rafmacalaba/data-use-mentions-extended huggingface dataset:
13

# **The Private Agricultural Technology Transfer Service (ATP2)**

In order to continue the diversification of its services, in mid-December 1994 INTA invited a number of private firms to jointly assess the possibility of providing private technical assistance to small and medium producers. The use of private firms to provide agricultural technology transfer services was enhanced by the need to: (a) use market incentives to provide better services; (b) minimize the risk of a larger public bureaucracy; and (c) reduce the costs to the public sector through a cost-sharing scheme by which the clients would participate in the financing of the service. During the first years of this program, most of the costs are expected to be covered by the government through a loan from the World Bank.

After several months of demand evaluation, the first contracts --between producers, the government, and private firms--were signed in August 1995. According to these tripartite contracts, the producers were expected to pay a sum covering about 20% of the cost in the first year, and their contribution is expected to increase to nearly 80% in about five years. At the time of writing, no producer was paying more than _50%_ of the service provision costs. An estimate of the average cost of public extension provision in Nicaragua (based on INTA budget for 1995), suggests a direct cost per producer of $115/year. This estimate appears to be quite high compared with actual data for ATP I and ATP2, as can be seen from the analysis in this paper. Producers in five regions were organized in groups and by end-July 1996 some 5,700 producers were served by 46 technicians and 7 supervisors contracted by 5 firms (each firm is responsible to provide extension services in a given region).

In contrast to the ATP1 concept, the service in ATP2 covers a wide range of farm production and marketing aspects. The size of the groups varies between 10 and 20 people. Each technician serves between 8
- **Jaynes, Gerald David.** 1982. “Production and Distribution in Agrarian Economies.” _Oxford Economic Papers_ , 34(2): 346–367.

- **Kaur, Supreet.** 2019. “Nominal Wage Rigidity in Village Labor Markets.” _American Economic Review_ , 109(10): 3585–3616.

- **Khanna, Gaurav, Emir Murathanoglu, Caroline B. Theoharides, and Dean Yang.** 2022. “Abundance from Abroad: Migrant Income and LongRun Economic Development.” NBER Working Paper 29862.

- **Kone, Zovanga L., Maggie Y. Liu, Aaditya Mattoo, Caglar Ozden, and Siddharth Sharma.** 2018. “Internal Borders and Migration in India.” _Journal of Economic Geography_ , 18(4): 729–759.

- **Kumar, Hemanshu, and Rohini Somanathan.** 2015. “State and District Boundary Changes in India: 1961-2001.” eSocialSciences Paper 7757.

- **Laffont, Jean-Jacques, and Mohamed Salah Matoussi.** 1995. “Moral Hazard, Financial Constraints and Sharecropping in El Oulja.” _Review of Economic Studies_ , 62(3): 381–399.

- **Lastarria-Cornhiel, Susana, Jolyne Melmed-Sanjak, and Beverly Phillips.** 1999. _Land tenancy in Asia, Africa, and Latin America: A Look at the Past and a View to the Future._ Land Tenure Center, University of Wisconsin-Madison.

- **Lewis, William Arthur.** 1954. “Economic Development with Unlimited Supplies of Labour.” _Manchester School of Economic and Social Studies_ , 22: 139–91.

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- **Madhok, Raahil, Frederik Noack, Mushfiq Mobarak,
# **4. Inequality in labor market impacts**

## **Methodology**

To shed light on how different groups were affected in terms of employment, we test whether changes in the early-phase and long-run labor market outcomes described above differ across types of workers and households. First, we regress the relevant work indicator on the respondent’s gender, age, education level, family composition,<sup>11</sup> urban vs rural location, and income diversification (whether the household had income from more than one type of source). We then additionally control for pre-COVID-19 industry of work, which is taken from the most recent face-to-face LSMS-ISA survey.

As described in the previous section, the samples are restricted to respondents who were working before the COVID-19 outbreak. To assess common patterns, we run OLS regressions on the pooled sample of respondents across the four countries, controlling for country fixed effects and with standard errors clustered by enumeration area. Estimation results for country-specific and rural and urban sub-samples are reported in Appendix Tables A6 to A11. In what follows, we first present and discuss results for earlyphase labor market impacts, and then turn to the one-year labor market impacts.

## **Results: early-phase impacts**

Estimation results for the early-phase labor market outcomes are reported in Table 1. In the early phase of the pandemic, women were significantly more likely to lose their job than men (column 1). This finding is in line with the gender difference in employment trends across 40 developing countries documented by Kugler et al. (2021), although the disadvantage for women is more pronounced in our sample.<sup>12</sup> Conditional on industry (column 2), the gender gap is slightly larger, suggesting it is driven by gender differences in job loss within (aggregate) industries. Again, this is in line with findings for other developing countries (Kugler et al. 2021).<sup>13</sup> The gender
made using a random sample of the cabin class configurations of aircraft used for the relevant flights. By using detailed information from several websites where such data are available, a fairly accurate match between the flights and the configuration of the aircraft used by the specific airline was made.<sup>24</sup> Of 1,219 airline/aircraft combinations, 304 could be directly verified, covering 123,972 flights. Estimates were made by counting the number of seats occupied in the same sized space between classes, which meant that multiples based on economy seats could be established for business and first class seats, reflecting not only the dimensions of the seats themselves, but also isle width and row spacing. From the 304 verified configurations, assumptions were made regarding the remaining 915 airline/aircraft configurations, making a total of 157,297 flights usable for analysis. These were then matched with known fuel consumption factors with tables provided by the International Civil Aviation Organization (ICAO).  In this phase of the calculations a single industry standard load factor of 71 percent (corresponding to the overall industry average for 2009) was applied for the carbon footprint calculations.<sup>25</sup>

> 24 The information sources included seatguru.com and seatmaestro.com.

> 25 The “load factor” corresponds to the share of available seats that is actually occupied on any given flight. Load factors can vary widely, by travel route and not least by flight class.

17
(0.009)|
|Observations|7,640|7,640|4,538|1,528|7,640|4,348|3,686|6,225|
|Mean of DV|0.000|0.000|0.003|0.000|0.000|0.001|0.001|-0.002|
|||Panel C: DV is a|(0,1) "good performan|ce" indicator from|public feedback sc|ores (feedback score|greater than 4.5/5)||
|(0,1) indicator that|-0.012|-0.009|-0.012|-0.003|-0.005|0.003|0.006|-0.020|
|worker is in India|(0.017)|(0.016)|(0.020)|(0.031)|(0.016)|(0.019)|(0.018)|(0.020)|
|Observations|5,935|5,935|3,642|1,245|5,935|3,347|2,883|4,838|
|Mean of DV|0.623|0.623|0.592|0.660|0.623|0.679|0.679|0.553|
|Relative effect|-0.019|-0.014|-0.020|-0.005|-0.008|0.004|0.009|-0.036|
||||Panel D: DV is a (0,1|) "good performanc|e" indicator from p|rivate post-job survey|||
|(0,1) indicator that|-0.032*|
errors at that same level to allow for correlation in the error term of household heads within a district. Ghana is administratively divided into 10 regions, 6 metropolitan assemblies and 55 municipal assemblies which are subdivided into 216 districts, each one with its own district assembly. Over time the number of districts has changed: in 1984 there were 141 local authorities which were reorganized into 110 districts in 1989 within a decentralization effort of the government, 28 districts were created by splitting some of the original 110 by 2006, in February 2008 the number was brought to 170. Since then, a further 46 districts were added bringing the total to 216 districts. We were able to map all the changes occurred over time and to match the administrative units of 1984 with the 170 districts available in GLSS-6.<sup>11</sup> Intuitively, the first stage is meant to capture a simple process: immigrants tend to settle where other immigrants from the same village (or town) of origin reside, and this is remarkably more so in a multiethnic country like Ghana where there is a strong overlap between administrative regions

> 11 This number (104) is well above the threshold of 30 clusters that Cameron et al. (2008) find to be the lower bound below which standard asymptotic tests can over-reject the null.

11
||0.0170**<br>(0.00688)|||0.0201**<br>(0.00895)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector2d|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10762276|10762276|10762052|9827417|9827417|9827251|
|R-squared|0.152|0.152|0.152|0.162|0.162|0.162|

_Note:_ The table presents the results from firm-level regressions of innovation intensity on ownership status. All specifications include fixed effects to control for initial firm size, initial firm age, 2-digit industry and year. Innovation is measured as the share of workers in technical and scientific occupations (PoTec) relative to total employment for firm _f_ in year _t_ . Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.16
longitudinal dataset, finds a drop in earnings of 30 percent at the time of displacement, which narrows to 10 percent after six years. In a seminal paper, using administrative data for workers in the state of Pennsylvania, Jacobson, LaLonde, and Sullivan (1993) look at the impact of largescale layoffs on workers. They report earnings losses of more than 40 percent in the year of displacement, and six years after displacement earnings are yet 25 percent below their predisplacement level. In a related paper for the state of Connecticut, Couch and Placzek (2010) find that earnings losses for workers displaced through mass layoff equal 32 percent at the time of job loss and six years later the reductions narrow to 14 percent. Davis and von Watcher (2011) calibrate a model of job search and unemployment fluctuations, and find that earning losses rise steeply with the unemployment rate at the time of displacement, concluding that tight labor market conditions at the time of displacement improve the medium- and long-term future earnings trajectory of displaced workers.

Bonikowska and Morissette (2012) look at the Canadian labor market and find that better labor market conditions mitigate earnings losses for displaced workers. In periods of low unemployment earnings losses are 10 percent while in periods of high unemployment they reach 21 percent. For Swedeen, Eliason and Sotrrie (2006) use linked employer‐employee data and follow displaced workers over a pre-displacement period of 4 years and a post-displacement period of 11 years. They conclude that displaced workers experience both earnings losses and worsened labor‐market position not only during a transitory period of adjustment but also in the long run. For Germany, Toulemon and Weber-Baghdiguian, (2016) find earnings losses of 10 percent but the effect vanishes after four years. They also find that other indicators of job quality are negatively affected. For the United Kingdom, Hijzen, Upward and Wright (2010) track workers for up to nine years after displacement and implement difference in differences estimators using 2
# **Figure 6-4 All taxes are progressive. The PIT is the most progressive tax, however the direct effects of**

**fuel excises are the most progressive component of the fiscal system.**

Progressivity of taxes, measured by the Kakwani Index at Market Income <!-- Start of picture text --> b. Progressivity of taxes<br>VAT (indirect effect) 3.2<br>Customs (direct effect) 3.7<br>Fuel excise (indirect effect) 4.9<br>Property 5.9<br>Pension contributions 10.4<br>Fuel excise (total) 11.9<br>VAT (direct + indirect effect) 12.6<br>PIT 14.1<br>VAT (direct effect) 19.5<br>Fuel excise (direct effect) 26.0<br>0 5 10 15 20 25<br>Kakwani Index w.r.t. Market Income<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017. Notes:

[1] The Kakwani Index is a summary statistic of progressivity [>0 = progressive].

[2] It is calculated for taxes by subtracting the Gini coefficient from the concentration coefficient.

47
<!-- Start of picture text --> 100%<br>7<br>90% 14<br>22<br>80%<br>28<br>70%<br>35<br>60%<br>45<br>50%<br>40%<br>64<br>30%<br>50<br>20%<br>31<br>10%<br>0%<br>1995/96 2003/04 2010/11<br>Poor Vulnerable Middle Class Upper<br><!-- End of picture text --> _Source_ : World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11 _Notes_ : The consumption aggregate used in this analysis is the 30-day recall version which is not the version used for official poverty estimates for 2010/11.

**Figure 6: Geographic Characteristics of the Poor, Vulnerable and the Middle Class, 2011**

<!-- Start of picture text --> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Rural 35 48 17<br>Urban 13 35 47<br>0<br>Eastern 30 51 18<br>Central 22 44 31<br>Western 29 46 24<br>Mid Western 45 45 10<br>Far Western 55 33 11<br>0<br>Mountains 39 46 15<br>Hils 30 42 25<br>Terai 30 48 20<br>Poor Vulnerable Middle class Upper class<br><!-- End of picture text --> Source: World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11 Looking at education and employment characteristics, it is clear that the likelihood of being
and are vectors with some standard child and mother/household characteristics known to affect newborn health: gender, an indicator of parity, the birth interval with respect to the cc pp ii,mm,tt ii,mm,tt XXprevious live birth, an indicator for a multiple birth (twins or more), for whether the birth was XX attended by a doctor or nurse, and for the day of birth (before/after the 15th of the month), the years of education of the mother, her age at birth, an indicator for whether she had any disability since birth or resulting from illness, the area of residence of the household (urban, rural or rural disperse), and a measure of multidimensional poverty.<sup>39</sup> are municipal characteristics previously defined; y y , m are time fixed-effects,<sup>40</sup> and mm are municipal fixed-effects. ii,mm,tt XX mmtth mm I exclude from the sample the children whose mothers migrated into municipalityγγ γγ γγ _m_ after _m_ after conception because they were exposed to different violence shocks. This could be solved by matching the violence data on the municipality of residence at the time of pregnancy, but it is not possible because DHS do not identify this place. Of the total sample of children with birthweight information, 15% of cases are excluded from the analysis for this reason,<sup>41</sup> but only a small proportion of mothers stated that they migrated as a result of violence: less than 6% of the pregnancies that occurred in a different municipality.<sup>42</sup> Instead, the most commonly stated reasons for migration, with about 44% of the responses each, were economic (‘looking for opportunities’ or ‘work') and family motives. I deal with migration arising from women moving out of municipalities exposed to violence as a robustness check.

# **V. Results**

# **A.
17

# Annex B: Metadata

Table B 1 shows each variable coded in the metadata of each FDP dataset identified in UNHCR/WB MDL.

## Table B 1: Metadata

|Name|Description|Source|
|---|---|---|
|id|Unique numeric ID|UNHCR/WB|
|idno|Unique stringID|UNHCR/WB|
|nation_abbreviation|ISO-3 countryabbreviation|UNHCR/WB|
|statement_title|Dataset title|UNHCR/WB|
|data_url|URL|UNHCR/WB|
|nation_name|Countryname|UNHCR/WB|
|year|Year of data collection|UNHCR/WB|
|producers_name|Name(s)of dataproducer(s)|UNHCR/WB|
|entity_name|Name(s)of entity/entities involved in data collection|UNHCR/WB|
|entity_contact|Contact information of involved entities|UNHCR/WB|
|abstract|Brief description and scope of the dataset|UNHCR/WB|
|coll_dates|Year,month,and daydata collection began and ended|UNHCR/WB|
|analysis_unit|Unit of analysis or data|UNHCR/WB|
|geog_coverage|Geographical coverage|UNHCR/WB|
|data_kind|Type of survey (e.g.,sample survey,census,event,multi-frame,no sampling)|UNHCR/WB|
|notes|Description of topics and modules covered in the survey|UNHCR/WB|
|method_data_collectors_name|Method of data collection|UNHCR/WB|
|method_sampling|Description of sampling|UNHCR/WB|
|method_coll_mode|Mode of data collection(e.g.,face-to-face,CAPI,CATI,Telephone,other)|UNHCR/WB|
|method_weight|Description of samplingweights|UNHCR/WB|
|topic_id|Numerical identifiers for which topics are covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|topic_words|Text identifiers for which topics are covered in
16.8%|35.6%|32.2%|16.7%|28.3%|
|High|26.9%|38.6%|30.0%|49.7%|25.5%|31.5%|15.4%|31.6%|
|ANOVA|||||||||
|F|16.83|92.14|24.11|361.41|28.49|31.56|322.54|12.71|
|Prob>F|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|

Notes: Table shows the poverty headcount rate across the three environmental risks categories as calculated for Figure 1 and statistics from a one‐way analysis‐of‐variance (ANOVA).

_Source: Authors’ calculation based on data from Brauer et al., 2015, Hansen et al., 2013, Vu et al., 2014b, Harmonized World Soil Database, Climate Research Unit, Bangalore et al., 2016, Winsemius et al., 2015, and Lanjouw et al., 2013._ 7
||**2008 – 2011**|**2012**|
|---|---|---|
|**Real**|All companies are eligible, n|o major changes|
|**Lump sum**|<br>Turnover of below<br>RwF20 million|<br>Turnover of between<br>RwF12 and RwF50<br>million|
||<br>4 percent of turnover|<br>3 percent of turnover|
|**Flat amount**|Not applicable|<br>Turnover of below<br>RwF12|
|||<br>Turnover based fee|

# **Annex 2: Tax declarations, tax revenue and taxable employment**

This paper relies heavily on tax declarations data to make inference on tax revenue and employment; this annex clarifies the relationship between the three.

Tax declaration data, unlike realized revenue, is available at company level, and can be checked against company registrations (see Annex 3 for detail). Although not perfect, there is a tight relationship between tax declarations and tax revenue at an aggregate level; with differences being accounted mostly by withholdings, pre‐payments, arrears, VAT refunds, audit and post‐audit adjustments, and fines.

This paper focuses on the three main tax categories: Corporate and Personal Income Taxes (CIT and PIT), employees’ withholding taxes (or pay‐as‐you‐earn, PAYE), and Value added Tax (VAT). The three constitute 70‐80 percent of overall tax receipts, with the remainder accounted for by excise taxes, land, property and rent taxes, and charges applicable to roads and motor vehicles.

Due to the above mentioned discrepancies between tax declarations and realized revenue figures quoted in this paper do not exactly correspond to national revenue declarations. To avoid confusion, this paper refers to tax figures in percentage of overall declarations.

As
|33,094|36,658|0.014|0.035|0.048|0.903|1.000|
|Meats and dairy|781|458|868|5,143|7,249|0.108|0.063|0.120|0.709|1.000|
|Grain milling|10,092|1,341|4,728|19,656|35,817|0.282|0.037|0.132|0.549|1.000|
|Sugar & bakery confectionary|3,151|1,400|6,280|20,475|31,307|0.101|0.045|0.201|0.654|1.000|
|Textiles and clothing|4,815|18,592|10,903|279,109|313,418|0.015|0.059|0.035|0.891|1.000|
|Leather and footwear|170|117|551|20,191|21,029|0.008|0.006|0.026|0.960|1.000|
|Wood and papers|30,504|7,720|79,746|115,781|233,751|0.130|0.033|0.341|0.495|1.000|

Source: Based on UN COMTRADE Statistics.

85
2) and 2019 (regression 3). Relying on the same years in which data is available for cloud and using its same long differences specification, also leads to consistent results (regression 4). In Table A4 we show the effect of the AIA on investment holds irrespective of whether we define the treatment status of the firm using a 2-year average based on the years 2006/07 (regression 1), or a 3-year average based on the years 2005/06/07 (regression 3).

In contrast to its effects on investment, AIA eligibility reduces the propensity to adopt cloud, in this case by 16.5 percentage points. The magnitude of the estimated coefficient is relatively large compared to the mean rate of cloud adoption, of 28% in our sample. These results reinforce the idea that firms view IT capital investment and purchases of cloud IT services as substitutes – a reduction in the relative price of IT capital leads to a substitution away from cloud services and towards tangible IT investment.

**Table 3:** Effects of AIA on total capital investment and cloud adoption, 2007-2013

|Regressions|(1)|(2)|
|---|---|---|
|Dependent variable|Total Investment|Cloud|
|AIA Dummy|0.481***|-0.165***|
||(0.105)|(0.040)|
|Observations|21,757|2,200|

Note: Time period: Total investment regressions use annual data for the years 2007 to 2013. Cloud regressions use data for 2007 and 2013. Total investment is in (log) thousands of pounds, while cloud is a dummy variable. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. Regressions use a binary AIA eligibility indicator. Regression 1 is estimated using the method outlined in
<br>and border control<br>Bilateral/Multilateral<br>Agreements<br>Labor taxes<br>Visa and work<br>permit fees|

What is the extent of market (and regulatory) failures that affect migration?

**Labor mobility.** Data on income or wage gaps across regions suggest that, in general, countries are very far from the spatial equilibrium discussed above; there is not enough labor mobility.<sup>23</sup> Gaps are especially large across countries--unskilled workers’ wages in rich and

23 While differences in economic prospects often dominate in shaping the prospect gaps, gaps in access to amenities can play an important role as well. Dustmann and Okatenko (2014) find for example that 60 percent of the variation in migration intention in Sub-Saharan Africa could be explained by differences in contentment with the local public services, while about 30 percent was explained by differences in