rafmacalaba/data-use-mentions-extended huggingface dataset:
gender. The “pooled sample” includes Lesotho and Swaziland only because in the empirical analysis we will first show results for Lesotho and Swaziland and subsequently compare them with Zimbabwe. In addition to HIV infection, we use behaviors and attitudes related to HIV prevention as dependent variables, such as abstinence, extra-marital sexual relationships and condom use. These sexual behaviors are at the heart of most prevention efforts, including the so-called "ABC" strategy ("Abstain, Be Faithful and use a Condom"), the most widespread information campaign on HIV prevention in many African countries. Abstinence, defined as not having any sexual intercourse in the last year, is generally higher for men than for women and is much lower in Lesotho than in the other countries. More precisely, the survey reports that about 34 percent of men and about 30 percent of women had no sexual relationships in the last year. This may be driven by the fact that men tend to marry later. Indeed, while the survey does not include age at first marriage, we observe that the average age for currently married men in the sample is 37 years, compared to 31 years for women.

Extra-marital intercourses, measured with an indicator equal to one if currently married respondents have at least one sexual relationship outside the marriage in the last 12 months, are frequently reported in all three countries and are more than 10 percentage points greater for men than for women. The highest percentage of extra-marital relationships is reported in Lesotho: figures from the DHS suggest that 29.8 percent of currently married men and 12.7 percent of currently married women had non-marital sexual relationships in the last year.

9
.112|0.138|
||(0.023)|(0.029)|(0.036)|(0.082)|(0.745)|(0.749)|
|Category FEs|NO|YES|YES|YES|YES|YES|
|District FEs|NO|NO|YES|YES|YES|YES|
|Industry FEs|NO|NO|NO|YES|YES|YES|
|Firm FEs|NO|NO|NO|NO|YES|YES|
|Year FEs|NO|NO|NO|NO|NO|YES|
|Observations|21684|21684|21670|21670|16610|16574|
|Pseudo _R_<sup>2</sup>|0.195|0.198|0.239|0.271|0.269|0.276|

Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01 Table 3: Self-reporting vs inspection data The fact that the firms are more in compliance when they self-report than when they are inspected can be rationalized through the costs the firms face when they are compliant with the environmental regulations. Nevertheless, it is intriguing since there is no penalty in India for noncompliance when the firms self-report. In the following analysis, the paper differentiates the data used in the estimation by inspections and self-reported data. Given the results in table 3, inspection data are more reliable, but carrying out the same type of analysis for the self-reported data will help to have a better idea of the kind of reforms that are needed in India’s monitoring system. Even if firms over-report compliance in self-reporting, it still is important information about the behavior of firms regarding environmental regulations.

# **
efects|Yes|Yes|Yes|Yes|
|Commune fxed efects|Yes|Yes|Yes|Yes|
|Individual controls|-|-|Yes|Yes|

Note: Panel A shows the event-study estimates of the aggregate effect of COVID-19 on household business outcome variables. The coefficients are estimated following Equation 1. Panel B presents the estimated effect for each month after January 2020. The coefficients are estimated following Equation 2. Data on household business suspension and closure filings are obtained from the Household Business Tax Census. Data on individual work hours and income are obtained from the Vietnamese Labor Force Surveys. Monthly income is detrended from pre-COVID linear trend and residualized from individual observables including age, gender, educational level, and occupation level. Standard errors are clustered at the commune level and are presented in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

18
These predicted consumption figures are then used to calculate poverty and inequality measures for every ward in the country. The poverty mapping data are missing for the islands of Zanzibar and Pemba, and so we restrict our analysis to mainland Tanzania.

Poverty maps are typically used by policy makers to distribute funding across large administrative units, such as districts or provinces, but not smaller ones, such as wards, because the standard errors for the poverty and inequality estimates become unacceptably large as the population size of the units decline. However, in this study, we are only using these estimates at the ward level to estimate the relationship between poverty and the likelihood of applying for and receiving TASAF II funds. In this sense, the larger standard errors at the ward level should only cause attenuation bias, which would drive the effects presented in Sections 5 and 6 to zero and therefore decrease our ability to draw inferences with confidence.

# _3.3.Electoral Data_

The final data used in the national analysis are from the 2005 presidential, parliamentary, and ward councilor elections. All data are available online at the website of the National Electoral Commission of Tanzania.<sup>7</sup> The presidential and parliamentary results are at the constituency level<sup>8</sup> , the councilor elections are at the ward level, and the electoral data is merged with the TASAF II institutional data and the poverty maps by ward. The elections took place prior to the announcement of the awards of TASAF II projects, and hence we take political outcomes as predetermined, and seek to understand how voter registration and turnout, as well as voting patterns, relate to application, approval, and funding patterns.

We calculate _voter turnout_ and _voter registration;_ the former given by the ratio of valid votes to registered voters in a ward, and the latter by the ratio of registered voters to population in a ward. Both of these variables are intended to measure the political activism of a ward, a feature which may make a
|
|||||||2002|68|
|||||||2003|68|
|||||||2004|157|
|||||||2005|171|
|Celtel Kenya|Greenfield project|Operational|Telecom|Telecom|15|1999|40|
|||||||2000|40|
|||||||2001|40|
|||||||2002|40|
|||||||2003|40|
|||||||2004|300|
|||||||2005|250|
|Econet Kenya Limited|Greenfield project|Construction|Telecom|Telecom|15|2004|75|
|**Transport sector**||||||||
|Mombasa Container Terminal|Management and lease contract|Canceled|Transport|Seaports|2|1996|-|
|Jomo Kenyatta Airport Cargo Terminal|Greenfield project|Operational|Transport|Airports|N/A|1998|21.4|
|Mombasa Grain Terminal|Greenfield project|Operational|Transport|Seaports|8|1998|32.0|
|Kenya-Uganda Railways|Concession|Operational|Transport|Railroads|25|2006|322.0|

_BOT=Build Owned and Transfer_ _Source:_ World Bank (2006c)

# **Official development assistance (ODA) in infrastructure (template 4)**

Indicative levels and trends of ODA in infrastructure according to various private participation modalities and arrangements can be obtained from the online database of the OECD‘s Credit Reporting System (http://www.oecd.org/statistics/). One template should be completed for each country.

Template 4 sets up an indicative listing of levels and trends of ODA in infrastructure as a percentage of GDP. The OECD Credit Reporting Database is a comprehensive information source for the completion of this template.

> 4 With the exception
negative impact on the labor market as it still employs a third of the domestic labor force, and provides at least partial subsistence to about half the population (Prohnitchi and Oprunenco, 2010).

Drawing on the MAMS database, the economic structure of the Moldovan economy in 2008 is summarized in Table 1. Primary exports represent only a moderate fraction of the total primary production while a quarter of the domestic demand for primary products is satisfied by imports. Therefore, the bulk of agricultural products are produced for the home market. This has important implications for the impact of changes in the agro-industrial sphere on the broader economy.

Moldova lacks own energy sources and imports all of its supplies of petroleum, coal, and natural gas, largely from Russia. It has been estimated that, by 2020, Moldova will only be able to meet around 10-12 percent of its energy needs from own (renewable) energy sources (Prohnitchi and Oprunenco, 2010). The availability and price of energy is thus a major factor causing uncertainty about the future economic development. In Table 1, fuel is part of an aggregate that also includes chemicals and fueland chemical-based products); for this broader sector, 80 percent of domestic demand is satisfied by imports.

The value added and employment shares indicate that private services have the highest level of value added per employee, while the opposite is true for agriculture. This relatively low level of labor productivity of is in part due to the fact that Moldova, despite its fertile soil and favorable climate, has not been able to reach yields that are on par with levels reached in EU or Central and Eastern European (CEE) countries (Prohnitchi and Oprunenco, 2010). Given this, there should be considerable scope for raising agricultural productivity.

**Table 1. Structure of production in 2008**

||Share of<br>production|Share of<br>value added|Employment<br>share|Share of<br>exports|Exports of<br>output|Share of
# **A Variable**

|Variable|Defnition|Source|
|---|---|---|
|**Investor/Fund inform**|**ation**||
|Active Share_i,t_|As defned by Cremers and Petajisto (2009).|CSE|
|Trading Interval_i,s,t_|Proportion of trades in stock _s_ executed in the closing batch.|CSE|
|Diferent Stocks_i,s,t_|Number of stocks traded during the day in the same direction of _s_.|CSE|
|Fund Turnover Ratio_i,t_|Minimum between yearly purchases and sells normalized by the average<br>of the fund’s assets.|CSE|
|Net Flows_i,t_|Daily fund net fows normalized by assets managed in t-1.|CSE|
|**Stock-Firm Character**|**istics**||
|_lsizes,t_|Firm size computed as the log of total assets at the end of the quarter.|SFC, Datastream|
|_B/Ms,t_|Book-to-market ratio, computed as the total net assets, divided by the<br>total market value of equity at the end of the quarter.|SFC, Datastream|
|_ctc_5_s,t_|Close to close return of last fve trading days.|Bloomberg|
|_otcs,t_|Open to close daily return.|Bloomberg|
|_ctos,t_|Overnight returns between t-1 and t.|Bloomberg|
|_PSs,t_|Price sensitivity, defned as _ln_(_ph/pl_)_/V olume_ where _ph_ and _pl_<br>are the highest and lowest price in each trading day.|Datastream|
|**Market data**<br>_mkt_25_t_|Indicator variable equal to one if the daily volatility of the COLCAP<br>is under the percentile 25 for the whole sample.|Bloomberg|
|_colrett_|Daily market returns.|Bloomberg|

42
# **4 Concluding remarks**

This paper explores the link between jobs, finance, and exposure to informal competition for firms in MENA countries. Using longitudinal data from the WBES survey, we document that job creation is facilitated by access to finance, which - in turn - is reduced when the firm is exposed to competition from informal firms. We provide suggestive evidence that a possible mechanism explaining this result is that firms that are more exposed to competition from informal firms have worsen expectations for future sales growth that in turn make them less likely to apply for a loan, depressing job creation.

Our results have some clear policy implications. The first concerns which policy to implement to favour employment creation in MENA countries. Because job creation is influenced by the functioning of the finance sector, our findings indicate that reducing the _disconnectedness_ which characterizes firms in MENA countries is a possible strategy to foster employment growth. Our results provide a novel demand-side view on the determinants of _disconnectedness_ , documenting that the presence of informal competition negatively influences the demand for finance. This implies that increasing the supply of credit and easing the access to credit should not be considered the only possible strategies to increase the use of finance by firms in MENA countries. Second, our results provide a novel motivation for reducing informality. Reducing informality would benefit the overall economy by inducing formal firms to increase loan applications and thus employment in response to a lower competition threat from informal firms. Third, our results show that policy interventions to support firms should be designed taking into account that the _perception_ of a constraint is as important as the existence of an actual constraint in driving firms’ behavior. As we document in our analysis, a firm’s decision not to apply for a loan is influenced by the perceived competition threat from informal firms, which is not necessarily correlated with an existing actual threat.

# **References**

- Abadie, A. and G. W. Imbens (2011). Bias-corrected matching estimators for average treatment effects. _Journal of
resources were available, conditions leading to the previous default or difficulties identified<br>and properly addressed and recovery prospects remained good.<br>.<br>The Government commissioned a study to assess the magnitude and nature of corporate distress and<br>recommend a project to facilitate the restructuring of potentially viable enterprises. The first phase of<br>the study which reviewed a sample of 214 firms concluded that a majority of them had good prospects<br>**for**viable onerations if they undergo financial, technical and managerial restructuring. The second<br>phase examined the altemative institutional anrangements for the restructuring and recommended the<br>establishment of an investment bank to carry it out. However, two new private merchant banks opened<br>in 1990 in addition to a previously existing Merchant Bank and thus the proposal was rejected.<br>In the end, enterprise restructuring became mired in political problems and assets were basically sold<br>off.<br>Instruments applied (partial debt write off, debt-to-equity swap, change of maturity)<br>Process of asset sale/disposition:<br>.<br>Negotiated sales. NPART advertised the assets subject to foreclosures in the local papers upon which<br>interested buyers approached NPART.<br>Purchaser of assets: Domestic investors mainly and some foreign investors purchased the assets of those<br>subject to foreclosures.|*****<br>Initially, the staff had to<br>determine the accuracy of the<br>loan records.<br>.<br>As of April 1993, the resolution<br>of loans of 203 enterprises with<br>debts of C38.45 billion (65% of<br>total NPART assets) was as<br>follows:<br>i.<br>foreclosures for 94 firms with<br>C14.5 billion (37.7%);<br>ii
growth. Figure 13 unveils that Argentina’s currency was markedly overvalued prior to the year 2000, with RER misalignment exceeding 100 percent for most of the periods. During this period, the country saw meager levels of exports and GDP growth. Similarly, Brazil’s exchange rate has been increasingly overvalued (from somewhat undervalued starting point in the 1960s) since the early 1970s, which was accompanied by sharp growth slowdown and smaller export growth. In Latin America, Mexico represented a somewhat anomalous case in that the country substantially undervalued its currency until the late-1980s. Undervaluation moved in sync with growth for most of the years before the early 1990s, when the comovement turned negative. Peru typically experienced considerable overvaluation for most of the 1980s and 1990s, which was generally associated with poor economic performance.

# **5.     The Macroeconomic Effects of Devaluation in Ethiopia**

# **5.1     The impact of devaluation on exports and growth**

In Ethiopia, empirical analysis using the RER shows that a 10 percent lower exchange rate is associated with an increase in export and real GDP growth rates of 0.52 and 0.23 percentage points, respectively (Nguyen, 2014). These upper-bound estimates suggest that an exchange rate adjustment that at least partially corrects the currency misalignment would significantly increase export and income growth. This does not come as much of a surprise considering that the country’s exports are predominantly low value-added primary commodities which compete more on price than on quality. Moreover, the decline in imports due to real depreciation is less likely to have significant negative repercussion given that the import-content of agricultural exports, which are the mainstay of Ethiopia’s exports, is relatively small compared to the industrial sector. The results in Nguyen (2014) are generally consistent with Moller and Walker (2015), who find that an overvalued real exchange rate held back some economic growth in Ethiopia over the period 2000-13.

13
examines whether the development of rural roads mitigated the welfare shocks from recent severe droughts.

The primary empirical approach is difference-in-differences (DID) matching based on road network and household panel data sets. A panel road network database is constructed for multiple years so that changes over time in rural roads and various welfare and economic outcomes can be compared. For the outcome measures, this study relies on the three waves of a panel household survey—the Ethiopian Socioeconomic Survey (ESS)—which were carried out in 2012, 2014, and 2016. The timing of the data collection was ideal to access the short- and medium-term impacts of rural roads that have been developed under the URRAP since 2010. In addition, the ESS represents a large part of rural Ethiopia, which allows this study to explore locational heterogeneity in the impacts of rural road development. Combining the road database and the ESS data, this study aims to estimate causal impacts by dealing with endogeneity due to nonrandom placement of rural roads. The primary strategy is a DID matching approach (Smith and Todd 2005), as used for the impact analysis of rural road access by Lokshin and Yemtsov (2005) and Mu and van de Walle (2011). Finally, this study analyzes one of the most relevant outcomes from a poverty reduction perspective: household consumption as a welfare measure.

The results of the econometric analysis suggest that Ethiopia’s recent rural road development has substantially increased household welfare, supporting households amid the recent severe droughts. It is estimated that rural roads increased household consumption by 16.1 percent between 2012 and 2016. In other words, thanks to the rural roads, households in connected communities increased consumption by 3.8 percent each year during the period. Moreover, households were less likely to fall into or remain in poverty when connected by all-weather roads in drought areas, indicating the mitigating role of rural roads for drought shocks. The study also finds the effects of rural road developments for agriculture and non-agriculture work in communities farther away from towns.
**Table 5. Proportion of Men and Women Using Computers on the Job, by Country**

||Men|Women|Total|N|
|---|---|---|---|---|
|Armenia|0.41|0.48|0.43|530|
|Bolivia|0.43|0.35|0.39|653|
|Colombia|0.34|0.41|0.35|832|
|Georgia|0.41|0.55|0.44|481|
|Kenya|0.28|0.22|0.24|1160|
|Ukraine|0.41|0.45|0.42|730|
|Vietnam|0.37|0.35|0.38|1394|

_Note_ . The sample includes 25- to 64-year-olds in self-employment or wage work. _Source_ : STEP Surveys (2014).

Looking at the distribution of men and women by the complexity of computer-related tasks that they engage in on the job (see Table 6), we find that in Armenia, Colombia, Georgia, Ukraine, and Vietnam, the largest proportions belong to the group using MS Office applications. The associated tasks are word processing, making presentations, and using basic graphic applications. In Bolivia and Kenya, the largest proportion of men and women are found using advanced programming tasks on the job (22 percent of men and 16 percent of women in Bolivia, and 14 percent of men and 8 percent of women in Kenya).

**Table 6. Proportion of Men and Women by Complexity of Computer Use, Both on the Job and by Country**

||Bro<br>based|wser-<br>tasks|Basi<br>Office|c MS<br>tasks|Ba<br>progra|sic<br>mming|Adva<br>progra|nced<
# **2. Review of the Literature**

Most of the literature on the accuracy of homeowner self-assessed housing values concentrates on the United States and compares self-assessed home values found in surveys with external information from professional appraisers, market transactions, tax assessments, other surveys, and so on. In their seminal paper, Kish and Lansing (1954) compare the homeowner estimates in the 1950 Survey of Consumer Finances and estimates of professional appraisers who were asked to evaluate the same properties. They found a large bias between the homeowner and professional evaluations, but, on average, the bias was negligible. Using a similar approach and similar data, Kain and Quigley (1972) find comparable results. By contrast, Ihlanfeldt and Martinez-Vazquez (1986) find that the owner estimates tend to be higher than the appraised values.

In a rare study on a developing country, Gonzalez-Navarro and Quintana-Domeque (2009) compare data from a household survey and appraisals of the same homes by a real estate agent in the city of Acayucan, Veracruz, central Mexico. They find large variability in the bias between homeowner and professional appraisal valuations. Only 11 percent of the estimates of survey respondents were within 10 percent of the appraiser values; 25 percent of the respondents believed their homes were worth at most 70 percent of the appraised values; and 35 percent of the respondents believed their homes were worth at least 150 percent of the appraised values. The length of tenure appears positively correlated with the bias: owners with long tenure overestimated the values of their homes, while families with tenure of less than two years seem to have reasonably accurate and unbiased estimates of the values of their homes.

Recent contributions exploiting data on housing transactions include Bigelow, Ifft, and Kuethe (2020), Chan, Dastrup, and Ellen (2016), and Corradin, Fillat, and Vergara-Alert (2017). Bigelow, Ifft, and Kuethe (2020) concentrate on farmland owners in New York State. They advocate comparisons across both self-estimates and market transactions because of key
are implemented.

On top of the time cost computed along the network elements, we account for processing times when a shipment is reaching / leaving ports, based on the estimates in Slack, Comtois, Wiegmans and Witte (2018). In their paper, Slack et al (2018) use data from Lloyds Intelligence Unit and base their estimates on a sample of 17,024 vessels in 2013. In our network, we use their regional averages reported in Table 2. In the context of our analysis, these processing times correspond to a “penalty” incurred when reaching a port, either from the land and from the sea, and correspond to the time it takes to load or unload the goods. In the improved scenario, when a project involves building a new port or upgrading an old port, we assume that the associated “processing time” decreases according to the following rule:

# **Improved Port Delay = Max (0.5*Baseline port Delay, Lowest Worldwide Processing Time)**

This rule allows us to account for a proportional decrease in port delay of 50 percent with a limit that no port can be more efficient than the worldwide lowest estimate of 17.2 hours.

_Table 2: Processing time in ports as estimated by Slack, Comtois, Wiegmans and Witte (2018)_

|**Region**|**Mean Ships Time In Port**<br>**(hours)**|
|---|---|
|Central and South America|23.5|
|South and South‐East Asia|26.5|

8