rafmacalaba/datause-encoder-data huggingface dataset:
points.<sup>17</sup> These results are in line with Bloom and Canning (2004), which suggest an increase of 1 to 1.4 percentage points for growth of the working age population over total population.

As suggested by the descriptive statistics, the increase in the share of working age population was also followed by an increase in years of schooling. It might be the case that having a larger share of working age population with additional years of education leads to a higher impact on per capita GDP growth, given that these results are usually driven by younger and better educated cohorts. Table 6 shows that the interaction effect between changes in the share of working age population and years of schooling is positive for first-difference and panel fixed-effects. However, the result does not seem robust under the GMM specification.

**Table 6 Growth of the working-age share of the population can increase real GDP per capita**

||Fist-Differ|ence||Panel Fi|xed-effects||Generaliz|ed Method o|f Moments|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of WAP|0.02|0.02|2.18***|‐0.30|‐0.27|1.99***|0.56|‐0.18|2.49***|
||‐0.93|‐0.93|‐0.52|‐0.98|‐0.98|‐0.50|‐1.50|‐1.34|‐0.89|
|||‐||‐<br>2.27*|‐|‐||||
|Initial GDP-pc|‐0.58***|0.58***|‐0.54**|**|2.28***|2.22***|‐0.73|‐1.26**|‐1.04|
||‐0.21|‐0.21|‐0.21|‐0.51|‐0.52|‐0.52|‐0.47|‐0.60|‐0.67|
|Schooling (years)|0.73***|0.73***|0.65**|0.16|0.17|‐0.06|1.13**|1.17*|1.18**|
||‐0.25|‐0.25|‐0.26|‐0.57|‐0.57|‐0.54|‐0.48|‐0.60|‐0.58|
|Δ Schooling (years)||‐0.06|0.01||0.77|0.91||2.04|1.05|
|||‐1.23|‐1.21||‐1.39|‐1.37||‐3.04|‐2.38|
|||||1.226||||||
|Δ share of WAP*|1.073**|1.073**||**|1.219**||0.43|1.05||
|Schooling (years)|‐0.52|‐0.52||‐0.56|‐0.55||‐0.83|‐0.72||
|Δ share of WAP*|||‐3.91|||‐2.75|||‐9.19|
|Δ Schooling (years)|||‐4.04|||‐3.94|||‐7.74|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Author’s estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

17 Using a similar specifications of savings, an increase of 1 percentage point in the share of working-age population is found to be associated with an increase by 0.6 to 0.8 percentage point in savings as a share of GDP.

16
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

**Table 17. Macro results of telecom investments, funded by import duties**

||||T<br>|elecom (fund<br>|ed Import duti<br>|es)<br>||
|---|---|---|---|---|---|---|---|
|Variable|Definition|Benin|Mali|Senegal|Tanzania|Uganda|Cameroon|
|Ym|Aggregate household income|0.48|0.46|0.62|0.31|0.12|0.60|
|EV|Equivalent variation|0.46|0.45|0.58|0.31|0.12|0.55|
|s|Wage|3.88|0.94|1.45|0.49|0.21|1.01|
|yg|Government income|12.79|2.77|5.13|4.73|2.73|5.53|
|ye|Firm income|–0.21|0.26|0.22|0.40|0.03|–0.27|
|g|Total government expenditure|8.85|1.28|3.98|2.61|1.84|5.38|
|It|Total private investment|0.24|0.38|0.05|0.73|–0.44|–0.07|
|e|Nominal exchange rate|–0.95|–0.32|–1.10|–0.64|–1.75|–1.55|
|GDP|GDP|0.44|0.44|0.55|0.45|0.13|0.41|

The impact of this type of funding on GDP is almost identical for all countries, as also found in the previous two simulations. For the foreign aid–funded telecom infrastructure investment, households improve their situation in contrast to the reference period baseline scenario; the greatest effects are seen for Senegal and for Cameroon. This differs from

**Figure 4. GDP and EV changes for telecom investments, funded by import duties**

<!-- Start of picture text --> 0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Equivalent variation GDP<br>Bnin Mali Sngal Tanzania Uganda Cameroon<br><!-- End of picture text --> the foreign aid case, where Cameroon was the most affected. When funded by foreign aid, the positive effect is stronger for households in all six countries. The country rankings are the same as in the previous simulation.

The impact on wages here is weaker than in the foreign aid–funded case, and the country rankings remain the same. The effect is greater for firms than for households; the situation is better in all countries than in the foreign aid–funded option. Households win over firms in all countries but Tanzania. In the foreign aid–funded option, investment increased slightly in only two countries, Mali (0.26 percent) and Tanzania (0.3 percent). But for the import duties funding option we observe investment growth in four countries while it plunges in Uganda and Cameroon, which suffered less negative investment with the foreign aid funding option.

Finally, in this case the nominal exchange rate is not under as much pressure as in the previous case; changes are all below 2 percent. The steepest drop is in Uganda (–1.75 percent) and in Cameroon (–1.55 percent). We observe declines in the nominal exchange rate for all six countries.

23
The primary dataset used for the analysis is the Peruvian household survey Encuesta Nacional de Hogares (ENAHO) collected by the National Institute of Statistics and Informatics. This annual household survey has been conducted for more than a decade, and the data used in this evaluation span ten years, 1998 to 2007. The survey is multi-topic, aiming to measure poverty-related variables, and it contains modules on household characteristics, economic welfare (including economic activity), social welfare, and agricultural activity. A feature of this survey is the availability of geographic coordinates for populated centers (referred to as “communities” in this paper). Project coordinates for the rehabilitation sites were documented by the implementation team making it possible for the location of the rehabilitation work to be identified and matched to the data.

ENAHO uses a three-stage stratified rotating sample of 22,000 households (8,800 in the rural area in the country from which 2,300 are in the rural coast) representative at the urban, rural and regional levels. The first stage uses the census to derive randomly selected primary sampling units or conglomerates with probability proportional to size. Once the conglomerates are selected, samples of 7 to 10 household are randomly picked. Households are surveyed over the course of a year, and in the next year, 7 to 10 households from the same sampling unit are randomly chosen to be surveyed. While households (and communities) within the sample regularly rotate, preventing the creation of household or community-level panels, districts stay in the sampling units over years which enable us to build district panels.

8
- [2] Armenter, R., Koren, M., 2014. Economies of Scale and the Size of Exporters. Forthcoming in the Journal of the European Economic Association.

- [3] Aw, B. Y., Roberts, M. J., and Xu, D. Y. , 2011. R&D Investment, Exporting, and Productivity Dynamics. American Economic Review 101(4), 1312-44.

- [4] Bernard, A., Redding, S., and Schott, P., 2011. Multi-Product Firms and Trade Liberalization. Quarterly Journal of Economics 126(3), 1271-1318.

- [5] Blum, B.S., Claro, S., and Horstmann, I. J., 2013. Occasional and perennial exporters. Journal of International Economics 90 (1), 65-74.

- [6] Bryan, G., Chowdhury, S., and Mobarak, A. M., 2014. Under-Investment in a Pro...table Technology: The Case of Seasonal Migration in Bangladesh. Econometrica 82(5), 16711748.

- [7] Chaney, T., 2008. Distorted Gravity: the Intensive and Extensive Margins of International Trade. American Economic Review 98(4), 1707–1721.

- [8] Das, S., Roberts, M., and Tybout, J., 2007. Market Entry Costs, Producer Heterogeneity, and Export Dynamics. Econometrica 75(3), 837-873.

- [9] Debaere, P., Mostashari, S., 2010. Do Tari¤s Matter for the Extensive Margin of Trade? An Empirical Analysis. Journal of International Economics 81(2), 163-169.

- [10] Demidova, S., Kee, H. L. , and Krishna, K., 2012. Do Trade Policy Di¤erences Induce Sorting? Theory and Evidence From Bangladeshi Apparel Exporters. Journal of International Economics 87(2), 247–261.

- [11] Development Initiative (2005): GSP, SAARC Cumulation and the RMG Textile Sector of Bangladesh, mimeo.

- [12] Dixit, A., 1988. Optimal Trade and Industrial Policies for the U.S. Automobile Industry, in: Empirical Methods in International Trade, ed. Feenstra, Cambridge, MA: MIT Press, 141-165.

44
yemen-humanitarian-response-plan-january-december-2019-enar.

Oxfam. 2019. ”The Humanitarian-Development Peace Nexus.” Report, Oxfam Discussion Paper, Oxford, England.

Sen, A. 1981. _Poverty and famines: An Essay on Entitlement and Deprivation.’_ Oxford: Oxford University Press, 1981.

Sundberg, R., and E. Melander. 2013. ”Introducing the UCDP Georeferenced Event Dataset.” _Journal of Peace Research_ 50 (4), 523-532.

TFPM. 2018. “Yemen- 17th Report.” International Organization for Migration, Sana’a, Yemen. Tandon, S. 2019. “When Rebels Attack: Quantifying the Impacts of Capturing Territory from the Government of Yemen.” _World Bank Economic Review_ 33 (2), 328-352.

Tandon, S. and T. Vishwanath. 2020. “The Evolution of Food Coping Strategies Over the Course of the Conflict in Yemen.” _World Development_ 130, 104922.

Tranchant, J., A. Bliznashka, A. Diallo, M. Sacko, A. Assima, and E. Siegel. 2019. ”The impact of food assistance on food insecure populations during conflict: Evidence from a quasiexperiment in Mali.” _World Development_ 119, 185-202.

U.N. 2017. ”Amid Humanitarian Funding Gap, 20 Million People across Africa, Yemen at Risk of Starvation, Emergency Relief Chief Warns Security Council.” Press Release, United Nations, New York, USA.

Verme, P., and C. Gigliarano. 2019. ”Optimal Targeting Under Budget Constraints in a Humanitarian Context.” _World Development_ 119, 224-233.

World Bank. 2017. “Yemen Poverty Notes.” Report, World Bank, Washington, D.C.

World Bank. 2018a. “Piecing Together he Poverty Puzzle: Poverty and Shared Prosperity 2018.” Report, World Bank, Washington, D.C.

World Bank. 2018b. ”United Nations, World Bank, and Humanitarian Organizations Launch Innovative Partnership to End Famine.” Press Release, World Bank, Washington D.C., (accessed January 2021) https://www.worldbank.org/en/news/press-release/2018/09/23/unitednations-world-bank-humanitarian-organizations-launch-innovative-partnership-to-end-famine.

World Bank. 2018c. ”New World Bank Support to Address Food Insecurity in Yemen Aims to Reach 9 Million Yemenis.” Press release, World Bank, Washington D.C., (accessed September 2018) http://www.worldbank.org/en/news/press-release/2017/05/19/new-world-bank-supportto-address-food-insecurity-in-yemen-aims-to-reach-9-million-yemenis).

WFP. 2016. ”Yemen- Special Focus: What Does the Conflict-Induced Public Sector Crisis Mean for Food Security in Yemen?” Report, World Food Programme, Sana’a, Yemen.

19
|||||**gro**|**wth**|
|---|---|---|---|---|---|
||**country**|||**2005-**|**2025-**|
|**ation**|**code**||**peak**|**2015**|**2035**|
|2|50|Swaziland|2008|-0.2%|0.5%|
|2|259|Tonga|2004|-0.4%|-1.9%|
|2|200|Trinidad and Tobago|2001|-3.4%|-0.2%|
|2|43|Tunisia|2004|-1.5%|-0.1%|
|2|122|Turkey|2020|0.4%|-0.3%|
|2|91|Turkmenistan|2008|-0.2%|0.2%|
|2|92|Uzbekistan|2009|0.1%|0.0%|
|2|104|Viet Nam|2007|-0.5%|-0.2%|
|2|26|Zimbabwe|2008|0.0%|0.3%|
|**3.**|**Countries with**|**peak between 2010 and 2030**||||
|3|108|Bahrain|2015|1.9%|-0.4%|
|3|80|Bangladesh|2039|1.1%|0.4%|
|3|204|Belize|2027|1.2%|-0.1%|
|3|214|Bolivia|2027|1.8%|-0.1%|
|3|29|Cameroon|2041|1.5%|0.4%|
|3|217|Colombia|2016|0.9%|-0.1%|
|3|218|Ecuador|2019|0.7%|-0.3%|
|3|39|Egypt|2031|0.3%|0.3%|
|3|206|El Salvador|2024|1.1%|-0.1%|
|3|208|Honduras|2032|1.7%|0.2%|
|3|82|India|2024|0.8%|-0.2%|
|3|113|Israel|2025|1.3%|-0.2%|
|3|114|Jordan|2021|1.9%|0.0%|
|3|99|Malaysia|2016|1.5%|-0.2%|
|3|237|Melanesia|2043|2.1%|0.3%|
|3|243|Micronesia|2026|1.1%|-0.1%|
|3|210|Nicaragua|2030|1.1%|0.0%|
|3|211|Panama|2027|1.0%|0.0%|
|3|240|Papua New Guinea|2044|2.5%|0.4%|
|3|223|Peru|2033|0.7%|0.2%|
|3|101|Philippines|2018|1.1%|-0.2%|
|3|251|Polynesia|2014|0.8%|-0.9%|

49
dil SPECIAL TopIC 1: PROJECmNG AIDS **MoRrAuTy** Table S2. Parameter values used In simulations

|Deiographic<br>variables|_|
|---|---|
|Fortitity|Total fertility<br>rate dect rnes from 6.5 during<br>1985-1995<br>to 6.2 in 1995-2000, and by 0.6 units<br>thereafter.<br>Age specific fertility<br>pattern like<br>Eastern Africa aggregate.|
|Fertility<br>adjustments<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|No fortility<br>adjustments because of HIV/AIDS are<br>_<br>made.|
|Mortality<br>(non-AIDS)|Mortality<br>from causes other than AIDS follows Eastern<br>Africa aggregate pattern.<br>Life expectancy gradually<br>increases from 55.6 during 1985-1990 to 68.7 during<br>2020-2025. Infant mortality declines during the same<br>period from 93 to 34.|
|Mlortality adjustments<br>|_______<br>______________________________|No interaction between AIDS mortalityandmortality<br>__<br>from other causes is assumed.|
|Age structure<br>___________________________________|The 1985 age-sex distribution equals the Eastern<br>Africa aggregate, with 46X below age 15 and 51X<br>between<br>ages 15 and 65.|
|Trmission<br>rates|__|
|Heterosexual contacts<br>l_______________________________________|oaleto female transmission averages 0.002; female to<br>male, 0.001.<br>Transmission rates vary by duration<br>since infection of the infected partner.|
|Homosexual<br>contacts|It is assumed<br>that homosexual<br>contacts are<br>insignificant<br>and can be ignored.|
|Blood transfusions<br>l_______________________________________|Receiving a blood transfusion with infected blood is<br>assumed to have a transmission rate of almost 0.999.|
|Injections<br>l_____________________________________|Receiving an inJection with an infected needleis<br>assumed<br>to have a transmission rate of 0.005.|
|Perinatal|Perinatal (or vertical)<br>transmission fromisassumed<br>to average 0.3.|
|Cofactors: ulcers|Amultiplicative<br>factor of 40 is applied to<br>transmission rates when a susceptible partner has<br>genitalulcers.|
|Cofactors: condom|Amultiplicative<br>factor of 0.2isapplied to<br>_ _ __<br>_<br>transmission rates when a condomisused.|
|Anal sex effect|No adjustment of transmission rates is assumed<br>for<br>either receptive orifnertiveanal sex.<br>|
|_<br>Risk groupeobershfpad**characte**|__1~<br>**ristics**<br>_|
|Risk group maemership:<br>males|30X monogamous;<br>40X low sexual activity; 20X mediuft<br>sexual activity;<br>10high sexual activity|
|Risk group mebership: females|50X monogamous;<br>30X low sexual activity;<br>15X percent<br>medium<br>sexual activity;<br>5X prostitutes|
|New contact frequency: mles|Monogamous:<br>0.05 new contacts per year; low: 2;<br>medium: 15; high: 50. Contact frequencies adjusted<br>in simulations with different<br>HIV prevalence levels|
to develop rural nonfarm enterprises.<sup>1</sup> The majority of studies on the investment climate, however, have not considered the heterogeneity of the investment climate across rural areas and industries. The standard approach is heavily biased toward registered (and bigger) enterprises in the manufacturing sector, which are typically located in urban areas. This paper aims to make an exception by looking explicitly at small and informal rural enterprises.

In Tanzania, where about 75% of the population resides in rural areas, it is particulalry important to understand how the economic environment affects enterprise growth. Some 28% of rural households reported that at least one member was working in a nonfarm business in 2005. The share of rural nonfarm self-employment income has increased, from about 6% in 1992 to more than 20% in 2005 (World Bank, 2007a). This is an important finding which requires further analysis.

Studies on the determinants of nonfarm enterprise growth and its correlation with the rural investment climate are very scarce, in particular for Sub-Saharan Africa. McPherson (1996) shows for five Southern African countries that firm age and size have a negative impact on firm employment growth. The analysis highlights also the importance of the proprietor human capital and gender for firm growth but does not assess the role of investment climate constraints. Liedholm and Mead (1999) show for small and very small enterprises in Africa (and Latin America) that location, vocational training and prior business experience matter for firm growth, beside firm age and growth. Also Sleuwagen and Goedhuys (2002) explained for Côte d’Ivoire firm growth by size and age. They do not provide direct evidence of enterprise constraints, but use descriptive firm perception data only to show that medium sized firms are negatively affected by infrastructure and financial constraints. Findings from Loening, Rijkers and Söderbom (2008) suggest that in Ethiopia enterprises show little growth and are mainly constrained from the demand-side. However, the sector

> 1 Our definition of nonfarm enterprises includes all rural businesses engaged in non-primary productive activities. This includes the transformation, transport, and marketing of primary products, but excludes agriculture, forestry, hunting, and fishing. Households primarily engaged in the production of goods and services for home consumption are excluded.

2
**Figure 2.4  The United States as the Leader of transformation : Shares in total employment for selected subsectors ranked by capital-labor ratio,1958-2005 (selected from the 99 industrial sectors)**

<!-- Start of picture text --> Fabric Mills Computer & mfg<br>0.03 0.015<br>0.02 0.01<br>0.01 0.005<br>0 0<br>Aircraft and Automobile Other plastic product and<br>fiber optics<br>0.03 0.04<br>0.025 Aircraft mfg<br>0.03<br>0.02 Automobile Mfg<br>0.02<br>0.015<br>0.01 0.01<br>0.005 0<br>0<br>Labor –intensive sector: (industry 313210: Broadwoven Fabric Mills); Mid level labor/capital intensity (industry<br>334111: Electronic Computer Manufacturing). High capital intensity: (336411: Aircraft 336111: Automobile). High<br>technical intensity (Industry 326199: Other plastic products mfg including fiber lens, windshield and optics).<br>Source: J. Ju, J.Y. Lin, and Y. Wang, ―Endowment Structure, Industrial Dynamics, and Economic Growth,‖<br>manuscript, based on the NBER-CES Manufacturing Industry Data for the period 1958-2005 in two versions. The<br>1997 6-digit NAICS codes (473 industries) was used.<br>1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003<br>1958 1963 1968 1973 1978 1983 1988 1993 1998 2003<br>1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002<br><!-- End of picture text --> Figure 2.4 below shows the employment shares in the United States during 1958-2005 for five subsectors selected from 99 manufacturing industries, ranked from most labor-intensive to most capital intensive. Overall, as the capital labor (K/L) ratio increases over time, the industrial and employment structures change dramatically. Specifically,

- The share of labor employed in the most labor-intensive sectors such as fabrics declined monotonically in the period.

- In sectors such as computer manufacturing whose capital-labor ratio was mid-range, the share of labor employed first increased and then declined, showing a hump or inverse V-shape.

- In industries such as aircraft and automobile manufacturing that are capital-intensive but subject to labor-saving scale economies, the share of labor showed a slow and declining trend.

22
10 levels although older cohorts in inner towns appear to be slightly less educated. The two groups of towns are similar also in terms of employment rates. In this respect, the census allows us to compare the populations also strictly before the quake, exploiting a recall question on employment status five years earlier, in 1976. Figure (4) displays the estimated differences by age and the corresponding 95 percent confidence interval, showing that before the quake employment rates were not significantly One concern with this evidence is that the census was run one year after the quake. In principle, there are at least two reasons why this could impair the relevant comparisons. First, populations from inner towns might have migrated elsewhere because of the quake so that those observed in 1981 might already be a selected subsample of those truly exposed to the quake and related interventions. Second, even if there is no migration the comparison might already reflect the presence of different effects of the quake. Geographic proximity should guarantee that both channels worked, if at all, in the same direction in both groups of towns.

To address empirically the selection effect induced by differential outward migration we examine yearly gross outflow rates from the sampled towns between 1974 and 1987. These are on average around 2 percent and the difference between towns just inside and just outside the quake region is an order of magnitude smaller, around 0.2 percent. To establish whether the quake led to a larger outflow from inner towns we regressed yearly outflow rates between 1974-1987 on a set of year dummies and their interactions with an indicator for towns inside the quake region. None of the interaction terms was statistically significant at customary levels: p-values are always well above 0.15 except for three years (1977, 1978 and 1981) for which they are still larger than 0.05<sup>11</sup> . Moreover, an F-test of the joint significance of the interacted terms does not reject the null with a p-value of 0.3. Overall, it seems that outflows were very similar between the two sets of towns, and relatively low both before

> 11We estimated a weighted regression with weights equal to average population size between t and t + 1 clustering standard errors at the town level.
developed and developing countries.<sup>7</sup> The dataset includes a large, random sample of firms across multiple manufacturing and service sectors. The surveys include both quantitative and qualitative information on barriers to growth, including sources of finance, regulatory burdens, innovations, access to infrastructure services, legal difficulties, and corruption.

The 2003 _World Bank Enterprise Survey_ in China asked additional questions on the market environment, such as the number and importance of supplier and customer relationships, and supplier and customer (“supply-chain”) financing. For the purpose of our analysis, the key questions regard the extension and terms of trade credit. Importantly, the survey asks both (i) whether firms offer trade credit to customers and (ii) whether customers accept trade credit from the firm.<sup>8</sup> This allows us to precisely identify the decision of firms to offer trade credit. From our sample of 2,400 firms, 2,295 firms report whether or not they extend trade credit to their customers.<sup>9</sup> Table 1 shows variable names, definitions, and means for all variables. We include measures of trade credit, general firm characteristics, indicators of market power of the firm (relative to its customers and to its suppliers), financial characteristics, and indicators of the collateral value of goods sold and customer creditworthiness. Detailed summary statistics are shown in Table 2 (the full sample) and Table 3 (disaggregated by firms that do and do not offer trade credit). Table 4 shows a correlation matrix of our explanatory variables.

Our main dependent variable is a dummy variable equal to one if the firm offers trade credit (accounts receivable), and zero otherwise ( _AR_d_ ). We find that 39% of the firms in our sample offer trade credit, and that the average percentage of goods sold on

> 7 The survey instrument and data are available at: www.enterprisesurveys.org. 8 Detailed questions on supply chain terms were not asked in any other country survey. 9 We exclude from our sample 157 firms that provide financial services.

10