rafmacalaba/data-use-mentions-extended huggingface dataset:
and Expenditure Survey (HIES 2016). The microeconomic model distributes shocks while keeping the consistency with the aggregate results obtained from the macro CGE model.

# 4.1.Employment and skills profile

**The Sri Lanka Household Income and Expenditure Survey (HIES) 2016 was utilized to examine the characteristics of workers at the sectoral and district levels.**<sup>15</sup> The HIES 2016 data set consists of approximately 21,755 households and 82,935 individuals found within these households. We focus on profiling workers in each sector according to the localized Sri Lanka official version of the 1-digit and 4-digit International Standard Industrial Classification (ISIC),<sup>16,17</sup> the province in which they live, median wage and skill level proxied by educational attainment.

**Women account for one-third of employment of 7.7 million workers and they are primarily employed in the agriculture (incl. vegetables and fruits) sector.** Male workers comprise 5.07 million (66 percent) and female workers comprise of 2.63 million (34 percent). Agriculture (25 percent), manufacturing (18 percent), trade and commerce (18 percent), and social services and others (12 percent) sectors are the four largest employers in Sri Lanka according to estimates from HIES 2016 (Table 1).<sup>18</sup>

> 15The survey was conducted by the Department of Census and Statistics (DCS) under the National Household Survey Programme (NHSP) of Sri Lanka. The survey is nationally representative, and the unit of analysis is the household with details on all the individuals comprising the household. The sampling frame consists of two-stage stratified sampling – area of residence (urban and rural) and the estate sectors in each district of the country were the selection domains. This makes our results representative at the district level too, which is essential for the results of the CGE-GIDD at the regional level.

> 16 <u>http
1943; Evans and Leighton, 1990; Storey, 1991 and 1994). The empirical evidence suggesting the important role of job losses in fostering entry is indeed quite robust (see Storey and Jones, 1987; Santarelli, Carree and Verheul, 2009). Using a panel of Italian data, Audretsch and Vivarelli (1995 and 1996) found that job losses represent an important ‘push factor’ in spurring new firm formation at the regional level (together with other factors such as the local industrial structure and the presence of agglomeration and external economies). At the end of the ’90s, in the UK the incidence of people starting a firm not because of a market opportunity but just because they had no better choice was about 22% (see Small Business Service, 2001, p.6). Likewise, unemployment has been found to be one of the most important determinants of ‘latent’ entrepreneurship in the stagnating Japanese economy of the second half of the ’90s (see Masuda, 2006)<sup>13</sup> .

Thus entry may be determined by a set of different environmental factors including some ‘progressive’ determinants such as profitability and promising technological opportunities, but also ‘regressive’ determinants such as low wages and the actual condition of being (or the fear of becoming) unemployed. In determining new firm formation, these environmental drivers interact with the potential entrepreneur’s personal traits (see next section).

# **3.2** **<u>The personal characteristics of the entrepreneur</u>**

New firm founders differ with regard to characteristics such as previous work experience, family tradition, financial status, personal motivation. To start with, the founder of a new firm is heavily influenced by his/her own background, with particular reference to his/her previous job experience; as already discussed in the previous section, on the one hand the importance of previous job experience explains sectoral inertia in entrepreneurship, while on the other the
|19.1|0.7|8.7|5.9|-2.8|
|Dhaka|16.0|19.2|3.2|7.2|4.8|-2.4|
|Khulna|27.5|28.4|0.9|12.4|6.7|-5.8|
|Rajshahi|28.9|27.8|-1.1|14.2|11.8|-2.5|
|Rangpur|47.2|45.8|-1.5|30.6|25.8|-4.7|
|Sylhet|16.2|22.8|6.6|11.5|6.8|-4.7|

Note: results for the Mymensingh division are available upon request. Source: Own estimations based on HIES 2016 and 2022 The imputation process has an impact on the poverty incidence levels within the divisions and their relative positions. However, it is expected that the relative positions of the divisions will not be significantly affected by this method. As expected, the results indicate only minor changes in the relative positions of divisions in both poverty and extreme poverty rankings. (Figure 4). However, these jumps are not significant enough to impact, for instance, the east-west regional divide. After imputation, Chattogram, Dhaka, and Sylhet are the divisions with the lowest poverty incidence, while Rangpur, Barisal, Khulna, and Rajshahi are in the opposite spectrum.

> 10 The simulated headcount was computed using the average of 1,000 simulations for per capita consumption with newly introduced items.

13
# 3 Pseudo-panel Estimation

We propose using pseudo-panel methods to consistently estimate the degree of income mobility in the presence of measurement error. A pseudo-panel tracks cohorts of individuals, such as birth cohorts, or birth-education cohorts, over repeated cross-sectional surveys. Since a new sample of individuals is taken in each period, the use of a pseudo-panel will also greatly reduce the effect of attrition on mobility estimates. The use of the pseudo-panel will capture mobility which is accompanied by movement within the cross-sectional survey domain. However, it will not capture mobility which arises from migration into or out of the survey area. Moffitt (1993), Collado (1997), McKenzie (2001, 2004) and Verbeek and Vella (forthcoming) discuss conditions under which one can consistently estimate linear dynamic models with pseudo-panels. Our aim here is to show that these methods can also deal with the measurement error problems facing panel data models.

Begin by taking cohort averages of equation (3) over the nc individuals observed in cohort c at time t : where Y c(t),t = (1/nc)<sup>Pn</sup> i=1<sup>cYi(t),tdenotesthesamplemeanofYoverthein-</sup> dividuals in cohort c observed at time t. With repeated cross-sections, different individuals are observed each time period. As a result, the lagged mean Y c(t),t−1, representing the mean income in period t − 1 of the individuals in cohort c observed at time t, is not observed. Therefore we replace the unobserved terms with the sample means over the individuals who are observed at time t − 1, leading to the following regression for cohorts c = 1, 2, ..., C and time periods t = 2, ..., T : 12
The main objective of this paper is to assess the educational effects of the conflict in Timor Leste. First, we analyze the short-term consequences of the last wave of violence in 1999 on school attendance and on grade deficit rates observed in 2001 among children who were of primary school age in 1999. Second, we examine the medium term consequences of the conflict on school attainment of the same cohort observed again in 2007. Third, we investigate the long term consequences of the most intense early years of the conflict between Timor Leste and Indonesia on school attainment outcomes of exposed individuals observed in 2007. Finally, we assess the average effect of the conflict on primary education outcomes in the longer term by looking at the average effect of exposure to the conflict as a whole. We focus on primary schooling because most individuals in Timor Leste (65 percent) have at most only primary school education (TLSS 2007b). The empirical study is based on two cross-sectional household surveys: the Timor Leste Living Standard Measurement Surveys (TLSS), conducted in 2001 and 2007, jointly by the National Statistics Directorate in Timor Leste and the World Bank. They are both nationally representative household surveys, and include a broad range of individual and household level indicators.

The TLSS 2001 surveyed 1800 households from 100 Sucos (villages) (covering nearly 1% of the population). The survey was conducted between August and November 2001. Interestingly, this survey includes very detailed information on the exposure of individuals and households to the wave of violence in 1999. Respondents were asked whether they were displaced and whether their house was destroyed due to the violent events that followed the withdrawal of Indonesian forces in 1999. We make use of this valuable information to get insights on the characteristics of individuals and households affected by the violent events in 1999.

The TLSS 2007 covered a sample of 4,477 households from all the 498 Sucos that form Timor Leste. The TLSS 2007 was undertaken over a period of 12 months between December 2007 and January 2008. The survey was
Figure 2- Internal Migration Rate by Country <!-- Start of picture text --> Internal Migration (% of Working Age<br>population)<br>70%<br>60%<br>50%<br>40%<br>30%<br>20% 8%<br>10%<br>0%<br>India 2001 Paraguay 2001 Morocco 1998 Honduras 2003 Congo, DR 2005 Armenia 1999 Albania 2005 Vitnam 1992 Colombia 1995 Brazil 2001 Nicaragua 2001 Haiti 2001 Kyrgyz Rep 1997 Croatia 2004 Cambodia 2004 Mongolia 2002 Madagascar 2001 Egypt 2009 Micronesia 2000<br><!-- End of picture text --> Sources: World Development Report 2010; Egypt: Authors’ calculations using Labor Force Survey 2009; India: Bahgat, Ram B. 2009.

Men and women show similar migration rates, with the rate of migration among males (6 percent) slightly lower than that of females (6.2 percent) (see Tables A1 and A2 in the appendix). However, the reasons for migration greatly differ between men women, as described later in the paper.

# **Direction of Migration**

Whether from urban or rural areas, most migrants prefer cities and towns as destinations. Rural migrants have a somewhat higher tendency to choose a rural locality (18.2 percent) compared to urban migrants (13.5 percent). In other words, urban migrants have a higher preference for cities and towns (86.5 percent) than rural migrants (81.7percent; Ssee Table 1).

Table 1 – Direction of Migration – Urban/Rural (classified according to place of origin)

||Current|Location||
|---|---|---|---|
|Previous Location|Urban|Rural|Total|
|Urban|86.5|13.5|100|
|Rural|81.7|18.3|100|
|
**Table A.17:** Dynamic Effects on Public Goods Provision

||2015-2020|2015-2017|2018-2020|2021-22 (placebo)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|New village head|0.503<sup>_∗_</sup>|0.902<sup>_∗_</sup>|0.440|-0.364|
||(0.263)|(0.529)|(0.340)|(0.549)|
|Observations|378|122|256|134|
|Control mean|0.23|0.33|0.15|0.45|
|Robust p-value|0.053|0.063|0.165|0.615|
|MSE-opt. bandwidth|18.7|18.4|19.0|22.2|
|Effective obs.|161|52|109|80|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). The dependent variable is the index of local public service provision constructed using the 2021 Podes data. We restrict the sample to villages that conducted their most recent election between 2015-2020 (column 1); between 2015 and 2017 (column 2) or between 2018 and 2020 (column 3). In column 4, we restrict the sample to villages that conducted their most recent election in 2021 or 2022, namely after data collection for the 2021 Podes survey. Thus, these regressions can be interpreted as placebo checks.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

**Table A.18:** Effects on Citizens
this section we describe each survey, summarize the preparation of our data, and provide some descriptive statistics.

# **2.1 The FAT data**

The FAT data is a nationally representative survey to measure firm-level technology adoption, which was first implemented in Cear ́a-Brazil, Senegal, and Vietnam on businesses with 5 or more employees in agriculture, manufacturing, and services ( _17_ ). This new survey collects data on the technologies used more often (from a list of relevant technologies available) in every general and sector-specific business function. The potential technologies associated with each business function follow a ladder of sophistication ranging from the most basic to the most sophisticated (from handwritten processes to Enterprise Resource Planning in the business administration function, for example). In this paper we exploit the firm-level technology sophistication measure for general business functions (GBFs) proposed by ( _17_ ).

GBFs are tasks common across firms regardless of their sector. They include: business administration, production planning, sourcing and supply chain management, marketing and product development, sales, payments, and quality control.<sup>1</sup> With the exception of the most basic option (e.g., handwritten processes for business planning), the technologies available to perform GBFs are predominantly digital. Thus, our measure captures the level of sophistication of digital technologies, whenever they are used to perform these tasks. The technology measure for each business function ranges from 1 to 5 to capture the different levels of sophistication of the most frequently used technology for every business function, and then averages these sophistication measures across GBFs within each firm. This average is our measure of pre-pandemic technology sophistication.<sup>2</sup> The FAT data also includes information about the characteristics of the manager of the firm and

> 1Business administration corresponds to human resources, finance, and accounting. Appendix A lists each technology associated with each GBF in the FAT survey. See also ( _17_ ).

> 2The list of key business functions and
Figure A5: Average quality of imported digital goods as result of the AfCFTA <!-- Start of picture text --> Cape Verde<br>Burundi<br>Mali<br>Nigeria<br>Libya<br>Gambia<br>Namibia<br>Seychelles<br>Lesotho<br>Mauritius<br>Botswana<br>South Africa<br>Swaziland<br>Tanzania<br>Morocco<br>Uganda<br>Egypt<br>Rwanda<br>Malawi<br>Comoros<br>Ethiopia<br>Angola<br>Tunisia<br>Senegal<br>Africa average<br>Niger<br>Madagascar<br>Central African Rep.<br>Mauritania<br>Ivory Coast<br>Kenya<br>Congo<br>Ghana<br>Zimbabwe<br>Benin<br>Togo<br>Burkina Faso<br>Zambia<br>Mozambique<br>Ex-post Ex-ante<br>98.5 99 99.5 100 100.5<br><!-- End of picture text --> Notes: Figure depicts expected change in the quality of digital goods due to the implementation of AfCFTA. Product quality by source country is estimated using the Feenstra and Romalis (2014) methodology, drawing on data for 2019 and is assumed to be constant. Estimated product quality changes are driven by changes in the shares of digital goods imports from each source country due to the implementation of AfCFTA.

37
will be achieved in Ukraine through a requirement that all state support be channeled through the SAR platform and its use as the basis for an EU-type integrated administration and control system (IACS), and regulation to govern data access. As <mark>the impact of new technology is sensitive to implementation (Muralidharan</mark> _<mark>et al.</mark>_ <mark>2023), this</mark> creates opportunities of great interest to other developing countries.

Third, a large and growing body of literature shows that weather-induced fluctuations in agricultural yields, the frequency of which is likely to increase with climate change, can trigger persistent conflict (Harari and La Ferrara 2018). Conflict <mark>in turn can l</mark> ead households to reduce human capital investment (Akresh et al. 2023; Büttner et al. 2022) and, by modifying their assessment of risk (Federle et al. 2022), influence activity choice (Arias et al. 2019; Fergusson et al. 2020), trust (Korovkin and Makarin 2023), social interaction (Couttenier et al. 2022), and long-term economic outcomes (Chiovelli et al. 2021; Ibanez et al. 2022). By reducing households’ vulnerability to shocks, c <mark>ash transfers can thus lessen conflict</mark> risk (Fetzer 2020) and increase resilience (Agwu 2023).<sup>6</sup> Survey-based evidence on the effect of transfers for farmers in conflict settings is more limited: <mark>in the Republic of Yemen, where conflict increased child malnutrition, cash</mark>

> 2 Whether or not this is size neutral is an actively discussed issue, a combination of digital new actors and public action, can improve access for all (Birner _et al._ 2021)  (Reinhardt 2023) and involves challenges (Finger 2023).

> 3 A complete overhaul of the Filipino farmer registry (https://www.pna
**Table 1**

|**Annual growth ra**|**tes of employmen**|**t in 3 major sect**|**ors (percentage)**|
|---|---|---|---|
||1987-90|1991-94|1987-94|
|Agriculture|2.9|2.1|2.8|
|Industry|4.1|0.3|2.5|
|Services|2.2|-0.3|1.4|

Data source: own calculations based on CSO statistics.

After the economic structural adjustment program was introduced and droughts took place, the rate of increase in employment slowed significantly in industry, as seen from Table 1. It barely increased in the 1991-94 period. In the same period employment in the service sector decreased yearly by 0.3 percent whereas in agriculture it increased 2.1 percent yearly. The employment loss has been concentrated in sectors vulnerable to competition from trade liberalization and rise in interest rates, such as, for example, textiles. The share of total employment in each sector in the 1987-94 period is pictured in Figure 4.

**Figure 4 Employment by sector (1987-94)**

<!-- Start of picture text --> Agric Mining Manuf Eiec Construc Finance Distrib Service Trrans&Comms<br>Data source: CSO<br><!-- End of picture text --> <!-- Start of picture text --> 7<br><!-- End of picture text -->
, although there is no limit for CCS supplies, regional resource bases in Japan and the Republic of Korea are constrained.

- Though most models include technology cost as an exogenous assumption, some modeling frameworks, such as WITCH, model technology costs endogenously.

- Finally, solution algorithms differ across models. Most technology-detailed bottom-up models are based on an intertemporal modeling framework, whereas others, such as GCAM, are myopic and their solution algorithm is recursive dynamic.

There are now many criticisms of Integrated Assessment Models (IAM) and these range from technical disputes regarding appropriate quantities for variables to more fundamental critiques of the assumptions, concepts and purposes of IAMs (for the latter see also Dale, 2018; Hickel, 2018; Murphy, 2018). IAMs give the impression of being rooted in data, which tends to give them status as science as well as policy influence in key decision making and advisory circles (governments, the IPCC, etc.). Climate and economy focused IAMs are, however, deeply unrealistic in how they represent energy, environment and human systems and the relation between them.

The recent IPCC Global Warming of 1.5°C report (IPCC, 2018) and the deficits published in the annual UNEP Emissions Gap reports (see Christensen & Olhoff, 2019) have placed greater pressure on governments across the world to immediately increase investment in mitigation and adaptation and take more urgent action to reduce emissions – and this has resulted in further negotiations via the COP process and the UNFCCC and different countries are now beginning to announce they will aim for ‘net zero’ emissions by mid-century (though currently statutory commitments, detailed plans and implementation are mainly lacking and there is considerable skepticism regarding what ‘net’ might mean). Still, it is increasingly clear that more delay and gradual incremental change will be insufficient. Moreover, it remains the case that the IPCC approach to change is itself not sufficiently ambitious. IPCC reports include various ‘simulated scenarios’ generated from IAMs.

35
per capita GNP in 1993. Predictably, test scores are generally higher among children in richer than poorer countries. The relation is relatively weak, however, with a regression R<sup>2</sup> statistic of only 0.17, implying substantial variation among countries. It therefore appears that differences in student learning is not just a function of country wealth, but is probably also the result of differences in policy choices that ultimately affect the effectiveness of education processes within the classroom.

> 3 For examples of studies on the relation between worers' cognitive sklfls and work productivity, see Murnane, Richard, J. John B. Willet, andFrank Levy (1994). 'Ihe growng impn tance of cognitive skills in wage determination," Harvard Graduate School of Education (rnimeo); and Bishop, John (1991) "Achievemen **t** , est scores, and relative wages," in Marvin H. Kosters (ed.) Workers and Their Wages, Washington D.C.: The AEI Press.

> 4 The raw data pertain to some 53 countres, and come from the various international science and mathematics tests (e.g. the Third International Mathematics and Science Study) and conveniently reported in Jong-Wha Lee and Robert J. Banro, 1997 "School quality in a cross-section of countries" National Bureau of Economic Research, Working paper no. 6198, Cambridge, Mass. Not all countries patcipated in all the tests We used regression estimates relating one test score to another to fill the gaps, and to produce a single test score incoporating all the available information. The procedure produced data on 44 observations.
compliant with the terms of India’s national debt moratorium policy in place at the time of our experiment.

## **3.2 Experimental Protocol**

The main experiment was implemented between November 2020 and March 2021, after India’s first phase of the national debt moratorium had ended and while lenders were encouraged, but not required, by the regulator to offer additional debt forbearance to their customers. This allows us to extend real stakes debt forbearance offers that are truthfully framed as being granted either as an initiative of the lender or as a result of government policy.

The experiment was conducted in several rounds, corresponding to the lender’s monthly repayment cycle. Each month, the lender shared a list of borrowers that had failed to make their required monthly loan payment. The size of each monthly borrower cohort was thus determined by the size of the lender’s 30-day delinquency portfolio at this point in time. This sample was further restricted to borrowers that had been making regular payments prior to this date and whose loans had not previously been restructured or modified. Through this process, the lender shared a sample of more than 51,660 customers that qualified for debt forbearance according to its internal criteria. We excluded from this sample any borrowers who, if they accepted a forbearance offer, would not complete

> 12 We use the Jan 1, 2021 exchange rate of Rp 73 per US$ for all currency conversions in this paper.

> 13 The median (average) consumer loan recorded in credit bureau data for this population has an amount of Rs 12,800 (Rs 15,000), a median (average) interest rate of 16.5% (19.5%), and a median (average) tenor of 7 months (8.9 months).

9