rafmacalaba/data-use-mentions-extended huggingface dataset:
The result would be that enforcing formalization may cause these firms to shut down, since they cannot afford to operate formally.

Table 4 examines this possibility by looking at the impact of the different treatments on firm closure. Firm closure is measured by whether the firm is observed to be closed at the time of the followup survey, coupled with information obtained from neighboring businesses. Between 14 and 16 percent of the control group is verified as closed at follow-up, with none of the treatments having any sizeable or significant impact on this closure rate. In particular, it is not the case that firms which received enforcement through the inspector treatment are more likely to have closed down.

# **5.4. Impact on Formalization**

Table 7 turns to the main outcome of interest, whether or not the treatments succeeded in getting firms to formalize. We use the administrative data to measure formalization, since this offers impacts without attrition, and offers substantially larger samples to measure impacts, offering the most power.<sup>12</sup> The first few columns compare the control firms to the free cost and communication firms. We see that assigning firms to the free cost treatment have a strongly significant negative impact on the likelihood of MEI registration (p=0.008 for definite matches, p=0.010 for probable matches). One possible explanation for this is that the free cost and communication interventions explained how to register for SIMPLES, but not how to register as a MEI. The free cost treatment also emphasized the need to have an accountant if you formalize, which is not required for MEIs. The unexpected policy change right after we launched this intervention increased the revenue thresholds under which firms could register as MEIs. It is plausible that firms receiving the communication and free cost intervention were less aware of this policy change given the information about the need to register for SIMPLES and get an accountant that they were given in person, causing them to decide not to register at all. The point estimates
statistics on respondents’ confidence using the formal financial services. The figure plots responses to the question “ _how comfortable are you making a transaction at a bank branch or mobile money agent?_ ”. Figure 2, panel (a) shows that a large majority of respondents in our sample are not comfortable using bank branches or mobile money agents, and Figure 2, panel (b) shows that this is even more true for respondents in the low-experience sample who have not used a bank or mobile account before.

# **3.2 Administrative Data**

In addition to the survey data, we obtained detailed transaction data for all participant accounts in our study. With the consent of participants, our partner bank and mobile money provider shared data on monthly account balances and the universe of transactions made in all accounts linked to our study. Each transaction record in this data set includes the transaction time, location, as well as information on the transaction type and fees charged. In total, this data set covers 903 bank accounts, 1,093 mobile accounts and records 82,055 individual transactions over a period of approximately two years. These data enable us to study consumer learning by comparing account usage over time between those receiving a payroll account versus a simple account. Moreover, access to the administrative data allows us to supplement and validate the survey responses. Notably, the administrative data reflects the full universe of workers receiving any account and is free from attrition concerns and concerns of enumerator demand effects. Having access to the complete transaction data for all accounts linked to our study allows us to test for these possibilities and verify the quality of the survey data.

We also received personnel data from the partner firms. While these outcomes are not the focus of our analysis, we report a summary of treatment effects in Appendix Table D.12.

# **3.3 Main Outcomes of Interest**

The focus of our study is the impact of payroll accounts on account use, consumer learning, and consumer
Figure A16: Specification Charts for Deviations in GDD <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .2<br>.05 .1<br>0<br>* Weather Product * 0 * Weather Product * -.1<br>MERRA-2ERA5 -.05 MERRA-2ERA5 -.2<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.04 .04<br>.02 .02<br>0 0<br>-.02<br>* Weather Product * -.04 * Weather Product * -.02<br>MERRA-2 -.06 MERRA-2 -.04<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.
of GDP and short-term interest rates; monetary policy stance is considered to be procyclical if the interest rates are high during the periods of low economic growth. Data on net foreign currency position is from Benetrix, Lane, and Shambaugh (2014), and refers to 1995.

In recent years however emerging economies appear to be making a transition from procyclical monetary policy to countercyclical monetary policy. The evidence points at the consistently improved net foreign exchange position of emerging economies--countries have reduced the mismatch in their external balance sheets (Figure 2), due to improved current account positions, larger foreign reserves (figure 3), shift in the composition of capital flows to equity from debt; and the success in developing local currency debt markets. Aizenmann et al (2015) particularly emphasize the role of larger reserve holdings of the emerging countries for this shift in the policy orientation of emerging countries.<sup>8</sup> A move to inflation targeting could be another reason for this transition, as noted in McGettigan et al (2013). Evidence therein shows that the countries that have successfully transitioned to countercyclical monetary policy are the ones which have adopted an inflation targeting framework, possibly reflecting strengthened monetary institutions allowing them to pursue independent monetary policy. Cordella and Gupta (2015) corroborate these findings, and in addition note that in recent years, monetary policy in emerging market economies is influenced less by exchange rate movements, and more by economic growth.

8 See Lane and Shambaugh (2010) for trends in the foreign currency position of emerging countries. Aizenman et al (2015) document the increase in the international reserves held by emerging markets since the early 2000s, attributing it to insure against volatile capital flows, mercantilist motive for specific countries, and possibly even to a regional demonstration effect—a country may gather reserves to keep up with the other countries in the region, termed as the “keeping up with the Joneses” effect.

6
data on unemployment stock data (ugam) from DIOC in conjunction with the unemploy ~~-~~ ment rates from the OECD Shor ~~t-~~ Term Labour Market Statistics, and the replacement rate of the national unemployment insurance scheme (3) from the OECD fiscal database. This allows computing wg,qn, the average income per active hour, and recalibrating ds~. as Wg Au (Qsam/Qsam) Validation of the calibration strategy. A key variable required in the analysis of the equilibrium outcomes is the share of total labor wage bill going to each type of worker in the labor force. These wage bill shares are generally computed by (1) using individual wages and total labor stocks from detailed labor force surveys; (2) using individual hours worked and total labor stocks from detailed labor force surveys; or (3) using total labor stocks and normalized wages, with one group of workers as the base group. Card and Lemieux (2001) and Manacorda et al (2012) present an application of these different approaches. The desired labor force surveys are not publicly available in such micro ~~-~~ format for most countrries; the available harmonised surveys for EU countries provide wage brackets, not the exact wages of individuals.

As a validation exercise, we thus compare our simulated wage bill shares with existing estimates available for 13 countries in the year 2010.'° The share of total wage bill going to a particular age group is the product of the size of that group and the normalized wage going to a given individual in that group, divided by the total wage bill of the entire economy. Results are depicted in Figure 4. The regression of observed wage bills on simulated shares shows a slope of 1.026 and an intercept of 0.003 (R ~~-~~ squared of 0.918), which is almost identical to the 45° line. This shows that the imputation strategy used to construct wage rate proxies (which involves
Reform Index) also produced by the authors that summarizes all the seven dimensions. Higher values of the index correspond to higher levels of financial liberalization. Finally, we have data from the Global Findex database, produced by the World Bank (Demirgüç-Kunt et al. 2018), on the share of the adult population that has an account in a formal financial institution. The data come from the 2011 and 2014 rounds of the Findex report and cover 150 countries. In the average country in our sample, around half (51%) of the population holds an account (median = 46%). We use this variable as a proxy for access to the formal 8
and we should pick the one that deviates as little as possible from the initial distribution (��∗ ). The solution to this problem is given by: where �<sup>�</sup> is the Lagrange multiplier for the constraint _E(X)_ , and Ω is the normalizing factor to scale the new population to the target size. In the simulation process we impose several population constraints which result from different growth rates by gender age group and governorate brackets.

The internally displaced population movement is taken into account after the above mentioned adjustment in weights has made. The input for this step is an out‐in flow matrix from the International Organization of Migration (IOM) which estimates the total number of families who had crossed governorate borders between June and December 2014. The lack of information about the characteristics of this population implied the following procedure: first randomly selecting families from governorates of origin like Anbar, Nineveh or Salahadin, and then allocating them to host governorates for instance, Dohuk, Erbil or Baghdad. It is relevant to note in this case that families and households are considered equivalent and all members of households classified as IDP are moved.

The rest of this simulation process follows Olivieri, S, et al. (2014).<sup>16</sup> Briefly, it uses predictions of behavioral models estimated on the baseline household data (as in step 1 above), and generates household and individual‐level predictions for employment, earnings and public and private transfers for 2014. Since an individual’s labor income depends on his/her employment status and labor earnings, how the output change in a particular sector is apportioned between employment change, earnings change and adjustments across sectors depends on how responsive (elastic) employment in that sector is to output change. It also implies that at the household level, the extent of the impact depends on the size of the aggregate change at the sector level and the demographics and characteristics of household members, which influence the labor
# **2. Background**

Despite the growing importance of FAFH in household expenditures, most national household surveys in developing countries largely focus on measuring consumption of food consumed at home. Furthermore, even when surveys attempt to better capture FAFH, little is known about how this information should be captured. This lack of attention and knowledge is the result of the difficulties in including FAFH within current food consumption modules, as well as the lack of rigorous evidence around best practices for collecting information on FAFH. With household surveys not well-designed to adequately capture these different types of consumption, FAFH consumption is often poorly measured.

A number of methodological challenges and various sources of measurement error arise when including FAFH. To begin with, simply distinguishing between ‘food consumed at home’ and ‘food consumed outside’ is not enough. Under this classification it is not clear where take-away meals belong (i.e. those ready-to-eat meals that are produced outside, for example at a restaurant, but brought to eat at home). Careful definition, classification of types of foods, and an organized framework is therefore needed to prevent double counting or omission.

In addition to clear classification of consumption types, there are myriad other potential sources of measurement error when collecting FAFH consumption. We focus in this study on three sources: omission, telescoping, and the level of disaggregation. Measurement of FAFH is particularly vulnerable to these issues, and as such, the need to address them formed the basis for our design of the different treatment arms.

Omission of consumption can occur due to accidental omission, purposeful deception, noncompliance, or lack of knowledge. Omission of consumption is likely to happen in any recall survey, but is expected to be more common when a single household member provides information for the entire household as a result of purposeful and unintentional misinformation as well as separate spending accounts for different members of the household (Dillman and House, 2012).<sup>2</sup> Reporting on food consumed away from home poses
Further, we look at two countries that experienced the largest changes in the level of economic freedom between 1970 and 2007. Uganda was one of the countries that registered the biggest improvement in economic freedom over the period. Both the level of GDP per capita and the level of economic freedoms have been steadily increasing over the period (Figure 7). In contrast, Zimbabwe was one of the countries where economic freedom recorded the biggest fall over the period. We note that both economic freedom and the level of GDP have been going hand in hand, increasing up to the late 1990s and falling afterwards—with the turning point of economic freedom preceding that of GDP (Figure 8).

**Figure 7.  Economic Freedom vs. per Capita Income in Uganda over 1982-2007.**

<!-- Start of picture text --> EFW and GDP per capita in Uganda<br>35 8<br>30 7<br>6<br>25<br>5<br>20<br>4<br>15 GDP<br>3<br>10 2 EFW<br>5 1<br>0 0<br>trillion 2005 USD)<br>GDP per capita PPP (constant<br><!-- End of picture text --> Source: Freedom House and World Bank.

Note: the EFW index is available only every 5 years between 1980 and 2000, so its values have been interpolated.

**Figure 8.  Economic Freedom vs. per Capita Income in Zimbabwe over 1980-2007.**

<!-- Start of picture text --> EFW and GDP per capita in Zimbabwe<br>4 7<br>3.5 6<br>3<br>5<br>2.5<br>4<br>2<br>3 GDP<br>1.5<br>2<br>1 EFW<br>0.5 1<br>
First, more understanding is needed of how statistical data are used. Some promising work has been done by PARIS21 on data use by the Executive Branch of Government and on data use by citizens but there is an important research agenda to be pursued. This research can lead to fruitful policy advice. For example, there is evidence that using volunteer data collected by citizens can encourage the public to participate more in environmental protection and enhance government ability to monitor and manage natural resources (Conrad and Hilchey, 2011). Statistics have no value unless they are used and it is only through an understanding of how they are used, the extent of that use and drivers for better use that statistical systems can be designed in a user-centered way. In this regard, we acknowledge that conceptually, while the new SPI is intended to provide the world with a new forward looking framework of how NSSs need to further evolve, the SPI scores are empirically based on the data currently available. As such, it must be further refined, based on collective investment in developing more relevant measurements and data sources.

Second, the United Nations Statistics Division global database for SDG indicators is not well populated, particularly for many high income countries.<sup>17</sup> A recent study suggests that data are available for just over half of all indicators and for just 19 percent of what is needed to comprehensively track progress across countries and over time (Dang and Serajuddin, 2020). In some cases it is likely that the data exists but has not found its way on to the database. This is a major problem for users and for those seeking to identify best country practice as a guide for their own statistical development. This issue is related to serious gaps in the data available on data sources. It would be important for the United Nations custodian bodies to work with countries to

> international organizations. While missing data pose no serious challenge, maintaining a database with more indicators and countries requires careful work. But this task can be
of registered transfers and mortgages. We describe the DID strategy used to evaluate the impact of titling and, as policy changes that affect everybody equally will not be identified by such an approach, complement it with spatial aggregation of administrative as the basis for before-after regressions using block fixed effects to provide _inter alia_ estimates of reform impacts on the likelihood of sporadically or systematically registered plots being subject to a registered sale or mortgage transaction.

# **3.1 Evidence from administrative data**

To obtain a complete set of registry and cadaster data covering the period of 1981 to 2019 inclusive for all of Maseru as a basis for assessing program impacts, paper records for all registered transactions (including transfers, and mortgages) for the period before LAA migrated to a functional digital platform were digitized. Although such data lack information on owners’ socio-economic characteristics or the presence, size or characteristics of structures, they include information on gender of registered owner(s) as well as start and end date as well as type of each transaction, allowing us to compute actual time required for completing a registration as an intermediate outcome.<sup>8</sup> Table 1 presents descriptive statistics using these data for the entire 39-year period (1981-2019) for which data are available in column 1 and three sub-periods, namely (i) the 1981-2009 pre-reform period (column 2); (ii) the 2010-2013 implementation period following passage of the Land Act and establishment of LAA (column 3); and (iii) the 2014-19 post-program period of routine LAA operation (column 4). Wherever possible, figures are disaggregated by subject of registration to assess the reform’s gender focus.

The rate of annual titles issued increased from 560 before reform to 12,450 during implementation before leveling off at 4,020 in the post-implementation period. While all registrations were sporadic before 2010, 87% and 39% followed a systematic approach during and after implementation, respectively, bringing the total number of systematically registered plots to 52,700 or
# **4 Results**

## **4.1 Evaluation of the performance of the models**

Figure 4 shows the prediction errors across the six combinations of target variables from Table 1 by each of the five machine learning algorithms we use (regular lasso, post-lasso, CARF random forest, conditional inference random forest, and gradient boosting). Note that regardless of which target variable combination we used (i.e. which panel of Figure 4), all the predictions are turned into poverty rates and the errors are evaluated over poverty rates. Hence, they are comparable to each other. As one example, the very first bar shows that when predicting poverty rates directly using a CART random forest (abbreviated as _carf_ ), then we on average get a predicted poverty rate 4.99 percentage points off the truth.

Note that each bar in panels 5 and 6 of Figure 4 have several possible realizations depending on which growth incidence curve or distributional assumption is used to convert means and Ginis into poverty rates. In Figure B.1 in the Appendix we show the best way of converting predictions of growth/levels in the mean and Gini into poverty rates. The best way of converting predicted levels of the Gini and mean tends to be by using a log-normal distribution, while the best way of converting predictions of growth in the mean and Gini tends to be by applying a linear growth incidence curve. Figure 4 shows the best performing options from Figure B.1 in panels 5 and 6.

Figure 4 reveals that generating poverty rates by predicting from the last observed survey (right column) gives lower errors than predicting poverty rates or the mean (and Gini) without accounting for changes from the last survey (left column). Nearly all methods trying to predict poverty rates directly give a higher error than the worst method trying to predict from the last survey.

The poor performance of models trying to predict poverty rates directly is probably not due the fact that they have poverty rates on the
# **3. Empirical Strategy**

where _Firm performance_ of firm _i_ in sector _j_ and country _k_ is captured by one of four variables: percentage change in sales; percentage change in employment; decreased employment; and anticipate falling into arrears. All firm performance variables are measured in Round 3 of the ES COVID-19 Followup Surveys.

_Government Support_ is a vector of one or more indicator variables equal to one if the firm received any type of government support and equal to zero otherwise.<sup>10</sup> We first use an indicator for having received government support in Round 1 or 2 of the ES COVID-19 Follow-up Surveys. Here, the coefficient represents the difference in Round 3 performance between firms that received support in Round 1 or 2 11 and firms that did not receive support in either of these rounds. ββ Next, we aim to unpack the role of government support in Round 1 versus Round 2. In this regression, we include three mutually exclusive indictors for government support: (i) receiving support only in Round 1, (ii) receiving repeated support in Rounds 1 and 2, and (iii) receiving support only in Round 2. This regression thus compares Round 3 performance of firms in these three categories to firms that did not receive any support in Rounds 1 and 2.

In identifying the causal effect of government support on firm performance, we face two main issues. The first is reverse causality. That is, government support may be determined by firm performance, for example if worse performing firms are more likely to receive support. We mitigate reverse causality by looking at firm performance one round after receiving government support, so that support is not directly influenced by firm performance. That is, we use Round 3 firm performance and government support in Round 1 and 2.

The second threat to identification is omitted variable bias. That is, other factors may determine both government support and firm performance. We mitigate omitted variable bias by controlling for firm background characteristics, as well as