rafmacalaba/data-use-mentions-extended huggingface dataset:
_Ain_ indicates the number of the _n_ th appliance owned by the household. Based on prior empirical research that has studied households’ appliance use decisions (Parti and Parti, 1980; Reiss and White, 2005), 15 distinct appliances are modeled in the demand equation, including refrigerators, freezers, televisions, computers, air conditioners, electric space heating, and swimming pools and so on. See Table 6 for a complete list of variables entering the model.

Appliance ownership ( _Ain_ ) is also assumed to be exogenous. This is somewhat contentious because it is possible that factors affecting appliance utilization behavior also affect decisions to acquire appliances. In the short run, however, the appliance stock is fixed, and existing evidence suggests that the bias from ignoring the possible endogeneity is likely to be small. See Reiss and White (2005) and Sebold and Parris (1989) for a discussion.

Previous studies indicate that weather conditions are important predicators of electricity consumption (Reiss and White, 2003). However, HBS 2008 and 2009 do not reveal household geographic locations, making it impossible to match weather data with sample households. As an alternative way to capture the weather effects, I add two dummy variables _SUMi_ and _WINi_ which take value 1 if the household is interviewed during the summer months (June – August) and winter month (December – February), respectively. For completeness, I also include month and year fixed effects that, as it turns out, deplete the explanatory power of price, while changing the coefficients of demand parameters only slightly. See Section V and Table 3 for details. This alternative approach, although capturing the seasonality in electricity demand, does not address regional variation in weather conditions. In Section V, I also take a formal approach to validating the model using out-of-sample testing.

Finally, is the error term; , and are unknown parameters to be estimated.

To test for the robustness of the above demand estimation, I also allow household income to enter the interaction term as a continuous variable
quantified their pretraining skills at a lower level than the level at which they had originally assessed their skills prior to receiving the skills training. The authors argue that the students reconstructed their past, guided by their subjective theories over what the skills training ought to have done. If African farmers hold implicit theories about the link between, say, labor inputs and production, then the report on the one may influence the report on the other. For example, in an end-of-season recall survey, labor may be retrospectively overstated during good harvests and understated during bad harvests.

# **3. EXPERIMENTAL DESIGN AND CONTEXT**

The goal of this study is to examine biases of the sort described above in agricultural labor data collected through household surveys. We focus on potential biases introduced by the length of the recall period and the frequency of reporting. We have conducted a large randomized survey experiment among smallholder farming households in rural Tanzania through which we have compared agricultural labor information collected in weekly surveys (our benchmark for the true labor estimates) with that collected in a single end-of-season survey. Understanding farm productivity at the lowest level entails studying inputs and yields on plots. From a broader perspective, questions of productivity in smallholder farming may require analysis of aggregated measures. We focus on both by examining plot-person labor reporting as well as aggregate household measures of family labor.

## _3.1. Experimental design_

We have conducted a survey experiment among 854 farming households in 18 enumeration areas in the Mara Region of rural northern Tanzania. Labor input was measured for the 2014 _masika_ (the main, long-rains season), running roughly from January to June 2014. Households were randomly assigned to one of four survey designs within each of the 18 enumeration areas. The differences in the four survey arms are the manner and frequency with which they are contacted.<sup>7</sup> Two survey designs entailed weekly interviews throughout the entire masika season either in person or by phone.<sup>8</sup> Face-to-face baseline survey
SADC separately. For the East African Customs Union, we took Kenya, Uganda, Rwanda and Burundi. For SADC we took Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe. Trade shares for the “Africa” region in our model are the sum of East Africa Customs Union plus SADC. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in appendix A. We used Tanzania as the reporter country for both exports and imports. Results for both exports and imports are reported in tables A2 and A3 of appendix A.

# **Tariff Data—Collected Rates at the Tariff Line Level**

We were fortunate to receive unusually detailed collected tariff data from the Tanzania Revenue Authority. That is, we received data on collected import duties (tariffs) and import values at the eight digit tariff line level. The collected tariff rates for the sectors in our model are obtained by first aggregating the eight digit tariff line level tariff collections and import values to the sectors of our model. The ratio of tariff collections to import values for each sector of our model is then calculated to give estimates of the collected tariff rates, which in turn are incorporated into our SAM. The tariff rates are shown in Table 4. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1.3% of GDP, which is consistent with collected revenues in Tanzania.19 The SAM has some detail on taxes, which include direct taxes on households and enterprises, import tariffs, producer taxes, indirect (sales) taxes and factor taxes. The data for import tariffs are replaced with collected tariff rate data for the year 2006.

Given that Tanzania participates in preferential trade areas with the East Africa Customs Union and the South African Development Community, it was necessary to make further adjustments. That
Table 6 reports then the results of the causality tests. We see from this table that there is no statistical evidence that Provision does not cause Participation when using both the MENA and OECD data. The same is true for Protection. These results suggest that a regression of (Participation) on (Provision) and (Protection) is warranted using both the MENA and PP3 OECD datasets, though the latter will only serve the purpose of a benchmark. We now turn to PP2 PP1 the regression analysis of our data.

|**Table 6.**Causality tests<sup>+</sup><br>H0<br>MENA<br> <br>|OECD|
|---|---|
|P2does not Granger-causeP3<br>7.22<sup>∗∗∗</sup><br><br>|6.59<sup>∗∗∗</sup><br>|
|<br>P1does not Granger-causeP3<br>3.29<sup>∗∗∗</sup><br><br>|2.39<sup>∗∗</sup><br>|
|<br><br>+StandardizedZstatistics reported; Optimal lag length= 1in all cases (choice bas<br>criterion);<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<sup>***</sup>:p< 0.01.<br>|ed on the Bayesian information<br>|

**_Probit analysis_** Our variable of main interest, , is "fractional," that is, it may take all possible values in the unit interval. Hence, a linear regression is not appropriate to analyze the effects of and PP3 on as it does not ensure that the predicted values of lie in the unit interval (
110 basic headings, 91 classes, 43 groups and 13 categories, and designing strict data cleaning procedures to ensure consistency in the standardization.<sup>17</sup> However, this standardization has limitations (Dupriez, 2007). Although consumption aggregates are standardized in the sense that they use a common data dictionary, the method of data collection and questionnaire designs affect the standardization directly and cannot be addressed.

Table 9 shows the average per capita consumption using both the standardized and national consumption aggregates. The table shows that, for the most part, there is not a large difference in expenditure per capita between the two aggregates. One exception is housing expenditure in urban India. Using the national consumption aggregates, imputed rent amounts to 70 cents per day (in USD 2011 PPP), but when imputed rents for homeowners are included in the standardized consumption aggregate, the housing expenditure jumps to USD 1.6 (in 2011 PPP). The standardization of the consumption aggregates decreases housing expenditure in Bhutan and Sri Lanka. Sri Lanka also sees a fall in food, and non-food non-durable expenditures due to standardization process.

> 15 Afghanistan is not included in Povcalnet because it lacks a PPP deflator and is not comfortable using a regression-based PPP. 16 As mentioned previously, we use the term national consumption aggregates to refer to the consumption aggregates created by the statistical offices of the respective countries, while the standardized consumption aggregates refer to the consumption aggregates obtained from the standardized consumption data sets created by the World Bank.

> 17  A detailed description of the standardization of the consumption aggregates in South Asia is available in Dupriez (2007).

14
1 (see more details below), which implies that mean consumption in the survey grows slower than the growth in national accounts.

The literature has discussed several channels for why there are systematic differences in growth of consumption as measured in national accounts and in the survey microdata; for example, see Ravallion (2003), Deaton (2005) and Pinkovskiy and Sala-i-Martin (2016).<sup>18</sup> First, there are

> 16 The S2S method has been used to generate plausible poverty estimates in other contexts and has often been successfully validated. Any imputation model is forced to make strong assumptions, such as the stability of the consumption model over six years (from 2011/12 to 2017/18) in our case. The availability of additional variables could also improve the predictive performance of our model. In some specifications, we successfully validate the 2014/15 estimates, as well as the 2004/05 estimates in urban areas, but do not accurately backcast the large decline in rural poverty between 2011/12 and 2004/05. This is likely because the estimated parameters, including the intercept term, is fixed at the estimated 2011/12 levels. Including a linear time trend in the model would greatly overestimate the decline in poverty between 2004/05 and 2011/12 in both urban and rural areas. This suggests that incorporating data on expenditures of particular services, which was possible when projecting into 2014/15 (Newhouse and Vyas 2019) but not into 2017/18, helps the linear time trend model perform much better at backcasting.

> 17 They fail to replicate poverty rates for urban areas in 2004/05, and make no reference to attempts to replicate 2009/10.

> 18 For a general discussion around the differences between household survey data and the data from national accounts, as well as potential adjustments for such differences, among others see Altimir (1987), Bourguignon (2015), and Prydz et al. (Forthcoming).

11
MINIMUM WAGES IN CAMBODIA 22 <!-- Start of picture text --> All<br>Garments Other sectors<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of hourly wage distribution<br>Point estimates 95% CI<br>Female<br>Garments Other sectors<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of hourly wage distribution<br>Point estimates 95% CI<br>.08<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br>Quantile treatment effects<br>-.06<br>-.08<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br>-.06<br>Quantile treatment effects<br>-.08<br>-.1<br><!-- End of picture text --> Figure 7. Quantile treatment effect of minimum wage hikes on hourly wage rate _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _α_ 2 + _β_ 2 (left panel), and _α_ 2 (right panel) from from quantile treatment effect estimation of Equation (6) for hourly wages. Quantiles are indicated by the horizontal axis. Sample restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for
Brazil, Senegal, and Vietnam, Cirera et al. (2021a) show that firms using more sophisticated technologies before the pandemic were more likely to increase the use of digital technologies in response to COVID-19 and achieved better sales performance, compared to firms that were similar in many other dimensions. Using a sample of more than 3,000 firms in 16 countries, including several developing countries, Grover and Karlus (2021) show that firms with structured management practices, which is highly correlated with productivity, show greater resilience in operational status and sales changes, plus they are also quicker to respond by adjusting their operating models.

The likelihood of exiting during the pandemic has also been correlated with burdensome business environment and lack of digital presence (Borino et al., 2021). Nonetheless, government support is pivotal. Using firm-level data for 17 countries, Gourinchas et al. (2020) suggest that in the absence of government support, the failure rate of small and medium enterprises (SMEs) would have increased by 9.1 percentage points. Likewise, evidence from Denmark also suggests that targeted policy support helped firms stay afloat and created incentives for job retention (Bennedsen et al., 2020). Although public support has played an pivotal role on avoiding business closures in high-income countries, prior work using the first round of BPS data shows that firms from middle- and lowincome countries are significantly less likely to receive public support (Cirera et al., 2021c). Some middle income economies, such as China, have nevertheless, witnessed an increase in the share of businesses that have received support (e.g., through online loans, Kong et al., 2021).

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impacts on the environment.<sup>8</sup> For example, fishing nets have to be of a specific mesh size (to prevent catch of juvenile species) and their use is often restricted in sensitive habitats—such as beaches, coral reefs, and spawning grounds.

In assessing the potential negative impact of different fishing gears on the environment, we rely on information collected during our focus group survey about how these different gears are actually used, as well as other studies that have taken place in Tanzania. Evidence suggests that there is a lack of compliance with some fishing gear regulations, and some illegal methods and gear continue to be used due to lack of enforcement and public awareness (Jiddawi and Ohman, 2002). Seine nets of small mesh size are commonly used and dragged on beaches and coral reefs, and sometimes even mosquito nets are used (MANREC, 2005; Berachi, 2003). Therefore, we classify gillnets, beach seine nets, and drive nets, as destructive fishing gears despite their legal status, because the way they are used are likely to cause significant environmental damage. This is in addition to illegal fishing gear, such as dynamite and poison, which are also considered destructive fishing gear.<sup>9</sup> Environmentally non-destructive fishing gear, on the other hand, are the types of fishing gear that are selective in catching the targeted species and do not result in large amounts of bycatch or result in negative impacts on marine habitats. We classify scoop nets, cast nets, ring nets, traps, fences, and hook and line fishing, as environmentally non-destructive fishing gear. As with the classification of environmentally destructive fishing gear, determining which types of gear are less destructive and belong in the non-destructive fishing gear category involves some judgment about how these gears are actually used in practice. Most of these gears we classify as non-destructive have traditionally been used by local fishermen. While they may generate some negative environmental impacts if used carelessly or by too many fishermen,
# **_Historically low national savings and rising investment gave rise to a savingsinvestment gap_**

**Both national saving and investment have grown since 2001, but investment increased more rapidly, leaving open a substantial gap financed by external funds.** At the time of the 2001 crisis, national saving was low, and reached a minimum of 19.9 percent in 2003. Since then, the national saving rate has been gradually increasing and as of the latest year stands at 25.8 percent of GDP – its highest level in the last four decades (Figure 12) and no longer low by international standards. The increase in investment can be seen evolution of constant-price shares of expenditure components of GDP (Figure 13). The only component that grew more than imports since 2001 was investment, which increased its share of GDP by nearly 11 percent. This was at the expense of private consumption and exports which both declined in relative magnitude over the period. While national accounts data are not available to disaggregate investment between the public and private sectors, fiscal data (Figure 14) show that government investment expenditure declined as a share of GDP over the period, so this increase in investment share can be attributed to private, rather than public, investment.

Figure 12: Saving and investment (percentage of GDP) <!-- Start of picture text --> 33%<br>Gross national saving<br>31%<br>29% GFCF<br>27%<br>25%<br>23%<br>21%<br>19%<br>17%<br>15%<br>Note: GFCF stands for Gross Fixed Capital Formation<br>Source: Haver Analytics<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> 11
11 especially high in those countries where unequal land distribution severely limit incomeearning opportunities in agriculture.

The Gini coefficient of percapita income for the sample is: 0.532. On the one hand, this Gini is quite a bit higher than the Gini (0.321) which can be calculated from per capita expenditure data for the sample. On the other hand, since income data include savings, it is natural to expect that the Gini coefficient of income will be higher than that for expenditure. Moreover, the Gini coefficient of income from this study seems well within the range of income Ginis recorded for other developing countries. For instance, the income Ginis recorded in the most recent edition of World Development Indicators (1998: Table 2.8) suggest that Gini coefficients of per capita household income range from a low of 0.420 (Bolivia) to a high of 0.601 (Brazil).<sup>9</sup> In Table 2 the five sources of income in rural Egypt are presented by income quintile group. The results demonstrate the importance of nonfarm income for the poor. The poor - that is, those in the lowest quintile group - receive almost 60 percent of their mean total per capita income from nonfarm income. This figure is 65 percent higher than that received by the poor from agricultural income, and more than ten times that received by the poor from transfer, livestock or rental income.'10 Evidently, the very real land constraints in rural Egypt - 75.7 percent of the households in the sample own no land" force the poor to seek the bulk of their livelihood from outside agriculture.

Table 3 presents another way of showing the dependence of the poor on nonfarm income. In this table households are ranked by size of land owned. Like other studies,'<sup>2</sup> the data reveal an inverse relationship between size of land owned and the share of nonfarm income. For the poorest (that is, landless group)
**Table 16. First Stage Regressions of Formal Labor Markets**

# **Dependent Variable: Covariate Violent Shock**

||**Household**<br>**head and**|**Female**|**Male**|
|---|---|---|---|
||**spouse**|||
|Land Gini|2.8306***|2.8883***|2.7739***|
||(0.9342)|(0.9179)|(0.9575)|
||-0.1989*|-0.2023*|-0.1943*|
|_Dummy_Native Populations in Colonial Times (1535-1540)|(0.1132)|(0.1138)|(0.1135)|
||0.0008|0.0031|-0.0014|
|Rural District Price Index|(0.0310)|(0.0315)|(0.0306)|
|Livestock Income as Share of Total Agricultural Income|-0.1087*|-0.1219**|-0.0955*|
||(0.0554)|(0.0551)|(0.0568)|
|_Dummy_Participation in Goverment Program Familias in|0.0250|0.0229|0.0283|
|Acción (1 if has acces to the progra; 0 otherwise)|(0.0236)|(0.0237)|(0.0251)|
|Ae|0.0011|0.0014|0.0010|
|g|(0.0008)|(0.0009)|(0.0009)|
||0.0007|0.0012|0.0005|
|Education (years)|(0.0030)|(0.0037)|(
regressions and other robustness checks are presented in section IV.

# **III.  Data Sources and Variable Definitions**

Data on trade flows, factor endowments and factor intensity are from standard sources. The year under consideration is 2000. Exports data at the SITC Rev. 3 5-digit level are from the OECD/UN International Trade Commodity Statistics (ITCS) database. We use the UN Broad Economic Categories (BEC) classification to define intermediate goods. We then map these data to 6-digit NAICS 1997 industries using the correspondence table from Feenstra et al. (2002). The BEC classification groups products into four categories of goods according to their main end use, i.e. intermediate, consumption, capital goods and not classified goods.<sup>8</sup> The world share of trade in terms of these four categories is reported in Table 1. As shown in the table, intermediate goods are the most important component of world trade both in terms of volumes and number of products.

> 8 This residual category includes for example passenger motor vehicles. Passenger motor vehicles are not classified by BEC because they are used as capital goods when purchased by a company to run its businesses, whereas they are used as consumption goods when bought by private households.

6
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE largest share of the gap can be traced to the power sector, followed by the ICT sector. Senegal has the potential to close this gap by bringing in more private sector investment and emphasizing projects in infrastructure with a regional dimension, taking advantage of its unique geographical location in the region.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure across almost all African countries, including Senegal. The results have been presented in reports covering various areas of infrastructure—ICT, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Senegal, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Two sets of African benchmarks will be used to evaluate Senegal’s situation: nonfragile, low-income countries, and middle-income countries. Detailed comparisons will also be made with immediate regional neighbors in West Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of the data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2008. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In common with the rest of the continent, Senegal’s growth performance improved markedly between the 1990s and 2000s. The overall improvement in per capita growth rates has been estimated at 1.03 percentage points, mainly from the ICT revolution (1.26 percentage points), while deficient power infrastructure