access Θ=40 (Residual)<br>coef = .061, se = .014 t = 4.23<br>.4<br>.2<br>0<br>ln NTL pc (Residual) -.2<br>-.4<br><!-- End of picture text -->

Nighttime Lights (NTL) vs. market access (controlling for locality, year and fatalities)

# **4.2 Fatalities data**

We use geo-referenced data on Israeli and Palestinian fatalities in the West Bank from B’Tselem<sup>16</sup> as our indicators of local conflict. For each violent incident, B’Tselem records the exact date, geolocation, and number of fatalities for Israelis and Palestinians separately. We combine these data with our geo-referenced locality data for the West Bank, to obtain the number of Israeli and Palestinian fatalities that have been recorded within a 5km radius of each locality during the course of the year, which we use as a control variable.

> 16B’Tselem is an Israeli independent non-profit organization that records a variety of statistics related to the conflict between Israel and Palestine. It refers to itself as an Information Center for Human Rights in the Occupied Territories. The statistics published by B’Tselem we use can be downloaded from: http://www.btselem.org/statistics.

25
(rather than general aggregate variation), and _statesharei,t−_ 4 is based on the eligibility rules for each specic policy change (rather than average transfer shares). Combined, this means that ∆ _tri,t_ reects actual changes in transfers to dierent states very accurately for example, in an IV specication rststage coecients are close to one and F-stats are above 500 rather than being an instrument correlated with transfer changes.

For the 2001 transfers and the 2008 middle-income tax refund the aggregate size of the payments ∆ _trUS,t_ in Equation 1 are taken from JPS and Parker et al. (2013) respectively (subtracting the size of the low income rebate from BEA (2009) in the latter case). Each state's share ( _statesharei,t−_ 4) is calculated using IRS state-level data on individual tax returns for the previous tax year (2000 and 2007 respectively), using eligibility rules for each specic payment. Eligibility for the 2001 and 2008 stimulus payments largely depended on income in 2000 and 2007, as full-year income was not known in 2001Q3 or 2008Q2 when the rst payments were made. The cross-state distribution of one-o transfers is plotted below in Figure 1.

For permanent Social Security benet increases, the aggregate transfer change ∆ _trUS,t_ is taken from Table 1 of Romer and Romer (2016) over 19521974.<sup>6</sup> I allocate the exogenous increase in aggregate Social Security payments across states in proportion to that state's share of Social Security payments a year before, _statesharei,t−_ 4 as in Equation 1 (see Appendix 1 for details). As the policy change essentially involves scaling up existing Social Security transfers, Equation 1 is exact using a contemporaneous _stateshare_ . In practice _statesharei_ is almost unchanged year-to-year, so Equation 1 is likely to be very accurate (see Appendix Figures 1.1 and 3.1B).<sup>7</sup>

> 6As Romer and Romer's data are monthly, whereas mine are quarterly, I spread the adjustments over two quarters if the permanent increase in payments occurred mid-quarter. For example, a permanent $1 increase on a $10 monthly payment starting on June 1 will become a 3.3% increase in Q2 and a _≈_ 6.6% increase in Q3.

> 7For example, in the case of an increase in Social Security benets in the rst month of the quarter, ∆ _trUS,t_<sup>_SS_isthedollarvalueofRomerandRomer'spermanenttransferchange,and</sup> _statesharei,t−_ 4 = _tri,t_<sup>_SS_</sup> _−_ 4<sup>_/_�50</sup> _i_ =1<sup>_tr_</sup> _t_<sup>_SS_</sup> _−_ 4<sup>isthatstate'sshareoftotalSocialSecuritytransfers</sup> four quarters before.

8
_(Table C.4 continued.)_

|Garment<br>knowledge<br>score|Nominee’s score on 81 garment knowledge questions, score out of 1<br>which represents aperfect score|
|---|---|
|Family support score|Nominee’s score on 8 family support questions, score out of 1 which<br>represents a perfect score<br>• 5 statements about family support, asks the respondent to re-<br>spond on a four-point scale from agree to disagree<br>• 3 questions about the level of support given to other women in<br>the family who work in garment factories|
|Interest score|Nominee’s score on 6 interest questions, score out of 1 which repre-<br>sents a perfect score<br>• 2 questions about whether respondent would want to be pro-<br>moted to supervisor or line chief<br>• 4 questions that indirectly ask whether they are interested in<br>the supervisor position, four-point scale from agree to disagree|
|Confdence score|Nominee’s score on 4 confdence questions, score out of 1 which<br>represents a perfect score<br>• Question how respondent would rate their performance com-<br>pared to a typical supervisor on a 5-point scale, if they were<br>promoted to supervisor today<br>• 3 forced choice questions between one statement that says “I<br>am confdent” using various words, and a dummy statement<br>about the factory|

# **Panel D: Production line statistics**

|Product complexity|Standard minute value (SMV) of garment. Time that the industrial|
|---|---|
||engineers of each factory estimate a production line with qualifed|
||operators working at a standard pace would require to produce one|
||piece of thegarment|

62
-11-

# CHAPTER II

# THE RECONCILIATION OF INDUSTRIAL CENSUS DATA WITH THE NATIONAL ACCOUNTS

If comparisons using the "industry of origin" approach are to have their full usefulness in growth accounts or are to be crosschecked with ICP results, it is essential to scrutinise the consistency of the information in censuses of manufacturing with estimates of manufacturing output in the national accounts. This is best done before embarking on very detailed commodity comparisons.

# Coverage of Industrial Censuses and Their Relation to National Accounts

In the case of Brazil, the national accounts for manufacturing are based directly on the manufacturing census, and tnere are no serious problems of reconciliation. In the U.S.A., the link is not direct as the national accounts are not derived from the industrial census results; we were able to make a rough reconciliation with the help of information supplied by the US Dept. of Commerce, but the census unfortunately does not contain as wide a range of information on inputs as is the case in Brazil and Mexico. In Mexico the national accounts make extensive allowance for informta economic activity; as a consequence the national accounts valuation of manufacturing value added is 38 per cent above that in the census. The country notes in this chapter explain these discrepancies in detail by industry branch.

# Definitions of Value Added in Industrial Censuses and National Accounts

The most readily collectable information on manufacturing output refers to physical product at producer prices. This kind of information is av7iafable in fairly comprehensive form in most censuses of production and can often be monitored successfully in intercensal years. This measure is usually called gross output, and refers to aggregate shipments by manufacturing establishments plus net changes in manufacturers' inventories.

However, this measure contains a good deal of duplication, and comparisons between countries on this basis can be misleading. In two countries producing a similar value added, the one with the most specialised plants will have a higher gross output because there will be more interplant shipments for intermediate processing. In order to eliminate this type of duplication and other differences in the degree to which plants use external inputs, the concept of value added was developed, and has now become quite familiar to the general public, because tax systems, particularly in EC countries, use this concept to measure economic activity. With the value added concept, the intermediate inputs used by a manufacturing establishment are deducted before arriving at the measure of output. All the manufacturing censuses we used show value added as well as gross output.

One major problem which arises in reconciling the census information with the national accounts, is that industrial census definitions of value added are less sophisticated and less standardised.
to various cities along the road.<sup>5</sup> Distances are then converted into travel time using available information about trucking and walking speeds along various types of roads in Nepal.<sup>6</sup> Off the road travel is assumed to take place by foot - a reasonable assumption for Nepal given the nature of the terrain.

Available information on distance to towns is summarized in Table 3. The average distance from surveyed wards to the nearest town is just under 4 hours, with large differences across wards. Around 30% of surveyed wards are located either within towns or very close to towns. Close to half the surveyed households live at most two hours travel away from a town or city; the median distance is 2 hours and 12 minutes. The other tail of the distribution is very long, reflecting the mountainous and isolated nature of much of Nepal. Twenty percent of surveyed wards are located more than 7 hours travel from the nearest town; ten percent are more than 10 hours away. One surveyed household in 30 is 15 to 30 hours travel away from the nearest town. This much variation should make it easier to identify the effect of distance on economic activity.

Combining information on distance to towns with data on population in these towns, we construct a measure of urban population at various time distances from each ward. These measures, denoted _pih_ for h _=_ 1, ..., H hours, only vary from ward to ward. They are organized as follows. Suppose that a ward i is 3 hours away from the nearest town, which has a population

> 5 This is a very time consuming process that requires a combination of various techniques. e.g., visual inspection of maps, statistical information on road grades, calculation of arc distances, comparisons across various measurements to identify shortest distances, etc. The assistance of Uwe Deichman and Jyotsna Puri (GIS lab, Department of Research of the World Bank) was essential to the success of this operation.

> 6Travel speeds are calculated for various terrains and types of road. Assumed travel times are as follows, in km/hour:

||Highway|Provincialroad|Secondaryroad|Offroad|
|---|---|---|---|---|
|Terai|60|35|10|5|
|Siwalik|51|29.75|8.5|4.25|
|Middlemountain|42|24.5|7|3.5|
|Highmountain|36|21|6|3|
|HighHimalayas|30|17.5|5|2.5|

These figures were obtained through discussion with various transportation experts and South Asia operations staff at the World Bank. Travel on highways and provincial roads is assumed to take place by truck; travel on secondary roads is assumed to be by cart.

15
# **Annex 3. Robustness checks**

The relationships between aspired and expected education and schooling outcomes shown in Table 1 are robust to several alternative specifications. First, the positive link between aspirations and expectations and grades and negative correlation with absenteeism holds with aspirations and expectations as dummies, taking the value of 1 for completing a postsecondary degree (as done in other studies like Favara [2017], for example) (Table A.3.1). Second, regressions with aspired and expected education levels as categorical variables instead of the quadratic terms confirm that the nonlinearity of the links between expectations and schooling outcomes is not solely due to the estimation equation functional form (Table A.3.2.). Taking general high school as the reference category, higher expected levels are most positively correlated with grades (and negatively with absenteeism) at the bachelor’s level but less correlated for higher levels. Finally, using a Tobit model for regressions on absenteeism to account for the fact that half of the students are never absent (thus have 0 days of absence) shows similar results as the regular OLS model (Table A.3.3).

34
Table 3: Examples of BDM use in developing country field experiments and reneging rate

|**HH**<br>**Delivery**<br>**Reneging rate**|Within 10 days<br>2.5%<br>Same day<br>1.1%<br>Immediate, or 2 weeks<br>5 to 8.2%|Immediate<br>8.9%|Following weekend<br>18%<br>Same day<br>6.7% (78%)<br>Immediate<br>0%<br>**Several weeks**<br>**54%**<br>-financing group, 0.7% in financing group.<br>nthly value.<br>ay for it. Only 17% paid the full price in<br>he items if not paid. Three products|
|---|---|---|---|
|**Monthly**<br>**income**|$122<br>n.a.<br>$75|n.a.|$362<br>$67<br>$51<br>**$100**<br>4% in non<br>to get mo<br>onths to p<br>ake back t|
|**Study**<br>**Country**<br>**Item**<br>**Product or**<br>**Service**<br>**Item cost**<br>|Ben Yishay et al. (2017) (1)<br>Cambodia<br>Improved latrine<br>P<br>$40<br>Berry et al. (2020)<br>Ghana<br>Water filter<br>P<br>$15<br>Cole et al. (2020)<br>India<br>Rainfall insurance<br>S<br>$2-$14|Cole et al. (2020)<br>India<br>Agric. information<br>S<br>$0.8-$5.2|Hoffman (2018) (2)<br>India<br>Water purifier<br>P<br>$18<br>Grimm et al. (2020) (3)<br>Rwanda<br>Solar kits<br>P<br>$13-$182<br>Grimm and Hartwig (2018) (4) Burkina Faso Eyeglasses<br>P<br>$9<br>**This paper**<br>**Jamaica**<br>**Business Training**<br>**S**<br>**$150**<br>**Notes:**<br>(1) Payment due on delivery in one arm, loan over a year in another arm. Reneg rate 4.<br>(2) Exchange rate of 55 Rs = 1USD. Daily household income is 664 Rs, we multiply by 30<br>(4) Exchange rate of 550 CFA = 1 USD used.<br>(3) Buyers were given the item, and randomly assigned to have 7 days, 6 weeks, or 5 m<br>the repayment period; and only 65% were fully paid within 14 months after threats to t<br>offered, cheapest was $13, most expensive $182.|

32
Table F.3: Productivity and Technology Sophistication Controlling for ln(K/L), GBF and SSBF EXT

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|ln(VAPW)|
|ln(K/L)|0.26***|0.26***|0.26***|
||(0.04)|(0.04)|(0.04)|
|GBF EXT|1.66***<br>(0.32)|||
|GBF EXT<sup>2</sup>|-0.21***<br>(0.05)|||
|GBF MOST||1.81***<br>(0.49)||
|GBF MOST<sup>2</sup>||-0.28***<br>(0.10)||
|GBF GAP||0.31***||
|||(0.10)||
|GBF EXT*AGRI|||0.25<br>(0.24)|
|GBF EXT*MANF|||0.54***<br>(0.07)|
|GBF EXT*SVC|||0.38***<br>(0.08)|
|SSBF EXT*Agriculture|-0.02|0.06|0.18|
||(0.27)|(0.26)|(0.33)|
|SSBF EXT*Food Processing|0.15|0.17|0.11|
||(0.18)|(0.18)|(0.18)|
|SSBF EXT*Apparel|0.34***|0.30***|0.24**|
||(0.11)|(0.10)|(0.11)|
|SSBF EXT*Retail and Wholesale|-0.04|-0.08|-0.06|
||(0.11)|(0.11)|(0.12)|
|SSBF EXT*Other Manufacturing|0.04|0.02|-0.02|
||(0.05)|(0.05)|(0.05)|
|SSBF EXT*Other Services|0.39*|0.44**|0.46**|
||(0.21)|(0.20)|(0.21)|
|Var(ABF EXT)|0.22**|0.30***|0.33***|
||(0.10)|(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,519|2,519|2,519|
|R-squared|0.55|0.55|0.54|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

96
Figure 8a: Real income gains by country: infrastructure improvements <!-- Start of picture text --> China Kazakhstan Kyrgyzstan Pakistan Tajikistan Turkmenistan Uzbekistan Aggregate<br>6%<br>5.6%<br>5.1%<br>4.9%<br>5% 4.4%<br>4%<br>3% 2.7% 2.7%<br>2.4% 2.3%<br>2.0% 2.1% 1.5%<br>2% 1.2% 1.6% 1.8% 1.7% 1.5% 1.4%1.9%<br>1.0% 1.4%<br>0.8%<br>0.9%<br>1%<br>0.3%<br>0.0%<br>0%<br>‐0.3%<br>‐1%<br>Direct Armington Monopolistic Competition IRS and Labour Mobility<br><!-- End of picture text --> Figure 8b: Real income gains by country; infrastructure improvements and border effects <!-- Start of picture text --> China Kazakhstan Kyrgyzstan Pakistan Tajikistan Turkmenistan Uzbekistan Aggregate<br>20%<br>17.7%<br>15% 14.2%<br>12.8%<br>12.4%<br>9.6%<br>10% 9.2%<br>8.8%<br>7.8% 7.8%<br>6.7% 6.7%<br>5.5% 6.2%<br>5.5%<br>4.8% 3.5% 5.0%<br>5% 4.0% 3.9% 3.6%<br>3.4%<br>2.2%<br>1.0%<br>0%<br>‐0.7%<br>‐5% ‐3.8%<br>Direct Armington Monopolistic Competition IRS and Labour Mobility<br><!-- End of picture text --> 27
<!-- Start of picture text --> Evolution of Water Consumption of Industrial Part<br><!-- End of picture text --> <!-- Start of picture text --> Simulated Values 1960 to 2030<br>60<br>a=]=a5=3 40* -oo<br>2So 30<br>3°<br>=s= 2010 ==—— VVvithoutVVith CooperativeCooperativeStrategyStrategy<br>te)<br>PLE?DS & EEFGG D@ SSPabv nO SFand od SP SFA SF© SSDDOGr SF VBPrasWN wm A PPDO - Ps©<br>Years<br>Graph 1<br>Evolution of Water Consumption of Rural Part<br>Simulated Values 1960 to 2030<br>35<br>30 -<br>5ec 25 ——<br>E 20<br>2 —— With Cooperative Strategy<br>8 15<br>== a —— Without Cooperative Strategy<br>=<br>5<br>fo}<br>SLES & FS© & SS4 SSAd gdh Se SISA © S& SSF2 «osSPSPspwr ww stA Fg?PD — we<br>Years<br><!-- End of picture text --> Graph2
# **Figure 5. Relationships among GDP per capita, Growth, Inequality, and Poverty**

Growth in average GDP per capita is strongly correlated with growth in the incomes of the poorest decile but has an ambiguous relationship with inequality.

- b. Market Gini and GDP per capita, 2000–10

- a. Income in Poorest Decile vs. GDP per capita, 2000–10

<!-- Start of picture text --> 4.0 80<br>3.5 70<br>3.0 60<br>2.5 50<br>2.0 40<br>1.5 30<br>1.0 20<br> 2 3 4 5 2 3 4 5<br>Average Log(GDP per capita) Average Log(GDP per capita)<br>d. Change in Market Gini and GDP per capita<br>Change in Income of Poorest Decile and GDP<br>Growth, 2000–19<br>per capita Growth, 1988–2008<br>40 10<br>30<br>20 5<br>10<br>0<br>0<br>-10<br>-5<br>-20<br>-30 -10<br>-10 -5 0 5 10 15 -5 0 5 10<br>Annualized GDP per capita growth, Annualized GDP per capita growth,<br>percentage points percentage points<br>f. Income Decile Growth and Correlation with<br>Sample of Growth Spells Lasting at Least<br>GDP per capita Growth, 1993–2008<br>Five Years, 1967-2011<br>20%<br>2.5<br>15% Correlation Decile growth<br>10% 2.0<br>5%<br>1.5<br>0%<br>-30% -20% -10%-5% 0% 10% 20% 1.0<br>-10%<br>0.5<br>-15%<br>0.0<br>-20%<br>1 2 3 4 5 6 7 8 9 10<br>-25%<br>Average income growth, percent Income decile<br>poorest decile)<br>Average market Gini<br>Average Log(Income in<br>percentage points<br>Change in market Gini<br>Annualized change in<br>income of poorest decile,<br>pecenrt<br>Income growth bottom 40%.<br><!-- End of picture text -->

- c. Change in Income of Poorest Decile and GDP per capita Growth, 1988–2008

- e. Sample of Growth Spells Lasting at Least Five Years, 1967-2011

_Sources_ : IMF staff and authors’ calculations using data from Dollar, Kleineberg, and Kraay 2016; World Bank Open Knowledge repository CC By-NC-ND 3.0; Standardized World Income Inequality Database (SWIID). _Note_ : In Panel b, market Gini is before taxes and transfers. All data on GDP per capita, income of the poorest decile, and their growth rates are in real terms.

10
In 2003, foreign equity investment stocks (inbound direct plus portfolio investment stocks)<sup>1</sup> exceeded the corresponding outbound investment stocks by a margin of over 4 to 1, as noted in Figure 1. Further, two-way investment between industrialized countries and Singapore exceeded that between developing countries and Singapore by more than 3 to 1. Most inbound investment comes from industrialized countries while most outbound investment goes to developing countries. Again, it is plausible that increasing relative Singapore skill endowments underlay the increase in outbound investment stocks, especially in developing countries.

Singapore poses an interesting study of the influence of skills on foreign investment because of its unique and dynamic nature. Singapore is no longer a developing country and it presents an interesting study of how foreign investment patterns change with economic development. It went from a small, labor-abundant developing nation in the mid-1970s to a larger, though still small (in terms of GDP), skill-abundant NIC today. In consequence, the nature of its inward foreign investment should have shifted from a labor-seeking orientation to a skill-seeking orientation. Meanwhile, its investment in developing nations should reflect a growing labor-seeking motive as international firms seek low-cost manufacturing locations to service from regional headquarters. We study these changes in this paper, through the prism of the knowledge-capital model.

The paper proceeds as follows. In the next section we review the literature that motivates the analysis. In Section 3 we specify the KK model for estimation and in Section 4

> 1 Data on foreign direct investment were only sporadically available before 1994 and hence foreign equity investment was used in this study.

4