rafmacalaba/datause-encoder-data huggingface dataset:
# **Section VIII. Lagged Spillovers between the Organized and Unorganized Sectors: The Issue of Jobless Growth**

# **a. Lagged Spillovers from the Unorganized Sector to the Organized Sector**

In this section we present a specification that asks whether the organized sector chooses as its location the states which already contain a large and diverse unorganized sector. That is rather than measure contemporaneous spillovers we look for lagged spillovers. The specification uses the fact that while we have only four years of unorganized sector data we have many more years of organized sector data spanning the years 1980-2007. Thus we can associate unorganized sector data in (2005, 2000, 1994, 1989) to organized sector data in (2007, 2002, 1995, 1991) respectively. However if we don’t use the 2005 unorganized survey then we can create greater leads of the organized sector activity. The specification equation is given below.

**Equation (5)** Y<sup>O</sup> jst+k<sup>=</sup>

β1HORIZONTAL Y<sup>U</sup> jst<sup>+ β</sup> 2<sup>FORWARD YU</sup> jst<sup>+ β</sup> 3<sup>BACKWARD YU</sup> jst<sup>+ δ</sup> 1<sup>TFP</sup> jstU + δ2FORWARD TFPjstU + δ3BACKWARD TFPjstU + γ1FORWARD Yjsto + γ2BACKWARD Yjsto + α + Ijs + It + Ijt + Istεijt Positive coefficients on the β’s would imply that there is greater organized economic activity in year t in state-industry cells that had more unorganized sector activity in previous years. Table 13 and Table 14 show the results from this exercise. Each panel specification includes state-year and industry-year fixed effects. Standard errors are clustered by state-industry. We find little consistent evidence that organized manufacturing activity is greater in state-industry cells that contain larger networks of unorganized suppliers of inputs. While two year leads of organized employment are positively related to current employment in unorganized suppliers this relationship turns negative when we consider 4 and 9 years leads. Few of the coefficients are significant when we consider real output of the organized sector as the dependent variable. Thus while ancillary unorganized industry is important for the organized sector it is not necessarily an important factor in the choice of location (i.e. state-industry cell) by organized sector plants.

# **b. Lagged Spillovers from the Organized Sector to the Unorganized Sector**

In this section we ask whether unorganized plants choose their locations based on the extent to organized sector activity that is already located in the state. That is, is greater organized sector 23
||(0.367)|(0.341)|(0.357)|(0.353)|(0.353)|(0.353)|
|---|---|---|---|---|---|---|
|Foreign Ownership (proportion)|-0.655|0.159|0.175|0.201|0.191|0.277|
||(0.866)|(0.901)|(0.967)|(0.960)|(0.954)|(0.955)|
|Female Ownership Y:1 N:0||6.156***|5.979***|5.931***|5.956***|5.981***|
|||(0.624)|(0.654)|(0.654)|(0.653)|(0.653)|
|Skills Obstacle (0-4)||0.582**|0.627**|0.615**|0.629**|0.630**|
|||(0.230)|(0.267)|(0.268)|(0.267)|(0.267)|
|How Much Of An Obstacle:||-0.005|0.010|-0.013|-0.018|-0.027|
|Transport?|||||||
|||(0.196)|(0.208)|(0.208)|(0.208)|(0.209)|
|Firm Purchased Fixed Assets Y:1||-0.969*|-0.995*|-0.981*|-0.917|-0.909|
|N:0|||||||
|||(0.523)|(0.556)|(0.556)|(0.557)|(0.558)|
|Power Outages (days)|||-0.150**|-0.143**|-0.146**|-0.144**|
||||(0.068)|(0.067)|(0.067)|(0.067)|
|Crime Losses Y:1 N:0|||-0.661|-0.701|-0.694|-0.711|
||||(0.671)|(0.671)|(0.673)|(0.673)|
|Firm Inspected Y:1 N:0|||-0.767|-0.804|-0.774|-0.768|
||||(0.604)|(0.603)|(0.598)|(0.599)|
|How Much Of An Obstacle: Tax|||-0.220|-0.248|-0.254|-0.262|
|Rates|||||||
||||(0.229)|(0.228)|(0.227)|(0.227)|
|How Much Of An Obstacle:|||0.053|0.059|0.049|0.047|
|Labor Regulations?|||||||
||||(0.300)|(0.300)|(0.299)|(0.298)|
|Population (logs)||||-33.168***|-31.558***|-31.448***|
|||||(10.906)|(10.966)|(11.101)|
|Urbanization (%)||||1.805***|1.735***|1.709***|
|||||(0.390)|(0.391)|(0.386)|
|WBL Index||||15.274*|15.788*|26.692**|

84
The implied long term effects of the transfers on living standards through the investment pathways are dramatic. In 2003, after five and one-half years on the program, households in the treatment group received an average cumulative transfer of $3,444 pesos per capita. Through the increased income from invested transfers, the average beneficiary consumed 62 pesos more per month<sup>20</sup> . Thus, after five and a half years, transfers increased consumption by 34% through the investment pathway<sup>21</sup> . Since the 34% increase in consumption is through investment in income generating activities, it should continue even if the household dropped out of the program.

These results also suggested that households were liquidity constrained. The estimated rate of return on investment, 17.55%, is substantially higher than the average annual real interest rate in the economy for the entire period (1997 to 2003), which was approximately 6.1%<sup>22</sup> . We take this result as evidence that these households face imperfect credit markets, since obtaining investments in productive activities would yield net gains. In fact, access to credit for the poor rural households in our sample is very limited. In the two rounds of the evaluation survey (May and November 1999) where questions on access to credit are asked, only 2.4% of households report using credit.

# **4.3 Heterogeneous Treatment Effects**

Table 10 presents 2SLS estimates of the effects of current and lagged transfers interacted with farm size in 1997, the age of the head of household, head’s education, and distance (in Km) to the closest urban center. The purpose of such interactions is to determine whether household heterogeneity affects preferences (the marginal propensity to consume) and/or productivity (the rate of return).

Non-agricultural households (households with no agricultural assets at baseline) have the highest marginal propensity to consume, while the other three types of households have a similar

> 20 “Permanent” rise in consumption estimated as average cumulative transfers per capita for beneficiary households in the treatment group times the coefficient on returns from investment,<sup>φˆ</sup> 2<sup>, estimated as 0.018.</sup> 21 The percentage increase is measured relative to first round of data where consumption data are available. In this survey eligible households in control communities have a real per-capita expenditure of $183 pesos. 22 The annual real interest rate (computed on the 28-day Inter-Bank Equilibrium Interest Rate) was 5.5% in 1997 and 2.8% in 2003. The average annual real interest rate for the whole period is 6.1% (source: Bank of Mexico and National Institute of Geography and Statistics, INEGI). We take this rate as our benchmark since investments are likely to have occurred over the whole period.

21
time trends – then this sort of continuous DID estimator suffers from the same problem as do conventional DID estimators that violate the standard parallel trends assumption. Analogous concerns would exist in a spatial framework.

This clever approach has been similarly used in other prominent recent papers. For example, Giovanni Peri (2012) investigates how intertemporal variation in immigrant populations differentially affects employment and total factor productivity growth among US states by interacting immigration population over time with time invariant distance from the Mexico border; Oeindrila Dube and Juan Vargas (2013) study the effects of international commodity price shocks on civil conflict within Colombia by interacting price time series with cross-sectional measures of intensity of production of the commodity in question. And Rema Hanna and Paulina Oliva (2015) explore how pollution reduction resulting from the closure of refineries affects labor supply in Mexico by interacting a time series on oil refinery closure with individuals’ time invariant distance to the refinery. As we describe below, in these other prominent cases that use this technique, the spurious trends we identify in the NQ study may not affect specific findings. But to date the literature does not appear to recognize the vulnerability of this method to the problem we highlight, and the approaches taken to argue for the validity of exclusion restrictions vary across papers. Hence the methodological caution we offer in unpacking one prominent, and highly policy relevant, recent paper.

# **I. The Nunn and Qian Estimation Strategy**

NQ construct an impressive panel dataset including 125 non-OECD countries over 36 years with information on the conflict status (defined as a country experiencing more than 25 battle deaths in a year), quantity of wheat delivered to 4
- [24] Immervoll, Herwig, and Stefano Scarpetta, 2012, ”Activation and employment support policies in OECD countries. An overview of current approaches”. IZA Journal of Labor Policy, (1): 1-9.

- [25] Larra ̃naga, Osvaldo, 2005, ”Focalizaci ́on De Programas En Chile: El Sistema CAS”. Serie de Documentos de Discusi ́on sobre la Protecci ́on Social No. 0528. The World Bank.

- [26] Larra ̃naga, Osvaldo, Dante Conterras and Jamie Ruiz Tagle, 2009, ”Evaluacin del impacto de Chile Solidario para la primera cohorte de participantes”, PNUD http://www.pnud.cl/areas/ReduccionPobreza/links_archivos/Evaluaci%C3% B3n%20Chile%20Solidario.pdf.

- [27] Larra ̃naga, Osvaldo, and Dante Contreras, 2010, ”Chile Solidario y Combate a la Pobreza”, Programa de las Naciones Unidas para el Desarrollo - Chile. Area de Reducci ́on de la Pobreza<sup> ́</sup> y la Desigualdad.

- [28] Lee, David S. and Thomas Lemieux, 2010. ”Regression Discontinuity Designs in Economics”, Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.

- [29] Mideplan, 2009, ”Costo del servicio de Apoyo Psicosocial del Programa Puente de Chile Solidario: Informaci ́on vigente para el a ̃no 2009”.

- [30] Moffitt, Robert. 1983. ”An Economic Model of Welfare Stigma,” American Economic Review 75: 1023-1035.

- [31] Mullainathan, Sendhil and Eldar Shafir, 2013. ”Scarcity: Why Having Too Little Means So Much”, Times Books.

- [32] Paxson, Christina and Norbert Schady, 2010. ”Does Money Matter? The Effects of Cash Transfers on Child Development in Rural Ecuador,” Economic Development and Cultural Change, University of Chicago Press, vol. 59(1), pages 187-229, October.

- [33] Porter, Jack and Ping Yu, 2014. ”Regression Discontinuity Designs with Unknown Discontinuity Points: Testing and Estimation”, MIMEO.

- [34] Raczynski, Dagmar, 2008, ”Sistema Chile Solidario y la Politica de Proteccion Social de Chile - lecciones del pasado y agenda para el futuro”, Corporaci ́on de Estudios para Latinoamrica, CIEPLAN, Santiago de Chile.

- [35] Romano, Joseph P. and Michael Wolf, 2005, ”Stepwise Multiple Testing as Formalized Data Snooping”, _Econometrica_ , Vol. 73, No. 4, Jul., 2005, pp. 1237-1282.

41
**Figure 4. WFH amenability index across countries.**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 0.8<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0 0<br>-0.2<br>-0.2<br>-0.4<br>-0.4<br>-0.6<br>-0.6 -0.8<br>-1<br>-0.8<br>-1.2<br>MEX PER ECU TUR CHL RUS GRC KAZ ITA IRL POL USA ESP NZL SVK SVN LTU KOR CZE GBR AUT SGP JPN FRA DEU EST ISR CAN HUN SWE DNK NOR FIN BEL NLD LKA LAO SLV GHA BOL COL KEN UKR ARM VNM PHL GEO KOS MKD SRB<br><!-- End of picture text --> Note: Each bar shows the number of standard deviations below/above the mean. A higher value indicates a greater amenability of jobs to working from home. The magnitude of the estimates is not comparable between the PIAAC and STEP datasets.

The difference between our results and those of Dingel & Neiman (2020a) seems to be driven by the fact that while the United States has a higher share of jobs in occupations that are more amenable to working from home than other countries, the tasks associated with these occupations are different across countries and tend to be less favorable to working from home in the United States. Figure A 2 in Appendix 2 illustrates this issue using Norway, the United States and Spain as examples. The United States has 61 percent of its jobs in the four occupational categories that are more amenable to WFH, a figure higher than for Norway (55 percent) and Spain (59 percent). If we imputed the US WFH measures to each occupation of these other two countries (as in Dingel & Neiman, 2020a), we would conclude that jobs are more amenable to WFH in the United States. However, when comparing the same occupations across countries, we find that most occupations in the United States are less amenable to WFH than in Norway and Spain. For example, the US WFH index for technicians is far lower than that for Norway and Spain. In other words, these findings illustrate the importance of using measures of tasks that vary across occupations and countries, since occupations are not associated with the same tasks in different economies.

12
**Mapping the risk posed to groundwater-dependent ecosystems by the uncontrolled access to photovoltaic water pumping in sub-Saharan Africa**

Guillaume Zuffinetti<sup>1,2</sup> , Simon Meunier<sup>1,2, *</sup> _1 Université Paris-Saclay, CentraleSupélec, CNRS, GeePs, Gif-sur-Yvette 91192, France_ _2 Sorbonne Université, CNRS, GeePs, Paris 75252, France_

- _Corresponding author email address: simon.meunier@centralesupelec.fr_

_This paper is related to the World Bank report "The Hidden Wealth of Nations: The Economics of Groundwater in Times of Climate Change" (2023)_

**JEL codes:** Q25, Q42.

**Keywords** : Groundwater-dependent ecosystems, Photovoltaic water pumping systems, Analytic Hierarchy Process, Sustainable groundwater management, sub-Saharan Africa
|Variable|(1)<br>Treated|(2)<br>Control|(3)<br>Difference|(4)<br>t statistic|(5)<br>p-value|
|---|---|---|---|---|---|
|Tech Sophistication|1.95|1.64|0.31***|8.33|0.00|
|Medium|0.27|0.25|0.01|0.51|0.61|
|Large|0.29|0.15|0.14***|5.42|0.00|
|Age 6 to 10|0.25|0.20|0.05**|1.97|0.05|
|Age 11 to 15|0.19|0.21|-0.02|-0.92|0.36|
|Age 16+|0.38|0.37|0.00|0.14|0.89|
|Export|0.26|0.17|0.08***|3.35|0.00|
|Foreign Owned|0.14|0.10|0.04*|1.95|0.05|
|Change in mobility|-32.84|-34.67|1.83|1.24|0.22|
|Work experience|18.35|17.64|0.72|1.03|0.30|
|Post-graduate degree|0.16|0.06|0.10***|5.27|0.00|
|Studying abroad|0.24|0.14|0.10***|4.02|0.00|
|Experience in large business|0.33|0.21|0.12***|4.47|0.00|
|Management Z-score|0.21|0.05|0.17***|4.19|0.00|
|<br>Senegal Agriculture|0.05|0.12|-0.07***|-3.95|0.00|
|Vietnam Agriculture|0.06|0.07|-0.01|-0.69|0.49|
|Brazil Manufacturing less exposed|0.02|0.00|0.01**|2.11|0.04|
|Senegal Manufacturing less exposed|0.04|0.04|-0.01|-0.75|0.45|
|Vietnam Manufacturing less exposed|0.12|0.12|0.00|-0.19|0.85|
|Brazil Manufacturing more exposed|0.06|0.01|0.05***|4.02|0.00|
|Senegal Manufacturing more exposed|0.08|0.10|-0.02|-0.98|0.33|
|Vietnam Manufacturing more exposed|0.06|0.07|-0.01|-0.62|0.54|
|Brazil Services less exposed|0.02|0.02|0.01|0.97|0.33|
|Senegal Services less exposed|0.17|0.14|0.02|1.00|0.32|
|Vietnam Services less exposed|0.19|0.16|0.03|1.23|0.22|
|<br>Brazil Services more exposed|0.00|0.01|0.00|-0.31|0.76|
|Senegal Services more exposed|0.06|0.08|-0.02|-1.06|0.29|
|Vietnam Services more exposed|0.06|0.04|0.02|1.47|0.14|

Table B7: Unbalanced Covariate Differences between Treated and Control Group. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses. Treatment group is the firms that increased use of digital technology.

37
We observe that the relationship between self-employment and migration is similar across all three levels, suggesting that our findings do not depend on the specific definition of migration adopted.

This tendency of the self-employed to be less likely to migrate than wage workers and those without jobs captures the joint effect of migrant selection and of any causal effect of self-employment on migration. We next attempt to isolate how much of this effect could be causal.

# **4 Causal analysis using randomized experiments**

As section 2.2 shows, theoretically the causal impact of becoming self-employed will depend on the extent to which it increases job opportunities at home (and hence the opportunity cost of migrating) and to which it involves a lock-in effect, versus the extent to which it alleviates credit constraints and changes human capital and hence job opportunities abroad. The result is that ”the” effect of becoming self-employed will likely depend on what causes an increase in the likelihood of self-employment. For example, becoming self-employed in order to take-over a family business when a parent or spouse dies is likely to have a different effect on the likelihood of migration from becoming self-employed because you have developed new entrepreneurial skills or had access to new forms of capital or credit. Our focus is on the impact of deliberate policy actions that increase self-employment. To examine this, we bring together data from seven randomized experiments, none of which had focused on migration as a key outcome, and examine the extent to which interventions to promote self-employment also change migration propensities.

## _Selection of studies_

We to collate as randomized as that attempted together many experiments possible were focused on or and stimulating supporting self-employment entrepreneurship, and for which we could measure migration as an outcome. We used our own knowledge of the literature, systematic reviews of business support programs, and web searches to identify studies which met the following criteria: (i) the intervention was aimed at self-employment or job creation and was successful in either stimulating new or in to be more to sur- self-employment, getting existing self-employed firms likely vive; (ii) outcomes were measured for a period longer than 15 months after the intervention took place, to allow time for migration to occur; (iii) the follow-up survey tracked respondents who moved and did not suffer from extremely high attrition; and (iv) data were either publicly available or shared by authors upon request.

11
When Winners Feel Like Losers: Evidence from an Energy Subsidy Reform<sup>I</sup> Oscar Calvo-Gonzalez<sup>a,</sup><sup>_⇤_</sup> , Barbara Cunha<sup>a</sup> , Riccardo Trezzi<sup>b</sup>

> _aThe World Bank Group, Washington, DC, USA._

> _bBoard of Governors, Federal Reserve System, USA._

_Keywords:_ Political economy of reforms, Negativity bias, Fuel subsidy.

_JEL:_ H23, H24, O54.

> IWe are grateful to _La Prensa Gráfica_ for making available the data for the public opinion surveys conducted in 2011 and 2012 and especially to Edwin Segura for his help and insights regarding public opinion polling in El Salvador. We are also grateful to Guillermo Raul Beylis, Augusto de la Torre, Marianne Fay, Auguste Tano Kouame, Alice Kuegler, Adrien VogtSchilb, and to the participants of the microeconomic shadow talks at the University of Cambridge for numerous comments and suggestions. Riccardo Trezzi is also grateful to the University of Cambridge for financial support. All errors and omissions are ours. This draft: April 2015. **Disclaimer: the findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the Board of Governors of the Federal Reserve Bank, 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.**

> _⇤_ E-mail address: ocalvogonzalez@worldbank.org.
For the multi-regional IO account outlined in equations (1) to (9), the major components are gross regional output (<sup>_x_</sup> _ir_<sup>), final demand (</sup><sup>_y_</sup> _ir_<sup>), primary factor payments (</sup><sup>_v_</sup> _ir_<sup>), international</sup> trade (<sup>_e_</sup> _ir_<sup>and</sup> _mir_ ), inter-industry transactions (<sup>_z_•</sup> _ijr_<sup>) and inter/intra-regional trade flows (</sup><sup>_d_</sup> _isr_ ). In many cases, data for several of these components are available from a single major statistical survey series—for example, in the United States<sup>_x_</sup> _ir_<sup>and</sup><sup>_v_</sup> _ir_<sup>are available from an Economic</sup> Census conducted every five years. Other components, for example<sup>_y_</sup> _ir_<sup>, may themselves require</sup> multiple disparate data sources to compile. While the strategic groupings may differ by country, it is likely that for large dimension (N × M) multi-regional accounts, primary data for individual regional sectors become sparse.

When the best available data are not consistent to the model structure, it may be necessary to restructure the adding up requirements in the model to accommodate the data. For example, in equation’s (2) and (3) of our model, the accounting identities require data for international exports (<sup>_e_</sup> _ir_<sup>) and imports (</sup> _mir_ ) on an origin of movement and destination of use basis respectively. However, in many countries such as the United Sates, port of entry/exit data are far more reliable. Therefore, different formulation of the corresponding accounting identities should be used.

For certain elements of the multi-regional account, very often only a purely theoretical inference is available to produce informed guesses about the initial estimates. A common example is the information about service trade flows within and between regions. In using a theory-based alternative to data, a case must be made for a prevailing empirical model that calibrates the unobserved activities to some other statistics or available survey data.

# **_Determine Model Dimensions Based on Maximum Concordance among Different Components_**

In compiling different components of the multi-regional account, the volume and nature of data available for each component can greatly vary. Detailed and survey based data may be 21
# **Dynamic Effects of Microcredit in Bangladesh**<sup>**1**</sup>

**Shahidur R. Khandker Hussain A. Samad Development Research Group World Bank March 2014**

JEL codes: G23; D69 Keywords: Microfinance, dynamic effects, Bangladesh

> 1 Shahidur R. Khandker is lead economist in the Development Research Group, World Bank, and Hussain A. Samad is senior consultant in the same group. This paper is based on a major research study funded by the Research Committee of the World Bank.  The Institute of Microfinance (InM), Bangladesh, has co-financed data collection of the study. The authors acknowledge gratefully the contribution of InM staff in carrying out the third round survey and data cleaning. The authors are grateful to Will Martin, Salman Zaidi, Johannes Zutt, and Tiemen Woutersen for helpful comments on an earlier draft. Views expressed in this paper are, however, those of the authors and do not reflect the views of the World Bank, InM, or any other affiliated institutions.