rafmacalaba/data-use-mentions-extended huggingface dataset:
does not lead to backlash against migrants, but does increase fears of crime (Afrobarometer), and slightly more experience with violence (ACLED). OLS models for Afrobarometer respondents within 150km with Nearest + 20km exposure measure interacting demographic controls with year, and including year and regional fixed effects. For ACLED, standard errors are clustered at the parish level. Estimates include 95% CIs.

When asked in Afrobarometer Round 6 about support for migrants’ free movement across borders (as opposed to restricting cross-border movement to protect own citizens), plot 2 shows that Ugandans experiencing greater presence of refugees responded no differently to those experiencing less. With respect to citizenship policy (plots 3 and 4), which was only included in Afrobarometer Round 5, citizens experiencing greater refugee presence were less supportive of granting citizenship based on birthplace alone, but more supportive of allowing migrants who have lived and worked in Uganda for many years (e.g., refugees) to be eligible for naturalization.

In the bottom row of Figure 6, _higher values_ correspond to _greater feelings of insecurity_ or _greater violence_ . More exposed citizens did not say they felt more unsafe in their community post-2014 (plot 1). In 2020, the effect was negative at -0.11 sd, which means that more exposed citizens felt _safer_ in 2020 than in 2011.

22
generation is also a good predictor for CO2 mitigation potential; for instance, in France where nuclear power accounts for almost 80% of power generation, an emission tax even at the relatively high level of US$50/tCO2 reduces emission by only 6.5%, while the same tax rate reduces emissions in the UK, where coal and gas account for the bulk of power generation, by about 28%.

**Table 7: Change in CO2 emissions (%) relative to baseline in 2020**

|**Carbon tax**|**US$10**|**/tCO2**||**US$25**|**/tCO2**||**US$50**|**/tCO2**||
|---|---|---|---|---|---|---|---|---|---|
|**Biofuels subsidy**|**0%**|**25%**|**50%**|**0%**|**25%**|**50%**|**0%**|**25%**|**50%**|
|**World total**|-12.9|-13.0|-13.7|-23.5|-23.7|-23.9|-32.8|-32.9|-33.1|
|**High-income**|-6.9|-7.0|-7.4|-13.7|-13.8|-14.0|-20.6|-20.7|-20.9|
|**Aus-NZ**|-10.9|
survey, we are left with a sample size of 1,493. Data were collected from September 2007 to August 2008. All households in a village completed their diaries over the same 14 days. Survey work in each district was completed in less than two months.<sup>6</sup> Each study household maintained a transaction diary for 14 days. The diary took the form of a paper log book, with a separate row for each transaction. On each day during the observation window, diary keepers noted the quantity, unit, value, and description of every item that entered or exited the household. If multiple items were purchased during a single shopping trip, each item received its own entry (row) in the diary. Purchases, gifts, own production, and stock adjustments were recorded separately. Because this paper deals with purchasing behaviors, we use only those rows of the transaction diaries that indicate purchases, unless otherwise specified.

After collecting the diaries from respondents, project staff used the item descriptions to assign each purchased item to one of 73 categories, covering 58 food items and 15 non-food items. These categories are similar to those that would be available in a typical consumption survey. However, because we have access to the raw diary data, we use the detailed descriptions of each item to further narrow the definitions of the study items. We do this by dropping entries for which the description does not match that of the modal entry within the item category. For example, we drop “unrefined sugar” from “Sugar,” retain only “dried beans” (excluding soy beans) from the original category of “Peas, beans, lentils and other pulses,” keep only “immature coconuts” in the “Coconut” category, restrict the “Dried fish” group to only “dried sardines,” excluding larger fish, and so on. This removes much of the bias from grouping goods of different variety under a single heading. The end result is a data set
either to pay for or to justify having bank accounts declined as income levels increased. While 75% among the lowest 20% of income distribution pointed to the cost of banking, or to their lack of resources, this is only an issue for 50% of the wealthiest 20%. The higher income groups point, instead, to not needing an account (24%) and to high management costs (13%). While banking services are considered to be costly by persons at all income levels, lower income groups in particular believe that their incomes are too low to justify using banking services. This confirms the finding of the supply side cost analysis (see Chapter 2) that the costs of banking services in Colombia are too high for low income people to afford, in particular those who earn less than one minimum wage per month.

**Figure 3. 16  Main reason for not having a bank account**

<!-- Start of picture text --> 12%<br>Q5 13% 24%<br>51%<br>7%<br>Q4 11% 17%<br>65%<br>6%<br>Q3 9% 15%<br>71%<br>6%<br>Q2 7% 14%<br>73%<br>8%<br>Q1 6% 10%<br>76%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>%<br>Lack of Resources Does not need an account High Costs Other<br>Population quintiles by income level<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_

# **3.7 ARE THE UNBANKED BANKABLE?**

31. Given their different socio-economic characteristics, the question has been raised whether the unbanked have “bankable characteristics”. Home ownership rates by
(e.g. the Institutional Profiles Database and the Cingranelli-Richards Human Rights Database), or because the methodology of the data source has changed so that it no longer meets the criteria for inclusion in the WGI (e.g. selected components of the Heritage Foundation Index of Economic Freedom, or the European Bank for Reconstruction and Development Transition Indictors, see footnote 7). We do however continue to use these dropped data sources in the WGI for the years for which they were available and suitable for the WGI (see Table 1).

We assign variables from the 35 data sources described above to each of these six governance dimensions. For example, an assessment of the quality of the bureaucracy would be assigned to Government Effectiveness, a question about confidence in the police or the courts would be assigned to Rule of Law, and a question about the perceived likelihood of having to pay a bribe would be assigned to Control of Corruption. In some cases, a single data source will have multiple questions that can be assigned to the same dimension. In this case, we use the average across all relevant questions from that data source.<sup>13</sup> Finally, we only assign each question from each data source to only one of the six governance dimensions, selecting the dimension that best matches the content of the question. Annex 2 in this paper provides a complete list of all the questions from each of the data sources assigned to the six aggregate indicators in the most recent year that the source appears in the WGI. As the specific questions available in each data source may vary over time, we also report the full list of questions assigned to each of the six governance dimensions over time at www.govindicators.org.

Naturally, the process of assigning questions to governance dimensions involves some judgment, including on whether the question – or even the data source itself – should be used at all, and if so, to which of the six governance dimensions it should be assigned. The
/sup><sup>_λR_+</sup><sup>_uR_</sup> _j_<sup>)</sup><sup>_R_</sup> _j_<sup>_∗_andsimilarly</sup> _Kj_<sup>_∗∗_</sup> = (1 + _λ_<sup>_K_</sup> + _u_<sup>_K_</sup> _j_<sup>)</sup><sup>_K_</sup> _j_<sup>_∗_,rearranging(11)givesthecondition</sup> where _Rj_<sup>_∗≡Rj_(</sup><sup>_K_</sup> _h_<sup>_∗,_</sup><sup>**_K_**</sup> **_h_**<sup>**_∗_**),</sup><sup>_R∗≡_�</sup> _j_ = _h_<sup>_R_</sup> _j_<sup>_∗_and</sup><sup>_ω_</sup> _j_<sup>_R≡R_</sup> _j_<sup>_∗/R∗_(with</sup><sup>_K_</sup> _j_<sup>_∗_,</sup><sup>_K∗_,and</sup><sup>_ω_</sup> _j_<sup>_K_</sup> defined analogously), and _uh_ absorbs the idiosyncratic elements in the comparison with the counterfactual of investment in _h_ being constrained to zero.<sup>47</sup>

> 46The distinction between the extensive and intensive margins has been made in previous work using FDI data: examples include Davies and Kristj ́ansd ́ottir (2010) and Canton, Solera et al. (2016).

47Specifically, 27
|Incumbent/Exit<br>Productivity|
||Difference|Difference|Difference|
|**Argentina**|0.006|-0.012|0.098|
|**Chile**|-0.015|-0.022|0.432|
|**Colombia**|0.005|0.008|0.627|
|**Estonia**|-0.008|-0.031|0.28|
|**Finland**|-0.003|-0.013|0.251|
|**France**|0.004|0.034|0.107|
|**Korea, Rep.**|-0.06|-0.122|0.495|
|**Latvia**|0.01|-0.001|-0.037|
|**Netherlands**|0.003|0.028|0.025|
|**Portugal**|-0.016|-0.039|0.394|
|**Slovenia**|0.014|0.059|0.252|
|**Taiwan**||||
|**(China)**|-0.019|-0.077|0.264|
|**UK**|0.008|0.148|0.051|
|**USA**|0.004|0.012|0.299|
|**West**||||
|**Germany**|0|0.001|0.274|

Notes: The reported figures are the time series averages. The first column is the product of the second and third column. However, since the reported figures are averages over time, the identity may appear not to hold (the
POLICY RESEARCH WORKING PAPER 1727

# **Summary findings**

Loayza presents the view that informal economies arise when governments impose excessive taxes and regulations that they are unable to enforce.

Loayza studies the determinants and effects of the informal sector using an endogenous growth model whose production technology depends essentially on congestable public services. In this model, changes (in both policy parameters and the quality of government institutions) that promote an increase in the relative size of the informal economy will also generate a reduction in the rate of economic growth.

Using data from Latin American countries in the early 1990s, Loayza tests some of the model's implications and estimates the size of the informal sector in these countries - identifying the size of the informal sector to latent variable for which multiple causes and indicators exisr.

'The results suggest that:

* The size of the informal sector depends positively on proxies for tax burden and restrictions on the labor market.

* It depends negatively on a proxy for the quality of governiment institutions.

* An increase in the size of the informal sector hurts growtlh by reducing the availability of public services for everyone in tihe economy and by increasing the number of activities that use some existing public services less efficiently or not at all.

This paper - a product of the Macroeconomics and Growth Division, Policy Research Department - is part of a larger effort in the department to examine the determinants of economic growth. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC, 20433. Please contact Rebecca Martin, room NI 1-059, telephone 202473-9026, fax 202-522-3518, Internet address rmartinl(@'worldhank.org. February 1997. (52 pages) _The Policy Research Working Paper Series disseminates the findings of work_ **_in_** _progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out_ **_quic_** _kly, even if the presentations are less than fully polished. 'The papers carry the names
policy measures.

It is notable that both Singapore and Malaysia face difficulties in setting the price and quantity of work permits in their highly regulated immigration systems where permit types vary according to nationality, skill level, and sector of employment, enforced with the help of high quality administrative capacity but also rather draconian measures that sometimes violate individuals' rights. Despite labor legislation promoting equitable conditions of employment, earnings evidence from Malaysia indicates that ethnic discrimination among citizens persists. Moreover, the proliferation of large informal sectors consisting primarily of foreign workers suggests imperfect enforcement. In view of these challenges, it may be more efficient to rely on market signals to address labor misallocations, for example through tradable work permits.
both greater privacy and control over their funds. The administrative costs for the social benefits program were 20 percent less for mobile transfers than for cash payments (Aker, et al., 2016).

Digitalization can also speed up the collection of social benefits payments. A recent study in Bangladesh on the impact of moving education subsidies from cash to digital channels found that nearly 80 percent of mothers preferred the digital payments because they removed the need to travel and wait for cash disbursements (Gelb, et al., 2019).

As evidenced by the examples above, government initiatives can play and have played a foundational role in expanding financial inclusion. India has been particularly proactive in this regard. A government program launched in 2016 provided every adult in the country a digital government ID, which was leveraged to open more than 250 million financial accounts (Agarwal, et al., 2017). This study also finds in some regions that this increase in account takeup also corresponds with increases in savings balances and the issuance of new loans.<sup>5</sup> The idea that some degree of government engagement helps to fuel financial inclusion is not unique to India. In fact, Findex data finds that 865 million account owners in developing countries (18 percent of adults), including 423 million women, opened their first account to directly receive some type of government payment, including a government wage payment, a government benefit, or a government pension.

# **_3.2 Private sector payments as a driver of financial usage_**

Government payments are not the only type of payment that can deliver benefits through digitalization. Receiving private sector wages directly into an account can also benefit recipients by being more convenient, safer, and sometimes less expensive, if the wage earner must travel to pick up their wages—just as government payments have proven to be more convenient, safer, and cheaper. In developing economies, 61 percent of adults receive their wages into an account, an increase from just 37 percent in
# **_<u>________________________________________________________</u>_**

|Report 2004; Overall<br>Trade Restrictiveness<br>Index(OTRI)|||economy|
|---|---|---|---|
|Doha Development<br>Agenda Trade Capacity<br>Building Database<br>(TCBDB)||annual|information on trade-related technical<br>assistance and capacity building projects|
|Technical Barriers to<br>Trade (TBT) Database|690 firms<br>in 17<br>countries|2001-2||
|Global Economic<br>Prospects 2004 database<br>(Chapter 5) : Based on<br>Wilson, Mann, Otsuki<br>(2003)|75|For year 2000|Port efficiency; Customs environment;<br>Regulatory environment; Service sector<br>infrastructure|
|European Union (EU)<br>Market Access database||current|Sectoral and Trade Barriers, Applied<br>Tariffs, Exporters' Guide to Import<br>Formalities|
|Tuck Trade Agreements<br>Database||2003|bilateral and regional free trade<br>agreements and customs union<br>agreements|
|PERINOM Database<br>|18<br>countries,<br>650,000<br>records<br>**CUSTOMS**<br>|**AND BORDER**<br>|Bibliographic database on standards and<br>technical regulations<br>**CROSSINGS**<br>|
|Doing Business: Trading<br>Across Borders|155|Newly<br>implemented<br>from 2005|Documents for import and export,<br>Signatures for import and export, Time<br>for import and export|
|International Exhibition<br>Logistics Associates<br>(IELA)|67|Current|Average days required for customs<
**Figure 2.8 Morocco: Evolution of investment efficiency, 2001‐17**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>y = 0.6187t + 3.2377<br>15<br>10<br>5<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>ICOR Linear (ICOR)<br><!-- End of picture text --> _Source_ : High Commission of Planning (HCP).

The diminishing efficiency of public investment, as well as possible crowding‐out effects (given a limited pool of national savings) and a limited complementarity effect associated with public capital, may help to explain why private sector investment in Morocco has remained at a relatively low level of around 16 percent of GDP (just over half of total investment) over the past decade.<sup>9</sup>

# **2.3.2 Persistent Unemployment, Inadequate Labor Quality**

# _Persistent Unemployment_

Despite Morocco’s favorable growth performance recorded over the past decades, the unemployment rate has remained stubbornly high, at about 10 percent in recent years, particularly for young and skilled workers. Youth unemployment exceeds 20 percent and the unemployment rate for graduates is 15 percent. Two out of three unemployed people are youth aged 15 to 24 years (with little or no education) and one of four is a higher education graduate (Figure 2.9). Almost 28 percent of the youth is neither employed nor in education or training. In addition, four of five unemployed are urban residents, one of two unemployed people is a first‐time job seeker, and nearly two out of three unemployed people have been unemployed for over a year. Thus, unemployment in Morocco is largely structural. Long‐term unemployment discourages job search and accelerates the loss of skills,
/sup> using the _leebounds_ command in Stata. We report these upper and lower bound treatment effect estimates which vary the assumptions regarding nonrandom selection of the FSW who attrited from the endline survey. This is a nonparametric bounding that drops a set of the Treatment arm observations to match the proportion of Control arm observations not attriting. The upper (lower) bounds are computed by dropping these Treatment arm observations from the lower (upper) end of the distribution of the dependent variable and re-estimating the unadjusted treatment effects model.

# _Ethics_

The RESPECT II trial received ethical approval from the Committee for Protection of Human Subjects at University of California, Berkeley (Protocol 2015-08-7849) and the National Institute for Medical Research in Tanzania <mark>(NIMR/HQ/R.8a/Yol.IXI 2770)</mark> . All data collection was carried out by research assistants from Innovations for Poverty Action Tanzania who were trained in
in the two data sets. Next, we focus on the names of politically exposed persons. Specifically, we match the names of the people reported in the PEP data set with the names of shareholders and owners listed in Orbis. Bussolo, Commander, and Poupakis (2018) exploit the network dimensions of these connections and provide a detailed discussion of these matching strategies.

Our sample consists of about 1.7 million observations from more than 460,000 firms from six countries over the period 2008-2013, of these 460,000 firms about 2,000 are classified as politically connected. Table 1 reports the distribution of firms across the six countries covered in our sample and Table 2 reports the descriptive statistics for all the variables used in our analysis (the top panel reports data for all countries and the mid and bottom panels separate connected and unconnected firms).

There are large differences in the number of firms reported by Orbis and in the share of connected firms. While Orbis includes data form more than 160,000 firms in Hungary and Romania, we have fewer than 50,000 firms for Serbia and the Slovak Republic, and fewer than 30,000 firms for Bulgaria and the Russian Federation (Table 1). The share of connected firms ranges from about 0.15% in Bulgaria, Hungary and the Russian Federation, to nearly 1% in the Slovak Republic (the values for Romania and Serbia are 0.5% and 0.3%, respectively).

Comparing median and average values highlights that the underlying distributions are skewed for most variables. To minimize the influence of outliers, we winsorize the investment, cash flow, and sales growth variables at 1%. Extreme values in the left (right) tail are replaced with the value at the 0.5 (99.5) percentile. We observe a large support for winsorized sales growth which in the full sample ranges between -0.9% and 6.6%. Despite the winsorization, the support of cash flow remains
, I present the placebo test results, using the pre-FPE data, assuming that FPE was introduced in 1999, such that the first FPE-treated cohort entered university in 2011. Third, I narrow the age window to include only students aged between 18 and 22 years old. Fourth, I present the semiparametric DID results controlling for some confounding factors, particularly the discontinuity of PESP at NUL, which affected all students doing physical science-based programs. Lastly, I present the doubly robust DID estimates that are robust to misspecification in either the propensity score ( _π_ 0( **X** _i_ ) _≡ P_ ( _D_ = 1 _|_ **X** _i_ )) or the outcome regression models for _E_ [ _Yit|D_ = _d, T_ = _t,_ **X** _i_ ],<sup>16</sup> following Sant’Anna and Zhao (2020).

## **6.3.1 Who entered the university post FPE?**

Given that I use university administrative data, which are not representative of the population, one basic concern could be that the students who got admitted into university post FPE program are different from those admitted pre-FPE in a number of ways. First, it could be that post FPE program, the number of those completing secondary school and qualifying for university entrance

> 15I explain the PESP program in detail below.

> 16Sant’Anna and Zhao (2020) shows that the outcome regression DID estimator is given as _δ_ ˆ _DID_ = _Y_ ̄1 _,_ 1 _−_ ̄ � _Y_ 1 _,_ 0 + _n_<sup>_−_</sup> _treat_<sup>1</sup> � _i|Di_ =1<sup>(ˆ</sup><sup>_μ_0</sup><sup>_,_1(</sup><sup>**X**</sup><sup>_i_)</
# **8.2 Payment Behavior**

We find a precisely estimated null effect of the engagement intervention for all primary payment outcomes and time periods measured in Table 4. The control group payments increase steadily over the nine-month period from 30.1% of property owners having made payments one month after the intervention to 55.8% having made at least one payment by nine months (cumulative). However, compounds being exposed to the engagement intervention track almost the exact same trajectory as their control comparison. The total amount paid, and proportion of balance paid off are similarly indistinguishable across treatment and control group.

In contrast to the engagement intervention, we find a sharp increase in payment behavior among compounds exposed to the disconnection notices (Table 5). The likelihood of payment within one month almost quadruples - increasing by 30 percentage points from 11 percentage points (p-value < 0.001). This difference in payment likelihood sustains through the nine-month period, although with a slight decline relative to the control group. A similar pattern is found for the total payments after one month, which increases by US$ 8.80 (p-value < 0.001) from a base of US$ 5.02. After this sharp initial increase, the difference remains roughly constant between treatment and control groups while both increase over time. Treatment compounds have paid off 11.3 percentage points more of their balance than control compounds after the first month of intervention (p-value < 0.001). Control compounds begin to catch up gradually over the nine months, closing this gap to 7.8 percentage = points (p-value 0.005).

Figure 5 presents the full time series data available from the daily payment information extracted from the utility billing database. The visualization strengthens the main messages identified through the regression results. First, we find strong evidence of balanced payment practices across treatment and control groups from 2014 when property owners first started connecting to October 2018 just before the enforcement intervention. Second, we see that