rafmacalaba/datause-encoder-data huggingface dataset:
Bank, 2001). Furthermore, despite recent amelioration in primary schooling and other social outcomes, Morocco’s social indicators (including enrollments in post-primary schooling) still lag significantly behind that of other countries in the region with similar levels of income.

The generally poor past state of education outcomes and low school enrollments are variously explained by poverty, cultural attitudes towards women’s roles, poor access and quality of educational facilities and parents’ general pessimism about schooling’s net rewards. A recent review of the education system shows that as recently as 1998, less than 45 percent of Douars (rural communes) had a primary school, for about 25% a facility existed only over 2 km away, while 6 percent provided no possibility of schooling at all. 80 percent of the rural primary school facilities did not have running water and 87 percent were without electricity (Commission Spéciale Education Formation 1999). Of course, access is even worse for higher levels of schooling.

The 1990s saw rapid urbanization in Morocco. In 1994, for example, the annual urban population growth rate was 3.6 percent compared to 0.7 percent in rural areas, where the fertility rate is nevertheless twice as high. 46 percent of the population remains rural. Urbanization is driven largely by rural migration. Many of these migrants are illiterate and enter low wage, low return informal sector activities. A new phenomenon in the Moroccan economy was a surge in women’s labor force participation starting around 1993. This reflects migration of rural women who had previously worked on the family farm (and were thus not counted as participating in the labor market) caused by a series of severe droughts in 1992, 93 and 95 (World Bank 1997).

A study of the incidence of public education spending based on the 1998/99 Morocco Living Standards Survey (MLSS) (discussed in section 5) finds that spending on the first 6 years of primary schooling is relatively pro-poor, in that, with the exception of the poorest decile, the 11
**<u>Table 1: Sample frame compared to national statistics</u>**

||<br>**Total**||**Farm si**|**ze in ha**||
|---|---|---|---|---|---|
|||**< 50**|**50-120**|**120-500**|**> 500**|
|**Panel A: No. of farms & medi**|**an/average fa**|**rm size**||||
|_Total Form 29_|36,184|16,138|5,401|7,144|7,501|
|ACUs %|78.9|82.0|79.7|76.2|74.0|
|AACs %|21.1|18.0|20.3|23.8|26.0|
|_Total SAR_|75,571|61,705|6,127|4,444|3,295|
|....Legal Entities %|20.8|9.0|58.4|76.3|97.1|
|FOPs %|11.2|9.3|28.2|21.1|2.0|
|Individuals %|68.0|81.7|13.4|2.7|0.8|
|Ratio SAR/F29|2.09|3.82|1.13|0.62|0.44|
|Median farm size, SAR|6.65|4.92|74 .99|206.60|1307.9|
|Med. farm size, Form 29|68.57|24.00|77.09|241.00|1298.6|
|Avg. farm size SAR|154.0|9.75|88.70|283.11|2802.5|
|Avg. farm size Form 29|777.7|25.07|79.43|264.67|3393.0|
|**Panel B: Area cult. (mn. ha)**||||||
|_Total Form 29_|27.85|0.40|0.43|1.89|25.14|
|ACUs %|75.4|79.8|79.5|75.9|75.3|
|AACs %|24.6|20.2|20.5|24.1|24.7|
|_Total SAR_|11.64|0.60|0.54|1.26|9.23|
|....Legal Entities %|90.5|33.0|63.9|82.8|96.9|
|FOPs %|4.4|13.6|25.5|15.3|1.1|
|Individuals %|5.1|53.4|10.7|1.9|2.0|
|Ratio SAR/F29|0.42|1.49|1.27|0.67|0.37|
|**Panel C: Informality (ACUs)**||||||
|Area cult. 2020 (mn. ha)|33.87|||||
|F29 overlapping (mn. ha)|6.42|0.09|0.20|0.67|5.46|
|F29 non-overlap (mn. ha)|14.59|0.23|0.14|0.76|13.45|
|SAR only (mn. ha)|3.14|0.43|0.24|0.28|2.20|
|Informal (mn. ha)|9.71|||||
|**Panel D: Informality (AACs)**||||||
|Area cult. 2020 (mn. ha)|11.86|||||
|F29 overlapping (mn. ha)|1.09|0.01|0.02|0.08|1.00|
|<br>F29 non-overlap (mn. ha)|5.75|0.08|0.07|0.38|5.23|
|SAR only (mn. ha)|0.22|0.02|0.03|0.05|0.12|
|Informal (mn. ha)|4.80|||||

_Source:_ Own computation from SAR, 2020 Form 29 data from SSSU and crop maps based on satellite imagery for 2020 as discussed in the text.

_Note:_ In panels C and D, ACU is areas controlled by Ukraine and AAC are areas affected by conflict. Region is not reported in the SAR for 1,629 farms cultivating a total of 39,735 ha.<sup>a</sup> Area cultivated 2020 is based on satellite imagery.

20
Health Service (NFZ), Ministry of Family, Labor and Social Policy (MRPiPS), Ministry of Health, and Ministry of Education. Finally, we use the 2010 Input-Output matrix to estimate the indirect effect of indirect taxes as described below and detailed in Appendix I.

# **Method and Approach**

To analyze the incidence of each fiscal intervention, and the impact of taxes and social spending on poverty and inequality, we follow Lustig and Higgins (2013) and measure per capita income before and after each fiscal intervention as described in Figure 1. In particular, for every household we define the following income concepts:

- _Market income_ includes pre-tax wages, salaries, and income earned from capital assets (rent, interest or dividends) and private transfers.

- _Disposable income_ is constructed by subtracting direct taxes and social contributions and adding direct transfers and social pensions to market income. Direct taxes include the personal income tax, capital tax, and farm taxes. Social contributions include those for disability and farmer pensions, health, accident and sickness insurance, as well as contributions to the labor fund, the fund of employee benefits, and to other farmer benefits. Direct cash transfers include housing benefits, child birth grant, nursing benefit, nursing allowance, social assistance, and family benefits. In addition, there are pre-retirement, family, disability, maternity, unemployment and farmer pensions which are treated as transfers.

- _Consumable income_ subtracts the impact of indirect taxes to disposable income. In Poland, indirect taxes included in this analysis include the VAT, excises on alcohol and tobacco, fuel and automobiles.

- _Final income_ adds in-kind benefits in the form of health and education to consumable income.

One area where there is no clear consensus in the literature is on how to treat contributory pensions and the related contributions. Arguments exist in favor of treating contributory pensions as individual savings or deferred income, while others argue that they should be treated as a government transfer, with the related contributions being treated as a direct tax. Following Lustig and Higgins, we present two scenarios. Under our “main” scenario all contributory pensions are treated as transfers, and the corresponding contributions are treated as taxes and therefore subtracted from disposable income, in line with standard EU measurement of disposable income. Under the “alternative” scenario, old-age contributory pensions are treated as deferred income, and the corresponding old-age contributions are treated as savings, and thus as part of disposable income.<sup>9</sup>

> 9 All other pensions are treated as transfers and the corresponding contributions as taxes under the alternative scenario. One could argue that there is some double counting under the alternative scenario, as pensions are included

12
associated with an increase of around 10 percent in the value of their country’s FDI stock in that sector over the timeframe of the analysis.

To gauge the relative effectiveness of IPAs, a country-interaction effect with the IPA sectoral targeting coefficient was incorporated into the same PPML model These findings suggest that there is considerable heterogeneity between IPAs in their ability to affect FDI stocks. Overall, 10 countries have positive and statistically significant results (classified as “high performing” IPAs). An additional 13 IPAs have statistically insignificant results, while 3 IPAs have negative and statistically significant results (which are jointly classified as “other” IPAs). Finally, 10 IPAs fell out of the country-interaction equation as they either did not have sectoral targeting or exhibited no variation in the timing of their sector targeting strategy.

The final section compared the 10 “high performing” IPAs to the 16 “other” IPAs across a range of characteristics from the WB-WAIPA survey, and used t-tests to consider which IPA characteristics significantly differ between the two groups. This suggests that effective IPAs are more likely to be private or semi-private agencies. Their mandate also tends to be focused narrowly on foreign investment and exclude responsibilities for domestic investment promotion. Such IPAs are also more likely to have a board of directors, while their staff tends to be better compensated. Finally, high-performing IPAs are also likely to engage in more investor services, partly by engaging smart, sectoral analytics and by adopting systems for identifying investor complaints or disputes.

This paper provides a modest contribution to two distinct bodies of literature. The first relates to economic literature analyzing the impact of IPA to attract FDI. Here, the added value is the paper’s use of a wide set of FDI source countries and actual FDI stock data (rather than FDI announcements). It also explicitly considers how an IPA’s targeting of a priority sector in one country may affect the relative effectiveness of IPAs in _other countries_ targeting the same sector, by including sectoral IPA targeting within a structural gravity model framework. The paper also contributes to the literature around characteristics of effective IPAs. This paper avoids some of these limitations of the literature by taking a data-driven approach to identifying ‘high-performing’ IPAs. In addition, it compares ‘high-performing’ IPAs to other IPAs across a highly detailed set of characteristics, so as to provide a detailed assessment of potential dynamics that may shape the effectiveness of IPAs in attracting FDI.

New research should focus on replicating the findings with a larger database of sectoral FDI. This would allow the analysis to study more IPAs. Having a broader timeframe would also be important (both on FDI stock, and on FDI sectoral targeting), as many of these dynamics are slow moving. Such analysis will likely be possible in the near-future as major FDI source countries and regions are increasingly publishing sectoral FDI statistics.

24
Finally, the OLS results show that children in communities that have either a feeding posts or a Partage program or both are taller; they are almost 0.3 SD higher compared to the height of the children who live in communities without these programs.<sup>4</sup> OLS estimates have also been performed on the underweight z score of the child. In this case, the presence of a nutrition intervention in the community no longer has a significant impact on the (standardized) weight of the child (it does in the preferred specification discussed in the next section). Expenditures on the other hand, continue to have a positive and significant effect. A doubling of household per capita consumption would generate an increase of the weight z-score of about 0.15 SD. Nutritional status still worsens with age, though this effect is more tenuous than in the case of stunting. Finally in this regression maternal education has a positive effect. So whereas stunting responded positively to additional education of the father, the weight z-score is responsive to additional maternal education. An additional year of maternal education increases the weight for age z- score of the child by 0.02 SD.

# **4.2 Instrumenting Program Placement and Observed Consumption.**

In seeking instruments to address the possibility of endogenous program placement it is necessary to find variables that have predictive power on the endogenous placement variables and, at the same time, are not correlated with the child’s health output and the measurement errors present in the explanatory variables. The set of instruments used to model Partage and feeding posts include the average per capita household consumption in the village and the average height of adults aged 25-32 in the village as proxies for the average nutritional status of the village before Partage or other programs were started. The set of instruments also includes the main shocks each community experienced in the previous ten years, the main causes of adult and children death in the community where the child resides and whether the village has a daily market.<sup>5,</sup> Whether and which type of disasters happened in the past ten years presents the appropriate features for being used as an instrumental variable of the program placement. While such shocks impoverish the village it is arguable whether they affect the health of the children in a direct way rather than

> 4 Different specifications to capture the effect of feeding posts on the nutritional status of the child were tried. In particular we explored whether the impact of the intervention on the child’s health varied with different household income levels. However, the results did not provide evidence in support of this assumption

> 5 See notes to Table 5.

9
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16 assessing external validity of social recognition interventions may require the collection of new kinds of data both at the individual staff level (e.g., pro-sociality, time discounting) and at the firm/ministry level (e.g., institutional or professional incentives including career dynamics, supervision structures, accountability). We speculate that the availability of institutional, psychological, and cultural variables might help pin down the mechanism through which social recognition may operate in a given context (e.g., comparison to peers, principal-agent concerns, a gift relationship between employers and employees, the concerns of patients and customers and reputation effects). Our findings are consistent with Kremer and Glennerster’s (2011) suggestion that the success of interventions in health systems may depend on institutional context. Future work could help distinguish these potential pathways as a function of the population and environment.

What we can say is that the RCT took place in two states that are very different in terms of human capital and bureaucratic organization. Across a number of indicators, Ekiti exhibits higher capacity than Niger, in terms of both bureaucracy and human capital (Table 1). In addition, figures from the 2013 Demographic and Health Survey (DHS), such as data on birth registration rates, suggest that there is more bureaucratic capacity in Ekiti. Similarly, among the population, adult literacy rates are much higher (see Table A2 and Figure A2 in the Appendix). This suggests, tentatively, that the social recognition incentive requires higher levels of training and organization on the part of public sector health officials in order to be effective. It may be that social recognition was motivating for officials in Ekiti, but not Niger, because in Ekiti health care workers believed that the bureaucracy had the capacity to use social recognition as a credible input into long-term career incentives. Niger and Ekiti states differ along many dimensions; in addition to the many demographic differences enumerated in this paper, the drug procurement systems for the public health systems in the two states depend on different fractions of public funding (Gauthier, Pimhidzai, and Saleh 2018, Chapter 5). As in many developing countries, the bureaucratic and managerial environment for service providers in Nigeria is highly heterogeneous, with variation between states and even within a single state (Rogger 2017). That an intervention was successful in one state, but not another, speaks to the importance of considering this variation when translating successful findings to a new context—even when the implementers in both cases are the same.
statistics are representative at least two groups of districts in Punjab: 15 stipend districts together as one treatment group and 19 non-stipend districts as one control group. Repeated crosssectional surveys, namely PIHS 2001-02 and PSLM 2004-05, are the only other independent sources of data available to us to compare our results from the provincial school census data.

The official age-group for middle school grades in Pakistan is 10-12 years. Age-grade profile from the survey data indicates that there are a large number of 13 and 14 year olds attending middle school grades because of either delayed entry to schools or higher repetition rates. For this reason, we focus on children of ages 10 to 14 years inclusive. Note that individual data from household surveys cover both public and private schools and the estimates on school attendance we present are for all types of schools. Descriptive statistics on the proportion of school attendance<sup>21</sup> for 10-14 year olds are shown in table 12. Levels and trends in these proportions follow similar patterns to those from the school census data. Girls in stipend districts have the lowest attendance rate in both periods, but they record the highest increase from 29 percent in 2002 to 48 percent in 2005. There is an increase of 7 percentage points for girls in non-stipend districts. Comparable figures for boys in stipend and boys in non-stipend are increases of 6 and 7 percentage points respectively.

We employ the same empirical techniques to estimate the impact of stipend program. Rather than schools as units of analysis, now the units of observations are individual children aged 10-14 years old. Because the surveys are repeated cross-sections and not pure panel at individual level, our regression specification includes interaction terms of stipend dummy and post-program time dummy in case of difference-in-difference (girls only) and a triple interaction term of stipend, post-program period and female dummy in case of triple differencing (girls _net_ of boys).

DD specification with RDD and covariates takes the following form:

> 21 One can also present results on an alternative indicator: proportion of out-of-school children. But as long as we measure progress in terms of percentage points change (and not percent change), results remain the same. For instance, increase in school participation rate from 29% in 2002 to 48% in 2005 implies proportion of out-of-school children decreased from 71% in 2002 to 52% in 2005. The change is 19 percentage points in both cases. In terms of percent change, one would be an increase of 66% while the other would be a decrease of 27%.

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morale (which is influenced by factors such as trust in institutions and perceptions regarding accountability and reciprocity) was likely to be sensitive to the changes occurring during the pandemic. However, it is safe to assume that the unrealized impact of these letters on compliance is a lower bound estimate of what could be expected in normal times because the costs of compliance (in terms of loss of liquidity) are elevated with high levels of uncertainty and reductions in the productive capacity of most firms and the effectiveness of the letters limited due to the high likelihood of letters not making it into the right hands.

In addition to uncovering important impacts of the letters on firm behavior, the experiment yielded substantial monetary gains with a single round of notifications in a very short period. Back of the envelope calculations suggest that letters sent to employers raised an additional 932,000 USD in PIT revenues that would have otherwise not been recovered in the absence of the letters.

Our results suggest that in crisis situations, positively framed language highlighting the benefits from tax compliance are more effective at generating immediate (positive) reactions from taxpayers while having meaningful impacts on revenues. Future efforts should be made to test the differential impact of soft vs. strong-tone language in a post-COVID environment to validate these findings. Moreover, our study was unable to identify how the employer-employee dynamics influences tax compliance. Future studies (in Albania and beyond) should explore this dynamic at the firm level (including bargaining and collusion) to improve the efficiency of tax administrations in dealing with the underreporting of wages in low enforcement capacity environments.

# References

Antinyan, A. & Asatryan, Z. (2020). "Nudging for Tax Compliance: A Meta-Analysis," CESifo Working Paper Series 8500, CESifo.

Allingham, M.G., Sandmo A., 1972. Income tax evasion: A theoretical analysis. _Journal of Public Economics_ 1, 323-38.

Callaway, B. & Sant’Anna, P. (2020). Difference-in-Differences with multiple time periods, Journal of Econometrics (in press) Cranor, T., Goldin, J., Homonoff, T. and Moore, L., 2020. Communicating Tax Penalties to Delinquent Taxpayers: Evidence from a Field Experiment, _National Tax Journal_ 73(2), 331-36.

Dalton, Abigail Goodnow; Manning, L.; Jamison, J.; Sen, I.; Karver, J.; Castaneda Nunez, J.; Guedes, L.; Mujica Estevez, S. 2021. Behavioral Insights for Tax Compliance. eMBeD brief. Washington, D.C. : World Bank Group.

Dhami, Sanjit and Ali al-Nowaihi. 2007. Why do people pay taxes? Prospect theory versus expected utility theory. _Journal of Economic Behavior and Organization_ 64, 171-92.

Engström, P., Nordblom, K., Ohlsson, H. & Persson, A. (2015), ‘Tax Compliance and Loss Aversion’, American Economic Journal: Economic Policy 7(4), 132–164.

Hallsworth, M., 2015. The use of field experiments to increase tax compliance, Oxford Review of Economic Policy, 30(4), Pages 658–679.

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Figure 34. Oaxaca-Blinder decomposition of real monthly labor income, ages 18-59 <!-- Start of picture text --> 52%<br>50%<br>40% 40%<br>33% 28%<br>30%<br>53% 27%<br>20% 39%<br>32% 34% 31%<br>10%<br>0% 0% -2% -1%<br>-8% -6%<br>-10%<br>2015 2017 2019 2020 2021<br>Explained Unexplained Estimated gap<br><!-- End of picture text --> Note: gap is estimated as a proportion of women's income. All estimates are significant at 1% (except 2020 which is not significant).