rafmacalaba/data-use-mentions-extended huggingface dataset:
Devereux (2011), Bjorklund and Salvanes (2011), and Corak (2013). A partial list of the contributions includes Bowles (1972), Becker and Tomes (1979), Atkinson et al. (1983), Solon (1992), Mulligan (1997), Arrow et al. (2000), Black et al. (2005), Bjorklund et al. (2006), Chetty et al. (2014), Lefgren et al. (2014), Black et al. (2015), Becker et al. (2015).

> 3Coresidency restriction results in a truncated sample, as the surveys do not gather any information on the family members who do not satisfy the coresidency criteria.

> 4Some of the children of the household head may not be part of the household at the time of the survey for a variety of reasons such as higher eduction, job, marriage and household partition.

> 5This is true even for a country such as India where there is a long tradition of high quality household survey data collection. Bardhan (2005) identifies intergenerational mobility as one of the under-researched areas of economic research in India.

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22 <!-- Start of picture text --> 1985 1990 1995 2000 2005 2010<br>year<br>Bulgaria Czech Republic<br>Hungary Poland<br>Romania<br>90<br>80<br>70<br>60<br>50<br>40<br><!-- End of picture text --> Notes: Yearly averages from the ICRG data set. Average of the first two months for the year 2011.

_Figure 2_ : Governance Quality of the First Group of Countries Between 1993 and 2011 <!-- Start of picture text --> 1995 2000 2005 2010<br>year<br>Estonia Latvia<br>Lithuania Slovenia<br>Slovakia<br>85<br>80<br>75<br>70<br>65<br><!-- End of picture text --> _Figure 3_ : Governance Quality of the First Group of Countries Between 1998 and 2011
above the Recommended Daily Allowance (RDA) of these micronutrients. By linking individual-level information on consumption from the post-experiment survey in 2005 with test score results in primary education, we can thus directly assess whether improvements in the nutritional intakes translate into improvements in subsequent performance in school. In addition, we use both the household survey data that were collected as part of the experiment and administrative data that were collected concurrent with the primary school exams to study how PAL impacts other determinants of school performance, such as child labor, school quality, and parental time investment.

Our main finding is that, 4 to 10 years after transfers began, in-kind food transfers had no impact on test scores while cash transfers negatively impacted test scores, relative to the control. Transfers, whether in-kind or in-cash, did not impact the likelihood that children took the tests, which rules out the possibility that these results are driven by sample selection.

2
migration as voluntary or involuntary. It would also have important implications for studying adaptation policies to climate change in rural developing contexts in Sub-Saharan Africa. For example, the systematic inclusion, in the roster of potential answers to the question on coping strategies, of the option “ _Drought, flood or other weather-related condition_ ” as in question Q.3.18 of the earlier rounds of the Uganda collection, which we mentioned above, would be a straightforward way to collect more information on climatic push factors. Even if a module should not be seen as a viable solution, the collection of additional questions and to make up for the current lack of an integrated framework on adaptation in household surveys such as the LSMS-ISA is in any case strongly encouraged.

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We have constructed our own per capita consumption series, based on data reported by households in the Socio-Economic Surveys of 2007 through 2010, in the bottom panel of Figure 4, and it paints a somewhat different picture: by this measure, real consumption spending was maintained until mid-2009, and then fell briefly, before reverting to trend by the end of the year.

<!-- Start of picture text --> 3100<br>3000<br>2900<br>2800<br>2700<br>y = 9.5766x + 2761.5<br>R2 = 0.3424<br>2600<br>2500<br>2007q1 2008q1 2009q1 2010q1<br>Figure 4.1. Private consumption expenditure, 1988 prices<br>Source: Reported by Bank of Thailand. Shaded area marks period of recession.<br>4600<br>4400<br>4200<br>4000<br>3800<br>y = 30.481x + 3805.4<br>R2 = 0.6463<br>3600<br>3400<br>3200<br>2007q1 2008q1 2009q1 2010q1<br>Real expenditure/capita, baht/month<br>Real expenditure/capita, baht/month<br><!-- End of picture text -->

**Figure 4.1.  Private consumption expenditure, 1988 prices**

**Figure 4.2.  Consumption expenditure per capita, 2007 prices.**

Source: Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010. Shaded area marks period of recession.

Losers and Winners in Recession: Thailand 2008-09 9
- 5 -

10. The paper explores this commute-wage relationship by using data from Antananarivo, the capital of Madagascar. Despite the accelerating urbanization in Africa, currently, little evidence is available for policy makers in the region. The current paper contributes to adding new evidence to the literature. Understanding how individual households decide to commute to work is essential for urban planners and transit developers. Methodologically, the paper relies on an instrumental variable (IV) technique to deal with potential endogeneity associated with the commute decision, which is another contribution to the literature.

11. The rest of the paper is organized as follows: section II provides a brief overview of Antananarivo; section III develops our empirical strategy, and section IV explains our data; section V discusses the main results; and section VI examines the robustness of the results; and then, section VII concludes.

# **II. OVERVIEW OF URBAN TRANSPORTATION IN ANTANANARIVO**

12. As experienced in many African countries, Madagascar is currently experiencing very rapid urbanization. According to UN-Habitat (2016), the population of Antananarivo, the capital of Madagascar, more than doubled in the last decade, from 1.2 million in 1995 to 2.6 million in 2015. It is the fourth largest city in East and Southern Africa, following Dar es Salaam (5.1 million), Nairobi (3.9 million) and Addis Ababa (3.2 million) ( **Figure 1** ). Attracting more people and more businesses, the city population is projected to continue growing to 4.1 million by 2025.

13. Similarly to other large cities in Africa, Antananarivo is the growth center in Madagascar. The country’s GDP per capita was about US$401 in 2016, and the total population is about 25 million. Thus, about 10 percent of the total population lives in Greater Antananarivo. More than half of Malagasy establishments, which amount to about 120,000, are located in Antananarivo ( **Figure 2
|2.13|0.67|2.15|0.69|2.09|0.64|0.0541**|
|<br>Locus of control|2.88|0.7|2.93|0.7|2.81|0.71|0.104***|
|Achievement|3.75|0.58|3.73|0.59|3.78|0.56|-0.0755***|
|Power motivation|3.35|0.81|3.28|0.83|3.44|0.79|-0.174***|
|Organized person|4.08|0.76|4.11|0.77|4.05|0.75|0.0779**|
|<br>Optimism|3.57|0.52|3.56|0.52|3.59|0.53|-0.0355|
|Observations|2,478||1375||1103|2478||

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85|104.63|
|Service|88.33|59.64|58.32|68.13|96.37|153.07|95.44|
|**Size**||||||||
|Small|66.96|49.92|50.82|60.04|93.79|127.72|69.15|
|Medium|131.40|109.98|88.33|61.97|118.59|179.86|137.33|
|Large|173.20|190.94|98.35|218.55|211.61|204.18|187.76|

Source: Author’s own elaboration from enterprise surveys

# **5.4 Overall ICT intensity**

The overall ICT index is a combination of the internet index and the computer and software index. Table 13 shows the ICT index by country, sector and firm size. Kenya shows the highest index followed by Zambia, Ghana, Uganda and Tanzania. Although the manufacturing sector shows a higher index, the difference is not substantial. On the other hand, larger firms have ICT index values that more than double those of small firms.

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that the firms that exit every year tend to be the least productive firms, while the more productive firms remain. As a result, the gains from reallocation of resources in the permanent sample are smaller than in the whole sample.<sup>17</sup> Last, it is important to note that for the permanent sample, the TFP gap is also decreasing in the level of flat tax rates, as seen in Figure 12 in the appendix.

# **7 Conclusion**

The objective of this paper is to quantify the effects of corporate tax rates on aggregate TFP through allocative efficiency. To do this, we set up a standard monopolistic competition model that includes firm-specific corporate tax rates as well as output and capital wedges. In our framework, firms can choose whether to face a positive tax rate and have non-negative accounting profits or face a tax rate of 0 percent and have non-positive accounting profits. We incorporate this exemption from the Chilean tax code to address the caveat that firms’ behavior can affect the effective tax rate they face. We calibrate the model and find that the TFP gains between the economy implied by the – Chilean tax code of the 1998 2007 period and a hypothetical economy without effective corporate tax rate inefficiencies are up to 11 percent. We also analyze how different levels of flat corporate tax rates affect TFP in an economy characterized by other distortions. We show that there is a monotonically decreasing relationship between the TFP gap and the level of the flat tax rate. We carry out a sensitivity analysis on parameters and robustness checks on our measure of effective tax rates and find that our results do not vary.

17. This finding is consistent with what Gopinath et al. (2017) find when analyzing Spanish data.

36
Brenton, P., C. Saborowski, and E. von Uexkull, 2010, “What Explains the Low Survival Rate of Developing Country Export Flows?” World Bank Economic Review 24(3), 474–499. Bricongne, J.-C., L. Fontagn ́e, G. Gaulier, D. Taglioni, and V. Vigard, 2012, “Firms and the Global Crisis: French Exports in the Turmoil,” Journal of International Economics 87, 134–146.

Bridges, S., and A. Guariglia, 2008, “Financial Constraints, Global Engagement, and Firm Survival in the United Kingdom: Evidence from Micro Data,”Scottish Journal of Political Economy 55(4), 444–464.

- Brown, J.R., G. Martinsson, and B.C. Petersen, 2013, “Law, Stock Markets, and Innovation,” Journal of Finance 68(4), 1517–1549.

Brown, J.R., G. Martinsson, and B.C. Petersen, 2017, “Stock Markets, Credit Markets, and Technology-Led Growth,” Journal of Financial Intermediation 32, 45–59.

- Cadot, O., L. Iacovone, M.D. Pierola, and F. Rauch, 2013, “Success and Failure of African Exporters,” Journal of Development Economics 101, 284–296.

- Cetorelli, N., and M. Gambera, 2001, “Banking Market Structure, Financial Dependence and Growth: International Evidence from Industry Data,” Journal of Finance 56(2), 617-648.

- Cetorelli, N., and P.E. Strahan, 2006, “Finance as a Barrier to Entry: Bank Competition and Industry Structure in Local U.S. Markets,” Journal of Finance 61(1), 437-461.

- Cho, I., S. Contessi, K.N. Russ, and D. Valderrama, 2019, “Financial Choice
_Figure 16: Factors behind well‐being improvement, vulnerable and non‐vulnerable groups, % of respondents_ <!-- Start of picture text --> 20%<br>18%<br>16% other<br>14% loan<br>12% change in hh composition<br>10%<br>additional income<br>8%<br>pension<br>6%<br>addittional work<br>4%<br>2% higher salary<br>0% employment<br>vulnerable average vulnerable average vulnerable average vulnerable average<br>2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

If we aggregate the responses on well‐being improvement and worsening related to employment and wages, we can see that the effect of employment during the crisis was quite small, while wage dynamics contributed much to the deterioration of well‐being of the population (Figure 17). Furthermore, it appears that those below the poverty threshold, as well as those who are vulnerable to poverty, suffered from falling nominal wages more than the population on average. Among the poor, according to the official definition, there was also a shift towards a more negative assessment of changes in the employment status.

The majority of the differences highlighted above are related to the specifics of regional economic development, and the way the crisis affected different sectors. Industrial and construction sectors suffered most from the crisis, leading to the fall in in economic activities in large cities. While labor market in Minsk is more flexible and reacted through employment reduction, in other cities employers, often state owned enterprises, tended to reduce wages more. In small cities and rural area labor demand was weak prior to crisis therefore it did not affect employment income in these areas that much (Figure 18).

_Figure 17: Balance of responses on the positive and negative role of employment and
# Sources of Wealth Data

Wealth data is collected less frequently than incomes, which are themselves not as common as consumption, particularly in low-income countries. But wealth surveys do exist- China and India have household surveys that collect detailed data on assets and liabilities of households- the Household Income Project (CHIP) for China and the All-India Debt and Investment Survey (AIDIS). There are also wealth focused surveys in Latin America- the Chilean Household Financial Survey, Colombian Household Financial Burden and Financial Education Survey, Mexican National Survey on Household Finances and the Financial Survey of Uruguayan Households (EFHU) (Gandelman and Lluberas, 2023). But such surveys are not common in LMICs. One recent example to improve data on assets is the World Bank Living Standards Measurement Study-Plus (LSMS+) program to measure the ownership of, and rights to, selected physical and financial assets in various African countries ( <mark>Hasanbasri et al., 2021</mark> ). These surveys collected the value of assets but do not measure liabilities, so net wealth cannot be computed.

Administrative data may provide information on wealth, although this is sometimes harder to utilize than administrative data on incomes. This is because most tax administration systems do not collect data on all or most forms of wealth directly. The most common wealth data collected through tax administration systems is estate records (Piketty and Saez, 2006). Wealth data is also collected when countries have wealth taxes, but these are not common. Some tax authorities ask questions about assets, even though they are not taxed, but, given that they are not taxed, the accuracy of the data can be questioned. To estimate wealth from income tax data, researchers use the capitalization method, in which capital income and an assumed or observed rate of return are used to estimate the value of the capital generating the observed capital income (Roine and Waldenstrom, 2015, Saez and Zucman, 2016).

In recent times other less traditional forms of data have been used to
|-|
||Large|14.2%|9.1%|17.6%|84.0%|-|

Source: Author’s elaboration from Enterprise Survey (2014) Further, the composition of the innovation portfolio highlights another difference between both country groups. While process innovation is more important in Bangladesh and India, product innovation is more important in Nepal and Pakistan. However, there is convergence in the pattern of non-technological

> 6 Size classification consists of: small, less than 20 employees; medium from 20 to 100 employees; large more than 100 employees.

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Small and Medium Enterprises (MSME) Survey for the year 2006–2007 suggests that 13% of firms are owned by a woman and 11% are female-managed (Chaudhuri et al., 2020).

**Stylized Fact #2** : Female-managed formal firms do not seem to differ widely in their demand for finance relative to male-led firms, but the former have limited access to the amount of credit, especially in traditional countries. Unconditional averages suggest that firms managed by a woman are more likely to apply for credit, their credit applications are less likely to be rejected and, consequently, female-managed firms are more likely to have an open line of credit. Nevertheless, on average, the credit received by female-managed firms is lower than that in male-managed firms (Table 1). Three points are worth noting here: _First_ , among the firms that did not apply for credit, 67% of female-managed firms and 62% of male-managed firms did not apply for credit because they believed their firms did not need loans, implying that women’s perception of the need for formal finance may be different. _Second_ , although both female and male-managed firms in our sample primarily use own funds to finance their fixed assets and working capital, there are modest gender differences in traditional countries (Appendix Table A.3). _Lastly_ , when separating the sample by social perceptions about women, differences in the unconditional average value of debt are higher in traditional countries (Table 1). These points suggest that while women-led firms are equally likely to apply for credit, they remain credit-starved. The reasons for not applying for formal finance may be embedded in their perception of the financial needs of the firm, or traditional social settings where finding own sources of finance may be more acceptable.

**Stylized Fact #3** : Female and male-managed firms differ in the underlying attributes that may matter for their demand and access to credit. On average, female-led firms are younger, smaller, have lower sales per worker and are more
the country, migrants often move across locations several times, using different internal routes according to their nationality (Di Maio et al., 2023). To study migrants’ movements within Libya, we use geo-localized monthly data collected by the IOM in 167 Flow Monitoring Points (FMPs) in Libya. FMPs are located at important points of permanence and transit for migrants. For each FMP, we have information on the country of origin of migrants located in the area, their destination countries, and their length of stay at the FMP. Second, we use data from the Global Database of Events, Language, and Tone project (GDELT)<sup>4</sup> to identify all the news articles related to migration published in any of the destination countries of the migrants located in all the FMPs in Libya. Importantly, GDELT provides a variable measuring the sentiment of each news article, allowing us to construct, for each FMP in each month, a measure of the average tone of all migration-related news articles published in the destination countries of migrants located near the FMP.

Our main result shows that a reduction in the measure of the sentiment of migration-related news articles published in destination countries increases the share of migrants remaining in their current location in Libya for a long period. The characteristics of migration flows in Libya

> 1See for instance, Benesch et al. (2019); Meltzer et al. (2021); Couttenier et al. (2023); Djourelova (2023).

> 2This is the case of the message broadcasted by Italian PM Giorgia Meloni on September 16 2023, `https: //youtu.be/gFrSJyF7xfU?feature=shared` or that by Florida Gov. Ron DeSantis `https://www.foxnews.com/ politics/desantis-warns-migrants-bused-texas-dc-florida-do-not-come`

> 3One important reason why Libya is the preferred transit country to Europe is the lack of border and internal controls, which has characterized the country since the beginning of the Civil War in 2011 (UNHCR, 2019b).
regime in case it reneges.‖ Some nondemocratic regimes such as Vietnam (Painter 2003) or China (Moon and Ingraham 1998) have undertaken significant public sector reforms (building on long if variable historical legacies of public sector meritocracy as noted by Fukuyama 2011); while other authoritarian as well as hybrid regimes have either not engaged in reforms, or have followed a pattern of partial or startstop reforms.

Despite, these numerous disincentives and obstacles, public sector reforms are on the agenda in many countries – and show progress at least in some. When then do such reforms become nonetheless feasible?

# **II.2 Political Economy Factors** **_Facilitating_ Public Sector Reforms**

The literature highlights several positive ̳macro‘ drivers, as well as institutional variables, and more proximate political economy reform facilitators. In contrast to the obstacles discussed above which are mostly linked to stakeholder interests, time horizons, and social structures, the enabling factors are often structural. However, the more proximate positive drivers also center around stakeholder constellations.

> 9 Electoral rules have also made a difference in countries, such as New Zealand, which embarked on major reforms in the 1980s. As Schick (1996) argues in case of economic management reforms with the first past the post-electoral arrangements then in effect, one party typically had a majority in Parliament and did not have to dilute its reform program to gain coalition support. Interestingly, voters then opted to change electoral rules in the country in the 1990s to more proportional representation, resulting in successive coalition governments and more compromises in reform efforts.

> 10 To support their finding, the authors draw on the ratings of 511 World Bank PSR loans in 109 countries which were systematically higher in countries with programmatic political parties.

10
- Non-routine cognitive interpersonal tasks (interactive, communication and managerial skills).

- Routine cognitive tasks (adaptability to work requiring limits, tolerances or standards).

- Routine manual tasks (repetitive physical movements).

- Non-routine manual tasks (physical movements requiring adaptability and dexterity).

Given that the O*NET is based on variables that are specific for the US, and such classification has not been repeated in developing countries, matching it using STEP surveys is not straightforward. We select the STEP variables which provide a good approximation for each of the five skill groups. The list of variables chosen to mimic the O*NET structure are reported in Table 1.

Regarding non-routine analytical skills, we select three STEP variables to measure the task “Analyzing data/information”: 1) ”Number of types of document typically read”; 2) ”Length of longest document typically read” , and; 3) ”Number of math tasks performed”. We use one variable to measure the task “Thinking creatively”, namely “How often the job requires thinking for at least 30 minutes”.

With regards to non-routine interpersonal skills, the task “Guiding subordinates” is elicited by a dummy variable capturing whether the job involves supervising co-workers. “Establishing personal relationships” is proxied by using the variable “How important interaction with people other than co-workers is”.

To estimate the routine cognitive content of jobs, we use three STEP variables: 1) “How often your work involves learning new things“ (mapped to the O*NET task “Importance of repeating the same task” (inverse)); 2) “How much autonomy you have in your work“ (mapped to “Structured vs. unstructured work”), and; 3) “How repetitive your work is“ (mapped to “Importance of repeating the same task”).

The routine manual content of jobs is estimated using the following variables: 1) a binary outcome capturing whether the work involves operating machines (mapped to
Development Economics,_ vol. 98(1), May 2012, pp 3-18.

Beegle, K. _et al._ (2016). _Poverty in a Rising Africa_ . Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22575 License: CC BY 3.0 IGO.

Biewen, M. (2002), “Bootstrap inference for inequality, mobility and poverty measurement”, _Journal of Econometrics_ , vol. 108, pp.317-342.

<mark>Boltz M., K. Marazyan and P. Villar (2019), « In</mark> come hiding and informal redistribution: A lab-in-the-field experiment in Senegal », _Journal of Development Economics,_ vol. 137, pp 7892 Bound J., C. Brown and N. Mathiowetz (2001), “Measurement Error in Survey Data” in J.J. Heckman and E. Leamer eds., _Handbook of Econometrics_ , Volume 5, Chapter 59, pp.3705-3843 Brown, C., R. Calvi and J. Penglase (2019), “Sharing the Pie: Undernutrition, Intra-Household Allocation, and Poverty”, available at SSRN: https://ssrn.com/abstract=3199129 or <u>http://dx.doi.org/10.2139/ssrn.3199129</u> 32