rafmacalaba/data-use-mentions-extended huggingface dataset:
|2,265|2,265|
|R-squared|0.118|0.807|0.037|
|Mean T 2017|12.36|12.71|107.65|
|Mean C 2017|12.28|12.67|102.03|

This table shows the direct implications of the randomized treatment variable in terms of the cash transfers received at the beginning of the program (1998-99). The outcomes employed are: (i) _Total transfers_ , which correspond to the accumulated amount of transfers received by the households (in real Mexican pesos); (ii) _Potential transfers_ , which refers to the amount of transfers that, given the program rules, a household would accumulate if there is perfect compliance; and (iii) _Total Payments_ , which indicates the total amount of installments that a household received since they were enrolled into the program. This information comes from administrative records from the program. Treatment is a dummy variable that identifies if the observation belongs to one of the 320 original Progresa RCT treatment localities. Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

As we use the initial RCT design for the analysis, the estimation of the effects of interest is rather simple and consists of the following specifications: where _Tj_ is a dummy identifying those households living in an early treatment locality _j_ (randomly determined), _Xij_ is a set of controls at the household ( _i_ ) and locality level ( _j_ ).<sup>16</sup> As standard in the literature we presents results on the three specifications above to provide evidence on the robustness of the results. In equation (1) we employ a simple cross-sectional
in and would result in completion and acceptance (by government) of state‐level action plans in eight states; an immediate implementation phase with constant monitoring, adaptation and learning over a yearlong period; and a completion phase characterized by reflection on results and capturing of lessons learned. This was originally expected to be completed by June 2017. Due to funding delays, however, the firm adjusted its efforts, such that the initial planning phase spanned a year‐long period, certain activities had to be repeated due to loss of momentum, and implementation and lessons‐learning phases were collapsed into six months.

23 Results from the 2018 survey round – reportedly completed in June 2018 ‐ are still not available at the time of writing.

24 In 2016 an alternative household survey – the Multiple Indicator Cluster Survey (MICS) – was used to determine progress on the six coverage areas and performance payments were awarded on the basis of that survey’s results. While some federal level government officials and Bank staff argue that there is a strong correlation between the MICS and the SMART surveys, the outcry generated among state officials after revelation of MICS results demonstrated that complementarity of the surveys was not a matter of consensus. Given that the MICS does not in fact measure one of the six key indicators – Vitamin A coverage – the states that 10
- 11 -

accountability. The reason is that technical and nontechnical losses are not distinguishable without sufficient meters. Therefore, the system losses may or may not be considered as their faults. The lack of capital investment in the network, for instance, may not fall on utilities. In Andhra Pradesh, the utility’s efforts to reduce the system losses began with recognition of the existing technical losses and theft. The technical losses, of which the former official estimate was 18 percent, were projected to be 38 percent (Antmann, 2009). This is a politically challenging yet essential step to strengthen the utility’s accountability.

Second, the absence of metering may also weaken enforceability of payments, even if the norm is applied to unmetered customers. The reason is that people can always claim to have used nothing. In addition, given the weak metering practices, utility officials would likely have incentives to practice irregular billing and over-charging. On the other hand, customers may have incentives to pay a bribe and ask for falsification of meter readings. A survey in South Asian countries indicates that about 20 to 30 percent of customers are faced with endemic petty corruption through meter readers (Transparency International; World Bank, 2009a). The same problem has been observed in the water sector. More than 70 percent water utility staff responded that meter falsification is very common to lower bills. And 41 percent of interviewed customers actually made informal payment to falsify meter readings (Davis, 2004; World Bank, 2008).

To deal with these problems, the private sector participation in the distribution network is one of the options that governments can take. Experience shows that it can promote proper metering, reading, billing and collection quickly, thereby improving the sector’s governance and performance. Apparently, nontechnical system losses are an obvious revenue leakage from the systems.<sup>7</sup> Therefore, private operators are willing to make efforts toward proper metering.<sup>8</sup> In Colombia, when
means that establishments with net sales larger than a certain threshold are always selected.<sup>17</sup> Therefore, the forzosa stratum allows to construct a data panel specification, where the starting year is reported but exit is not reported in the data. Exits could reflect establishments’ output falling below the threshold as well as real exit.

The survey includes information related to establishment location, industry classification (following ISIC revision 4), sales, value added, intermediate input costs, number of employees, wages and book values of assets. Our main variables of interest are establishment TFP and markups, which require information on the value added, the capital stock (tangible fixed assets), intermediate inputs, the number of employees, the total wage bill, and establishments’ age. All monetary variables are deflated using constant industry price indices (constant 2007) at the most disaggregated level available.

# **4.2 Subnational regulatory reform data**

We use a unique data set for Peru that captures the elimination of all regulatory barriers across more than 1,800 municipalities as a response to legislative changes in 2013 and 2014, strengthening the power of the national competition authority (INDECOPI) to enforce the elimination of regulatory barriers to market entry and competition that are inconsistent with national legislation. The initial list published by INDECOPI includes 613 and 1,577 different eliminated regulatory barriers in 2013 and 2014, respectively, the majority of which were eliminated by subnational governments. The list includes regulatory changes that apply only to consumers but not to firms. We thus restrict the analysis to eliminated regulatory barriers that impact firms by easing licensing requirements to operate in a specific location or economic activity, removing technical requirements to enter local markets or sectors (such as specifications for buildings or transport vehicles), and reducing charges and fees for regulatory procedures.

This leaves us with 594 (149) regulatory changes eliminating barriers by subnational governments in 2014 (2013), of which 473 (80) were implemented at the district-municipality level
for each randomization strata to increase power (Bruhn and McKenzie, 2009). 11 The footnote of Table 2 provides the full list of control variables. Given there was some attrition in the baseline phone survey, in this specification we impute the sample mean to missing observations and add a dummy variable for missing observations in order not to reduce the sample size due to missing controls.

> 12 The standard-errors are clustered at the level of the randomization strata, i.e. gender and 14 study subject groups.

11
-0.116*|-0.114|-0.117*|-0.115|-0.115*|-0.112|
||(0.0693)|(0.0692)|(0.070)|(0.070)|(0.070)|(0.070)|(0.069)|(0.069)|
|Const|1.9873***|2.2671***|1.988***|2.275***|1.987***|2.282***|1.988***|2.255***|
||(0.1199)|(0.2538)|(0.120)|(0.265)|(0.120)|(0.263)|(0.120)|(0.256)|
|N. Obs|14,656|14,656|14,656|14,656|14,656|14,656|14,656|14,656|
|R2|0.0642|0.0666|0.064|0.067|0.064|0.067|0.065|0.067|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule o|f Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

29
help improve smallholders’ resilience to climate shocks, either via learning about (crop-specific) moisture saving technology or via support (e.g. with tractors) to create moisture conservation structures, we estimate where _Yikt_ is the log of the yield of crop _k_ and **_R_** _it_ is a vector of indicator variables specifying, for each pair kebele _i_ and year _t_ , whether rainfall was above or below the long-term mean by less or more than one standard deviation using the pixel-level data on precipitation discussed above,. The elements of **_β_** _1_ will then indicate if small farms closer to large ones be more able to maintain yields in the presence of rainfall shocks.

# **3. Descriptive evidence on large and small farm performance**

Data on the country’s main cereals highlight striking, though commodity-specific, differences in yields and input use between large and small farms. Limited presence of non-farm employment and rainfall variability. Maps illustrate the expansion of large farm presence over time. As this provides options for interaction and exchange of technology, it makes positive technology spillovers via informal interaction plausible and justifies efforts to try and identify other types of externalities from large farm formation in our data.

# **3.1 Comparing large and small farmers’ productivity and input use**

Table 1 provides data on large farms overall and for our four crops. Mean commercial farm area is 266 ha, with wheat farms largest (552) and sorghum and teff (235 and 238 ha) ones smallest. About 25% were established during the 2007/08 peak, some 22% each in 2002-2006 and 1992-2002, and some 15% and 8%, respectively in 2009/10 and 2011-13. Compared to 47% of farms overall, 68% and 61% of large teff and maize farms were established after 2007 with a corresponding shift in the level at which negotiations were held. With 55 ha per permanent employee, the average farms employs has less than 5 permanent workers; wheat
# **I. Introduction**

At 21.16 percent, the female labor force participation (FLFP) rate in Morocco ranked 180th out of a sample of 189 countries in 2018,<sup>1</sup> meaning that 78.4 percent of Moroccan women between 15 and 65 years old were neither employed nor looking for a job. Currently, the FLFP rate is lower than it was two decades ago, despite lower fertility rates and better access to education, factors usually associated with greater chances of employability.

While the education profile of the female population has improved, it has not translated into a greater share of educated women in the labor force. For instance, 40 percent of unemployed women have a college education (while 8 percent have no education), and among employed women in rural areas (half of the total employed women), 77 percent have no formal education and less than 1 percent have a college education.<sup>2</sup> Although women have better access to education, the participation rate among those educated has decreased over time. Female inactivity for women holding a superior education rose from 13 percent in 2010 to 19 percent in 2018 in rural areas and from 23 to 24 percent in urban areas in the same period. The overall low education profile of employed women signals constraints to participation, a possible mismatch between firms’ needs and their skills, and other cultural and social norms that could possibly limit the employment of women. Also, vulnerability in the form of unpaid or informal work is highly prevalent among employed women.

This paper provides insight into the persistent challenge that Morocco faces in bringing women into the labor market. From a macroeconomic point of view, increased FLFP would put more labor inputs to use, thus improving the country’s productive capacity. From a

> 1 World Development Indicators 2020.

> 2 In urban areas, education levels of the employed are somewhat higher and more evenly distributed.

2
comprised of the location mentioning messages associated with the REACH data, includes messages from 770 unique locations, 54 of which are mentioned more than 10,000 times, 229 of which have returnees, and 129 of which have IDPs. A heat map showing the distribution of mentions of locations overall, locations with returnees and locations with IDPs by source is in Appendix 8.1, as are maps with locations mentioned by returnee status and year of return. The majority of locations mentioned in the `Location Mentions` dataset are in northeastern Syria in Idlib, Aleppo and northern Ar-Raqqa, an outcome of REACH data emphasis, as can be seen in Section 4 Figure 1. For 218 of these locations return first occurred during the time of data collection, the `Return Date` dataset. Of these locations, 119 have both returnees and IDPs. In the overall REACH data it is also true that most locations with IDPs also have returnees (26% of locations have IDPs and no return). Table 1 shows the size of the datasets and the percentage of messages within that dataset that are from areas with returnees and IDPs. For example, in the main `Location Mentions` dataset there are 2,360,559 messages of which 1,264,871 are from Telegram. Of the total messages, 16% (370,345 messages) are from areas with only return, 5% are from areas with only IDPs, 62% are from areas with neither and 17% are from areas with both. Of

> 5To truncate the dataset to Arabic, we first obtained the language of a message using _langid_ (Lui and Baldwin (2012)), an off the shelf tool for detecting language from text. The tool is based on pre-trained machine learning models and can detect over 90 languages. Before beginning data analysis, we performed basic cleaning on the text data using Nielsen’s stemmer (Nielsen (2017)), but prevented the stemming of proper nouns like key locations and political figures. Next
Since 2008, the only “truly” reliable data on sovereign defaults is for sovereign bonds, which are tracked meticulously by rating agencies and the global press. Sovereign defaults on other external creditors (official, banks, suppliers) are likely underestimated, even though they make up the bulk of total external lending to governments. In fact, sovereign bonds accounted for only around 30%-40% of total international sovereign lending by developing countries in the 2010s (Schlegl et al. 2019).

**_Features of China’s debt restructurings_ .** The limited information on restructuring terms available in the case of Chinese lending does not allow for a systematic analysis on the size of debt relief (or “haircuts”), as in Sturzenegger and Zettelmeyer (2007), Cruces and Trebesch (2013), Reinhart and Trebesch (2016), Asonuma et al. (2017) or IMF (2020).<sup>7</sup> But the data show clearly that China’s large state-owned banks very rarely agree to provide deep debt relief to their debtors.

Between 2000 and 2019, the majority of restructuring events associated with debtor debt distress were reschedulings that do not reduce the nominal debt burden but extend the repayment period by increasing the maturity and/or grace periods (45 of the 51 cases prior to the Covid-19 crisis).<sup>8</sup> Interest rates on the loans were unaffected by the restructuring in most cases, with evidence for a reduction of rates in only 10 of 51 cases. Only four cases involved face value debt stock reductions and these are outlier deals that took place in exceptional circumstances.<sup>9</sup> China’s preference for limited cash flow debt treatments has only been reinforced since the outbreak of the Covid-19 crisis. In 2020 and 2021, all 33 Chinese debt restructuring agreements that we can document were reschedulings or payment suspensions with no nominal debt reduction.

Figure 3 compares the Chinese debt restructurings with those involving the Paris
The researchers administered a baseline household survey in 2005 and another survey 15 to 18 months after the branch was introduced to approximately 65 households in each neighborhood (6,850 households total) to measure the program‟s impact on outcomes related to consumption, new business creation, business income, education, health and women‟s empowerment. It is worth noting that other microfinance institutions operated in the area and some of them opened up in both treatment and control areas. The evaluation strategy is an intentto-treat analysis that compares averages in treatment and comparison areas, averaging over both borrowers and non-borrowers. The study finds no effect of the program on average monthly expenditure per capita but that expenditure on durable goods increased in treatment areas and the number of new businesses increased by one-third in treatment areas. They find that treatment effects were heterogeneous, with households with a high propensity for starting a business cutting back on non-durable consumption in an apparent effort to save for the fixed costs of starting a business. The study finds that there were no discernible effects on education, health or women‟s decision-making.

# **4.1.5  Expanding Credit in Thailand: The Thai Million Baht Village Fund Program**

Kaboski and Townsend (2007) study the effectiveness of microcredit programs in the context of rural Thai villages. In particular, they develop and structurally estimate a dynamic model of credit-constrained households deciding on consumption, indivisible investment, and savings and study how these households were affected by the introduction of a large-scale government microfinance program, the Thai Million Baht Village Fund Program. This program, begun in 2001, transferred one million baht (about $25,000) to each of almost 80,000 villages in Thailand to start village banks that would lend to local households. The total amount of funding to each village was the same regardless of the size of the village, so village size provides a plausibly exogenous source of variation in the amount of credit increase per household. The funds were not directly targeted at women, but women
**Figure 5: Informal employment rates estimated using ESS**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>ESS informal Rosstat official<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <u>Source: Authors’ calculation using ESS.</u> <u>Note: In the ESS, informal employment is defined as working without a contract in the main job. Rosstat’s official measure of</u> informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are weighted. 95% confidence intervals are shown using dotted lines.

**Figure 6: Informal employment rates in 2016 across different surveys**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Official Rosstat ESS LFS (broad) RLMS (primary) LiTS<br><!-- End of picture text --> <u>Source: Rosstat and authors’ calculation using various surveys.</u> <u>Note: Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in</u> their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. In the LFS, informal employment includes workers that are employed but not at an enterprise in their primary job
# <u>Table 1 Description of analyzed datasets</u>

|||||Teachers<br>|Percent of total|
|---|---|---|---|---|---|
|Country|Source|Year<br>|Number<br>surveyed<br>|Percent of total<br>surveyed wage workers<br>|surveyed work force<br>(ages 15-64)<br>|
|||(1)|(2)|(3)|(4)|
|Burkina Faso|Continuous Multisectoral Survey|2014|343|13%|1.3%|
|Côte d'Ivoire|National Employment Survey|2013|201|6%|1.1%|
|Democratic Republic of<br>Congo|National Household Survey|2012|1,104|18%|3.1%|
|The Gambia|Integrated Household Survey|2010|243|10%|1.9%|
|Ghana|Ghana Living Standards Survey 6|2012-2013|772|16%|2.4%|
|Liberia|Household Income and Expenditure<br>Survey|2014-2015|171|14%|2.2%|
|Malawi|Integrated Household Panel Survey|2010|393|12%|1.9%|
|Namibia|Labor Force Survey|2013|359|8%|3.7%|
|Niger|National Survey on Household<br>Living Conditions and Agriculture|2014|119|12%|1.6%|
|Nigeria|General Household Survey|2015|240|20%|2.8%|
|Senegal|Poverty Monitoring Survey|2010|331|7%|0.8%|
|Sierra Leone|Labor Force Survey|2014|220|27%|3.1%|
|Tanzania|Labor Force Survey|2014
|Sub-Saharan Africa|5.2|1.0|6.9|1.5|

Source: The Doing Business Database

**Table 7**

**Outstanding Guarantees as a % of GDP**

|**Country**|**2007**|**2008**|**2009**|
|---|---|---|---|
|Egypt|0.08%|0.08%|0.09%|
|Jordan|0.07%|0.07%|0.07%|
|Lebanon|0.85%|0.84%|0.87%|
|Morocco|0.33%|0.35%|0.41%|
|Palestine|0.05%|0.16%|0.27%|
|Saudi Arabia|0.01%|0.01%|0.02%|
|Tunisia|0.34%|0.39%|0.50%|

Source: Saadani, Arvai, and Rocha (2010)
fit the De Soto view, where firms have some potential to grow, yet due to burdensome regulations, they are not able to join the formal sector. Firms above the 75<sup>_th_</sup> percentile threshold could be classified as "parasite" firms as, conditional on their characteristics, they have a level of productivity comparable to or even higher than the top quartile of formal firms but they still remain in the informal sector.<sup>8</sup> Under this framework, we can classify all firms in our sample into four categories: (1) Informal-necessity, (2) Informal-De Soto view, (3) Informal-parasite, and (4) Fully formal. Figure 1, shows the kernel density of this conditional productivity for the four types of firms in our sample, using PCA(5) as the informality index. As expected, the informal firm’s productivity distribution has a heavy left tail compared to formal firms that have a thicker right tail. Yet, there is a huge overlap in the productivity distribution across these firms, suggesting that the reasons for staying informal at the lower end of the distribution might be very different than at the higher end.<sup>9</sup> This also points to the possibility that the shock may not affect all informal firms uniformly.

Figure 1: Distribution of conditional productivity across types of informality <!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>-5 0 5 10<br>IHS transformation labor productivity<br>conditional on size, sector, age, female ownership, and country<br>Fully formal Necessity<br>De Soto Parasite<br>Informal (all types)<br>Kernel density<br><!-- End of picture text --> Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS
sup> Loan-level transactions reported in DRS are confidential. However, the aggregation of this loan-level data to the country-year level provides the basis for country-level debt data published by the World Bank in its annual Global Development Finance publication.<sup>8</sup>

> 6 See the World Bank's Operational Manual, BP14.10, available at: http://web.worldbank.org/WBSITE/EXTERNAL/PROJECTS/EXTPOLICIES/EXTOPMANUAL/0,,contentMDK:20064540~ menuPK:4564187~pagePK:64709096~piPK:64709108~theSitePK:502184,00.html

> 7  Details on reporting requirements can be found in the World Bank Debtor Reporting System Manual, available at <u>http://siteresources.worldbank.org/DATASTATISTICS/Resources/drs_manual.doc.  These loan-level transactions</u> are typically provided as paper records or in spreadsheet format, and staff in the Development Data Group of the World Bank enter it manually into DRS.

> 8 To my knowledge, the loan-level data in DRS has been used only in a handful of previous scholarly papers, all of which are focused in one way or another on alternative characterizations of the financial value of concessional loans (Chang, Fernandez-Arias, and Serven (2002), Dikhanov (2004), and Dias, Richmond, and Wright (2011)).

10