rafmacalaba/datause-encoder-data huggingface dataset:
effect, which is commonly referred to as the _firm wage premium_ .

We estimate the following regression: where _i_ indexes individuals, _j_ indexes firms, _t_ indexes time, _yit_ is logged normalized earnings for worker _i_ at firm _j_ in year _t_ (normalized earnings are total payments from firm _j_ per year divided by the number of full time days worked at firm _j_ ), the _αi_ are worker fixed effects, the _ψj_ ( _i,t_ ) are firm fixed effects, the _θit_ include a set of year indicators and a flexible polynomial of worker _i_ ’s age, and _εit_ is an idiosyncratic log wage component, capturing purely transitory earnings fluctuations. In this estimation, worker effects _αi_ and firm effects _ψj_ ( _i,t_ ) are separately identified by workers who change employers.<sup>12</sup> In the AKM framework, _αi_ reflects the worker’s time-invariant human capital, such as education and ability, that is rewarded equally across different employers, while _ψj_ ( _i,t_ ) gives the percentage wage premium enjoyed by every worker employed at firm _j_ . Such a premium could represent rent-sharing (Card, Cardoso and Kline, 2016), strategic wage posting behavior (Burdett and Mortensen, 1998; Moscarini and Postel-Vinay, 2013), other components of the wage structure such as an efficiency wage premium, compensating differentials (Sorkin, 2018), or variation in general payment practices (e.g., the presence of unions, corporate culture, or negotiating power). Appendix Figure C1 shows the distribution of and worker For the estimation of the AKM model, we use a number of sample restrictions. A possible concern is that consolidations can impact firms’ wage structure, and hence the estimated firm fixed effects. To avoid such endogeneity, we drop observations during and after the year of the first consolidation we observe in the time period for any firms that eventually consolidate. For workers with multiple jobs within a given year, we keep the job with the highest associated earnings.

> 12The crucial assumption for this interpretation of the AKM decomposition to hold is that the idiosyncratic log earnings component _εit_ is unrelated to the sequence of worker _i_ ’s employers _j_ ( _i, t_ ). For a discussion and test of the validity of this conditional exogenous mobility assumption, see Abowd, Kramarz and Margolis (1999) and Card, Heining and Kline (2013). Schneck (2021) presents an application using similar Dutch micro data as ours.

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# **_D. Integration with non-traditional data sources_**

As for the opportunities offered by the integration with non-traditional data sources, also known as ‘digital trace’ data (Kirchberger, 2022), such as big data and citizen-generated data, our assessment is that there are both pros and cons, and that while, at the moment, improving interoperability of household surveys with non-conventional data presents complex issues, this is likely to be the way to go in a notso-far future. On the one hand, big data, such as mobile phone data or smartphone app data, have indeed the potential to complement traditional data and address significant spatial and temporal gaps. As emphasized by the International Organization for Migration (IOM, 2018), the main strengths of these new data sources lie especially in their wide and continuous coverage, flexibility, timeliness, frequency, high spatial, relatively low cost, and their potential to track temporary, circular, and seasonal patterns of migration, which are difficult to capture through traditional sources. Additionally, big data can be a promising tool for better ex-ante sample design, such as first-stage sample selection, and allow one to compute new covariates that would otherwise be costly or difficult to collect at scale, such as labor market referrals, social networks, or mobility and social contact (Kirchberger, 2022). On the other hand, the use of big data comes with significant challenges, among which: i) ownership by the private sector; ii) privacy, data protection and ethical issues in using data automatically generated by users, and human rights concerns; iii) their volume, complexity and “noisiness”; iv) the fact that big data reflect behavioral patterns which may not be representative of the population (selection bias); v) reliability of the selfreported information on social media; difficulties in applying statistical definitions of migration.

<mark>Using the case study of a data science challenge involving West African mobile phone data, Taylor (2016) argues that big data carries with it the dual risk of rendering certain groups invisible, and of misinterpreting what is visible a</mark> nd, in addition, raises concerns about the lack of information concerning the context of behaviors and activities ‘observed’ using big data analysis. Others have argued that the distance between the researcher and the research looms large for remotely generated data, posing challenges to validity and ground truth, as well as for the reliability and interpretation of research results (Kraly and Hovy, 2020). Operationally, while the potential for using such data to better understand migration dynamics has yet to be fully explored, integrating multi-topic household surveys with varied sources of big data requires not only addressing the significant technical and ethical challenges just highlighted but also the development of new methodologies that consider complex interactions over differing geographic and temporal scales (Franklinos et al., 2020). Furthermore, to leverage big data as a meaningful source of information for migration analysis, national statistical offices would need to work 34
The key research questions that guided the MEXA experimental design included: 1) Does interviewing only the “most knowledgeable” household member, as is typically done in household surveys, yield comprehensive information about individual-level asset ownership and control for both men and women? 2) How much can we improve understanding of (i) intra-household asset ownership and control and (ii) inter-relationships between reported, economic and documented ownership of, and rights to assets by interviewing multiple household members, as opposed to the most knowledgeable household member? 3) Do partners provide different information about personal and each other’s asset ownership when interviewed separately versus together? 4) Do individuals provide different information about personal asset ownership when interviewed separately but asked to report only on assets they own versus assets owned by any household member, including themselves?

In view of the prevailing protocols on respondent selection and fieldwork implementation, and the research questions, MEXA tested 5 survey treatments, each of which sought to establish a different interview setting while uniquely identifying, at the asset-level and across 13 asset classes, reported owners, economic owners, documented owners and holders of rights to (i) bequeath, (ii) sell, (iii) rent out, (iv) use as collateral, and (v) invest/make improvements. Within each enumeration area, 4 households were assigned at random to each treatment arm.

Arm 1 (standard of practice) interviewed the individual who, following the enumerator’s introduction of the survey, was identified to be the “most knowledgeable” household member.” This respondent was asked about the assets owned by each member of the household, exclusively or jointly with others within or outside the household, in each asset class.<sup>10</sup> Arm 2 interviewed the randomly selected member of the principal couple while Arm 3 interviewed the principal couple together. The questionnaire for Arm 2 and Arm 3 was otherwise identical with respect to Arm 1. Arm 4 and Arm 5 each interviewed up to 4 adult household members, 18 years and above; attempts were made to conduct the interviews simultaneously. In each case, an attempt was made conduct the interview without others present.

Identical to Arms 1 through 3, each respondent in an Arm 4 household was asked independently about the assets owned by each member of the household, exclusively or jointly with others within or outside the household, in each asset class. In this respect, _reported_ owners, _economic_ owners and _documented_ owners (as applicable) were uniquely identified based on ID numbers sourced from household roster through the following

> 10 In line with the prevailing implementation protocols, the selection of the most knowledgeable household member was a function of the adult individuals that were available at the time of the interview. This could have meant that the first choice for the most knowledgeable member was not interviewed if he/she was unavailable during the time that the field team was going to be in that enumeration area.

10
As a benchmark, we also use OLS to estimate an augmented Cobb-Douglas production function. To make it comparable to the Ackerberg et al. procedure, we regress real firm value added (defined as above) on real labor and capital inputs as well as measures of services reform and other control variables:<sup>18</sup> ln _VAijt_   _i_   1 _j Kit_   2 _j Lit_   3 _Services_ Index jt_  1   4 _Tariffit_  1   5 _Input tariff it_  1   6 _Foreignit_   _t_  _it_ (4) where _VAijt_ stands for the value added of firm _i_ observed in year _t_ (and manufacturing industry _j_ ), _Kit_ denotes capital, and _Lit_ labor. Note that we allow the coefficients on capital and labor inputs to differ across 11 manufacturing sectors. As in specification (3), we include firm and year fixed effects and cluster standard errors at the sector-year level.

Our point estimates for the production function coefficients, presented in Table 2, have reasonable values. On average, the labor coefficient is 0.73 in the OLS and 0.75 in the Ackerberg et al. specification, and the capital coefficient is equal to 0.27 in both cases. In 9 of 11 industries, the coefficient on the capital input is higher in Ackerberg et al. procedure, which is what we would expect to observe under plausible assumptions (Olley and Pakes, 1996). The average returns to scale are very close to constant (1.00 and 1.01).

> 18 A specification with output on the left-hand side and industry-specific coefficients on material inputs, services inputs and energy leads to very similar results.

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of 10 employees and contribute almost half of Mexico’s GDP. Access to external financing for these firms is limited by governance issues arising from their family-based structure and by their relative lack of collateral, financial and credit history information, which is partly a function of the high level of informality in the economy<sup>44</sup> . High transaction and monitoring costs and small loan amounts further restrict the economic appeal of lending to these companies. A joint program by the Ministry of Economy and development bank _Nacional Financiera_ (NAFIN) entitled _Sistema Nacional de Financiamiento PyME_ was incepted in 2001 to foster micro and SME credit through the provision of direct guarantees<sup>45</sup> , but its impact is still relatively small with guaranteed loans representing less than 7 percent of bank commercial credit portfolios as of December 2005 (see figure 32). The development of a web-based reverse factoring program for trade receivables (“ _Cadenas Productivas_ ”) by NAFIN has also contributed materially to greater bank lending to SMEs<sup>46</sup> .

<!-- Start of picture text --> Figure 31: Number of Companies in Figure 32: Evolution of the SME Financing<br>Mexico by Size (1999) Guarantee Program (2002-2005)<br>120,000 30<br>Medium<br>Large 0.9% Small<br>0.2% 3.9% 100,000 25<br>80,000 20<br>Micro<br>95.0%<br>60,000 15<br>40,000 10<br>Size Manufacturing Trade Services 20,000 5<br>Micro < 30 < 5 < 20 - 0<br>Small 31 - 100 5 - 20 21 – 50 2002 2003 2004 2005<br>Medium 101 - 500 21 - 51 - 100<br>Number of companies Volume of loans guaranteed<br>100<br>Large 500+ 100+ 100+<br>MXP Billion<br>Number of companies<br><!-- End of picture text --> Source: Ministry of Economy, INEGI. Note: Size of firm refers to number of employees.

Although data on the evolution of commercial lending rates are unavailable, anecdotal evidence suggests that credit affordability has improved for larger companies. In particular, there is no published commercial loan product rate or spread (in relation to 44 For example, only about 15 million out of an estimated 42 million workers receive social security and other benefits; see Mehrez G. (December 2005). See Beck T. and Demirguc-Kunt A. (November 2006) on the link between access to finance and growth for SMEs.

45 The focus of the credit application process shifted from the characteristics of the company, provision of collateral and evaluation of the project, to basic requirements of formalization of companies and assessment of the experience and credit record of the owners-managers.

46 In this program, the factor (i.e. the bank) only purchases accounts receivable on a non-recourse basis by pre-identified large buyers, thereby addressing some of the SME direct financing problems linked to lack of information, risk of fraud and high transaction costs. The program has been successful in attracting large corporates and providing liquidity to their SME suppliers, while the use of an electronic platform has reduced transaction costs and fostered competition among financial institutions. See de la Torre A., Gozzi J.C. and Schmukler S.L (April 2006) for a description.

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# **1 Introduction**

Ghana’s GDP per capita growth rates increased steadily in the past two decades and varied around 5-7% between 2007 and 2010 (see Figure 1). Over the same period, poverty rates decreased and other, non-monetary, welfare indicators improved. In order to assess the sustainability of these positive developments, their determinants need to be detected and quantified. This investigation of historic growth rates can further be used for predicting future GDP growth rates under different scenarios.

This piece of research uses panel growth regressions to address the question about the drivers of Ghana’s historic growth rates, emphasizing in particular the role of natural resources. Cocoa, timber, minerals and gold account for a large share of total exports of the country (see Lej ́arraga (2010), for example), and the recent discovery of oil raises the question about the expected impact of natural resources on growth. The estimates obtained from the cross-country panel analysis are used to predict the growth potential for the country under different scenarios.

<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>.1<br>.05<br>0<br>5-year-average GDP per capita growth rate<br>-.05<br><!-- End of picture text -->

**Figure 1** – Growth rate of per capita GDP in Ghana, averages over 5 years: Data from 1970-2010 are used. Values are 5-year averages for the years 1970-2006, and averages of 1-4 years for values that correspond to the years 2007 and after.

The choice of variables that are used in the empirical analysis is based to a large extent on the findings in Barro (2003), but additionally considers the aim of assessing the growth performance of the country of Ghana by estimating various specifications that tackle the impact of natural resources.

2
-10-

The board of each association is composed of seven members, four representing minor farm holdings (of less than 20 hectares) and three representing major holdings. The board members are elected for two years, but can be re-appointed indefinitely.

The seasonal cultivation and irrigation program is submitted for approval to a consultative committee composed of representatives of the large and small farm holdings, regional directors of HIMAT and representatives of other agricultural institutions (credit, extension services) at the discretion of the water users'associations.

Acuerdo No.41 also defines the book-keeping responsibi'Lties of the district management as including:

- Master list of users,

- Record and file of works and procurement contracts,

- Updated inventory of the District's works and equipment,

- Hydrological and meteorological records,

- Records of rainfed and irrigated crops,

- Record of irrigation distribution orders,

- Record of volumetric consumption,

- Books of inventories, materials, equipment control, unit costs, yields, etc.

- O Accounting and cash books,

- Correspondence files,

- Files of channel and on-farm water measurement records and topographical records,

- Personnel records,

- Book of minutes of the meetings of the Coordination

Commitee, meetings with users, Users' Associations, etc.,
loom and weaver. The second set of practices most likely to be dropped were ones that required daily attention from management: monitoring defects on a daily basis; meeting daily to discuss quality defects and gradation; and updating visual aids of efficiency on a daily basis. They were thus costly, and presumably seen as not very valuable.

In contrast, we see that many of these practices are very sticky. Of our 38 practices, once adopted, 14 are not dropped by a single plant, and a further 8 are dropped by at most one-quarter of those adopting. Particularly noticeable among these sticky practices are that those which were adopted by 10 or more plants and then never dropped. These relate very closely to the most immediate improvements in quality and inventory levels that we saw from the original consulting intervention: recording quality defects in a systematic manner (defect-wise); having a system for monitoring and disposing old stock; and carrying out preventative maintenance. Finally, we note that not all daily activities were susceptible to being dropped, with those most closely tied to keeping machines running quite persistent: firms still maintained daily monitoring of machine downtime and had daily meetings with the production team.

# **_III.D. Results on Long Run Performance_**

The other question we investigated when returning to the plants was the long-run performance impact of the original management interventions. Because of budget limitations and the reluctance of firms to share financial data, we are not able to undertake a detailed analysis of TFP.<sup>10</sup> We were able, however, to collect basic information on plant size and looms in 2014 and 2017 to supplement our original data for 2008 and 2011. Since there were changes over time in the number of plants per firm, and the management practices have converged across plants within firms, we examine performance at the firm level.

We run Intention to Treat (ITT) panel regressions over four years (2008, 2011, 2014 and 2017) at the firm level with firm and year fixed effects and standard errors clustered at the firmlevel: i,t = aTREATi,t + bt + ci +i,t 10 In our original study the consulting firm spent many months extracting production data from firms’ log books and production records, which were used to construct a measure of TFP. We were not able to extract this data in our longer-term follow-up.

15
towns and villages near the border within a radius of roughly 50 km.

5. If the goods are destined to be delivered further afield, other transporters take over the load.

**Although the border crossing point at Bouchebka is one of the most important on the Algerian border, there seems to be very little traffic there.**<sup>26</sup>

**According to the local authorities, informal trade takes place at points other than the official border crossings.** Customs officials explained that the activity we observed at the crossing points was normal and that the number of border crossings was in line with the daily statistics for individuals and vehicles seen in 2012.<sup>27</sup>

**This led us to look elsewhere for information, namely to traders and transporters of goods across the border living in Kasserine and in nearby border towns.** Despite the difficult security situation and the climate of suspicion and distrust in the region, we were able to collect 107 responses to the questionnaires we distributed among these individuals. An initial assessment showed that there is a considerable difference in prices for certain products. These price differences appear to be the main reason for informal cross-border trade in the region (see Table 7).

**Table 7: Prices of various goods in Tunisia and Algeria**

|Product|Unit|Tunisian price<br>(in TND)|Algerian price<br>(in TND equivalent)|
|---|---|---|---|
|Cheese(gruyère)|kg|30|10|
|Roasted coffee|kg|9|4|
|Tea|kg|5|3|
|Juice|1 liter|2|1|
|Sparklingdrinks|1.5 liter|2|1|
|Gasoline|1 liter|2|0|
|Fuel oil|1 liter|1|0|
|Round<br>steel<br>reinforcement bars|per ton|1,600|900|
|32" LCD TVs|per unit|770|450|
|Air conditionersper 12|,000 BTU|900|450|
|Foreign cigarettes|perpacket|5|1|

**The most common form of transport used in this type of informal trade is the van, with 3,000 of these vehicles being used to transport goods illegally across the Algerian-Tunisian border, according to those we interviewed.** On average, these vans make one crossing per day.

> 26 In the space of three hours, we saw just six Tunisians on foot, three Algerian private cars, two Tunisian articulated trucks filled with full gas bottles, four empty Algerian articulated trucks cross into Tunisia, and just three Algerian articulated trucks carrying Tunisian white cement crossing into Algeria.

> 27 A total of 65 private cars per day (10 Tunisian and 55 Algerians) and 15 trucks (3 Tunisian and 12 Algerian) are recorded as both entering and leaving Tunisia.

19
# Fi ure 2c: Correlation in female activit across sectors b state <u>g y y</u>

<!-- Start of picture text --> A. Shares of female-owned establishments C. Segmentation in female-owned firms<br>40% 90%<br>85%<br>35%<br>80%<br>30%<br>75%<br>25%<br>70%<br>20% 65%<br>60%<br>15%<br>55%<br>10%<br>50%<br>5%<br>45%<br>0% 40%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 40% 50% 60% 70% 80% 90% 100%<br>Manufacturing Manufacturing<br>Services<br>Services<br><!-- End of picture text --> <!-- Start of picture text --> B. Share of employees in state who are female<br>D. Segmentation in male-owned firms<br>40%<br>100%<br>35%<br>95%<br>30%<br>90%<br>25%<br>20%<br>85%<br>15%<br>80%<br>10%<br>75%<br>5%<br>0%<br>70%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>60% 65% 70% 75% 80% 85% 90% 95% 100% 105%<br>Manufacturing<br>Manufacturing<br>Services<br>Services<br><!-- End of picture text -->