rafmacalaba/datause-encoder-data huggingface dataset:
4 as agricudtur waste nd firewood. Urban low-income households wore observed to have the highest budgat shares for commercial energy. In absolute terms, the highest budget shares for kerosene lie between 3.6 and 6 percent. For electricity the relevant range is 1 to 9 percent.

There are few reliable estimates to gauge the flexibility of households with respect to interfuel substtion and subsiton between energy and other consumption goods. Where elasticity estimates are available, the elastcity of demand for kerosene is close to -1, i.e., a one percent increase in price leads to close to a one percent fill in consumption. The price elasticity of demand for electricity is relatively low and lies between -0.08 and -0.32. Ihe loss of welfare depends on the elasticity of demand, the expenditure share and the price change; the upper bound on the welfare effect is when the demand elasticity is zero. Table 2 swunrizes the welfare effect of domestic energy price changes for the countries analyzed.

Analysis indicates ta the welfare effect (expressed as a loss in consumer surplus) due to an increase in domestic kerosene p:ices is equivalent to 1.3 to 2.23 percent of household income For electricity price chnges, the welfare loss was estimated to be between 2.2 and 3.35 percent of household noome. Th figures reported in table 2 are for groups of household **s** howing the greatest loss as compared to oder households in the countzy. In all 1he cases these are low-income ihouseholds residing in urban area.

_Table 2: Welfare Impacts of Energy Price Increases: The Household Sector_

|Countzy|**Year**|Price'<br>(% change)|Fuel|Budget<br>share (%)|Assumed<br>elasticity|Welfare<sup>2</sup><br>(% change)|
|---|---|---|---|---|---|---|
|Malaysia|1985|33.0|Kerosene|4.0|-0.81|-1.3|
|Indonesia|1983|50.0|Kerosene|6.0|-1.02|-2.23|
|Ghana|1983-87|39.0|Kerosene|3.6|0|-1.4|
|Zimbabwe|<br>1984|38.7|Electricity|5.0|-0.08|-2.2|
|Colombia|1985-88|35.5|Electricity|9.4|0|-3.35|
|Turkey|1988|80.0|<br>Electricity|3.01|0|-2.4|

_Noir._ 1. Price chanes genc;dly zfcr to chne n real prices (1986 local cunrracy) over the prcvious year's prcs-for Ohana **the avmg** ince over the ya For Columbia, cstma refer to changes in price between 1985 and 1988. 2. Wefarc impt mt tD the k in counwmer _swplus_ (CS). Estimates are sportd ass peent ofincom fer the housholds **vih lighcat om. FwTue,C Ghana nd** Columbia, the estimates of welfae ss can be interprted asthe dwoft eae umuio **as** itis baned **on thc asumpin th** price **dLsticity** of **dmnd** is equal to zero.
employment rate was only 60 percent. There are also major differences in rural and urban labor market outcomes, as shown in Table 3. Participation in the labor force is substantially more likely in rural than urban areas. This is mainly driven by employment in subsistence family farming in rural areas, where family labor (often unpaid) is the primary factor of production. Since the rural rate of poverty stood at 24.5 percent in 2005 (compared to 11.2 percent in urban areas), inactivity and unemployment are typically not in the choice set of rural individuals (World Bank & INSTAT, 2006). This is not the case in the urban economy where insufficient labor demand breeds inactivity and unemployment among the working-age population.

Moreover, Table 4 demonstrates distinct differences in employment outcomes across men and women. The LSMS data indicate that the unemployment rate is considerably higher for women. In 2004, the women’s unemployment (employment) rate was 5 (22) percentage points higher (lower) than the rate for men. Among the unemployed, women are also more likely to be discouraged than men, as measured by the differential between the standard and relaxed unemployment rates. While the 2004 difference was about 10 percentage points for women, it was 6 points for men. Breaking down rural/urban labor market statistics by gender in Table 4 also enables to determine the factors driving the overall differences in labor market outcomes. Aggregate indicators are significantly better for both men and women in rural areas where gender disparities are also narrower.

As transfer recipients adjust their labor supply conditional on labor market dynamics and the availability of social transfers and other non-labor sources of income, gendered and spatial differences in labor market outcomes may imply unique incentive effects of social protection programs for men/women, and across urban/rural areas. In particular, one expects work relaxed definition recognizes the validity of the discouraged worker effect in Albania, and yields an unemployment rate of 13.4 percent in 2004.

12
14 Table 4: Country differences in performance

|Leading performers|Lagging underperforners|
|---|---|
|_Change in annusa acerage rate ofGDP growth,1985-88 compared with 1970-80_<br>||
|Korea|Nigeria<br>|
|Mauritius<br>|Philippines<br>|
|Morocco<br>|Malawi<br>|
|Ghana<br>|C8te d'lvoire<br>|
|Thailand|Mexico|
|_Saving rate in current pries, 1985-88 wom_<br>_pared with 2970-80_<br>||
|Korea<br>|Nigeria<br>|
|Chile|Zambia|
|Costa Rica<br>|Malawi<br>|
|Jamaica<br>|Philippines<br>|
|Mauritius|Senegal|
|_Inwvstment rate_in_curmnt prkis, 1985-88 compared with 1970-80_||
|Costa Rica<br>|Cote d'lvoire<br>|
|Korea|Malawi<br>|
|Jamaica<br>|Nigeria<br>|
|Chile|Zambia|
|Kenya|Philippines|
|_Investment rae_in constant_prces, 2985-88onmpared_<br>with_1970-80_<br>||
|Korea|Malawi<br>|
|Mauritania|Zambia<br>|
|Mauritius|Nigeria<br>|
|Togo<br>|C8te d'lvoire<br>|
|Madagascar|Phillppines|
|_Export-to-CDPratiIn_current_prices, 1985-88 compared with 1970-80_<br>||
|Jamaica|Kenya|
|Mauritius|Senegal<br>|
|Chile|Malawi|
|Korea|Zambia<br>|
|Mauritania|Brazil|
|_Export-to-CDP_<br>_ratio incnstant_<br>_prices, 1985-88 compared_<br>_with1970-80_||
|Jamaica<br>|Kenya<br>|
|Mauritania|Zambia<br>|
|Korea<br>|Nigeria<br>|
|Togo|Madagascar|
|<br>Mauritius|Malawi|
# **Appendix A: Two Theoretical Results**

We want to show that both the probability of specializing in export crops and expected income are increasing in the number of intermediaries in a district. That is, that _π_ ( _p, n_ ) and _Y_ ( _p, n_ ) from equations (8) and (9) are increasing in _n_ when keeping _p_ constant. Both equations can be generically written as an integral of the form The derivative of the generic integral with respect to the number of intermediaries is which is strictly positive for any function _g_ strictly increasing in _x_ .

In the case of the probability of specializing in export crops, _π_ ( _p, n_ ), _g_ is equal to the probability of specializing in export crops at a given distance _x_ , This function is strictly increasing in _x_ . In the case of expected income _Y_ ( _p, n_ ) which is also strictly increasing in _x_ . Thus, both _π_ ( _p, n_ ) and _Y_ ( _p, n_ ) are strictly increasing in _x_ , the result we wanted to show.

# **Appendix B: Household and District Controls**

Table A1 shows the results of the IV linear model of poverty on market density.

# **References**

Appleton, (1999). “Poverty Rates in Uganda,” The World Bank.

Blundell, R. and J. Powell (2004). “Endogeneity in Semiparametric Binary Response Models,” _Review of Economic Studies_ , vol. 71, pp. 655-679.

24
23

# Bibflography

Das, Satya P. and Shabtai Donnenfeld, 1987, Trade Policy and its Impact on Quality of Imports: A Welfare Analysis, Journal of Intemationl Economic, 23, 77-95.

Das, Satya P. and Shabtai Donnenfeld, 1989, Oligopolistic Competition and International Trade: Quantity and Quality Restrictions, Journai of Internatiinal Economis, 27, 299318.

Krishna, Kala, 1984, Protection and the Product Line, NBER LW srk Paper no- 153.

Krishna, Kala, 1987, Tariffs Versus Quotas With Endogenous Quality, lournal of International Economics, 23, 91-122.

Messerlin, Patrick A., 1990, Antidumping Regulations or Procartel Laws? The EC Chemical Cases, World Bank Paper Series, no. 389.

Nattino, Elbio, 1989, Politica Antidumping o Aranceles Diferenciales?, !CERES, Montevideo.

Rodriguez, Carlos A., 1979, The Quality of Imports and the Differential Welfare Effects of Tariffs, Quotas, and Quality Controls as ?rotective Devices, Canadian Journal of Ecwnmica, 12(3), 439-449.

Santoni, G.J. and T.N. Van Cott, 1980, Import Quotas: The Quality Adjustment Problem, Southern Economic Journal, 46(4), 1206-1211.

Snence, A.M., 1976, Monopoly, Quality and regulation, Bel oloua Economics, Autumn, 417429.
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# Table 1: Baseline Model.

|VARIABLES|(1)<br>TFP|(2)<br>TFP|(3)<br>TFP|(4)<br>TFP|(5)<br>TFP|(6)<br>TFP|(7)<br>TFP|(8)<br>TFP|(9)<br>TFP|(10)<br>TFP|
|---|---|---|---|---|---|---|---|---|---|---|
|Policy Restrictiveness in<br>Services FDI|-0.903*|-0.940*|-0.981**|-0.982**|-0.851*||||||
||(0.493)|(0.513)|(0.433)|(0.435)|(0.432)||||||
|Policy Restrictiveness in<br>Manufacturing FDI||-1.288*|-0.255|-0.255|-0.248||-1.195|-0.162|-0.163|-0.155|
|||(0.749)|(0.718)|(0.718)|(0.754)||(0.763)|(0.684)|(0.685)|(0.732)|
|Tariffs on Inputs (Effective)|||-0.00759*|-0.00759*|-0.00717|||-0.00754*|-0.00759*|-0.00722|
||||(0.00436)|(0.00441)|(0.00501)|||(0.00437)|(0.00443)|(0.00501)|
|% of Output by Foreign<br>Firms in the Sector||||1.50e-05|-0.000151||||-0.000152|-0.000297|
|||||(0.000424)|(0.000395)||||(0.000427)|(0.000400)|
|Output Tariffs (Effective)|||||-0.000758<br>(0.00339)|||||-0.000725<br>(0.00351)|
|FDI Flows in the Service||||||-0.124|2.817|3.338|3.307|3.556|
|Sector||||||(0.127)|(3.735)|(3.077)|(3.097)|(3.212)|
|Constant|0.336***|0.382***|0.393***|0.393***|0.389***|0.0954***|0.284***|0.288***|0.291***|0.299***|
||(0.0437)|(0.0558)|(0.0479)|(0.0489)|(0.0471)|(0.0130)|(0.0317)|(0.0308)|(0.0343)|(0.0351)|
|Observations|40,336|40,336|40,336|40,336|40,208|164,908|40,336|40,336|40,336|40,208|
|Firm-level Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects<br>Standard Errors|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered ISIC<br>2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|Yes<br>Clustered<br>ISIC 2/Year|
|R-squared|0.026|0.027|0.029|0.029|0.029|0.026|0.026|0.028|0.028|0.029|
|Number of id|25,492|25,492|25,492|25,492|25,425|29,456|25,492|25,492|25,492|25,425|
|Robust standard errors in<br>parentheses|||||||||||

*** p<0.01, ** p<0.05, * p<0.1

21
negative effect of political monopoly may reflect other omitted governance variables, we also further control for “voice and accountability” and executive competition.

Third, some argue that Africa is particularly affected by a “natural resource curse”. Some economists have argued that SSA countries are more likely to be located in tropical areas, which have poorer quality of soil and a worse disease environment and are therefore bad for development (Sachs and Warner 1997). Other economists have argued that countries with a heavy reliance on natural resource exports tend to have worse economic performance due to a number of reasons such as the lack of diversification. To capture the natural resource curse, we include three measures: the share of land area in the tropics, the ratio of petroleum exports to GDP (lagged by one year), and the ratio of raw material exports to GDP, lagged by one year.<sup>31</sup> Fourth, since the business environment is a large category that covers many aspects, we try to control for further subjective measures, including the local share of firms viewing the following as moderate or severe constraints: electricity, transport, informal competition, tax rate, land access, financial access, and labor regulation. Since entry barriers are particularly important (Klapper, Laeven and Rajan 2006), we also control for an objective measure of entry barrier, namely, the logarithm of minimum capital to start a business (from Doing Business).

Overall, our results are generally robust (see Table 7). Africa’s advantage in TFP and labor productivity remain, with small changes in magnitude. However, the African conditional disadvantage in export intensity becomes more pronounced, at 5 percent and significant. The positive effects of telecom and the negative effects of bribes become significant for investment rates. The negative effect of firing difficulty on productivity growth also becomes significant. The negative effects of political monopoly on productivity and sales growth rates remain

> 31 The first measure is from http://www.cid.ha, while the latter two are both from WDI of the World Bank.

26
Policy Research Working Paper 10804 Household Business Performance in Ghana The Role of Personality Traits and Gender Role Attitudes _Nkechi S. Owoo Akuffo Amankwah Pauline Castaing Amparo Palacios-Lopez_ Development Economics Development Data Group June 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
# **Locating Crash Events**

As demonstrated in Table S1 in the example tweets from @Ma3Route, the geoparser has to handle different tweets in different ways. For example, tweet 1 is simple, including the name of one road and one landmark. Tweet 3 is short and clear as well; however, it identifies the crash location by a junction instead of a landmark. Tweet 8 uses the Swahili word ”apo”, which is commonly in front of a landmark word. Accident 2 includes the location of the crash and the location where traffic starts. This section outlines in detail the different components of the geolocation algorithm, which are meant to handle these situations.

The algorithm to locate an event location from text starts by cleaning the text and extracting location names of landmarks, roads and areas (e.g., neighborhoods) from the text. Next, the algorithm restricts location names and their locations to consider; for example, if two landmark names are found, and one is contained within the other, we only keep the longer one; in addition, where possible, we restrict locations to those near mentioned roads. The algorithm then chooses the location names that reference the event location, prioritizing location names primarily by the words that precede them (e.g., “just after [location]” is used over “toward [location]”). If the chosen location is not near a mentioned road, we search for landmarks that have a similar name but are near a mentioned road. Next, we snap the location to the road network. Finally, the algorithm implements select checks to determine whether no location should be outputted; for example, if a road is mentioned but the chosen location is not within 500 m from any mentioned road, the algorithm does not output a location. The algorithm is described in detail below.

S15
43 with all vaccinations at district level. Vaccination records are not available for deceased children. For definition of "all vaccinations" see Appendix. Significance levels are marked as follows: * 10%, ** 5%, *** 1%.

**Table 6: Regression results for infant mortality in 2003 and 2008 in Kenya**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Intermediate malaria risk zone (IMRZ)|0.0124|0.0121|0.0157|0.0128|0.0136|0.0152|0.0080|
|High malaria risk zone (HMRZ)|0.0720***|0.0701***|0.0774***|0.0761***|0.0743***|0.0757***|0.0473**|
|Interaction LMRZ and ITN ownership|0.0222|0.0224|0.0190|0.0207|0.0200|0.0193|0.0225|
|Interaction IMRZ and ITN ownership|-0.0097|-0.0098|-0.0125|-0.0083|-0.0061|-0.0076|-0.0113|
|Interaction HMRZ and ITN ownership|-0.0440**|-0.0439**|-0.0477**|-0.0438**|-0.0447**|-0.0470**|-0.0506***|
|2008 survey|-0.0145|-0.0119|-0.0072|-0.0087|-0.0075|-0.0059|-0.0144|
|Full immunization (district level)||-0.0167|-0.0218|-0.0155|-0.0219|-0.0222|0.0347|
|Birth in clinic|||0.0218**|0.0268**|0.0269**|0.0258**|0.0233**|
|Antenatal tetanus protection|||0.0078|0.0077|0.0081|0.0077|0.0094|
|Antenatal antimalarial drugs|||-0.0107|-0.0104|-0.0103|-0.0112|-0.0085|
|Antenatal iron supplement|||-0.0033|-0.0035|-0.0047|-0.0049|-0.0087|
|Unimproved water source (not surface)||||-0.0168|-0.0166|-0.0177|-0.0199|
|Improved water source||||-0.0066|-0.0061|-0.0095|-0.0091|
|Unimproved sanitation||||-0.0134|-0.0168|-0.0218|-0.0200|
|Improved sanitation||||-0.0382**|-0.0487**|-0.0553**|-0.0555**|
|Mother has primary education|||||0.0193|0.0189|0.0086|
|Mother has secondary education|||||-0.0060|-0.0072|-0.0190|
|Mother has tertiary education|||||0.0622|0.0623|0.0484|
|2nd wealth quintile||||||0.0004|-0.0013|
|3rd wealth quintile||||||0.0281*|0.0295*|
|4th wealth quintile||||||0.0013|0.0059|
|5th wealth quintile (richest)||||||0.0152|0.0224|
|Rural area|||||||0.0219|
|Central province|||||||-0.0343|
|Coast province|||||||-0.0186|
|Eastern province|||||||-0.0471|
|Nyanza province|||||||0.0387|
|Rift Valley province|||||||-0.0281|
|Western province|||||||-0.0110|
|North Eastern province|||||||0.0028|
|Constant|0.0539***|0.0633***|0.0539***|0.0690***|0.0622***|0.0600***|0.0470|
|Number of observations|4668|4668|4668|4668|4668|4668|4668|
|R-squared|0.0120|0.0122|0.0143|0.0163|0.0194|0.0211|0.0277|
|F-statistics|3.6371|3.1176|3.0139|2.6566|2.4190|2.4445|2.2881|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata.

Notes: Linear probability model, pooled sample. Dependent variable is a dummy variable on child's survival status. It takes value 1 if the child died in postneonatal or infant period. For categorical variables with more than two categories, the left-out categories are: Low malaria risk zone (LMRZ), Surface water, No sanitation, Mother has no
while a stable flow of individuals continue to emigrate towards increasingly more distant destinations, a growing number of returnees are establishing residence back home. A fair share of these returnees use (some of) their foreign earnings to set up low-return family businesses, often small replicas of the businesses they were exposed to while abroad (Labrianidis and Hatziprokopiou, 2006). The probability of establishing such small enterprises increases with the time they spent abroad (Kule et al., 2002).

In view of its increasing importance, and the dearth of information on return migration, this study uses data from the 2005 Albania Living Standards Measurement Study (ALSMS05) survey and assesses the impact of past migration experience of Albanian households on non-farm business ownership. We also hypothesize that such impact will depend on the earning potentials and opportunities for skill acquisition in different destination countries. For this purpose we differentiate the impact of past migration experience by main migrant destinations, namely Greece and Italy<sup>5</sup> . We also test for the hypothesis of the existence of migration cycles, by differentiating the time spent abroad based on the year of return. The assumption is that earlier returns are more indicative of stability in Albania, thus being positively associated with business ownership. The impact of more recent migration is ambiguous, as migrants are less likely to have completed their “migration cycle” or to have accumulated enough savings.

The remainder of the paper is organized as follows. Section 2 attempts to establish the theoretical link between past migration and business ownership, based on a succinct literature review. Section 3 describes the data set, while section 4 presents the empirical approach adopted and the model. In Section 5, we report the regression results, before concluding in Section 6.

# **2. Exploring the Nexus between Return Migration and Non-farm Investments**

In order to provide context for the analysis of the role of household past migration experience on non-farm business ownership, we first provide an overview of contending migration theories underlying the development of the empirical model.

> 5 An increasing number of Albanians are now migrating to further destinations, such as the United Kingdom, Germany, the United States and Canada.  However, the numbers are still quite small and in this paper we combine the migrants to these countries into an “Italy and beyond” category.

- 4 -
# **Table 3e.  Trade Flows of COMESA**

|||Imports|||Exports||
|---|---|---|---|---|---|---|
||USD in|||USD in|||
||<br>billion|% of total|% of supply|<br>billion|% of total|% of output|
|**Business Services**|||||||
|Air Transport|1.3|2.0|49.2|2.1|4.0|63.4|
|Communication|0.4|0.6|4.1|1.3|2.4|16.3|
|Insurance|1.0|1.5|26.8|0.3|0.5|10.5|
|Business Services nec|2.9|4.4|16.1|1.7|3.2|11.6|
|Financial Services nec|0.2|0.3|2.9|0.4|0.7|7.1|
|Transport nec|1.2|1.8|8.5|8.1|15.4|44.2|
|Water Transport|0.2|0.3|7.4|0.2|0.4|7.1|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|13.7|20.9|32.1|6.6|12.4|19.9|
|Energy and Minerals|1.7|2.6|7.9|14.6|27.5|42.8|
|Food Products|4.4|6.7|11.7|2.0|3.8|6.0|
|Petroleum and Coal Products|3.4|5.2|15.9|3.3|6.2|15.8|
|Other Manufacturing|20.1|30.7|49.4|1.7|3.1|8.5|
|Textile and Apparel|3.6|5.5|14.7|3.1|5.9|13.6|
|Wood and Paper Products|2.6|3.9|25.6|0.6|1.1|7.9|
|**CRTS**|||||||
|Agriculture and Forestry|5.0|7.6|11.2|2.7|5.1|6.5|
|Other Services|3.0|4.6|3.6|3.6|6.8|4.2|
|Trade|0.9|1.4|3.1|0.8|1.5|2.8|
|Utilities|0.1|0.1|0.5|0.1|0.2|0.8|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

51
**Table 3. Addressing possible endogeneity concerns about access to credit**

|**Explanatory Variables**|**Dependent Variable: The fir**<br>|**m introduced a newpr**<br>|**oduct or service**<br>|
|---|---|---|---|
|Has a bank account?|0.002|0.002|0.002|
||(0.032)|(0.033)|(0.032)|
|Positive credit shock|0.047|0.044|0.045|
||(0.039)|(0.037)|(0.039)|
|Positive credit shock X State bank|0.172***|0.053|0.173***|
||(0.040)|(0.041)|(0.041)|
|Positive credit shock X State bank||0.206*||
|X bank health||(0.088)||
|Positive credit shock X Foreign|0.024|0.064|0.025|
|bank|(0.051)|(0.059)|(0.050)|
|Positive credit shock X Foreign||-0.057||
|bank X Bank health index||(0.048)||
|Positive credit shock X State|||0.897***|
|foreign bank|||(0.126)|
|Ln(age)|-0.002|-0.001|-0.002|
||(0.009)|(0.009)|(0.009)|
|20-99 emloees|0.031|0.033|0.031|
|py|(0.017)|(0.018)|(0.017)|
|>99 emloees|0.036|0.038|0.035|
|py|(0.022)|(0.020)|(0.022)|
|SOE|-0.206**|-0.171**|-0.207**|
||(0.072)|(0.052)|(0.073)|
|Fi ?|0.103**|0.103**|0.103**|
|oregn company|(0.031)|(0.031)|(0.031)|
|Country FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|R2|0.13|0.14|0.13|
|N|22080|22080|22080|

Note: OLS regression. The dependent variable is whether the firm has introduced a new or improved product or service over the last three years ("innovation diffusion or new to the firm"). Robust standard errors clustered at the sector level are reported in brackets. ***, **, * represent significance at the 1%, 5%, and 10% level.

# **3.2.Discussion of the transmission channels for state banking and innovation**

Borrowing from state-owned banks can help firms innovate in several ways. This subsection discusses some possible ones. First, state-owned banks may ease access to external finance for risky firms by being more willing to lend to firms that may not qualify for loans from private banks. By having access to capital, firms can invest in research and development, hire new employees, or purchase new equipment or technology to innovate (Berger, Klapper, Peria & Zaidi, 2008; Mohsni & Otchere, 2014). However, the large firms may benefit from the relationship with a state-owned bank and not the smaller firms that are 8