rafmacalaba/datause-encoder-data huggingface dataset:
# **Table E1:** **<u>Correlation between Macroeconomic Indicators and School Closure Policies due to COVID-19 in 2020</u>**

||||||||School cl|osure due to|COVID-19|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|Labor force participation rate,<br>female (% of female population|-0.004||||||||-0.002|||||||
|ages 15+)|[0.00]||||||||[0.00]|||||||
|Freedom House Index||-0.008||||||||0.065*||||||
|||[0.03]||||||||[0.04]||||||
|Individuals using the Internet (% of<br>|||-0.005**||||||||0.000|||||
|population)|||[0.00]||||||||[0.00]|||||
|Men's Individual Beliefs (Share 0-<br>||||-0.901||||||||-0.310||||
|1)||||[0.67]||||||||[0.66]||||
|Women's Individual Beliefs (Share<br>|||||0.683||||||||0.839|||
|0-1)|||||[0.79]||||||||[0.77]|||
|Men's Perceived Community Norm||||||-0.589||||||||-0.094||
|(0(0.1)1)||||||[0.38]||||||||[0.40]||
|Women's Perceived Community|||||||-0.538*||||||||-0.117|
|Norm(0(0.1)1)|||||||[0.31]||||||||[0.32]|
|GDP per capital (Ln USD)||||||||-0.102***|-0.094**|-0.149***|-0.115**|-0.097***|-0.105***|-0.099**|-0.097**|
|||||||||[0.04]|[0.04]|[0.04]|[0.05]|[0.04]|[0.03]|[0.04]|[0.04]|
|Constant|0.793***|0.592***|1.042***|1.399**|-0.091|0.968***|0.924***|1.623***|1.657***|1.877***|1.816***|1.858***|0.844|1.650***|1.648***|
||[0.15]|[0.13]|[0.21]|[0.63]|[0.76]|[0.27]|[0.22]|[0.38]|[0.38]|[0.40]|[0.37]|[0.67]|[0.85]|[0.40]|[0.38]|
|Observations|289|289|259|289|289|289|289|289|289|289|259|289|289|289|289|
|Adjusted R-squared|0.3|0.29|0.34|0.3|0.29|0.3|0.3|0.32|0.32|0.33|0.36|0.32|0.32|0.32|0.32|
|Region FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Sample|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|Wave|
||1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|1-6|

Note [1] OLS pooled regressions at the country level includes Region of the world Fixed Effects and Time Fixed Effects.

65
with mechanizing the sewing process.<sup>8</sup> At least for now, the ready-made garment industry appears to offer some opportunity for some nations in Africa.

# _The Business Environment_

Much has been made of the poor business environment in Africa and business environment does matter, of course. But as nations across Asia have shown, where there are profits to be made, businesses find a way to work around business environment problems. Similarly, despite the business environment in Africa, formal manufacturing firms have performed well in terms of productivity growth (Diao et al. 2021).

Indeed, measuring the business environment by the World Bank Doing Business index, many countries of Africa compare favorably to countries of Asia that have experienced rapid growth. In 2013, for example, Ghana ranked 27 countries ahead of Vietnam in the Doing Business indicators. According to these indicators, it was considerably easier to get credit in in Ghana than in Vietnam, paying taxes was less of a hassle, insolvency was much more quickly resolved, and access to electricity was less problematic. In terms of how well investors are protected, there was a 40-point difference between the two countries in favor of Ghana (McMillan et al. 2017). A comparison between the rankings of countries in Africa and those of countries in Asia with established bases in manufacturing for the year 2019 offers several similar examples. Rwanda ranks 40 points ahead of Vietnam at 29, Mauritius and Kenya are also ranked ahead of Vietnam at 21 and 61 respectively. 17 African countries rank ahead of Cambodia. Bangladesh has 5 million garment workers (ILO 2020) but out of 48 countries in Africa only eight countries are ranked below Bangladesh and seven of these countries are at war. Nigeria is ranked 30 points ahead of Bangladesh.

An important aspect of doing business not covered by the World Bank’s index is the ease and cost of international travel. For example, Campante and Yanagizawa-Drott (2018) find that air links increase business links and that the movement of people fosters the movement of capital; the advent of just-in- time manufacturing has also raised the importance of short-term air shipping. In a novel study of traders in Lagos, Nigeria, Startz (2021) uses Nigeria’s 2016 ranking in Henley and Partners Visa

> 8 Interview with Willy Shih, Robert and Jane Cizik Professor of Management Practice, Harvard Business School, May 7, 2021.

24
|

!

: :

|

| | |

| | |

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS730|Wage and Employment Policies in<br>Czechoslovakia|Luis A. Riveros|July 1991|V. Charles<br>33651|
|WPS731_|Efficiency Wage Theory, Labor<br>Markets, and Adjustment|Luis A. Riveros<br>Lawrence Bouton|July 1991|V. Charles<br>33651|
|WPS732|Stabilization Programs in Eastern<br>Europe: AComparative Analysis of<br><br>the Polish and Yugoslav Programs<br>of 1990|Fabrizio Coricelli<br>|Raberto deRezende Rocha|July 1991<br>|R. Martin<br>39065|
|WPS733|The Consulting Profession in<br>Developing Countries: A Strategy for <br>Development|Syed S. Kirmani<br> Warren C. Baum|July 1991|INUDR<br>33758|
|WPS734|Curricular Content, Educational<br>Expansion, and Economic Growth|Aaron Benavot|July 1991|C. Cristobal<br>33640|
|WPS735_|Traditional Medicine in Sub-Saharan<br>Africa:<br>Its Importance and Potential<br>Policy Options|Jocelyn DeJong|July 1991|O. Nadora<br>31091|
|WPS736|Wages and Employment in the<br>Transition to<br>a Market Economy|Simon Commander<br>Fabrizio Coricelli<br>Karsten Staehr|August 1991|O. DelCid<br>39050|
|WPS737|External Shocks, Adjustment<br>Policies, and Investment:<br>illustrations<br>fromaForward-lookingCGE Model<br>of the Philippines|Deifin S. Go<br>|August 1991|A. Bhaila<br>37699|
|WPS738|Tax Competition and Tax<br>Coordination: When Countries<br>Differ in Size|Ravi Kanbur<br>Michael Keen|August 1991|J. Sweenev<br>31021|
|WPS739|{Managing Financial Risks in Papua<br>NewGuinea: AnOptimal External<br>= <br>Debt Portfolio|Jonathan R. Coleman<br> YingQian|August 1991|J. Carroll<br>33715|
|WPS740|The Onchocerciasis Control Program<br>inWest Africa: ALong-term<br>Commitment to Success|Bernhard H. Liese<br>John Wilson<br>Bruce Benton<br>Douglas Marr|August 1991|O. Nadora<br>31091|
|WPS741|When Does Rent-Seeking Augment<br>the Benefits of Price and Trade Reform<br>on Rationed Commodities? Estimates<br>forAutomobiles and Color Televisions<br>inPoland|David Tarr|August 1991|D. Ballantyne<br>37947|
Table 3: Measures of Gender Norms and Female Wage Discrimination

||Gender|Norms|Wage Dis|crimination|
|---|---|---|---|---|
||1970-75|2010-18|1970-75|2010-18|
||(1)|(2)|(3)|(4)|
|**_Panel A. Sectors_**|||||
|Agriculture|0.73|0.67|0.42|0.29|
|Manufacturing|0.73|0.66|0.48|0.32|
|Services|0.63|0.38|0.43|0.29|
|**_Panel B. Occupations_**|||||
|Professional|0.73|0.40|0.36|0.31|
|Clerks|0.59|0.28|0.15|0.14|
|Craft, Trade, Service|0.66|0.54|0.52|0.34|
|Agricultural|0.73|0.69|0.42|0.29|
|Machine Op., Elementary|0.73|0.61|0.48|0.28|
|**_Panel C. Countries_**|||||
|India|0.70|0.77|0.34|0.37|
|Indonesia|0.50|0.48|0.41|0.27|
|Brazil|0.87|0.47|0.46|0.33|
|Mexico|0.81|0.57|0.34|0.25|
|Canada|0.64|0.26|0.56|0.30|
|United States|0.50|0.16|0.53|0.32|

_<u>Notes:</u>_ This table reports the gender norm _λoj_ and wage discrimination estimates in the 1970s and 2010s for the countries in our core sample. Panels A-C present average barriers by sector, occupation, and country. Averages are weighted by the total income in each occupation-sector and country-year.

**Model Validation.** As a validation, we examine whether our estimates of gender – – barriers which are inferred as structural residuals relate to measurable changes in social norms and women rights. To do so, we use the World Bank’s “Women, Business, and the Law” (WBL) database, which evaluates 35 aspects of countries’ legal code to construct indicators that measure gender equality in the labor market, at the workplace, and in the legal system over the past five decades and across 190 countries (World Bank, 2019; Hyland et al., 2020).

We merge the data sets at the country-year level and regress our estimates of wage ˆ discrimination _τojct_ or gender norms _λ_<sup>ˆ</sup> _ojct_ on the different indicators from the WBL data for each country-year, controlling for the log of GDP per capita.

occupation against GDP per capita.

26
index (Calderón and Servén 2010; see Timilsina, Hochman, and Song 2020 for a review). We follow this approach and combine the logs of phone lines, mobile phones, internet connections, electricity access, and secure internet connections, all taken from the WDI.<sup>14</sup> Each of those five infrastructure variables is first Pearson standardized to a sample mean of 0 and a sample standard deviation of 1. Thereafter, they are weighted by the number of available original observations in each year, divided by the total number of original observations from all five series in the same year. For example, if 150 original observations for log phone lines were available in 1970, and 50 observations were available for electricity, the former gets a weight of 3/4, and the latter gets a weight of 1/4, while the remaining three infrastructure variables are not part of the composite index in 1970. This weighting reflects two aspects: statistical reliability and economic relevance. Fewer observations of a variable for a given year mean more predicted values for that variable. Statistically, less weight should be attributed to the variable in this case, as predicted values are less reliable than original observations. Having fewer observations also reflects that the indicator has less relevance. For example, secure internet connections or mobile phones were simply not available or not as important in the 1970s; hence, their availability is not recorded in the data. This constitutes an economic reason for attributing less weight to series with fewer original observations.

# 3.2.5 Political violence

We include data on major episodes of _political violence_ from Systemicpeace.org. The variable _actotal_ in the original data set is the total summed magnitude of all the societal and interstate episodes of political violence, with the magnitude of each episode ranging between 1 (lowest) and 10 (highest).<sup>15</sup> The link from conflict, political violence, and fragility to growth is well established (e.g., Benhabib and Rustichini 1996; Rodrik 1999; Amodio and Di Maio 2018). Our measure exhibits considerable variation over time, which is a key advantage over most common measures of institutional quality, which often come out highly insignificant in growth regressions due to a high noise-to-signal ratio for individual countries over time (e.g., Araujo et al. 2016).

> 14 Since those variables have descending order of availability, we use stepwise prediction with a country fixed effects and country-specific time trend model to interpolate the missing data. Details are available upon request. The interpolated data are down-weighted in the index as explained below.

> 15 The Systemicpeace.org data do not include 2019.

-10-
|,<br>_f t_<br>_PK_|price (per unit of) of capital stock_f_|
|---|---|
|,_c t_<br>_PM_|price for import of_c_(in_C_) (LCU) (includes import tariffs and<br>distribution margin)|
|, ,<br>_c d t_<br>_PQD_|composite commodity price for_c_(in_C_) for domestic demander<br>(type)_d_(in_D_) [includes commodity subsidies, all taxes (including<br>VAT and sales tax),and distribution margins]|
|,,<br>_c t_<br>_PQS_|composite commodity price for_c_(includes import tariffs and<br>distribution margins but not sales tax,commoditysubsidies,or VAT)<br>|
|,_c t_<br>_PX_|producer price for commodity_c_|
|,<br>_a t_<br>_PVA_|value‐added price for activity a|
|,_a t_<br>_QA_|level of activity_a_|
|,_c t_<br>_QD_|quantity sold domestically of domestic output_c_|
|,_c t_<br>_QE_|quantity of exports of commodity_c_(in_C_)|
|, ,<br>_f a t_<br>_QF_|quantity demanded of factor_f_by activity_a_|
|, ,<br>_i f t_<br>_QFINS_|endowment of institution_i_(in_INSD)_of factor_f_|
|,_c t_<br>_QG_|quantity of government consumption of commodity_c_|
|_t_<br>_QGSCAL_|government consumption scaling factor|
|, ,<br>_c h t_<br>_QH_|quantity consumed of commodity_c_by household_h_|
|, ,<br>_c a t_<br>_QINT_|quantity of commodity_c_as intermediate input to activity_a_|
|,_c t_<br>_QINV_|quantity of investment demand for commodity_c_(investment by<br>source)|
|_t_<br>_QINVSCAL_|investment scaling factor|
|,_c t_<br>_QM_|quantity of imports of commodity_c_(in_C_)|
|,_c t_<br>_QQ_|quantity of composite demand (and supply) of commodity_c_(in C)|
|,_c t_<br>_QT_|quantity of trade and transport services demand for commodity_c_<br>(in C)|
|,_c t_<br>_QX_|quantity of domestic output of commodity_c_(in C)|
|_t_<br>_RGDPMP_|real GDP at market prices (at constant base‐year prices)|
|_t_<br>_SAVF_|foreign savings (FCU)|
|_t_<br>_SAVG_|government savings|
|,_i t_<br>_SAV_|savings of domestic non‐government institution_i_(in_INSDNG_)|
|, ,<br>_i f t_<br>_SHIF_|share for institution_i_(in_INSD_) in the income of factor_f_|
|������|government spendingon commoditysubsidies|
|,<br>_a t_<br>_TFP_|total factor productivity for activity_a_|
|_t_<br>_TFPSCAL_|scaling of total factor productivity|
|_t_<br>_TRDGDP_|real foreign trade (exports+imports) and GDP ratio|
|, ',<br>_i i t_<br>_TRII_|transfers to institution i (in_INS_) from domestic non‐government<br>institution_i’_ (in_INSDNG_)|

‐33‐
whether to attend the training before learning its treatment group. Therefore, we are confident that the decision to attend training is orthogonal to treatment status and did not introduce selection bias into the study design. As such, we use the sample of the 1,498 firms that attended training in studying the program’s impact, with the caveat that this could slightly limit the external validity of our findings.

The training sessions took place either on the tax authority’s premises or in the implementing partner’s office. At the beginning of training (before revealing treatment status), the baseline survey was self-administered (completed on paper forms by firm representatives) with detailed instructions and examples provided by implementing partner trainers.

Groups A and B received identical training which included both a general tax presentation and an e-filing presentation and demonstration, with a question-and-answer session at the end. The training for Group C (control group) differed in that it did not include the e-filing presentation and demonstration. On average, training lasted for two hours in Groups A and B and for one hour in Group C. A few days after each training, the implementing partner called back all firms in Group A and assisted interested firms in registering for e-filing. This logistical assistance involved visiting each firm, collecting all necessary documentation, submitting it to the tax authority on the firm’s behalf, obtaining the e-token, and installing the software.

# **4 Conceptual Framework**

In this section, we outline a simple framework for thinking about a firm’s decision to adopt e-filing. A firm will switch from paper filing to e-filing if the expected discounted value of the benefits associated with e-filing adoption is greater than the cost of adoption. That is, 12
3 policy distortions to producer and consumer prices. By drawing on a recent comprehensive database covering most of Sub-Saharan African agriculture, we generate annual country level indexes for the past half-century, as well as region-wide aggregates including for individual commodities and a sense of the relative importance of the key policy instruments used. In doing so we make a methodological advance by incorporating a number of key nontradable products in our estimates of the indexes, which turns out to be important in the African agricultural policy context.

Data for construction of the indexes come from the World Bank’s Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008). The database gives consistent measures of price-distorting policies for 75 countries for the period 1955 to 2007. The data for the 21 African countries in that database is discussed comprehensively in Anderson and Masters (2009). In this paper we focus on 19 of those African countries, leaving aside Egypt and South Africa because they are both large and very different from the others. That sample comprises five countries of eastern Africa (Ethiopia, Kenya, Sudan, Tanzania, and Uganda), four in southern Africa (Madagascar, Mozambique, Zambia, and Zimbabwe), five large economies in Africa’s western coast (Cameroon, Côte d’Ivoire, Ghana, Nigeria, and Senegal), and five smaller economies of West and Central Africa for which cotton is a crucial export (Benin, Burkina Faso, Chad, Mali, and Togo). We concentrate on the period 1961 to 2004, since those are the years for which the African data are most complete.

This paper is structured as follows: the next section presents the methodology we use. This is followed by a discussion of the data in the World Bank’s Agricultural Distortions database. We then report our estimates of the series of indexes before presenting our conclusions and listing some caveats and areas for further research.

# **Methodology**

There is a growing literature that identifies ways to measure the trade- and welfarereducing effects of international trade-related policies in scalar index numbers. This literature serves a key purpose: it overcomes aggregation problems (across different intervention measures and across industries) by using a theoretically sound aggregation procedure to answer precise questions regarding the trade or welfare reductions imposed by each country’s trade policies.
# **4 Household-level analysis: Pricing and pro-poor targeting**

In this section we estimate the demand curve for land titles, exploiting the random variation in prices induced by the voucher experiment. We begin by presenting the econometric estimates of the price- and wealth-elasticity of demand, and then use these econometric estimates to address a range of policy questions.

## **4.1 Price and income elasticities of demand**

To test the relationship between randomized voucher values and the subsequent purchase of CROs, we estimate a linear probability model of the form: In this equation, the dependent variable _qi_ is a binary indicator of whether household _i_ purchased and fully paid for a CRO. The key parameter of interest is the coefficient on _pi_ , the randomly assigned price of a title expressed in thousands of Tanzanian shillings, net of all voucher discounts. For all demand estimates, we restrict the effect of voucher values to be linear, which appears to be a reasonable approximation of the underlying data.<sup>11</sup> The vector **x** _i_ indicates household and parcel-level characteristics from the baseline survey, which will be included in some specifications.

Equation (2) implicitly assumes that general vouchers and conditional vouchers (which require a female co-signatory on the title) can be treated interchangeably in calculating net price offers. The experiment is designed to test this assumption, which is the focus of Section 5. For now we pool all the voucher values to maximize the precision of our estimates of the general price elasticity of demand.

Turning to the results in Table 4, it is reassuring to see the demand curve is significantly downward sloping. Column (1) shows the results from estimating equation (2) without baseline controls. An increase in price of 10,000 shillings reduces the probability of buying a title by 3%, significant at the 1% level. This coefficient is essentially unchanged by the inclusion of socioeconomic controls. Predicted take-up rates are shown for each price level in Figure 1. With no voucher discounts and a maximum price of 100,000 shillings, predicted take-up at mean values of the socio-economic controls is just under 20%. This rises to over 30% at a price of 60,000 shillings and nearly 45% at a price of 20,000 shillings.

Land titles appear to be a normal good, but price sensitivity does not vary much by income. Column (1) of Table 4 shows that an increase of assets or income by one standard deviation increases take-up by 2% and less than 1% respectively, though only the asset coefficient is statistically significant. The interaction between price and either income or

> 11This can be seen in Figure 3. More formally, table 7 in the appendix displays the results from a series of tests which fail to reject linearity.

14
traditional stoves. In both scenarios, we assume that households with improved stoves consume 25 percent less biomass than with traditional stoves.

The trends in energy poverty status resulting from the simulated changes clearly show that energy poverty declines rather significantly as people adopt more modern forms of energy (Figure 6). However, the decrease in energy poverty is higher among poor households compared to wealthier ones since electrification among rich households is already high and they have less energy poverty. Overall, energy poverty decreases from the current situation to 40 percent in scenario 1 and to 35 percent in scenario 2. Given these findings, policies that promote electrification of rural households have the potential of lowering energy poverty, as do policies that promote replacing traditional biomass stoves with more efficient stoves. Further reduction in India’s rural energy poverty is possible if rural households, like their urban counterparts, eventually switch to LPG as their cooking energy. Also, these predictions are based on constant changes in energy use, and we know that the results might actually be higher because of the multiplier impacts that energy has for development.

<!-- Start of picture text --> .7<br>.6<br>.5<br>.4<br>.3<br>.2<br>1 2 3 4 5 6 7 8 9 10<br>Income decile<br>Current situation 75% electrification & 50% improved stove<br>100% electrification & 100% improved stove<br>Energy poverty headcount<br><!-- End of picture text --> Source: IHDS, 2005.

**Figure 6: Alternate energy poverty scenarios in rural India, by income decile**

25
# **1 Introduction**

The 2008-2009 financial crisis without doubt is the most severe crisis since the Great Depression. Initially thought to be limited within the U.S.’s subprime housing market, the crisis quickly spread across sectors and countries after the collapse of Lehman Brothers in September 2008. Many developing countries also saw large declines in investment and output, in many countries much larger declines than the U.S.’s (see Figure 1). These developments call for explanations about how a crisis can spread from advanced countries to developing ones.

<!-- Start of picture text --> Changes in GDP (Quarter1/09−Quarter3/08), %<br>−50 −40 −30 −20 −10 0<br>Kyrgyz Republic<br>Romania<br>Bulgaria<br>Botswana<br>Latvia<br>Lithuania<br>Russia<br>Turkey<br>Belarus<br>Slovak Republic<br>Estonia<br>Hungary<br>Georgia<br>Croatia<br>Slovenia<br>Greece<br>Malta<br>Malaysia<br>Czech Republic<br>Singapore<br>South Korea<br>Brazil<br>Argentina<br>Iceland<br>Cyprus<br>Mauritania<br>India<br>Peru<br>Jordan<br>Costa Rica<br>Poland<br>UNITED STATES<br>Jamaica<br>Indonesia<br>Thailand<br>Israel<br>Colombia<br>Chile<br>Morocco<br><!-- End of picture text --> Figure 1: GDP growth rates- Source: International Financial Statistics The question of interest is why a negative shock in an advanced economy can have large (sometimes larger) and persistent impacts in terms of investment and output on a developing country. In this paper I study the following mechanism: a negative productivity shock in a large, advanced economy drives down international asset prices. If a small, developing country has to collateralize its assets to borrow funds to invest, falling asset prices worsen its ability to borrow and hence to reinvest. Investment in the developing country declines, and capital repatriates to the advanced country. As less capital now can be pledged as collateral, 2