rafmacalaba/data-use-mentions-extended huggingface dataset:
microSD cards (16 GB capacity), and external battery packs.<sup>13</sup> Teams of four enumerators and one supervisor were provided with a mobile generator using fuel to ensure that tablets could be charged overnight. Computer Assisted Personal Interviewing (CAPI) data collection can be used to improve data quality by imposing sophisticated systems of constraints on the enumerators’ entries. This was particularly relevant for consumption and price data, which need to be measured precisely as a prerequisite for a reliable poverty analysis. Indeed, CAPI has been experimentally shown to improve data collection while minimizing the potential for enumerator error (Caeyers et al., 2012; Fafchamps et al., 2010). Furthermore, it can be used to create more sophisticated questionnaires, with elaborate conditional skipping patterns that are much easier to implement (De Leeuw et al., 1995).

The rapidly expanding cellular network in South Sudan meant that the data could be transmitted via mobile networks and made available quickly to data analysts (Pape and Mistiaen, 2014). The near real‐time transmission of data to a cloud enabled the implementation of a monitoring system including a dashboard tracking the cumulative number of interviews, the fraction of missing variables, as well as additional quality indicators at any level of disaggregation.<sup>14</sup> This helped to identify challenges in the field work as well as weak enumerators early on and mitigate their impact on data quality, e.g. by providing individually tailored extra trainings for selected enumerators. In addition, the real‐ time analysis code calculates core indicators of the survey, e.g. consumption, educational attainment, and unemployment, to check incoming data while field work is still ongoing. This head‐start on building the analysis code ensures that swiftly after the end of data collection the cleaned data can be made available, which considerably accelerated the process from data collection to the publication of results.

The availability of real‐time data facilitated monitoring by allowing much closer tracking of
# 6. References

Arthi, V., Beegle, K., De Weerdt, J., & Palacios‐López, A. (2016). Not your average job: measuring farm labor in Tanzania. _Working Paper, World Bank._ Baah‐Boateng, W. (2015). Unemployment in Africa: how appropriate is the global definition and measurement for policy purpose. _International Journal of Manpower, 36_ (5), 650‐667.

Bardasi, E., Beegle, K., Dillon, A., & Serneels, P. (2011). Do labor statistics depend on how and to whom the questions are asked? Results from a survey experiment in Tanzania. _The World Bank Economic Review, 25_ (3), 418‐447.

Barrientos, S., Kabeer, N., & Hossain, N. (2004). The gender dimensions of globalization of production. _ILO Working Paper._ Bassi, F., Padoan, A., & Trivellato, U. (2008). Inconsistencies in reported employment characteristics among employed stayers. _IZA Discussion Paper._ Beegle, K., Carletto, C., & Himelein, K. (2012). Reliability of recall in agricultural data. _Journal of Development Economics, 98_ (1), 34‐41.

Benes, E., & Walsh, K. (2018a). ILO LFS Pilot Studies experimental field tests: methodology, process and outcomes. ILO.

Benes, E., & Walsh, K. (2018b). ILO LFS Pilot studies in follow up to the 19th ICLS: background, objectives and methodology. ILO.

Benes, E., & Walsh, K. (2018c). Measuring main activity in labour force surveys. Main findings from the ILO LFS pilot studies. ILO.

Benes, E., & Walsh, K. (2018d). Measuring employment in Labour Force Surveys: main findings from the ILO LFS pilot studies. ILO.
, we leave this question for future research.<sup>52</sup>

# **REFERENCES**

Ades, Alberto F and Edward L Glaeser, “Trade and circuses: explaining urban giants,” _The Quarterly Journal of Economics_ , 1995, _110_ (1), 195–227.

51The UT sector (MFG & FIRE) is more formal than the UNT sector (verified using both IPUMS and I2D2 data; not shown). As such, it may have smaller congestion externalities on other sectors and the city as a whole. Akbar et al. (2018) find that the roads of Indian cities are particularly congested in the middle of the day when few workers are commuting. Indeed, the (more informal) wholesale and retail trade sector (incl. roadside vendors) often causes traffic congestion. Burger et al. (2022, Table 4) find that the AEs of UT firms are reduced to a lower degree by congestion than the AEs of UNT firms, possibly because UNT firms are more dependent on local consumer markets, while UT firms rely less on local markets and employ workers who are better able to locate close to their workplaces and/or afford faster transport services.

> 52Nonetheless, Venables (2017) shows theoretically that a city is unlikely to produce UTs if it faces high urban and hinterland demand for UNTs. That is especially the case with increasing returns in tradable production that make cities specialized in UNT globally uncompetitive for the production of UTs.
and Collins 2009).

**_4.2.4 Women’s empowerment and gender roles (via television)_**

**There is a small but important body of evidence on the impacts of television on gender roles and women’s empowerment, substantiating the frequent references to this in the case study literature and identifying a potential causal mechanism for greater women’s empowerment in electrified vs. non-electrified villages in Bangladesh as identified by Barkat (2002) and impacts of electrification in Bhutan found by ADB (2010).  For example, Chong and La Ferrara (2009) find that access to the television network that carries telenovelas in Brazil resulted in higher divorce rates, which the broader literature suggests may be related to empowerment of women although it may have adverse consequences for children.  More directly, Jensen and Oster (2009) find that access to cable television results in lower acceptance of spousal abuse, lower son preference, more autonomy, and greater likelihood of sending young girls to school in rural India.  This suggests a potentially important impact channel for achieving the third Millennium Development Goal, albeit depending on the complementary input of television programming that directly or indirectly promotes women’s empowerment. The impact may stem from imitation of role models of emancipated women in fictional TV dramas.**

Cecelski (2004) argues that the question of ―rural electrification‘s impact on gender relations‖ is critical and neglected. She notes that some researchers are skeptical about the possibility that electricity alone could empower women, but suggests that there are several possible pathways. For example, ESMAP studies in India and the Philippines found that women in electrified households read more, and EnPoGen studies in Sri Lanka and Indonesia concluded ―that electrification strengthened women‘s position in the household, by encouraging families to spend time together in the evening and giving women the same access to entertainment and information from TV‖ (Cecelski 2004). Barkat (2002)

> 48 Jensen and Oster (2008) estimate fixed effects regression of fertility on
21|
|Post-secondary (academic)|0.26|0.44|75,107|21|
|Means and standard deviations are weighted by sampling weights that have been adjusted to give<br>numeracy, and problem-solving skills, mean and SD are estimated using plausible values.|equal weight t|o each c|ountry; fo|r literacy,|

|Table 2. Descriptive statistics of the industry-level dataset<br>|||||
|---|---|---|---|---|
|Variable|mean|SD|obs.|countries|
|Cog. skills per worker (literacy skills, 2nd percentile set as zero)|127.98|18.55|373|22|
|Output per worker (euros 2012)|67,820|140,618|356|21|
|Capital per work (euros 2012)|471,920|1,661,381|319|19|
|Emissions per worker (tonnes)|24.80|81.74|356|21|

13
determinant of exports to this market in the context of the treatment effects regressions, where the probability of being eligible for preferential access is endogenously determined by U.S. geostrategic interests. The significance of distance reappeared in the Heckman-selection models where program utilization is determined by U.S. non-economic motivations, but in this case the estimated effect of the Andean trade preferences was not significant.

Of particular current interest is the finding that FTA members have significantly larger exports to the U.S. compared to excluded countries. And this effect seems to be larger than the also positive effects of CBI, ANDEAN, and AGOA. Another important finding is that the estimated benefits of preferential market access programs decline considerably when we take into account the rules of origin requirements and other bureaucratic barriers by using utilization ratios in the estimations. GSP is the only exception in terms of providing benefits to beneficiaries and seems to have negative effects in several specifications.

Among the interesting methodological conclusions, the presence of zeroes in the productlevel exports data tends to produce severe biases in linear estimations. These biases are particularly influential on the estimated coefficients of some preferential programs, AGOA in particular, and they also severely biased the impact of distance in the OLS linear regression. Thus truncated data techniques seem to be more appropriate in this type of application. The inclusion of a direct proxy for international transport and transactions costs seems to be quite important in these models. Indeed, all of our estimates of the impact of these costs on trade volumes indicate that they are economically and statistically important. Moreover, in the treatment regressions, which also control for the endogeneity of program eligibility, the distance variable became statistically irrelevant, whereas the direct proxy of international transport and transactions costs remained robustly significant with economically large coefUndoubtedly, there is need for further analysis of the effects of preference programs and more to be learned from gravity models in this context. One main limitation of this study is the focus on U.S. programs. The European Union has similar reciprocal and unilateral
the trends in wage inequality (which are arguably exogenous). We believe that our strategy of matching sectoral tariffs to sectoral wages through two episodes or trade liberalization and one episode of reversal to protection provides a good and valid identification strategy of the effects of trade liberalization on wages and wage inequality.

To end, we use our data to ask whether trade liberalization has had an effect on the wages of young (versus more tenured) workers. This is an interesting question for it may indicate an additional channel by which trade can affect income inequality and poverty. Our findings reveal, however, that there is no evidence that trade protection has affected the experience premium; indeed, the coefficients of the interactions of trade with age (and age squared) are statistically insignificant. In the end, we conclude that trade has had an impact of the skill premium but not on the tenure premium.

# 4 Conclusions

This paper proposed to explore the links between trade liberalization and skill premium by exploring a historical dataset of Argentine trade policy and labor force surveys. To this end, we put together information spanning almost 30 years of recent Argentine history, from 1974 to 2001. This is thus one of the few cases where such a data set is constructed. A similar case is Attanasio, Goldberg, and Pavcnik (2004), who carry out an analysis covering the 1984-1998 period in Colombia. But while they explore one episode of trade liberalization (using 8 labor force surveys from 1984 to 1998), we exploit two episodes of trade reforms separated by a decade of reversion to protection (using 40 surveys from 1974 to 2001).

The 1974-2001 period under study is one of active and fluctuating trade reforms and wage inequality in Argentina. Tariff reforms accelerated in the late 1970s and early 1980s, stagnated during the 1980s, and picked up further momentum during the 1990s. The skill premium, in contrast, increased during the 1970s, declined during the 1980s, and increased again during the 1990s. In addition, the share of labor
Despite the new insights into patterns of urbanization both across and within SSA countries generated by our analysis, there are important limitations that should be noted. Hence, our analysis is limited to a single cross-section in time, _circa_ 2015. This is because of the limitations of the underlying gridded population data that we rely on as input into our analysis. Thus, the detailed map of all building footprints in the region that underpins the derivation of the gridded population data we use is only available for _circa_ 2015. Moreover, even for this year, although it is the best available, the quality of the population data that we use is ultimately limited by the quality of the underlying population data available from national population Censuses in the region. For some SSA countries, this data is quite outdated, while, for many, it is only available for large administrative regions as opposed to, for example, small census tracts, which explains the need for top-down gridding of the data in the first place. In this sense, improving the quality and spatial resolution of available population Census data for SSA countries is crucial to further progress on the understanding of urbanization in the region.

25
reconcile the difference in results observed above (between cross section and panel) by decomposing growth “convergence” (or lack of) into 3 components: (i) a coefficient representing the “structural’ mobility, or change in inequality, (ii) the “pure” mobility effect (which tries to address the Galton fallacy) and the “re-ranking” effect. The authors of this study will explore this approach as a next step of the analysis with the aim to assess the relative important of “pure” mobility versus re-ranking and therefore the relative role of short term (re-ranking) versus medium to long term changes in the distribution.

# **VII. Conclusions**

Looking at cross sectional data for 2005 and 2008, the paper demonstrates that Egypt achieved impressive poverty reduction between 2005 and 2008, thanks to rapid economic growth. However, inequality also increased during this time, attenuating the impact of growth on poverty reduction. The shape of the growth incidence curve for overall Egypt suggests that the rich gained more than the poor, especially in rural areas. However, growth incidence curves ignore the fact that households can move to a different ladder in the distribution over time. With the panel data available for Egypt 2005-2008, it is actually possible to trace such movements between 2005 and 2008.

Longitudinal changes capturing actual movement of households suggest a very different pattern of mobility compared to cross-section growth incidence curves. Growth incidence curves constructed on panel data allow seeing how each person’s welfare evolved over time. From the panel perspective, growth in Egypt was pro-poor. The welfare of an average person who was poor in 2005 increased by almost 10 percent per year between 2005 and 2008; this was sufficient to move this household out of poverty. But growth also exposed some non-poor to negative dynamics, making them poor. Panel data also reveal that many middle-class households were exposed to significant risks. In the period 2005-2008, only 45 percent of the population in Egypt remained out of poverty and near-poverty. This means that
13 the firms in which AFPs have invested is a little higher (lower leverage, higher coverage ratio, and higher profitability) than that of firms that are eligible for AFP investments but in which they have not invested. However, the dispersion of financial performance is large for both groups of firms (with AFP investments or not). In fact, all groups of firms contain badly performing firms and performing firms in similar proportions. The group of eligible firms in which AFPs have acquired equity contains 17 firms with a score below 5 (i.e. scoring poorly on more than half of the 10 indicators used). By contrast, there are 33 eligible firms in which AFPs have not invested which have a liquid stock and a score of 7 or higher. The existence of such a plethora of performing stocks left aside by institutional investors and the fact that there is little pension fund money going to new market entrants is likely to create a disincentive for firms thinking about issuing securities.

**Figure 13:  Performance of Firms With and Without AFP Investments**

<!-- Start of picture text --> Financial performance of eligible Financial performance of eligible<br>companies with no AFP investment companies with AFP equity investment<br>Financial performance index Financial performance index<br>20 16<br>2015 14 12 16 15 16 13 15 12 11 13<br>8 9 10 7 7 9<br>10<br>5 2 1 1 5 0 0 3 4<br>0 0<br>0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10<br>Financial score Financial score<br>Source: Author’s calculations based on data from the FECUs and the SAFP 6<br>Number of firms<br>Number of firms<br><!-- End of picture text --> The closed family owned structure of many firms may also be at the root
quarter of 2009. Since the Great Recession hit China’s exports in the last quarter of 2008, the quarterly data allow us to analyze the initial impact of the Great Recession on China’s processing trade. As it is shown in Table 8, the initial impact of the crisis was huge. In the first quarter of 2009, demand for China’s exports experienced a stunning contraction of 19.9% compared to the previous year, from US$304 billion to US$243 billion. We now turn to the analysis of the three effects.

> 20 Mason-Jones and Towill (2000) provide the following explanation of a bullwhip effect: “If demand for products is transmitted along a series of inventories using stock control ordering, then the demand variation will increase with each transfer”.

29
# **1 Introduction**

Corporate tax regulation generates heterogeneity in the effective tax rates faced by firms due to exemptions, deductions, and deferrals. At the same time, there is a large amount of dispersion in firm-level revenue productivity even within narrowly defined industries. This suggests that effective corporate tax rates can potentially generate an inefficient allocation of resources across firms by directly impacting revenue productivity. An inefficient allocation of resources will have a direct negative effect on total factor productivity (TFP).

This paper quantifies the effect of effective corporate tax rates on aggregate TFP through allocative efficiency. First, we use Chilean manufacturing census data for the years 1998 to 2007 and document several characteristics of the effective tax rate distribution. Two important findings are a large dispersion in the effective tax rate faced by firms and a mass of firms with a 0 percent tax rate. Next, we incorporate these features into a standard monopolistic competition model with capital and output wedges, where firms endogenously choose the tax rate they face. We then calibrate the model and find that TFP would increase between 4 and 11 percent if the economy implied by the Chilean tax code of the 1998 – 2007 period changed to a hypothetical economy without effective corporate tax rate inefficiencies. Afterward, we study the effects of imposing the same tax rate on all firms, which we call a flat tax rate policy. We find a monotonically decreasing relationship between the level of the flat tax rate and TFP.

To carry out our analysis, we use the ENIA (Encuesta Nacional Industrial Anual), a plant-level manufacturing census from Chile that covers all establishments with more than 10 employees, for the time period 1998-2007.<sup>1</sup> The data set is an unbalanced panel that contains detailed balance sheet and production information. Importantly, it specifies net after-tax firm income and corporate taxes paid by firms. We use these two variables to construct the average effective tax rates faced by firms, which is essential for our analysis.

1. Different years of this data set
032|-|
|Log Labor Force|-|-15.487***|-|-15.901***|
|Log gross fixed capital formation|-|7.880***|-|7.164***|
|Log exports of goods and services|-|3.043**|-|3.765***|
|Log REER|-|2.477|-|2.754|
|World oil price index|-|0.069***|-|0.064***|
|World metal price index|-|0.004|-|0.009|
|Gini index|-|-0.616|-|-0.616|
|Observations|873|873|873|873|
|R2|0.119|0.445|0.911|0.863|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Accounting for the heterogeneity between country income groups yields a very different picture than that shown in the baseline models. From a methodological perspective, this heterogeneity affirms the importance of considering structural differences between countries at different stages of development. From a macroeconomic perspective, it lends credibility to the idea that the “resource curse” is a function of country-level structural characteristics, such as the management of natural resources rents, the sophistication of public financial management systems and the impartiality of policy processes.

# 4.3 Analyzing a Subsample of Resource-Rich Countries

The model presented below focuses on a subset of countries defined as nonrenewable resource rich according to the IMF’s 2013 Resource Governance Index Report.<sup>16</sup> The full sample included countries in which nonrenewable natural resources played a relatively modest economic role. Excluding these countries can shed light on the possibility that the empirical associations between resource dependence and the Gini index and GDP
of 37,000 households in 1,394 communities in 350 counties, which are located in 29 provinces.

There are several major advantages of using CHFS-III for this research. First, apart from information on demographics, CHFS contains a wide range of information on income/expenditure, financial assets, real estate, asset structure, among others. Second, it has details on household financing behavior and historical debt. Third, the 2015 survey additionally included a module on fraud victimization. This is perhaps the first nationally 11
within-student across-subject comparisons. In addition, we build on Metzler and Woessmann’s (2012) proposal to use contemporaneous within-student within-teacher acrosssubject variation to identify impacts. This approach, initially applied by Melzer and Woessmann (2012) to Peruvian data, was subsequently implemented by Bietenbeck et al. (2017) and Hanushek et al. (2018) in multi-country settings (in 13 Sub-Saharan African countries spanning years 2000 to 2007 in the former and 31 countries in the latter).

In order to obtain consistent estimates of the impact of teacher content knowledge using a contemporaneous specification one needs to assume either (i) that only contemporaneous teacher’s knowledge matters to the production of current achievement or (ii) that there is no correlation between the current teacher’s knowledge and past teachers’ knowledge (Todd and Wolpin 2003). We relax both assumptions by exploiting the fact that we have data for both the current and previous teacher. As a result, our approach is more in line with the value-added approach where, as a rough approximation, prior achievement is used as a sufficient statistic for the history of prior teacher inputs.

# _2.2 Data_

We use data from the Service Delivery Indicators (SDI)—an ongoing Africa-wide program that aims to collect informative and standardized measures of what primary teachers know, what they do, and what they have to work with. The SDI program—piloted in Tanzania and Senegal in 2010 (Bold et al. 2010, 2011)—grew out of concern about poor learning outcomes observed in various student tests, as well as evident shortcomings in school-level service delivery in fast-expanding education systems.

To date, the SDI program has collected data from a total of seven countries (eight surveys): Kenya (2012), Mozambique (2014), Nigeria (2013), Senegal (2010), Tanzania (2010, 2014), Togo (2013), and Uganda (2013).<sup>6</sup> In each country, representative surveys of between