rafmacalaba/datause-encoder-data huggingface dataset:
|Hired Labor Days per Acre (log)|0.021***||-0.019|0.022***|-0.005|
|---|---|---|---|---|---|
||(0006)||(0035)|(0006)|(0032)|
|Reverse Dummy for Family Labor|.<br>-0.003<br>||.<br>-0.001<br>|.<br>-0.003<br>|.<br>-0.014<br>|
||(0.005)||(0.010)|(0.004)|(0.009)|
|Reverse Dummy for Hired Labor|<br>0.002||<br>-0.064|<br>0.002|<br>-0.069|
||(0.005)||(0.110)|(0.005)|(0.103)|
|Aggregate of Physical Inputs|<br>0.019***||<br>0.009|<br>0.025***|<br>0.007|
||(0.005)||(0.011)|(0.005)|(0.010)|
|Any Pesticides Applied|0.009***||-0.002|0.015***|0.002<br>|
||(0.003)||(0.007)|(0.003)|(0.006)|
|Any Manure Applied|<br>0.002*||<br>0.008|<br>0.000|<br>0.006|
||(0.001)||(0.006)|(0.001)|(0.006)|
|Any Fertilizer Applied|<br>-0.002||<br>-0.002|<br>-0.002|<br>-0.001|
||(0.002)||(0.003)|(0.002)|(0.003)|
|Improved Seeds|<br>0.010***||<br>0.006|<br>0.012***|<br>-0.000|
||(0.003)||(0.007)|(0.003)|(0.006)|
|Any Erosion Control or Water Harvesting Structure|0.001||0.017|0.001|0.015|
|<br>|(0.001)||(0.029)|(0.001)|(0.019)|
|**Individual & Community Characteristics**||||||
|Manager Received NAADS Extension Services||||0.005**|0.005|
|||||(0.002)|(0.013)|
|Manager Received Other Extension Services||||0.000|0.007|
|||||(0.003)|(0.009)|
|Education Years of Manager||||0.010|-0.072*|
|||||(0.009)|(0.043)|
|Value of Household Assets||||-0.004**|0.031|
|||||(0.002)|(0.020)|
|Value of Household Livestock||||-0.000|0.036***|
|||||(0.000)|(0.013)|
|Child Dependency Ratio||||-0.000|0.125***|
|||||(0.001)|(0.042)|
|Number of Household Members||||<br>0.001|<br>0.028|
|||||(0.001)|(0.074)|
|Age of Manager Binary Variables||||<br>-0.001|<br>0.001|
|||||(0.007)|(0.013)|
|HH Distance to Nearest Major Road (Km)||||-0.003|0.061*|
|||||(0.002)|(0.034)|
|Deviation from Average Rainfall & Squared||||-0.002|0.017|
|<br>Deviation from Average Rainfall||||(0.001)|(0.025)|
|<br>Western Region||||<br>-0.036***|<br>0.040***|
|||||(0.005)|(0.014)|
|Northern Region||||-0.003<br>|-0.004<br>|
|||||(0.003)|(0.015)|
|Eastern Region||||<br>-0.007**|<br>-0.008|
|||||(0.003)|(0.012)|
|Central Region||||-0.003|-0.017*|
|||||(0.003)|(0.010)|
|Household Fixed Effects|-0.021||0.060|||
||(0.032)||(0.039)|||
|Community-Season Fixed Effects|<br>-0.022||<br>0.374***|||
||(0.032)||(0.098)|||
|Constant Term|||<br>-0.020||0.390*|
||||(0.249)||(0.231)|
|Number of Observations||6,999||6,|<br>999|
|note: *** p<0.01, ** p<0.05, * p<0.1||||||

21
# **3 How to use the** **`outdetect` command**

This section is devoted to illustrating the typical use of `outdetect` . After explaining the syntax, we provide examples of the command “in action”, using a sample of 12,447 households from Malawi’s Fourth Integrated Household Survey (2016-2017).<sup>9</sup> `outdetect` can be installed from the Statistical Software Components Archive by typing `ssc install outdetect` . Stata 15.1 is the earliest version that can run `outdetect` . The core of the command is written entirely in Mata to minimize computation time. The command handles complex survey settings automatically, when `svyset` is used to declare the sampling design features of the data to Stata (see `help svyset` ). The use of `pweights` is also allowed. The general syntax of the command is as follows:

## `outdetect` _varname_ [ _if_ ] [ _in_ ] [ _weight_ ] [ _, options_ ]

`outdetect` identifies extreme values, either “too small” or “too large” observations, in the distribution of `varname` . We shall call these observations bottom outliers and top outliers, respectively (small values being at the bottom of the distribution of `varname` , and large values being at the top). By default, `outdetect` creates a new variable, _ `out` , containing numeric codes that flag outliers of `varname` : 0 observation is not an outlier 1 observation is a bottom outlier (“too small”) 2 observation is a top outlier (“too large”) The output of `outdetect` reports “Raw” statistics (computed using `varname` as is), as well as “Trimmed” statistics (computed using just those observations of `varname` that are not flagged as outliers). A full description of all the available options is provided in the `outdetect` help file.

## **3.1 Basic usage**

In this example, the target variable is per capita annual household expenditure (here called `pce` ) in Malawi (monetary amounts are expressed in thousands of Malawian kwacha

> 9 The data set is part of the Rural Livelihoods Information System (RuLIS), and is also publicly available at the World Bank Microdata Library.

11
household members that have returned from abroad in the past 5 years and when they returned, but with no additional information. In contrast with Bangladesh and Nepal, no information on current migrants is collected. As the number of return migrants from overseas captured by each wave of the Pakistan LFS can be small relative to the working age population, we pool together the 2014/2015 and 2017/2018 waves of the survey to increase the number of return migrants in the Pakistan sample. In all three countries, an individual is categorized as a return migrant if he has returned from overseas in the past 5 years, which is the recall period used in all three surveys. Return migrants in the sample are thus relatively recent returnees, which must be kept in mind in the interpretation of the results. For all three countries, since over 95% of temporary migrants (and thus return migrants) are males, we restrict our analysis to males of working age (between age 20 and 59).

# **3.2 Descriptive Statistics**

Descriptive statistics of the sample of return migrants compared to nonmigrants are reported in Table 1 for individuals aged 20 to 59, as well as the p-value for the corresponding t-tests between two samples.<sup>8</sup> As shown in Table 1, return migrants to South Asia are by a very large majority males, reflecting the very low share of female migrants (Ahmed and Bossavie 2021).<sup>9</sup> This gender pattern is driven by very low levels of domestic female labor force participation (Farole et al. 2017; Amir et al. 2018; Bulmer, Shrestha, Marshalia, et al. 2020), combined with difficult work conditions and risk of abuses at destination, as well as regulations in sending countries which in some cases explicitly restrict female migration to avoid abuses (Ahmed and Bossavie 2021). Given the very low incidence of migration among female workers, we restrict the remainder of our analysis of return migrants’ characteristics and labor market outcomes to males.

As shown in Table 1 and Figure 1, return migrants are somehow higher skilled than nonmigrants in all three countries. The difference in the average years of schooling attained by return migrants and nonmigrants is substantial and statistically significant in Bangladesh, Nepal and Pakistan. In particular, the share of return migrants who have not attended school is much lower than for nonmigrants in all three countries, and workers with lower secondary education are over-represented among return migrants. However, return migrants are less likely to have some tertiary education than nonmigrants, especially in Nepal and Pakistan.

> 8Summary statistics for the full samples of male workers in Bangladesh, Nepal and Pakistan are reported in Table A1.

> 9In Bangladesh, according to the Bangladesh Return Migration Survey (BRMS), about 4 percent of return migrants from overseas are females, a similar share as the proportion of female outmigrants from the country. In Nepal, the proportion of females among return migrants is also around 4 percent and lower than among current migrants, which partly reflects an increase in female migration in recent years.

8
cross equation restrictions<sup>comingfrom</sup><sup>**b.**Onewaytoimposetheserestrictionsis to</sup> first estimate the global factor<sup>using principal componentsandthenuseit in the system</sup> of equations described<sup>**by (1)-(3).**</sup> The empirical section<sup>thatfollowsalsopresentsresultsfromamodel whichisesti-</sup> mated under the assumption<sup>thatwe,aseconometricians, observethe global factors.In</sup> this approach we use<sup>thesameglobalvariablesthatwereusedintheprincipalcompo-</sup> nents analysis as the<sup>_defacto_globalfactors.Intherobustnesssectionweestimatethe</sup> latent global factor<sup>with</sup><sup>**GMMby**imposing the cross equation restrictions directlyin the</sup> estimation of the<sup>investment equation</sup><sup>**(1).**</sup>

# **3 Empirical Analysis**

In this section, we implement<sup>andestimatethemodel describedin equations</sup><sup>**(1)-(3)**us-</sup> ing cross-country<sup>andtime-seriesdata.Throughthiscountry-levelexerciseweexamine</sup> the significance of<sup>varioustheorieson thedeterminantsof foreigndirectinvestmentdis-</sup> cussed above. Furthermore,<sup>weusetheestimatedmodel andtheglobalizationmeasure</sup> to ascertain the relative<sup>importanceof localand global factors in explainingthe variation</sup> of foreign direct investment.<sup>**By**examiningthe time seriesbehavior of the globalization</sup> measure and how it<sup>differsacrossindustrialanddevelopingcountries,wederiveconclu-</sup> sions on how the process<sup>of market integrationisaffectingthebehaviorof foreigndirect</sup> investment.

This section is organized<sup>aroundthemajorempiricalobjectivesandcorresponding</sup> exercises outlined above.<sup>First,wepresentthevariablesincludedin our empiricalmodel</sup> of foreign direct investment<sup>andprovidetheirbasicdescriptivestatistics.Second,we</sup> discuss the results of<sup>foreigndirect investmentregressions for various countrysamples and</sup> different restrictions<sup>oncountry-specificparameters.And third,wereportanddiscuss</sup> the variance decomposition<sup>resultsand theglobalizationmeasure.</sup>

**_9_**
Deign, Jason (2019). 8 Countries (Besides the US) With Solar Under $25 per Megawatt-Hour. GTM, October 31, 2019. <u>https://www.greentechmedia.com/articles/read/8-countriesbesides-the-us-with-solar-under-25-per-megawatt-hour.</u> Dobrotkova, Zuzana, Kavita Surana, Pierre Audinet (2018). The price of solar energy: Comparing competitive auctions for utility-scale solar PV in developing countries. Energy Policy, Vol. 118, pp. 133-148.

Energy Information Administration (EIA) (2020). Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2020. Available at https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf (accessed on April 18, 2020).

- International Energy Agency (IEA) (2015). Projected Costs of Generating Electricity; 2015 Edition. IEA, Paris.

International Renewable Energy Agency (IRENA) (2018), Renewable Power Generation Costs in 2018, IRENA, Abu Dhabi.

International Renewable Energy Agency (IRENA) (2020). Renewable Power Generation Costs in 2019. IRENA, Abu Dhabi.

Joskow, P.L. (2011). Comparing the costs of intermittent and dispatchable electricity generating technologies. American Economic Review 101 (3):238-241.

Kelly-Detwiler, Peter (2020). Record Low Solar Prices in Qatar. Forbes, Feb 3, 2020. <u>https://www.forbes.com/sites/peterdetwiler/2020/02/03/record-low-solar-prices-in-qatarand-four-other-energy-stories-that-matter-from-week-5-of-2020/#4c8063f12215.</u>

- Landis, Florian and Govinda R. Timilsina (2015). The Economics of Policy Instruments to Stimulate Wind Power in Brazil, World Bank Policy Research Working Paper, WPS 7376, World Bank, Washington, DC.

Lazard (2019). Lazard’s Levelized Cost of Energy Analysis - Version 13.0. <u>https://www.lazard.com/perspective/lcoe2019</u> Available at (Accessed December 2019).

35
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Zimbabwe’s fuel levy and public contribution fall short of the road network’s maintenance and rehabilitation needs**

<!-- Start of picture text --> Niger<br>Sierra Leone<br>Zimbabwe<br>Cote d'Ivoire<br>Ghana<br>Benin US cents per liter<br>Mali<br>0 10 20 30 40 50<br>Optimal fuel levy for maintenance plus rehabilitation Optimal Implicit Actual<br><!-- End of picture text --> _Source:_ Gwilliam and others 2008.

**Figure 14. Trading across borders is expensive in Zimbabwe**

<!-- Start of picture text --> a. Export costs b. Import costs<br>CAR CAR<br>Niger Zimbabwe 5.101<br>DRC Rwanda<br>Zimbabwe 3.28 DRC<br>Rwanda Niger<br>Botswana Botswana<br>Zambia Zambia<br>Sudan Sudan<br>SSA 1.96 SSA 2.49<br>South Africa South Africa<br>Tanzania Tanzania<br>OECD OECD<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>US$ thousand per container, year 2010 US$ thousand per container, year 2010<br><!-- End of picture text --> _Source:_ World Bank’s ―Doing Business‖ Database 2010. _Note:_ CAR = Central African Republic; DRC = Democratic Republic of Congo; OECD = Organisation for Economic Co-operation and Development; SSA = Sub-Saharan Africa.

By far the most problematic road border crossings in southern Africa are those at Beitbridge (Zimbabwe to South Africa) and Chirundu (Zambia into Zimbabwe). The combined four-day delay at this pair of crossings is much higher than at any other major crossings in Africa, meaning as much as 50 percent of transport time between Durban and Lusaka is lost at the Zimbabwean border. There is therefore an urgent need to create one-stop border crossings at these locations, and to rehabilitate existing border-post infrastructure. The overall investment cost of improving the performance of border posts along the corridor is relatively modest, lying between $20 million and $30 million. But challenging 33
Table 2: The impact of Chinese unilateral trade liberalization on the intensive margin of exports

|Dependent variable|Log|Export sales of frm|_f_ in country _j_ inye|ar_t_|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Tariff_fj,t−_1|-0.868***|-0.876***|-0.834***|-0.350*|
||(0.182)|(0.182)|(0.184)|(0.195)|
|Tariff_fj,t−_1_×_China_j_||||-2.678***|
|||||(0.416)|
|RER_j,t−_1||-0.536**|-0.279|-0.441**|
|||(0.212)|(0.221)|(0.221)|
|GDP_j,t−_1|||0.305***|0.169|
||||(0.117)|(0.118)|
|Firm fxed effects|Yes|Yes|Yes|Yes|
|Country fxed effects|Yes|Yes|Yes|Yes|
|Year fxed effects|Yes|Yes|Yes|Yes|
|Observations|34,327|34,327|34,327|34,327|
|_R_<sup>2</sup>|0.574|0.574|0.574|0.576|

_Notes_ : The regressions are OLS estimations of equation (2) for the period 1999-2005. The dependent variable is the logarithm of firm _f_ ’s export sales to country _j_ in year _t_ . Fixed effects by firm, country and year and a constant are included in all specifications. The destinations are China and other Asian countries that already integrate WTO such as India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand. Tariff _fjt_ is the tariff measure faced by firm _f_ when exporting to country _j_ in year _t_ . Tariff _fjt ×_ China _j_ is an interaction term between the firm level tariff measure and a dummy variable equal to one when the country of destination of exports is China and zero otherwise, China _j_ . GDP _jt_ is the natural log of the GDP of country _j_ from the Penn World Tables. RER _jt_ is the bilateral real exchange rate between France and China and countries in the control group using producer prices of France and importer countries from the International Financial Statistics (IFS). Heteroskedasticity-robust standard errors clustered by firm-country pairs are reported in parentheses. _∗∗∗, ∗∗,_ and _∗_ indicate significance at the 1, 5 and 10 percent levels respectively.

# _Unilateral Trade Liberalization and Foreign competition_

The findings presented in the previous section might suffer from an important omitted variable bias related to foreign competition faced by each firm in the destination market. To control for this issue, the baseline specification in (2) is extended to include our three firm-level measures of foreign competition.<sup>20</sup> A positive and significant coefficient for both Herfindahl indexes implies that an increase in foreign competition in destination _j_ faced by each firm (i.e. a reduction in the firm level Herfindahl concentration index) reduces the number of products exported and export sales to that destination. On the other hand, for the intensive margin competition measure, we expect a negative and significant coefficient. This measure captures the variation on the volume of imports by incumbent competitors towards the liberalized market. Chinese unilateral liberalization will increase the volume of imports of firms/countries 19
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data The next table reports the estimates for the MIMIC model using Maximum Likelihood estimation.<sup>29</sup> Panel A presents the results for the structural equation with dependent variable (equation 7) and panel B and panel C show the results for the two measurement equations with dependent variables log of sales reported to the tax office (equation 10) and log of sales reported in the survey (equation 11) respectively. Panel D provides the multivariate normality tests on the residuals. Column (1) shows the results without IC variables, column (2) and column (3) add _credit_ and _corruption_ respectively, and column (4) adds both. Notice that we drop the constant in equation 10) as this allows for identification of the model without relying on the non-linear constraint (see Appendix B) and, unlike the other two constants in the model, was found to be insignificant.

Panel A shows the estimates for the structural equation for the latent variable of true sales. The coefficients for the three industry dummies suggest that true sales in construction, service and tourism are respectively 32%, 26% and 171% higher than in manufacturing (column (4))<sup>30</sup> , although only the coefficient for tourism is significant. The coefficients for _medium_ and _large_ are both significant at 5% level and imply that firms of medium and large sizes produce almost 0.6 and 2.7 times more than small firms respectively, holding everything else constant. oreover, firms’ sales increase with increasing capital and higher capacity utilization. The dummy variables _credit_ and _corruption_ control for the impact of the investment climate on productivity and prices and consequently on true sales. The

> 29 The estimated coefficients of the city dummies in are small and (jointly) insignificant and were excluded from in the final specification. Similarly the estimated coefficients for Skill and Experience in and Capacity in were insignificant and small and therefore omitted from the final (more parsimonious) specification.

30 Exp(0.279)-1=0.32, exp(0.234)-1=0.26, and exp(0.998)-1=1.71.

26
# **Table 4. Augmented Gravity Model Estimations**

Dependent variable: Total bilateral exports (in logs)

|||Specification||
|---|---|---|---|
|Variable|14|15|16|
|Log of exporter's GDP|1.207|1.251|1.265|
||(64.35)|(64.77)|(75.57)|
|Log of importer's GDP|0.962|1.000|0.956|
||(47.38)|(48.23)|(54.17)|
|Log of distance|-1.383|-1.395|-1.390|
||(-38.27)|(-38.64)|(-39.02)|
|Log of exporter's GDP|0.323|0.082||
|per capita|(7.46)|(1.46)||
|Log of importer's GDP|-0.001|-0.195||
|per capita|(-0.02)|(-3.59)||
|Log of exporter's average time|-0.519|-0.349|-0.373|
|for all procedures|(-7.37)|(-4.68)|(-5.24)|
|Log of importer's average time|-0.137|0.124|0.171|
|for all procedures|(-1.87)|(1.53)|(2.13)|
|Log of exporter's MaxTime-AvgTime|-0.273|-0.235|-0.236|
|difference|(-5.07)|(-4.25)|(-4.28)|
|Log of importer's MaxTime-AvgTime|0.189|0.120|0.090|
|difference|(2.45)|(1.57)|(1.18)|
|Log of importer's total cost|-0.569|-0.518|-0.492|
|of import-related procedures|(-12.14)|(-11.0)|(-10.68)|
|Exporter's Corruption Perception Index||0.165|0.188|
|||(7.2)|(10.82)|
|Importer's Corruption Perception Index||0.183|0.134|
|||(7.22)|(6.27)|
|Regional trade agreement|0.270|0.332|0.343|
|dummy variable|(3.71)|(4.58)|(4.73)|
|_K_|12|14|12|
|_R_-squared|0.712|0.717|0.717|
|Adjusted_R_-squared|0.711|0.717|0.716|
|Observations|5149|5149|5149|
|_F_-statistic|1305.7|1101.0|1287.0|

Note: Dependent variable is total bilateral exports (in logs) in 2003 or latest year available. Corruption Perception Index is for 2004, from Transparency International. Figures in parentheses are _t_ -statistics (based on robust standard errors). Constant terms are not shown. Source: Authors' calculations.

18
- 21 -

1990-2000. Summer data are taken from either January or July whichever has higher monthly temperature.

Finally, the exchange rate data are calculated from _International Financial Statistics_ ; the exchange rate variable in our model is defined as annual changes in the euro per the local currency. The exchange rate appreciation is expected to increase product prices at the destination market and reduce competitiveness.

# **IV. ESTIMATION RESULTS AND POLICY IMPLICATIONS**

**Coffee and cocoa.** The three-stage least squares estimation is performed for coffee and cocoa, and dairy products separately (Tables 6 and 8). First of all, in the coffee and cocoa case, the price coefficient is negative and significant; an increase in product prices would lower competitiveness and reduce the market share.
# **Figure A.3. Predicted USAID FGT indexes, Tanzania 2019/20- 2020/21**

<!-- Start of picture text --> Panel 1: FGT 1 Panel 2: FGT 2<br>14<br>30<br>28 12<br>26<br>10<br>24<br>8<br>22<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 3: FGT 3 Panel 4: FGT 4<br>5<br>7<br>4<br>6<br>5 3<br>4 2<br>3 1<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 5: FGT 5<br>3<br>2.5<br>2<br>1.5<br>1<br>.5<br>m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

60
_Figure 5: Relationship between initial labor productivity and its growth across Peru's departments_ The results of our convergence regressions presented in **Table 7** confirm that initially lagging departments, in terms of labor productivity, have been catching up in the subsequent decade. This result is robust to estimating pooled convergence regressions for all sub‐sectors of the economy and conditioning on department fixed effects and initial poverty levels ( **Table 8** ). The rate of convergence is 1.9 percent; this is slower than the one for either manufacturing or mining and is driven by the fact that labor productivity in other sub‐sectors (i.e. agriculture, services) is not converging robustly. As shown earlier, while agriculture is converging, the rate is low, and statistically significant only in the conditional convergence specifications. In the tertiary sector, only the restaurant and hotels industry exhibits evidence of convergence.

**_Table 7: Labor productivity convergence regressions, All sectors_**

|||Disaggre|Overall<br>ated by|||
|---|---|---|---|---|---|
||Total<br>aggregation<br>|<br>Primary, Se<br>Terti<br>|<br>condary &<br>ary<br>|Disaggreg<br>sect<br>|ated by all<br>ors<br>|
||(1)|(2)|(3)|(4)|(5)|
|Dependent variable:||||||
|Growth of VAper worker over a decade||||||
|Log initial VA per worker|‐0.019***|‐0.019***|‐0.030**|‐0.020***|‐0.048***|
||(0.007)|(0.006)|(0.011)|(0.005)|(0.013)|
|Constant|0.199***|0.197***||0.201***||
||(0.060)|(0.057)||(0.049)||
|Department FE|�|�||�||
|Industry FE|�|�||�||
|Observations|24|72|72|255|255|
|R‐squared|0.266|0.215|0.792|0.106|0.386|
|Stardard errors in parenthesis.||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||

17