rafmacalaba/data-use-mentions-extended huggingface dataset:
15

# Protection in MENA

The last point brings to the fore the issue of the repression of agriculture in MENA by inappropriate economywide policies. Despite the importance of agriculture and, more generally, the rural economy in the region, protection from imports of manufactures and other products remains high in many countries **in** the Middle East and North Africa, contributing significantly to overvaluation of the real exchange rate (the relative price of nontraded goods to traded goods), and thereby to repression of domestic relative prices in subsectors of agriculture in MENA that are internationally competitive.

Table 6 indicates the magnitude of protection during the last decade in the MENA countries for which data on import tariffs and nontariff barriers (NTBs) are available.'<sup>9</sup> The data on tariffs reveal the combined magnitude of ad valorem customs duties and other fiscal charges on imports (so-called para-tariffs). The NTB data, on the other hand, indicate the percentage of national tariff schedule lines in each product category affected by quantitative restrictions and other nontariff barriers, as compiled by the U.N. Conference on Trade and Development (UNCTAD) for its inventory of trade control measures in industrial and developing countries (UNCTAD 1994).

The most open countries in MENA are the high-income Persian Gulf countries (Kuwait and Saudia Arabia), with average tariff rates not exceeding about 10 percent and average NTB rates not exceeding about 5 percent. Most other countries in the region are inward-looking, with average tariff rates widely exceeding 20 percent and average NTB rates widely exceeding 30 percent. This is true of low-income countries such as Egypt and Sudan, but also middle-income countries such as Algeria, Iran, and Turkey. The high nontariff barriers are particularly costly in economic terms

> '9 The discussion here focuses on protection in output markets for agricultural and other products. Although protection for production inputs is also important, estimates of effective rates of protection are beyond the scope of the present analysis. Notably however, computed effective rates of protection are
# **Appendix A: Trade Data and Tariff Rates for Tanzania’s Trade Partners**

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database.

The regions of our model are Tanzania, the European Union, the East African Customs Union plus SADC and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In this appendix, we calculate and report data for the East African Customs Union and 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 the first table below. We used Tanzania as the reporter country for both exports and imports. Results for both exports and imports are reported in the subsequent two tables.

# **Tariff Data.**

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 of the main
**<u>Table A.5. RDD regressions of households’ income and poverty using VHLSSs 2004 to 2008</u>**

|||||Dependent<br>|variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|Poor<br>households<br>(using<br>constant<br>povertyline)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program|-0.1970**|-0.9491|-0.6676|0.1892|-0.4467|-0.5750**|-0.2603|0.1224*|
||(0.0931)|(0.5946)|(0.4126)|(0.2526)|(0.4421)|(0.2485)|(0.2403)|(0.0644)|
|(Poverty rate – 50)|0.0050|0.0938*|0.0168|-0.0284|-0.0102|0.0509*|0.0047|-0.
Against the backdrop of the ongoing aggressive tightening of Fed policy, this paper explores the impact of rising U.S. interest rates on EMDE financial and economic conditions and fiscal outcomes. In particular, it aims to answer the following questions:

- What mix of inflation, reaction, and real shocks have driven changes in U.S. interest rates in recent years?

- How do the different types of shocks behind changes in U.S. interest rates affect EMDEs financial markets, capital flows, borrowing costs, and fiscal outcomes?

- How do the real, inflation, and reaction shocks behind changes in U.S. interest rates affect the likelihood of EMDE financial crises?

Because each of these questions requires the analysis of different sets of data available at different frequencies, and in order to keep the models used to examine those datasets as parsimonious as possible, the paper employs three distinct empirical methodologies. To identify the mix of real, inflation, and reaction shocks that have been driving U.S. interest rates, the analysis applies a _signrestricted Bayesian VAR model_ to monthly U.S. data on bond yields, stock prices, and inflation expectations. It then estimates _panel local projection models_ to assess the impact on EMDE financial, macroeconomic, and fiscal variables at a quarterly frequency of the different types of U.S. interest rate shocks identified by the VAR model. Finally, a _logit model_ is applied to annual data to determine how these different types of interest rate shocks affect the probability that an EMDE will experience a financial crisis.

The paper reports the following key findings. First, since the start of 2022, rising rates have been driven almost exclusively by continued increases in inflation expectations and a perceived hawkish shift in the Fed’s reaction function as it pivoted toward an exclusive focus on reversing the surge in inflation. Most observers and market participants anticipate some further Fed tightening as inflation remains well above target and the Fed continues to reaffirm that price stability is its highest priority.

Second,
calculate the contribution of structural transformation (as proxied by the rate of net employment creation) to the change in the intersectoral productivity gap between 2014 and 2015 based on the following equation:

> S = ∑L ∆∅ +<sup>∑</sup> L ∆∅ −<sup>∑</sup> L ∆∅ −<sup>∑</sup> L ∆∅ ,  (19) NN NN NN NN NN NN NN NN rr rrN rr rrN rr rrSSr rrSSr rrN rr rrN rr rrSSr rrSSr ∆LLPP where S = contribution of structural transformation to the change in the intersectoral labor productivity gap in region rr , L r = labor productivity of -type enterprises in sector in region ∆LLPP kk , ∅r = employment share of rr -type enterprises in sector kk in region , = informal or formal,  jj kk = industry (I ) or services (S ), and ∆ = 2014 −2015. rr kk jj rr kk It is not feasible to measure the change in labor productivity at the enterprise level. jj II SS Given this, Equation 19 provides a partial picture of the total change in the intersectoral productivity gap over time (using a standard shift-share decomposition framework). Assuming own-sector productivities remain constant, that is, ∆L N = ∆L N = ∆L N = ∆L N = NN NN NN NN 0, Equation 19 approximates the contribution of structural transformation to the change in the rr rr rr rr regional intersectoral productivity gap. We calculate ∆∅N , ∆∅ , ∆∅ , and ∆∅ and NN NN NN NN labor productivities based on IBES data. rr SSr SSr SSr

Figure 12 compares the actual intersectoral productivity gap and the predicted intersectoral productivity gap calculated as the sum of the actual intersectoral productivity gap and the contribution of structural transformation to the predicted change in the intersectoral productivity gap in each region. At the national level, the contribution of structural transformation to the change in the intersectoral productivity gap is positive,
governance determinant affects performance, in which case, the share of provision is less relevant than the amount of knowledge that can be learned from analyzing that particular organization or segment of the health sector.

It is beyond this paper to propose governance measurements for all potential units of analysis. Therefore, the remainder of this note focuses on governance of direct public <u>provision of health care services because the experience of seeking governance indicators</u> for this segment is more extensive than any other part of the health system. This is not meant to imply that it is either the most important or even the central feature of a health care system. In fact, in many countries, direct public provision is quite marginal relative to social insurance plans or private providers. Similar exercises could be undertaken for the governance of public health insurance organizations (Savedoff and Gottret 2008), non-governmental or commercial health care providers, and public health regulation (e.g. of medical and pharmaceutical supplies, medical education).

# **4. What to Measure?**

A complete data collection effort on governance in the health sector requires gathering a range of standard information in addition to specific governance measures. Data on health service inputs and demographics are necessary for judging the constraints and ______________________________________________________________________________________

-17-
.3|3.1|4.6|3.5|

_Source:_ Authors’ calculations.

# **Sri Lanka (HIES 2006–2007)**

The data are taken from the 2006–2007 Household Income and Expenditure Survey (HIES) administered by the Department of Census and Statistics in the Ministry of Finance and Planning of the Government of Sri Lanka. The fieldwork for the survey was carried out between July 2006 and June 2007.

The value of all non-purchased items was about 18 percent of total purchased expenditure, while non-purchased food accounted for about 25 percent of all non-purchased items. These ratios were nearly constant across quintile groups. All expenditures on food items were deflated using a Laspeyres index provided in the survey data set.

The questionnaire asked about the average expenditure on and quantity of LPG purchased in a month as well as the household’s primary cooking fuel. Twenty-six percent of households reported LPG purchase in the previous month while 17 percent named LPG as their primary cooking fuel. Firewood was the most commonly cited primary cooking fuel (Figure A2.4), even in the top quintiles. According to a separate question, 83 percent of households collected some firewood and a quarter of households purchased it.

Figure A2.4: Main source of energy for cooking across all households in Sri Lanka <!-- Start of picture text --> Firewood 80<br>LPG 17<br>Kerosene 2.4<br>Sawdust/paddy husk 0.21<br>Electricity 0.14<br>Others 0.55<br>0 20 40 60 80 100<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.
**Figure 2.2 From "Twin Surpluses" To "Twin Deficits" for Country Groups in MENA Region**

<!-- Start of picture text -->
1. Fiscal balance  2. Current account<br>(Percentage of GDP) (Percentage of GDP)<br>15<br>30<br>GCC OEC OIC 25 GCC OEC OIC<br>10<br>20<br>5<br>15<br>10<br>0<br>5<br>-5<br>0<br>-5<br>-10<br>-10<br>-15<br>-15<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text -->

Sources: International Monetary Fund, World Economic Outlook Database October 2017; and World Bank Staff calculations. Note: Current account balance of country groups are average current account balance across member countries w eighted by gross domestic products. GCC: Gulf Cooperation Council countries. OEC: Oil exporting countries other than GCC countries. OIC: oil importing countries. Forecast Starting 2017.

Even if many MENA countries, especially oil exporters, appear to have relatively low levels of debt, there are important contingent liabilities related to state-owned enterprises—such as non-performing loans—that suggest an escalation of public debt. With little ability to increase spending to embark on the kind of transformational agenda that is required to address the socio-economic challenges they face, MENA countries will have to mobilize private sector financing.

# **Infrastructure needs and challenges**

The infrastructure needs in the region are large. They are estimated to be around US$100 billion annually. Most of the needs are in electricity generation and transportation, followed by water and sanitation, and information and communication technologies.

Infrastructure traditionally has been financed, and managed, by governments
On the same subject, but through a rigorous analysis, Farzin (1991) concludes that “Somalia still depends heavily on imported food, and especially food aid, as a result of donor continuation of illplanned food aid programs”. In fact, Farzin concludes that the insufficient aid organization, combined with a disequilibrium between food aid assistance and little farmer support produced a vicious circle that imprisoned Somalia in a growing dependency. For Cote d’Ivoire, an analysis involving different aid types and savings, Ouattara (2007) finds that “aid tends to worsen Cote d’Ivoire’s dependence”.

Ethiopia is a pretty well researched country, for which we would like to report four studies. The first is an anecdotal report by Salisbury (1992), narrating that Ethiopians remarkably altered their behavior (giving up their work, planting trees upside down), in order to secure the extension of the international food aid programs. These behaviors are totally consistent with our hypothesis and represent a form of “community moral hazard”. The second comes from an interesting householdsurvey study on food aid targeting in Ethiopia, (Clay et al 1999) which reports that there is “no significant association between household food availability (need) and food aid receipts” and recognize that “research is also needed to examine the potential disincentive effects that observed targeting errors may exert”. Another household survey research always on Ethiopia (Jayne et al : 2002) concludes that “the single most important determinant of whether a wereda (county) receives free food or food for work is whether that wereda has been a recipient in previous years. On its face, it is unclear whether historical use should be interpreted as indicating that inertia is determining allocations”. These two conclusions may be consistent with the result implying that in presence of badly targeted aid programs, needless people may react by joining the safety net and dropping work hours, so that it is unsurprising to find that needless individuals receive aid transfers. The last, which somehow, concludes on
0.1|4.7|||
|Parts of apparatus of division 76---|7649|22235|0.4|0.5|0.0|0.0|0.0||
|Kraft paper and paperboard, in rolls or sheets|6413|22040|0.4|6.2|0.2|0.0|||

_N Source_ : Calculated using UN COMTRADE Database _Notes_ : A value of 0.0 represents less than US$100 in exports; blanks represent zero exports.

34
an initial overview of specific issues that should be highlighted in surveys to ensure more accurate counting of rural women’s employment, and in turn improving links to the design of rural employment policies. When collected over time, additional data in these areas can also help provide insights on the dynamics of women’s employment during economic cycles and rural economic shifts, and help in broader economic planning as well.

# References

Anderson, C. Leigh, Travis W. Reynolds and Mary Kay Gugerty. 2017. “Husband and Wife Perspectives on Farm Household Decision-making Authority and Evidence on Intra-household Accord in Rural Tanzania.” _World Development_ 90: 169–183.

Beegle, Kathleen, Emanuela Galasso and Jessica Goldberg. 2017. "Direct and Indirect Effects of Malawi’s Public Works Program on Food Security.” _Journal of Development Economics_ 128: 1-23.

Benes, Elisa, and Kieran Walsh. 2018a. “Measuring Employment In Labour Force Surveys: Main findings from the ILO LFS pilot studies.” International Labour Organization.

Benes, Elisa, and Kieran Walsh. 2018b. “ILO LFS Pilot Studies Cognitive Interviewing Tests: Methodology, Process and Outcomes.” International Labour Organization.

23
data on non-RTP3 activities and GIS data on roads were prohibitively difficult to retrieve, if not nonexistent. The data on the pre-intervention quality of the road were not available either. In the end, we were able to collect data covering project activity, start and end dates, and details of the road segment improved from 17 of the 33 project provinces.

The resulting data set covered 1,369 road segments, which pass through 1,222 communes in 240 districts. Activities are classified into four types of improvements—road rehabilitation, periodic maintenance, routine maintenance, and repairs due to flood (Table A.2). Repairs due to flood are an emergency response. Including them in estimation of RTP3 impacts could conflate the impact of the flooding with the impact of response to the disaster. Consequently, we dropped RTP3 roads where these emergency repairs due to floods took place. This left us with 1,350 roads.

# _Qualitative data collection_

We complemented these quantitative data with qualitative data collected from three RTP3 project sites. The objective of qualitative data collection was to understand the mechanisms through which better roads affected socio-economic outcomes. We placed specific emphasis on capturing gender-specific factors, which may facilitate or hinder benefitting from improved rural roads.

Our primary data collection took place between October 21 and November 2, 2018. We selected one RTP3 road in three provinces that were covered in VARHS: Lào Cai, Nghệ An, and Quảng Nam. We designed the 8
# **1 Introduction**

The global digital economy accounted for 15.5 percent of the world’s GDP in 2016 ($11.5 trillion). Yet, not everybody has benefited equally from the arrival of digital technologies. There are still huge disparities across and within countries when it comes to the adoption and usage of digital technologies (Comin and Mestieri 2018). While more than half of the world’s population now has access to the internet, the penetration rate in the least developed countries is only 15 percent, or 1 in 7 individuals (World Development Report 2019).<sup>1</sup> The benefits of adopting digital business solutions like email, launching a business website, or connecting to two-sided digital platforms can be substantial especially for firms (Goldfarb and Tucker 2019). The transfer of information and data over the internet helps reduce production costs and expand demand for a firm’s goods and services and thus for its factors of production. Reductions in search costs enable buyers and sellers of products or services to get better access to the other side of the market, by increasing the speed or efficacy with which firms find workers or input suppliers (De Loecker 2019). Digital business solutions also help expand market opportunities. Reductions in search, transaction, or tracking costs allow firms to overcome geographical barriers, penetrate new markets, and enlarge the volume of trade (World Development Report 2020).

The existing evidence on the impact of digital-technology adoption on productivity and factor demand, however, is surprisingly thin, especially for developing countries. It is even thinner when it comes to quantifying these effects using firm-level data. This paper aims to fill these gaps in the literature. Specifically, we estimate the effects of adopting digital business solutions, namely email to communicate with clients and suppliers and launching a business website, on firm-level revenue-based total factor productivity (TFPR) and demand for labor and capital. We rely on publicly available data from the World Bank’s Enterprise Survey
0.028)||(0.030)||(0.025)||(0.027)|
||T2=T3|0.109|0.072*|0.287|0.325|0.255|0.177|0.300|0.140|
|F-test for Equality of Parameters - p-value|T2=T4|0.000***|0.000***|0.000***|0.000***|0.024**|0.027**|0.281|0.213|
||T3=T4|0.032**|0.088*|0.002***|0.003***|0.327|0.415|0.980|0.757|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age Dummies?||No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations||1,938|1,938|1,937|1,937|1,934|1,934|1,938|1,938|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at the child level using baseline and 18-month data on Stimulation, Positive Practices, Parenting Stress Index and The Center for Epidemiologic Studies Depression Scale. Standard errors in parentheses are clustered at the CBCC level. The Parenting Stress Index had its score inverted so that a higher score is better. The variable referred to as 'baseline control variable' is the lagged (baseline) value of the dependent variable, except in column (8), where it is the baseline value of the Center for Epidemiologic Studies Depression Scale. We use all children
# **Appendix Table A1: Baseline summary statistics by business practices at baseline**

||Below<br>Percenta|median<br>ge of business p|Above<br>ractices used at|median<br>baseline|
|---|---|---|---|---|
||Control<br>Mean/SD<br>(1)|Treatment<br>Mean/SD<br>(2)|Control<br>Mean/SD<br>(3)|Treatment<br>Mean/SD<br>(4)|
|_Panel A: SEBRAE-PR data (2017/2018)_|||||
|Used SEBRAE service during the year b|efore the interve|ntion|||
|Any type of service|0.151|0.179|0.266|0.194|
||[0.360]|[0.384]|[0.445]|[0.396]|
|Material|0.093|0.161|0.228|0.157|
||[0.292]|[0.368]|[0.422]|[0.364]|
|Training|0.070|0.060|0.114|0.078|
||[0.256]|[0.237]|[0.320]|[0.269]|
|Business practices used (%)|0.295|0.264*|0.709|0.717|
||[0.151]|[0.152]|[0.146]|[0.135]|
|_Panel B: RAIS data_|||||
|Number of employees (2015)|3.953|3.301|3.785|4.275