rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Industry and Spatial Agglomeration for High-Growth_**

The positive and highly significant coefficient for the agglomeration center dummy suggests a significantly higher probability in achieving high-growth among plants located in agglomerations, relative to those located over 150 km from the nearest center. In fact, plants located in towns that are within 50 km of the nearest agglomeration center also have a higher chance of experiencing a high-growth episode relative to those located farther away (although the coefficient for the dummy variable recording plant location within 50 km from agglomeration center is not always significant). Qualitatively, these results are consistent with standard arguments that go in favor of agglomeration and clustering (e.g. due to noted benefits from thicker markets for resources, knowledge spillovers and reduced transaction costs).

By comparison, the coefficient on initial industry-level clustering, measuring using EGI, is negative and significant, implying that agglomeration within the same broadly defined industries generates congestion externalities and competition for similar resources which discourages a high-growth spell. These results are consistent with the findings of Bigsten et al. (2012) who use the same Ethiopian data-set to suggest that although agglomeration generates positive externalities, it also increases competitive pressures in underdeveloped factor and product markets.

By introducing the interaction of initial industry clustering with plant location, the full specification in column 3 seeks to understand the association of clustering in agglomeration centers with the probability of attaining high-growth. Although the coefficient for the interaction term is positive (but insignificant), its sum with EGI and with agglomeration center dummy is negative (e.g. -.054 in column 3), implying that

> 38 The coefficient size with respect to TFP growth rather than levels is smaller in magnitude. The effect is significant only with the Birch definition. First, firms that have higher levels of TFP are not the same as those with higher growth in TFP. Second, Birch HGF firms are more than double the size of OECD defined HGFs and their TFP levels are also, on average nearly three times higher.

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0.41}***|{0.23}***|
|No. Observations|12946|12959|25903|12946|12959|25903|
|||**Estima**|**tes of Mobility**|**from Quadrat**|**ic CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.63|4.67||**_IGME0 _**|0.49|0.63|
|**_ES5 _**|8.93|7.55||**_IGME5 _**|0.429|0.518|
|**_ES10_**|10.92|9.87||**_IGME10_**|0.369|0.407|
|**_ES16_**|12.91|11.93||**_IGME16_**|0.296|0.272|

Notes: (1) The data used are IHDS 2012 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at statelevel in braces; *** p<0.01, ** p<0.05, * p<0.1.
through temporary migration decision. Since Egypt is a non-dependent oil economy, the exclusion restriction holds if oil prices at the age of 25 are not correlated with employment and wages in Egypt. To provide support to our identifying assumption, in Figure 1 in Panel A, we present oil prices against key aggregate economic indicators in Egypt including GDP annual growth rate, male labor force participation rate, male employment in agriculture (% of total male employment), male employment in industry (% of total male employment), and male employment in services (% of total male employment).<sup>20</sup> The top panel in Figure 1 provides support to our exclusion restriction

> 19 See the Data Appendix B.2 for a description of the Monadic Conflict Onset and Incidence data set from the Uppsala Conflict Data Project (UCDP).

> 20 Labor Force participation rate, employment in agriculture, industry, and services come from Key indicators of the Labor Market Database of the International Labor Organization. GDP growth rates come from the World Bank National accounts data files. The choice of the time period is dictated by data availability.

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Figure 18: Prevalence of Part-time employment among salaried employees.

(Early 2000s/Mid-2010s) <!-- Start of picture text --> 16% Starting point<br>14% Ending point<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Russia Georgia Kyrgyz Turkey Armenia Albania Moldova<br>Republic<br><!-- End of picture text --> Source: Own calculations based on Household surveys.

Figure 19: Prevalence of Temporary employment among salaried employees.

(Early 2000s/Mid-2010s) <!-- Start of picture text --> 25%<br>starting point<br>20%<br>ending point<br>15%<br>10%<br>5%<br>0%<br>Georgia Kyrgyz Republic Turkey Armenia<br><!-- End of picture text -->

# Source: Own calculations based on Household surveys

The older age profile of part-time employment in most of the analyzed countries of Eastern Europe and Asia is consistent with the age profile observed for total employment. In fact, the mean age of the labor force rose 4.5 years on average in the countries included in the study. That is, the older age profile of NSE workers reflects the aging trend of the overall labor force.

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||[709]|[422]|[287]|
|---|---|---|---|
|All|0.33|0.36|0.32|
||(.31-.36)|(.32-.40)|(.29-.35)|
||13,374|5,915|7,458|

Notes: The table reports the share of providers prescribe antibiotics for diarrhea, conditional on the diagnosis. All individual country statistics are calculated using country-specific sampling weights. 95% confidence intervals in parenthesis and number of observations in parenthesis.

57
easier way for getting at this true cost than aggregating up from plant level estimates.<sup>10</sup> We therefore would like to think of our (plant-level) approach to the analysis of the effects of business environment as a natural complement, rather than alternative, to aggregate analyses at the industry or sector level.

One advantage of estimating productivity effects of business environment at the level of the plant rather than the industry is that it enables us to identify these along side the effects of geographic variables. Simultaneous identification of the effects of institutions and economic geography would seem to be more and more difficult as we work at higher and higher levels of aggregation. It would indeed be impossible in cases where the identification of the effects of business environment has to rely on (individual) fixed effect techniques in which the effects of economic geography would have to be “differenced out”.

On the other hand, as Dollar et al (2004) point out, plant level analysis of the productivity effects of the business environment needs to address econometric problems that would not arise in the analysis of longitudinal aggregate data. One of these is the possibility of selectivity bias in least squares estimates of the effect of business environment on productivity arising from the correlation of unobservable location or firm characteristics influencing plant productivity with those affecting location decisions. There is also the possibility that the firm level incidence of business environmental bottlenecks could be endogenous even in the absence of selectivity, say, because inherently more productive firms somehow cope better with adversity in their external environment.<sup>11</sup> 10 Long run effects can be identified along side short run effects based on plant level analysis. However, this requires longitudinal census data. Even then it is difficult to imagine an easier way of identifying long run effects as defined here than aggregating estimation at least at the industry level. The reason for this is that most competitive industries seem to be subject to simultaneous entry and exit, for which the spatial analogue would seem to be
of bank accounts provided under the PMJDY scheme since 2014.<sup>2</sup> . This indicates that the provision of a bank account alone was possibly not sufficient to increase credit access to micro-enterprises.

Almost half the enterprises reported facing some problem in this period, mainly the lack of demand and the inability to recover financial dues. However, leass than 6% mentioned facing problems due to the unavailability of electricity and less than 8% mentioned problems in accessing credit. Less than 1% of the enterprises claimed to have receive any assistance from the government over the past year.

# **2.3 Household Consumption Data**

Our data on household consumption is sourced from the household surveys conducted by the Centre for Monitoring the Indian Economy (CMIE). The surveys, collectively known as Consumer Pyramids (CP), were initiated in 2014, with a sample size exceeding 170,000 households. Households are surveyed thrice a year and information on their consumption, investment and borrowing over the past three months are obtained, in addition to household demographic characteristics. While the CP data oversamples urban households, we apply household-specific weights to make the data nationally representative. For the state of Rajasthan, the sample size is based on almost 2,500 rural and over 6,000 urban households. We restrict our sample to October 2016, prior to the “demonetization” episode. As the “demonetization” intervention is perceived by many economists to have started the current

> 2 The Pradhan Mantri Jan Dhan Yojana (PMJDY) was launched in September 2014 and aimed to provide a zero-cost banking account to every Indian adult.

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Policy Research Working Paper 10736

# **Abstract**

The data for estimating monetary poverty in the Syrian Arab Republic are outdated. In the context of data scarcity, this paper aims to propose a methodological approach to address the knowledge gap regarding welfare in Syria over the past decade. In particular, the analysis provides (i) updated pre-conflict poverty baseline estimates based on grouped data from the 2009 Household Income and Expenditure Survey; (ii) supporting evidence on the viability of using Humanitarian Needs Assessment Programme Demographic and Water Supply, Sanitation, and Hygiene 2022 survey data for the estimation of monetary poverty in 2022; and (iii) supporting theoretical and empirical evidence to identify growth in per capita gross domestic product in current prices deflated by Consumer Price Index as the best metric to project poverty using a nowcasting approach. Based on this analysis, the paper proposes to use 2022 Humanitarian Needs Assessment Programme–based poverty estimates to anchor the most recent estimates to the best available evidence, and to interpolate the poverty evolution obtained from back-casting 2022 and nowcasting 2009 poverty estimates over 2009–22 using the growth rate of per capita gross domestic product in current prices, deflated by the Consumer Price Index with a passthrough of 0.7.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at sredaelli@worldbank.org.

_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
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# **APPENDIX 2**

|Fig A2. 1-A2|.3 Poverty incidence curves for 1985/86 and 1992/93<br>60-61|
|---|---|
|Fig A2.4-A2|.6 Poverty deficit curves for 1985/86 and 1992/93.<br>62-63|
|Fig A2.7-A2|.9 Poverty severity curves for 1985/86 and 1992/93.<br>63-64|
|**APPENDIX**|**3**<br>65|
|Table A3.1|Rural-Urban per capita expenditure differentials<br>65|
|Table A3.2|State Price Deflators<br>68|
|Table A3.3|Regional Cost of Living Indices by State (1985-93)<br>70|
|Table A3.4|Population Estimates and Extrapolation Factors by State and Region 71|
|Table A3.5|Composite Consumer Price Indices (1985-93)<br>72|
|Table A3.6|Unemployment Rates in Nigeria (1985-93)<br>73|
|**APPENDIX**|**4**<br>74|
|Table A4.1|Summary of Past Studies on poverty in Nigeria<br>74|
|**APPENDIX**|5<br>75|
|TableA5.1|<br>The estimation of Household accounts from the<br>National Consumer Survey<br>77|
|Table A5.2|Per-Capita Expenditure by Decile (Untreated data: 1985/86)<br>79|
|Table A5.3|Per-Capita Expenditure by Decile (Untreated data: 1992/93)<br>79|
Customs data allow assessment of the degree of unfulfilled potential in fragile states or, in other words, the scope for market creation. These data enable identification of the sectors in which no cross-border transactions—whether imports or exports—are recorded for years. The World Bank _Exporter Dynamics Database_ is a unique source of information to assess that gap.<sup>18</sup> Drawing on data sets covering the universe of cross-border transactions that are obtained directly from customs agencies in 70 countries allows assessment of the extent to which products are missing from fragile states.

The differences therein are striking: an average of 50–70 percent of product lines are missing from custom transactions in fragile states, as opposed to only 20–30 percent in the rest of the world. The gaps are persistent over time, and illustrate the absence of large-scale domestic production, as well as a missing market for foreign suppliers. In less distorted markets, such gaps reveal investment opportunities that result in high entry.

That is not the case with fragile states. The level of their failures and risks in fragile situations is prohibitive for most foreign suppliers, as illustrated by lower import entry rates, relative to other markets. As fragility imposes fierce selection at the borders, only the exceptional few make it in, which, in turn, translates into greater resilience post-entry for those that are able to overcome the sizeable entry barriers (see Figure 3).

What these dynamics suggest is also a significant ‘survivor bias’ in what we know about pioneers in fragile situations. The information we have comes from the few that succeed and survive, and who capitalize on capabilities that deviate substantially from entrepreneurs in the rest of the world. The characteristics of those who did not survive are unobserved, and, importantly, so are the characteristics of those that could potentially create markets with the support of financial institutions.

# **_First-mover advantages are not sufficient to secure credit_**

As corroborated by an array of studies, there are benefits to being a
spending needs model for transport is based on a spatial analysis that assesses the costs of linking economic and demographic nodes through transport infrastructures with the objective of achieving regional, national, urban, and rural connectivity. The model identified key geographic and demographic features of each country, using geographic information system (GIS) data to measure the necessary distances. Costs are based on condition, type and standard of investment, and maintenance needs.

• _Irrigation._ The spending needs model for small-scale irrigation combines spatial information on current rain-fed crop production, irrigation potential, and exploitable runoff. The results provide unit revenue increases to be expected with the implementation of small-scale irrigation.

• _Water and sanitation_ . This model builds on the standards methodology developed by the Joint Monitoring Program of the United Nations to estimate the cost of meeting the Millennium Development Goals (MDGs) for water and sanitation. Estimates were based on minimum acceptable asset standards. It was assumed that relative prevalence of water and sanitation modalities remain broadly the same between 2006 and 2015, and services are upgraded for only a small segment of customers.

• _Power._ A dynamic model that combined economic growth and electrification targets was used to estimate power spending needs. The model simulated optimal (least-cost) strategies for generating, transmitting, and distributing electricity in response to demand increases. It also estimated the cost of 34
ment growth across over 600 industries is plausibly uncorrelated with local economic shocks or unobserved local factors, affecting local employment growth only through the industry composition at the local level.<sup>2</sup> Our data – administrative data on BF beneficiaries as well as matched employer-employee data on formal employment – enables us to follow movements in and out of BF as well as formal dependent employment without interruption from 2012 up to 2019.<sup>3</sup> Our primary results suggest that local employment growth leads to a small – yet statistically significant – increase in the probability of beneficiary families leaving the BF program. A 10% growth in local employment raises the exit probability by 0.342 percentage points, increasing the average transition rate of program exit from 8.61% to 8.69%. In addition, we find that these exogenous “rising tides” at the local level cause an increase in the probability of having at least one adult family member in a formal dependent job. Several robustness checks, such as replicating the analysis at the municipality level and checking for administrative capacities of the local authorities confirm our results.

Those findings indicate that the poor are responsive to local improvement in the labor market and confirm that a program can be operated in ways that are responsive to such income changes.<sup>4</sup> Nevertheless, the small size of the effects also highlights the relevance of policies that strengthen the capabilities and support of the poor to seize labor market opportunities.<sup>5</sup> Heterogeneity analysis of our results, in fact, shows that exits are concentrated among those households that are less likely to face labor force participation constraints, with fewer young children and more spare adult labor. This is consistent with the descriptive findings of another recent study on the family-level correlates of the BF benefit duration (Morgandi _et al._ , 2023).

The question is highly relevant for governments in many developing countries. After deploying CCTs on a large scale,
(2016), and Tria _et al._ (2022). In Viet Nam, Nguyen (2008) and Lensink and Pham (2012) find evidence of microcredit's poverty-reducing effects.

Table 6 presents our estimation of the program impact on households' loans from various sources. The results demonstrate program positive effects on borrowing from formal sources and microcredit. Specifically, the program increases the probability of obtaining formal loans and microcredit by 0.13 and 0.09, respectively. However, the program effects on the loan size are not statistically significant. When considering the sample of both borrowers and non-borrowers, we find positive program effects on loan size.<sup>9</sup> Our findings align with Thanh _et al._ (2019), who find positive impact of microcredit on self-employed nonfarm employment in Viet Nam.

Table 7 estimates the impact of the 30A Program on infrastructure using communelevel data and the same model as in equation (3). The VHLSSs provide information on whether communes have basic infrastructure, and we select outcome variables that correspond to the list of infrastructures provided by the 30A Program in the program areas. Our findings indicate marginally statistically significant but positive program effects on the availability of all-weather roads in the villages (column 1). Specifically, an all-weather road is defined as one that remains passable throughout the year. These positive effects on rural roads could stimulate local market development (Mu and van de Walle, 2011), which might in turns serve as a mechanism through which the project promotes nonfarm employment and income. Indeed, studies on other developing countries such as Bangladesh and India also find that rural roads help local people to find more nonfarm self-employment opportunities (Khandker _et al._ , 2009; Asher and Novosad, 2020).

9 We address zero loan values by adding 1 before taking the natural logarithm of the loan values. 24
(1), 25-29.

- Phillips, J., & Simon-Davies, J. (2016). Migration to Australia: a quick guide to the statistics. Canberra: Parliamentary Library.

- Shergold, P., Benson, K., & Piper, M. (2019). Investing in refugees, investing in Australia. Department of Home Affairs (Australia).

- Slewa-Younan, S., Rioseco, P., Guajardo, M. G. U., & Mond, J. (2019). Predictors of professional help-seeking for emotional problems in Afghan and Iraqi refugees in Australia: Findings from the Building a New Life in Australia Database. _BMC Public Health_ , _19_ (1), 1-11.

- Spike, E. A., Smith, M. M., & Harris, M. F. (2011). Access to primary health care services by community‐based asylum seekers. _Medical Journal of Australia_ , _195_ (4), 188-191.

- Steel, Z., Momartin, S., Silove, D., Coello, M., Aroche, J., & Tay, K. W. (2011). Two year psychosocial and mental health outcomes for refugees subjected to restrictive or supportive immigration policies. _Social Science & Medicine_ , _72_ (7), 1149-1156.

- Tahiri, S. (2017). Not working: experiences of refugees and migrants with Jobactive. Sydney, NSW: Fairfield Multicultural Interagency in partnership with Refugee Council of Australia.

- United Nations High Commissioner for Refugees (2017). Projected Global Resettlement Needs 2017. Geneva: United Nations High Commissioner for Refugees.

> 19 Some other refugee employment programs such as _Career Pathways Pilot_ (for newly arrived humanitarian refugees) and _Youth Transition Support_ (for young people) have been shown to be ineffective in terms of participation rates and outcomes achieved. For example, the number of clients enrolled in the _Career Pathways Pilot_ program was 65% of the expected amount, and only
|R<sup>2</sup>|0.126|0.114|
|---|---|---|
|Mean of the dependent variable|0.249|0.256|
|N|4,212|11,200|

> <sup>†</sup> These are actual values, not log.

Note: The level of FE is than times year. For the estimation, data from 2000, 2005 and 2010 rounds of HIES are combined and then households from only those thanas that are common to all three rounds are kept. Figures in parentheses are t-statistics in the marginal Impacts column and standard deviations in Mean column. Regressions additionally include community prices of consumer goods, daily wage, etc., and agroclimate characteristics (land elevation, average number of sunny months, and excess rain amount per month). Sources: HIES surveys, 2000, 2005 and 2010.

38
[0.00038]|0.1394|[0.11356 ]|0.00028|[0.00086]|Yes|No<br>No|No|17271|20200.75<br>|s the estimation<br>dummy that tak<br>|e 1 if the frm re|
|ble 10: Qual<br>Pr(IQ=1)|0.00326|[0.00064]***|-0.000243|[0.00035]|0.12837|[0.10445]|0.00026|[0.00079]|Yes|No<br>No|No|17271|20200.75<br>|s table present<br> variables is a <br>|hat takes valu|
|Ta|Know.spillovers||Distance to Innovators|*Know.spillovers|International Know.spillovers||Distance to International Innovators|* International Know.spillovers|Firm ’ s characteristics|Firm s and Industry-Country’ s<br>characteristics<br>Country-Time fxed efects|Industry fxed efects|Num. of observations|Log pseudolikelihood<br>|Note: Thi<br>dependent <br>|a dummy t|

31