rafmacalaba/data-use-mentions-extended huggingface dataset:
years of retrospective livestock history to examine wealth dynamics in southern Ethiopia, finding some support for a poverty trap in livestock wealth. However, panel data on assets is rare in developing countries, especially outside of an agricultural context, and even when available, is likely to suffer from the same measurement error and attrition issues as data on income.

This paper proposes a dynamic pseudo-panel approach to the estimation of nonlinear income dynamics and uses this method to test for the presence of poverty traps in Mexican income and expenditure.<sup>5</sup> The use of pseudo-panels can greatly mitigate the three major data issues facing the empirical study of poverty traps: a lack of long panels, attrition, and measurement error. A pseudo-panel tracks cohorts of individuals over repeated cross-sectional surveys (Deaton, 1985). Since such surveys are often available over longer time-periods than genuine panels, this approach can allow for the study of longer-term dynamics than is usually possible with panels. The Mexican Urban Labor Force Survey (ENEU) used here is a quarterly rotating panel available from 19872001. This enables us to use 58 quarters of data in the pseudo-panel estimation, compared to panels of only 5 quarters.

Non-random attrition is much less of a problem in pseudo-panels than in panels since a new sample of households is drawn in each period. In Antman and McKenzie (2005), we show that construction of a pseudo-panel eliminates the measurement error bias from dynamic linear models. This bias is found to be large: panel data estimation would suggest that one-third of the gap in income between two randomly selected households would close within one quarter, while pseudo-panel analysis shows only 1.2 percent of this gap would be eliminated. This paper extends the study of pseudo-panels to the non-linear dynamic models needed for the study of poverty traps. We employ the functional form of Lokshin and Ravallion (2004), in modelling income as a polynomial of lagged income.

> 5 Only limited data on asset ownership, in the form of housing infrastructure, is available in
|40.3|67.3||71.3||119.3||
|2013|38.9|66.9||69.8||119.9||
|2014|37.4|65.9||68.3||120.5||
|2015|36.8|66.6||68.0||123.2||
|2016|38.0|70.6||69.7||129.7||
|2017|38.5|73.6||70.4||134.4||
|2018|39.0<br>|76.3||70.8||138.7||
|2019|39.1<br>39.1|78.5|78.5|71.0|71.0|142.7|142.7|

Note: the table reports the results of the backcasting exercise using a pass-through of 1. The backcasting exercise uses sectoral GDP data from the World Bank MFM-Tool to backcast household consumption from the 2018/19 NLSS survey using information on household head’s sector of employment to map macro- and micro-data. Estimates are reported at the US$1.90 and US$3.20 poverty line and show the poverty headcount rate and the equivalent number of people living below each poverty line. Population data are from the WDI.

33
comes heterogeneous treatment effects by pre-treatment GPAs, we find that students in higher terciles clearly benefited more; for students in the upper tercile, the intervention had a positive effect on GPAs at the 1% level, increasing GPAs by about 8% SD in the short-run, 21% in the medium-run, and 46% in the long-run in the teacher-delivered treatment (effects are also significant, although lower, in the student self-learning treatment). Finally, when estimating seemingly unrelated regressions for all results on Roma students (on average, by gender, and by student achievement), we can reject the null that the coefficients of the student-teacher treatment are jointly equal to zero across models at the 1% level.<sup>27</sup>

# **5.3 Robustness**

# _Attrition_

So far, we have a different number of students in our analysis of socio-emotional skills and our analysis of GPAs, the difference arising from attrition in our survey data, mainly due to some surveys not being returned, being unreadable, or being only partly filled out. Our analysis of socio-emotional skills, therefore, includes all students with complete surveys, whereas our analysis of GPAs includes all students who are present in official, administrative records by the Ministry of Education in North Macedonia (and should have participated in the intervention according to the randomization routine). This approach is agnostic as it simply takes all observations that are available. It is also conservative, in the sense that our estimates on GPAs are again likely to be downward biased, as some, potentially non-treated, students may be allocated to either of the two treatment groups.

> 27 A more complex issue arises in case that the intervention increased motivation and effort on the side of teachers such that teachers in any of the two treatment groups (and potentially differently by treatment group) recorded GPAs more swiftly post-treatment. Graphical evidence, however, suggests that the temporal distribution of when GPAs are recorded is similar between any of the two treatment groups and the control group
# **Appendix 4. Additional information on the Policy Priority Inference Model Setup**

Besides the data on the network structure, PPI also requires data on 1) government expenditure over time (during the retrospective analysis), 2) data on the quality of law and monitoring, and 3) data on the initial estimates for the growth probabilities c which in the initialization are assumed to be fixed values within the data (they do not change over the calibration period). ee (tt)

**Government expenditure data:** For the retrospective analysis, we approximate the government expenditure using annual data from the World Development Indicators on general government final expenditure as a percentage of GDP. We transform this data to be in per capita constant 2021 USD BB(tt) (in PPP) simply by multiplying with World Bank’s GDP per capita estimates. PPI also requires the government expenditure data to be de-trended we do this by estimating a linear trend regression model

0 1 BB(tt) = aa + aa tt+ uu(tt), and removing the trend component from the model. That is, for each year we calculate an estimate for the government expenditure and use it as an input in PPI as 0 . Also, PPI typically runs its calibration process with time increments that are much smaller than a year (in order BB<sup>�</sup> (tt) BB<sup>�</sup> (tt) = aa + uu(tt) to converge PPI requires more than 50 simulation time steps). Here we assume that there are 6 simulation steps in a year (meaning that we calibrate PPI with a total of 54 steps). Due to this, the yearly data needs to be transformed such that the total budget of the country within the six steps that correspond to a one year matches the government expenditure. We do this by simply assuming that the government distributes its budget equally within each simulation step in a year ( /6)
the nature of informality better and to provide empirical support for the theories that best depict the reality on the ground, with an eye for what policies might be most appropriate in a setting of heterogenous firms and workers. The remainder of this section reviews previous studies on the informal sector and informal employment in Indonesia and ends with recent developments relating to informality.

# **The Informal Sector in Indonesia**

As in other countries, measuring the size of the informal sector in Indonesia is a challenging task. One of the key estimates of the size of the informal sector comes from Rothenberg et. al (2016). Using the 2013 IMK Survey, they found that 98 percent of firms with fewer than 10 employees are unregistered. Using Hsieh and Olken’s (2014) previous finding that 95 percent of firms in Indonesia have fewer than 10 employees, Rothenberg and co-authors (2016) projected that 93 percent of Indonesian firms are informal.

Informal enterprises in Indonesia share a number of characteristics that align with previously documented patterns in other countries. For instance, more than 90 percent of informal enterprises in the country are either micro or small firms – suggesting a substantial overlap between MSEs and the informal sector. This contrasts with the situation in Malaysia and Vietnam, both of which have a thriving MSE sector characterized by much lower rates of informality at 9 percent and 27 percent, respectively (Nugroho, 2021). Notably, an estimated 64.5 percent of MSMEs in Indonesia are owned by women, highlighting the important role of women in this space (Susanti et al., 2023).

In addition to firm size, informal enterprises in Indonesia are also characterized by low levels of productivity. For example, estimates from the 2009 ISS showed that labor productivity in the informal sector ranged from 9 percent (in the city of Yogyakarta) to 15 percent (in the province of Banten) of productivity in the formal sector (ADB & BPS, 2011). This reflects the fact that most informal enterprises are primarily labor
the UK it has been found that campaigns explaining the benefits of declared work were more efficient than punitative measures (Willliams, 2008).

# **Box 1 Recent measures in Latvia to combat the shadow economy**

In April 2010, the government of Latvia approved a set of measures to reduce the shadow economy. The government emphasized that a broad approach is needed including strengthening tax enforcement but also changing society‘s opinion of the shadow economy and encouraging legal business transactions. The main measures are:

- Reducing cash transactions in wholesale trade from LVL 3 000 (4240 €) to LVL 1000 (1413 €). If cash transactions exceed this amount the taxpayer must provide a declaration on transactions with the given partner in the previous month. The reasoning is that cash transactions are more prone to tax evasion and fraud than bank transactions;

- Providing access to the tax administration (State Revenue Service) to the Loans Register at the central bank.  This measure aims at identifying those persons whose expenditures exceed income;

- Public awareness campaign on how tax revenues are being used, and how paying taxes benefit taxpayers. For this purpose the Finance Ministry‘s website will also be upgraded;

- Encouraging crime reporting to the authorities;

48
first deflates household expenditure by a price index that adjusts for spatial and monthly price variations _within_ a survey year, and then adjusts for inflation _between_ survey years. Third, the two aggregates are deflated to different base periods. The international measure uses 2011 as the base period while NISR (2016) uses January 2014 as the base period.

IV.1. Estimating international poverty rates for Rwanda using the World Bank’s methodology To estimate the international poverty rates, we first convert the $1.90 per day per capita line to an annual per capita poverty line expressed in local currency units (LCUs), and then compare it to a welfare aggregate also expressed in annual per capita terms.

We do this by first expressing the international poverty line (IPL) in annual terms (multiplying it by 365). We then convert the annual line to local currency units (LCUs) using the 2011 Purchasing Power Parities (PPPs), and finally adjust the resulting poverty line to reflect average 2011 prices. This is done by multiplying the annual PPP adjusted line with the inflation rate between the 2011 average and the EICV survey month average. Thus, the IPL for EICV3 can be written as: 15
32 investing across borders: do fdi-specific regulations matter?

than countries like Afghanistan and Haiti which receive insignificant amounts of FDI. Obviously BRIC offers conspicuous advantages like market size to investors that small, conflict-ridden economies do not. That, on paper, the poorer countries allow 100 percent ownership of equity should foreigners invest does not seem to matter when most other determinants of FDI are accounted for. This is illustrated in Figure 7 with outward _sectoral_ FDI data from the United States to 87 IAB countries.<sup>28</sup> There is no systematic relationship between countries’ openness score and actual FDI received by sector.

In the bias-corrected models of column 3 and 4 in Table 1, the elasticity of FDI with respect to distance drops dramatically, by two-third, from that in the (biased) benchmark model in column 2. The positive effect of a shared border increases, but the coefficient of common colony falls modestly in the bias-corrected models. Coefficients on remoteness and GDP are also subdued in the bias-corrected models. The coefficient of GDP per capita is not statistically significant in any of the regressions. Coefficients on the average education level of the adult population and the mean level of weighted tariff on manufactured import in host countries are also not statistically different from zero. I detail the estimation procedure and results in the rest of this section.

In Table 2, FDI-receiving OECD countries in the sample are dropped so that the FDI relationship is now between the 24 OECD source countries and 65 nonOECD host countries. Coefficients on FDI Arbitration and FDI Procedures are both statistically significant although the magnitudes are lower than in the fuller sample (Table 1). The elasticity of distance in the biased regression is much higher than in the main regression, which drops by about one-third when biases are corrected. This indicates that the inclusion of 15 OECD countries in the sample overestimated the effect of FDI-specific provisions and underestimated the hurdle posed by bilateral distance. When the proportion of developing countries in the sample grows,

> 28 The 5 sectors are mining,
br>fair condition|0|86|79|82|68|
|Unpavednetworkcondition|% of unpaved roads in good or<br>faircondition|30|56||58|61|

_Source:_ Derived from AICD calculations.

_Note:_ For South Sudan: the paving ratio is calculated based on the classified road network that is paved, the paved network condition refers to regional and national roads, the paved road network does not include roads of fair quality because the length of roads with fair quality is unknown.

GIS = geographic information system; AADT = average annual daily traffic.

16
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- Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.

- World Bank (2006). Nepal - resilience amidst conict: an assessment of poverty in nepal, 1995-96 and 2003-04. Technical report, World Bank, Data.

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- Yang, D. and Choi, H. (2007). Are remittances insurance? evidence from rainfall shocks in the philippines. The World Bank Economic Review, 21(2):219248.

22
Figure 8. Progressivity of Indirect Taxes (Kakwani Index) <!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Sales Tax Excise Customs Green tax<br><!-- End of picture text --> _Source_ : World Bank staff estimation using BLSS 2017.

Figure 9. Marginal Contribution of Indirect Taxes to Change in Poverty and Gini <!-- Start of picture text --> Green tax<br>Customs<br>Sales Tax<br>Excise<br>-0.5 0 0.5 1<br>Change in Poverty Headcount Change in Gini<br><!-- End of picture text --> _Source_ : World Bank staff estimation using BLSS 2017.

Indirect transfers are progressive and poverty-reducing. The Kakwani index exhibits progressivity for indirect social transfers and positive redistributive effects (figure 10, figure 11). In absolute terms, education benefits accrue slightly more among households in the bottom of the distribution: for example, 12.1 percent go toward those in the bottom 10 percent. This is likely because those households have more children and are less likely to send them to private schools.

Health benefits are highly evenly distributed across the distribution, with households across all deciles accruing around 10 percent of total health spending (figure 12). This likely reflects the fact that health services are almost exclusively funded and delivered by the public health care system.

Figure 10. Progressivity of Indirect Transfers (Kakwani Index) <!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30
similar coefficients to our baseline, the AIA policy reduced cloud use for treated firms by between 15-20 percentage points (depending upon estimation period, 2007-2013 or 20072019). Since around 43%-46% of firms were treated by the AIA policy, this implies an aggregate reduction in cloud use of 7-9 percentage points. Over our sample period aggregate cloud use has been increasing roughly 6 percentage points per year (whether measured over 2007-2013 or 2007-2019). Thus, the AIA policy appears to have slowed down cloud diffusion by 1 to 1.2 years.

Again we note the robustness of these results if we define firms’ treatment status using a 2-year average of investment based on the years 2006/07 (regression 2), or a 3-year average based on the years 2005/06/07 (regression 4) (Table A4), or, as we show in Table A5, if we measure the effects of cloud for the years 2007 and 2015 (regression 1), 2017 (regression 2) or 2019 (regression 3). Across these regressions the estimated effect of the AIA on cloud adoption was between -11 and -17 percentage points.

# **5.2 Types of cloud and capital investment and the measurement of the AIA**

The detailed nature of the UK data allows us to further explore how the AIA policy is linked to different types of investment and different types of cloud services. Within Table 4 Panel A, we begin by separating the aggregate investment (shown in the previous Table 3) into hardware (column 1), software (column 2), plant and machinery (column 3), vehicles (column 4), and land and building investment (column 5). Similarly, in Panel B we study the effect of the AIA on the different types of cloud included in the E-Commerce survey. These are cloud databases (column 6), storage (column 7), customer relations management (CRM) software (column 8), office software (column
(SIC) codes in the Economics and Statistics Association of the US Patent and Trademark Office’s 2012 report on intellectual property. Finally, because the impacts of IPAs membership might vary by countries’ comparative development levels, we allow for such effects to vary by discrete income groups, as designated by the World Bank.<sup>9</sup> We use unchanging income-group designations for countries based on 1995 definitions, classifying countries as low-income (LI), lower-middle income (LMI), upper-middle income (UMI), or high income (HI). In all cases we estimate trade effects in samples excluding trade with partners (both before and after the entryinto-force of the PTA) in the treatment IPAs. As discussed below, this approach excludes potential endogeneity between existing trade linkages with those partners and decisions to join such PTAs.

Turning to bilateral trade among all country pairs, we adopt a gravity specification in which we identify specific coefficients on imports and exports of IP-sensitive goods, using the disaggregated sectoral breakdowns. This approach permits estimation of the impacts of membership in an IP-related PTA on sectoral trade with bilateral trade partners outside the agreements.

With this background, our identification relies on three types of variation. First, during our sample some countries entered into IP-related trade agreements, as we define them below, while others did not (note that countries rarely exit PTAs once they have joined). We also distinguish among countries at varying income levels, noting that both their membership decisions and their economic responses to such agreements may vary. Second, as already noted, we distinguish between sectors in terms of their apparent relative usage of intellectual property, computing trade impacts in high-IP and low-IP industries compared to the same sectors in control countries. This distinction is important, for if IP chapters matter for trade, in comparison with just the impacts of membership in a PTA generally, the effects should show up in relatively greater impacts in the high-IP set of industries. The third important element for identification is to control for TRIPS
The wage curve can be writtenaas w=w(u), where w is the wage level and _u_ is the unemployment rate. This curve replaces the conventional labor supply function and slopes downwards in the wage-unemployment space. It is often estimated by the following equation: ln W = a ln U + other terms where In W is the log of wages, In U is the log of the unemployment rate, and the other terms are control variables. The 9 coefficient denotes elasticity of pay with respect to unemployment. The equation reads that workers earn less in periods of high unemployment or underemployment than in periods with low joblessness. The wage curve, that links the level of pay to the unemployment rate, is concerned with equilibria and not a description of temporary phenomena or transitory dynamics. The Phillips curve, on the other hand, which links the rate of changes of wages to the unemployrnent rate, can be seen as such a disequilibrium adjustment mechanism. Hence, both models are contained in a single wage determination model when modeling wages in errorcorrection form (see below).

The unemployment elasticity of pay, X, has been estimated using microeconomic data to take the value of -0.1 in many developed countries, for example, in the US, the UK, and Norway (Blanchflower and Oswald; 1995). This implies that a hypothetical doubling of unemployment is associated with a drop in pay of 10 percent. Hence, the unemployment elasticity of pay appears to be of similar magnitude across developed economies, despite sometimes dramatic institutional differences, implying that countries exhibit more uniformity in wage flexibility than a priori expected.

Studies analyzing wage curves in developing countries are scarce. One study by Hoddinott (1996) addresses the question of whether the rate of unemployment affects the wage level in C6te d'Ivoire. The estimated wage-unemployment elasticity is -0.12. The 12
, & Vargas, J. C. (2020). The spectre of Haiti: structural antiblackness, the far-right backlash and the fear of a black majority in Brazil. _Third World Quarterly_ , 41(4), 645-662.

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Arampatzi, E., Burger, M., Ianchovichina, E., Röhricht, T., & Veenhoven, R. (2018). Unhappy development: Dissatisfaction with Life on the Eve of the Arab Spring. _Review of Income and Wealth_ , 64, S80-S113.

Arampatzi, E., Burger, M. J., Stavropoulos, S., & van Oort, F. G. (2019). Subjective well-being and the 2008 recession in European regions: The moderating role of quality of governance. _International Journal of Community Well-Being_ , 2(2), 111-133.

Arampatzi, E., Burger, M. J., & Veenhoven, R. (2015). Financial distress and happiness of employees in times of economic crisis. _Applied Economics Letters_ , 22(3), 173-179.

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. _The Review of Economic Studies_ , 58(2), 277297.

Arrondo, R., Cárcaba, A., & González, E. (2020). Drivers of Subjective Well-being in Spain: Are there gender differences?. _Applied Research in Quality of Life_ , 1-24.

Autor, D., Dorn, D., Hanson, G., & Majlesi, K (2017). A note on the effect of rising trade exposure on the 2016 presidential election.’ MIT Working Paper https://economics.mit.edu/files/12418; Baltar, P. E. D.