rafmacalaba/datause-encoder-data huggingface dataset:
- Timilsina G. and Shrestha R., 2006. “ _General equilibrium effects_ of a supply side GHG mitigation option under the clean development mechanism”, Journal of Environmental Management 80, p.327-341.

- Timilsina, G.R. and A. Shrestha, 2010. “Biofuels: Markets, Targets and Impacts,” World Bank Policy Research Working Paper, WPS 5364, July 2010.

- Timilsina, G.R., J. C. Beghin,D. van der Mensbrugghe and S. Mevel, and (2010), The Impacts of Biofuels Targets on Land-Use Change and Food Supply: A Global CGE Assessment, World Bank Policy Working Paper, Unpublished Draft.

- Hertel T., Rose S., and Tol R., “Economic Analysis of Land Use in Global Climate Change Policy”, Routledge, April 2009.

- Hertel T., Tyner W., and Birur D., 2008. “Biofuels for all? Understanding the Global Impacts of Multinational Mandates”, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, REVISED November 2008, GTAP Working Paper No. 51, 2008

- Horridge, M. 2008. “SplitCom: Programs to disaggregate a GTAP sector”. Melbourne, Australia: Centre of Policy Studies, Monash University.

- Van der Werf E., 2008. “Production functions for climate policy modeling: An empirical analysis. Energy Economics, 30, pp. 2964-2979.

27
critical to analytical findings. However, if an assumption is not critical (and a small value of government exports is unlikely to make any qualitative difference to any analysis based on a macro SAM), it may be better to retain the erroneous assumption and save time for other tasks.<sup>14</sup> Apart from the assumptions, we also note that, according to the national accounts, government value added is a payment to labor, i.e. the capital share is zero. Furthermore, since services cannot be stored, the demand for stock change must be for the private commodity since, as opposed to the government commodity, it includes goods (which are storable).

Mathematically, the split of the balanced accounts act and com of Macro SAM I into private and government accounts (act‐gov, act‐prv, com‐gov, com‐prv) is a balanced extraction to the government activity and commodity accounts (act‐gov, com‐gov) of selected payments from the aggregate activity and commodity accounts (act and com), drawing on the above‐mentioned data and assumptions. After this, the accounts act and com, which remain balanced, are renamed to act‐prv and com‐prv.

As stated in Table 3.5, the only new information that is needed (beyond what is found in Macro SAM I) is the ratio between intermediate inputs and (labor) value added for the government, a piece of information that we extracted from the GTAP database (1). The rest of Table 3.5 shows the details of how the accounts act and com are split. Cells not involved in payments to or from act or com are unchanged compared to Macro SAM I. Government consumption is moved to a new cell (2) and, given the values for government consumption and the ratio between government intermediates and value added, the government payment to labor is defined using a formula that is derived in the table note (3). The government payment to intermediates is what remains of the government consumption value (4). Government output sales may be defined as government consumption or the production cost of the government activity (5). The private activity payments to labor and for intermediates are the differences between total activity cells in Macro SAM I and what the government pays (6‐7). Only the private

> 14 Cf. Rodrik (2016, p. 94‐98) on assumptions in economics, including the observation that is echoed here: there is no need for assumptions to be true if they make no critical difference to findings.

‐14‐
Obstfeld, Maurice, and Kenneth Rogoff, 2007. “The Unsustainable US Current Account Position Revisited,” in Richard Clarida (ed.), _G7 Current Account Imbalances: Sustainability and Adjustment_ , University of Chicago Press.

Rajan, Raghuram G., and Luigi Zingales, 1998. “Financial Dependence and Growth.” _The American Economic Review_ 88(3): 559-586.

<mark>Shen, Leilei, 2013. “Financial Dependence and Growth: Diminishing Returns to Improvement in Financial Development</mark> .” _Economics Letters_ 120(2): 215-19.

Stehrer, Robert, Neil Foster, and Gaaitzen J. de Vries, 2012. “Value Added and Factors in Trade: A Comprehensive Approach,” WIOD Working Paper No. 7.

Timmer, Marcel P., 2012. “The World Input–Output Database (WIOD): Contents, Sources, and Methods.” Version 0.9.

Timmer, Marcel P., Bart Los, Robert Stehrer, and Gaaitzen J. de Vries. 2013. "Fragmentation, Incomes and Jobs: An analysis of European competitiveness," _Economic Policy_ 28(76): 613–61.

Timmer, Marcel P., Erik Dietzenbacher, Bart Los, Robert Stehrer, and Gaaitzen J. de Vries, 2015. "An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production", _Review of International Economics_ 23: 575–605 Urata, Shujiro, and Kozo Kiyota, 2003. “Services Trade in East Asia,” in T. Ito and A. O. Krueger (eds.), 2003, _Trade in Services in the Asia-Pacific Region_ , NBER-East Asia Seminar on Economics, Vol. 11, University of Chicago Press, Chicago and London.

U.S. International Trade Commission (USITC), 2013. “The Economic Effects of Significant U.S. Import Restraints.” _Investigation #332-325_ , Publication 4440.

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu, 2013. “Quantifying International Production Sharing at the Bilateral and Sector Level.” NBER Working Paper No. 19677.

Wang, Zhi, Shang-Jin Wei, Xinding Yu, and Kunfu Zhu, 2017. “Measures of Participation in Global Value Chains and Global Business Cycles.” NBER Working Paper No. 23222.

28
percentage of GNI (Doing Business, World Bank); and a dummy variable equal to 1 if the law prohibits discrimination in hiring practices based on gender (WBL, World Bank).

Additional firm-level controls taken from WBES include a dummy variable equal to 1 if the firm currently uses technology licensed from a foreign-owned firm and 0 otherwise; a dummy equal to 1 if the firm spent on R&D activity during the last fiscal year and 0 otherwise; a dummy variable equal to 1 if the firm has a checking and/or savings account and 0 otherwise; a dummy variable equal to 1 if the firm competes against unregistered or informal businesses and 0 otherwise; number of times either inspected or required to meet with tax officials during the last 12 months; proportion of the firm’s senior management time spent in dealing with government regulations in a typical week over the last 12 months ( _Time tax_ ); a dummy variable equal to 1 if the firm suffered losses due to crime during the last fiscal year and 0 otherwise; and a dummy variable equal to 1 if the firm reports that typically firms make bribe payments to government officials to “get things done” and 0 otherwise.

# 3.6 _Interaction variables_

For testing _Hypothesis 1_ , we use interaction term between bank credit and industries’ dependence on external finance ( _Financial dependence_ ) as reported in Rajan and Zingales (1998). For robustness, we also use the percentile ranks of _Financial dependence_ . For testing _Hypothesis 2_ , we use interaction term between bank credit and (one year lagged values of) a dummy variable equal to 1 if credit bureaus are present in the country-year and 0 otherwise. For robustness, we show that our results with (one year lagged values of) the percentage of the total population in the country-year that is covered by credit bureaus ( _Credit bureau coverage_ ). The data source for the credit bureau dummy and coverage is Doing Business, World Bank. For _Hypothesis 3_ , we use the 15
# **Graph 3: Commitments to roads by province (in percentage of total amount), 2008 - 2011**

<!-- Start of picture text --> Luapula<br>Central<br>Western<br>Northern<br>Southern<br>Lusaka<br>Eastern<br>Copperbelt<br>North Western<br>0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00<br><!-- End of picture text --> _Source:_ AWP 2011.

The distribution of allocations by funding source and province reveals that some of the provinces benefited from both external and Zambian funding sources (see Graph 4). For example, the Northern province has been one of the top beneficiaries of Zambia’s Road Fund, government funding and to a lesser extent, donors. The Eastern province has also received a large share of funds from these three sources. Other provinces, on the contrary, have been allocated funds only from one source. The Western province has received the largest share of donor funds, about 54%.

# **Graph 4: Share of road fund, GRZ funding and donor funding in Commitments to roads by province (in percentage of total committed amount by province), 2008-2011**

<!-- Start of picture text --> Share of donor funding Share of GRZ funding Share of road fund<br>Luapula<br>Central<br>Western<br>Northern<br>Southern<br>Lusaka<br>Eastern<br>Copperbelt<br>North Western<br>0.00 10.00 20.00 30.00 40.00 50.00 60.00<br><!-- End of picture text --> 11
48 Table 1: Hypothesized effects of shifting from line-item budgets, and main findings

||Fee-for-ser<br>|vice (FFS)<br>|Patient-basedp<br>|ayment (PBP)<br>|
|---|---|---|---|---|
||Hypothesized effect|_Estimated effect_|Hypothesized effect|_Estimated effect_|
|No. admissions|Positive.|_A significant increase of 2-_<br>_7.5%._|Positive (larger than FFS<br>effect).|_No effect._|
|Average length of<br>stay|Uncertain.|_No effect._|Negative.|_A significant reduction of_<br>_3.5-4.5%._|
|No. beds|Uncertain.|_No effect._|Uncertain.|_No effect._|
|Bed-occupancy rate|Positive, assuming hospitals<br>can alter their bed stock.|_A significant increase_<br>_(mostly after one year) of_<br>_8%._|Positive, assuming hospitals<br>can alter their bed stock.|_A contemporaneous_<br>_negative effect offset by a_<br>_lagged positive effect._|
|Cost per admission|Uncertain but probably<br>positive.|_Cannot test._|Negative.|_Cannot test._|
|Quality of care|Uncertain but possibly<br>positive.|_Cannot test._|Uncertain but possibly<br>negative.|_Cannot test._|
|Total hospital costs|Uncertain but probably<br>positive.|_Cannot test._|Uncertain.|_Cannot test._|
|Total health<br>expenditure|Positive.|_Significant increase of 18-_<br>_19%, with similar_<br>_percentage increases in_<br>_public and private_<br>_spending._|Uncertain.|_Significant increase of 11-_<br>_21%, with similar_<br>_percentage increases in_<br>_public and private_<br>_spending._|
|Amenable mortality|Uncertain but probably<br>negative.|_No effects._|Uncertain.|_Significant negative effects_<br>_for some conditions in some_<br>_model specifications._|
havens (Switzerland, Luxembourg, Belgium, Jersey, Guernsey and the Isle of Man) and 55% can be assigned to 11 non-havens. Among the havens, for which bilateral deposit information is publicly available, Switzerland is by far the most important. Around 16% of the total foreign deposits cannot be assigned to individual banking centers. Even if all these unallocated deposits are held in havens like Cayman Islands, Singapore and the Bahamas where public data is not available at the bilateral level, the public series still allocate almost two thirds of all haven deposits to individual havens for this particular sample.<sup>19</sup> We first re-estimate the baseline model with the (incomplete) measures of haven and nonhaven deposits based on publicly available information while using the same sample period as in the baseline analysis, 1990-2010. As shown in Column (1) of Table 7, an aid disbursement equivalent to one percent of GDP in a given quarter induces a statistically significant increase in haven deposits of around 2.5% and, as shown in Column (2), an insignificant decrease in non-haven deposits of around 2%. The increase in haven deposits, measured over and above the increase in non-haven deposits, is around 4.1%, as shown in Column (3). These estimates are similar to the baseline estimates based on restricted deposit information (Columns 1-3 in Table 2), but somewhat smaller. A possible interpretation is that the havens not allowing for public release of bilateral deposit data are also the havens where deposit responses to aid disbursements are largest. Extending the sample period to include the most recent observations in the public data yields almost identical results, as shown in Columns (4)-(6).

Next, we show results by individual banking centers; an exercise we are not allowed to conduct with the restricted dataset due to confidentiality requirements.<sup>20</sup> As shown in Columns (7)-(10), the overall increase in haven deposits around aid disbursements is driven by accounts in Switzerland and Luxembourg while the responses in Belgium and Jersey (combined with Guernsey and Isle of Man) exhibit statistically insignificant changes. This is consistent with the notion that the increase in haven deposits around aid disbursements reflect diversion to secret private accounts. Throughout the period 1990-2010, Switzerland was a leading haven with some of the strictest bank secrecy rules in the world and a share of the global market for private wealth management of around 40% (Zucman, 2013; Zucman, 2017). There is evidence that as much as 90-95% of the wealth managed in Switzerland is hidden from the authorities in the owners’ home country (Alstadsæter et al., 2019).

> 19For the rest of the world (Column 2), the coverage of the publicly available deposit information is lower with 35% that cannot be allocated to individual banking centers.

> 20A limitation of this analysis is that the total deposits owned by small and relatively poor countries in small and relatively unimportant banking centers are not rarely zero, which translates into missing observations with our log-transformation of the dependent variable.

18
To answer these questions requires extending the time series of estimates of distortions in the Krueger/Schiff/Valdes sample and extending the sample to a wider range of countries. That is the focus of a new research project getting under way at the World Bank. But in the meantime it is possible to answer the question in the title of the present paper using a new database for 2001. Specifically, here we address the question: What would be the consequences for agricultural markets and farm incomes if all countries were to reduce their trade distortions simultaneously (as in an ambitious WTO round), as distinct from just reducing their own distortions?<sup>1</sup> That is, the aim of this paper is to make use of a new database and a global general equilibrium model to assess how agricultural markets and value added in agriculture would change if, over the next decade, the world were to remove all merchandise trade barriers and agricultural subsidies. While no-one anticipates such a radical reform, the analysis serves as a benchmark to suggest what is at stake in the WTO’s current round of multilateral trade negotiations – as well as in further unilateral reforms. It can also give a better indication of agricultural comparative advantages around the world than is available by looking at indicators in the current distortion-ridden situation.

Specifically, we make use of the World Bank’s recursive dynamic model of the global economy known as LINKAGE (van der Mensbrugghe 2004), which has formed the basis for the World Bank’s standard decade-long projections of the global economy and of its earlier trade analysis (e.g., World Bank 2002, 2004). The distinction is made in our welfare results between

> 1 The Krueger, Schiff and Valdes (1988) and Jensen, Robinson and Tarp (2002) studies focused on effects of just own-country policies, the first using partial equilibrium and the second using national general equilibrium models. On the relationship between those two methodologies, see Bautista, Robinson, Wobst and Tarp (2001).

3
# 6.2. Firm-level capabilities and skills

Poor labor productivity trends among formal Zambian firms in the non-mining industry and service sectors also reflect firm-level capability and technological constraints. Firms often lack critical managerial, technological, and vocational skills. The Zambian training system provides inadequate training with respect to business and entrepreneurial skills, which are necessary for the establishment and growth of businesses (Government of the Republic of Zambia 2020). A relatively low percentage of firms in Zambia have licenses for the use of international technologies (Figure 37) or have internationally recognized quality certifications (Figure 38), proxies for the level of technology adoption, and managerial and operational sophistication. As a result, Zambia ranks 100th out of 132 countries with respect to business sophistication in the World Intellectual Property Organization’s Global Innovation Index (2022), below countries such as Peru (49th), Thailand (43rd), and even Zimbabwe (90th) (World Intellectual Property Organization 2022).

<!-- Start of picture text --> Figure 37. Firms using technology licensed from Figure 38. Firms with international recognized quality<br>foreign companies certification<br>(Percent of latest Enterprise Survey respondents) (Percent of latest Enterprise Survey respondents)<br>40% 25%<br>35%<br>20%<br>30%<br>25% 15%<br>20%<br>15% 10%<br>10%<br>5%<br>5%<br>0% 0%<br>Cross-country averages Structural peers Aspirational peers Zambia Cross-country averages Structural peers Aspirational peers Zambia<br>Note: Select aspirational peers such as Thailand also have lower scores, although in such cases the low score reflects the ready<br>availability of domestically developed technology rather than low business sophistication.<br>Source: World Bank staff calculations based on Enterprise Survey data and the Investment Climate Assessment 2.0 toolkit<br><!-- End of picture text --> Zambian firms also face skills and capability constraints at the worker level. Zambia’s Human Capital Index (HCI)—a measure of health and education’s contribution to worker productivity—is well below most aspirational and structural peer countries (Figure 39).<sup>14</sup> Zambian firms are unable to overcome these challenges after onboarding workers. Although a high portion of formal Zambian firms report offering formal training, they offer it to a low share of workers (Figure 40).

> 14 The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education.

22
# Figure 5. Growth of world output

## A. World output growth

## B. World per capita output growth

<!-- Start of picture text --> Percent Percent<br>9 6 Market weights<br>Market weights<br>PPP weights<br>PPP weights 4<br>6<br>2<br>3<br>0<br>0<br>-2<br>-3 -4<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: Feenstra, Inklaar, and Timmer (2015); Kose and Terrones (2015); World Bank.<br>Note: Shaded areas indicate global recessions in 1975, 1982, 1991, and 2009, and global downturns in 1958, 1998,<br>2001, and 2012. Data for 2019 and 2020 are estimates and forecasts, respectively.<br><!-- End of picture text --> 34
given the positive effect of the interventions on agricultural productivity for the interaction term and the joined effects. On the other hand, for the female spouse, one possible explanation would be a lack of control over agricultural outputs. Controlling the final output than the process might be more important to invest her time and resources. Future studies might be benefited if they look into the intensity of the technology tried and the decision-making power of the female spouse on agricultural outputs.

_Table 4: Impact on Agricultural Productivity_

||Productivity<br>(birr/ha)|Cultivated<br>land (ha)|Value of<br>harvest (birr)|Total crop<br>sale (birr)|
|---|---|---|---|---|
|round 3|1,342.883***|0.184|1,293.943***|495.901**|
||(342.614)|(0.221)|(480.581)|(216.961)|
|FIF*round 2|-374.879|-0.045|-70.964|199.867|
||(313.285)|(0.082)|(305.701)|(170.585)|
|FIF*round 3|-331.530|0.182|-61.910|52.874|
||(436.389)|(0.183)|(495.877)|(272.181)|
|WIF*round2|-33.265|-0.397***|-515.366|-436.536|
||(624.458)|(0.142)|(591.326)|(267.706)|
|WIF*round3|1,106.025|0.081|764.276|723.916|
||(996.272)|(0.354)|(919.612)|(705.185)|
|WIF*FIF*round2|2,311.051***|0.296*|1,199.889*|7.982|
||(822.542)|(0.155)|(636.566)|(435.012)|
|WIF*FIF*round3|460.993|-0.629|-39.290|-476.813|
||(1,081.062)|(0.406)|(1,030.121)|(815.535)|
|Observations|2,428|2,428|2,428|2,428|
|R-squared|0.100|0.045|0.248|0.118|
|Estimates: FIF2 + WIF2 + FIF_WIF2|1903|-0.146|613.600|-228.700|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.003***|0.311|0.220|0.392|
|Estimates: FIF3 + WIF3 + FIF_WIF3|1235|-0.367|663.100|300|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.099*|0.055*|0.461|0.472|
|Control Mean(BL)|4936|2.302|5887|1165|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. Productivity is calculated by dividing the value of harvest by the size of cultivated land. Productivity, cultivated land, the value of harvest, and crop sales are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **_Livestock_**

We expect increased extension service information and the easing of financial constraints could lead to an increase in the reported value of livestock and poultry. Turning to Table 5, we find that such an increase only happens when both spouses are targeted.

21
involves increased job creation in the non-agricultural sectors of the economy, where productivity tends to be higher.

# _Employment_

The first decade after the end of the civil war saw quick growth in non-farm employment, especially in urban areas, but since then, the structure of primary employment has changed very little –primary employment growth in each segment has just kept up with labor force growth (table 1). In 2009 only 8 percent of the primary employment was in non-farm enterprises. This is substantially lower than other SSA countries (figure 2). Mozambique even lags behind countries with similar income levels (Fox and Sohnesen, 2012).

**Table 1 Structure of primary employment by area (age 20+), 1997-2009**

|_Type of Employment_||_Urban_|||_Rural_|||_Nationa_|_l_|
|---|---|---|---|---|---|---|---|---|---|
||_1997_|_2003_|_2009_|_1997_|_2003_|_2009_|_1997_|_2003_|_2009_|
|Agriculture|66.7|46.7|44.7|94.0|92.3|93.2|86.8|78.2|79.6|
|HEs|10.1|19.0|22.7|2.3|3.8|2.8|4.4|8.1|8.4|
|Non-farm Wage Employment:|23.2|34.3|32.7|3.7|3.9|3.9|8.9|12.6|12.0|
|Private sector|7.6|21.9|22.5|1.3|2.2|2.1|3.0|7.8|7.8|
|Public sector|15.6|12.5|10.2|2.4|1.7|1.8|5.9|4.7|4.2|
|Total|100|100|100|100|100|100|100|100|100|

_Source:_ Authors calculations based on IAF 1996/97, 2002/03, and IOF 2008/09.

Owing to the lack of educational opportunities during the twenty-year civil war and its aftermath, Mozambique’s labor force is poorly educated with 69 percent of the labor force having less than completed lower primary (table A2), despite the impressive gains in access to education realized in the last decade. This represents a huge challenge to improving employment outcomes and household incomes. Low education levels could be one reason why primary employment in the HE sector in Mozambique is undeveloped compared with other SSA countries (figure 2).

**Figure 2 Employment in HEs as share of total primary employment in SSA countries**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source:_ Fox and Sohnesen 2012 6 | P a g e