rafmacalaba/datause-encoder-data huggingface dataset:
in 1960 (and 1984, when there are two endogenous variables). The identification assumption is that the past populations a↵ect the current labor outcomes only through the current population size. As is mentioned in the previous literature, we consider this instrumental variable as a strategy to reduce the bias rather than a perfect solution for the endogeneity issue.

where _Zj_ is historical population of town _j_ . The second stage estimation is To examine heterogeneity in the agglomeration e↵ects, we estimate the 2SLS models in equations (3) and (4) for subsamples based on workers’ demographic and educational characteristics as well.

# **5 Results**

## **5.1 Employment Types: Self-Employment versus Wage Employment**

We first report OLS estimation results for employment types, measured as the binary indicator. Table 2 shows the association between the probability of individuals engaging in self-employment and the town population. The baseline result with no controls in column 1 shows the coefficient estimate for the log of population as -0.067, meaning that a log increase in population size is associated with a 6.7 percentage point decrease in the chance of individuals engaging in self-employment (or the share of self-employment at the town level) as opposed to wage employment. Additional controls for Addis Ababa fixed e↵ect (column 2), regional fixed e↵ects (column 3), demographic (column 4), and education (column 5) do not substantially change the estimates.

We then estimate the 2SLS models in equations (3) and (4) to reduce an endogeneity bias. Table 3 summarizes the results of 2SLS estimations with subsamples, as well as OLS results for the sake of comparison. The results confirm that the town population size is negatively correlated with the probability of workers taking on self-employment jobs (as opposed to wage employment). The estimated probability change is 4.5 percentage points (column 2), which is smaller than the OLS estimate (5.8 percentage points in column 1). Nevertheless, the magnitude is not negligible as a log increase in population is associated with 4.5 percentage points higher chance in wage employment.

13
characteristics, including information on the timing and quality of the inputs and of the technical assistance.<sup>28</sup> For individuals in the comparison group, the survey collects information on whether they have submitted a project proposal and its current approval status.

Table 2 shows the sample structure. The baseline survey covers 309 program participants (covering 301 households and a total of 1,340 individuals) and 244 non-participants (covering 244 households and a total of 1,116 individuals). During the one year period, some productive projects failed, beneficiaries left the project or they simply could not be found by the interviewers in the follow up survey. Hence, only 86% of the individuals in the baseline survey (or 85.3% of the households) were followed up in 2005 survey.<sup>29</sup> Since we will use a differences-in-differences methodology, we will restrict the analysis to the balanced sample. Our final sample covers 476 individuals (covering 465 households and a total of 2,104 individuals) and a total of 113 projects.

Table 3 compares the demographic, employment and household characteristics in our final sample with a random sample of _Jefes_ beneficiaries in the GBA area taken from the Permanent Household Survey (EPH) at the same survey time (the end of 2004 and 2005).<sup>30</sup> Those individuals interested in starting a self-employment activity are predominantly females (as in the case of _Jefes_ , more than 2/3 of the individuals), have more than 8 years of schooling and are on average 39 years old. A notable difference with respect to the average _Jefes_ participant is due to the fact that more than 70% are heads of household. Their average household size tends to be smaller than for the average _Jefes_ (4.5 persons versus 5.2 persons in 2004). As found for other

> 28 As explained in Section 2, the associability requirement of the project required 3 _Jefes_ beneficiaries to submit a proposal for a self-employment project. However, only one of the participants reported information on project characteristics in the survey ( _Referente_ ).

> 29 Table A1 in the annex reports a probit regression of sample attrition on relatively exogenous individual characteristics. We do not find any evidence, both in treatment and in the control group, that those individuals that exit from the sample differ significantly in their socio-demographic characteristics.

> 30A random sample of 226 _Jefes_ beneficiaries in the GBA area was extracted from the Permanent Household Survey (EPH) at the end of the fourth quarter of 2004 and 2005.

17
# **Annex 1: Detailed Tables**

## **Table A 1: Balance Table for the Village Selection Model: Madhya Pradesh**

||**Me**|**an**|||
|---|---|---|---|---|
|**Variables**|**Treated**|**Control**|**Percent Bias**|**P-**<br>**value**|
|Tube Wells/Borehole Status - Census 2011|0.30|0.42|-27.20|0.06|
|Proportion of either SC or ST Population - Census 2011|0.81|0.71|40.30|0.00|
|Interaction of Proportion of Literate and Proportion of Female Population - Census 2011|0.24|0.25|-11.40|0.46|
|Households with PTG Members – SECC 2013|20.59|22.67|-6.20|0.71|
|Total Unirrigated Land Area and Area Irrigated bysource - Census 2011|296.07|284.74|4.50|0.79|
|Self - HelpGroup (SHG)Status - Census 2011|0.62|0.64|-4.00|0.78|
|Public/Private Bus Status - Census 2011|0.47|0.49|-3.90|0.78|
|Agricultural Credit SocietyStatus – Census 2011|0.10|0.09|3.10|0.81|
|Forest Area - Census 2011|169.93|302.64|-27.90|0.04|
|Proportion of Government SectorJobs Households – SECC 2013|0.04|0.03|16.20|0.19|
|Proportion of Public SectorJobs Households – SECC 2013|0.00|0.00|-11.90|0.34|
|Proportion of Private SectorJobs Households – SECC 2013|0.00|0.00|-2.10|0.63|
|Proportion OwningMobile OnlyHouseholds – SECC 2013|0.27|0.30|-11.40|0.39|
|Proportion OwningIrrigated Land Households – SECC 2013|0.08|0.10|-13.50|0.35|
|Proportion OwningUnirrigated Land Households – SECC 2013|0.35|0.28|29.50|0.07|
|Proportion OwningOther Irrigated Land Households – SECC 2013|0.03|0.04|-8.20|0.60|
|Percentage of households with main source of light as electricity– Census 2011|0.51|0.56|-12.20|0.39|
|Percentage of households with latrine facilities within thepremises – Census 2011|0.06|0.10|-26.30|0.06|
|Logof District HeadQuarter Distance – Census 2011|3.85|3.82|4.80|0.72|
|Proportion OwningIrrigation Equipment Households – SECC 2013|0.08|0.13|-28.40|0.03|
|Proportion HavingKisan Credit Card Households – SECC 2013|0.03|0.04|-14.00|0.11|
|DensityPrimarySchool Numbers – Census 2011|0.86|0.90|-1.40|0.75|
|DensitySecondarySchool Numbers – Census 2011|0.06|0.07|-1.30|0.96|
|DensityMiddle School Numbers – Census 2011|0.22|0.24|-3.70|0.75|
|Proportion of Total Population Female – Census 2011|0.50|0.50|12.10|0.29|
|Proportion of Literate Population Person – Census 2011|0.49|0.50|-13.70|0.37|
|Proportion of Non-WorkingPopulation Person – Census 2011|0.48|0.50|-33.40|0.01|
|Observations|||772||

27
Guinea is considered one of the poorest countries in Africa despite its rich endowment in agricultural, mineral and energy resources. The United Nations Report of Human Development has, for the past decade, ranked this country last or near the bottom among some 174 countries. In 1994, income poverty incidence was estimated at about 40 percent based on a poverty line set at about US$300 _per capita_ and _per annum_<sup>_10_</sup> . This is about 56 percent of the 1994 GDP per capita (US$532). Table 3.1 shows a profile of income poverty in Guinea, as measured by members of the Foster-Greer-Thorbecke (FGT) family of poverty measures and the Watts index. All measures indicate that poverty is essentially a rural phenomenon. About 53 percent of the rural population lived in poverty in 1994. The urban centers outside the capital city of Conakry represent the next poorest areas with poverty incidence estimated at 24 percent. Poverty is lowest in the capital city, regardless of the measure used.

Figure 3.1 provides a global picture of poverty for the whole country based on the FGT measures. This is the so-called _three I’s of poverty (TIP) curve_ of Jenkins and Lambert (1997)<sup>11</sup> . The curve shows graphically three dimensions of aggregate poverty as follows: (a) the length of the non-horizontal segment reveals incidence, (b) the height of the curve at any point on the horizontal segment represents intensity, and (c) the degree of concavity translates inequality among the poor.

In January 2002, the Government of Guinea published a _Poverty Reduction Strategy Paper_ (PRSP) describing an integrated and participatory approach to fighting poverty. The three pillars of this strategy are (Republic of Guinea 2002): (1) Boosting

> 10 In local currency (Guinean Francs) this was equivalent to GNF293,714.

> 11 The curve is obtained by ranking individuals from the poorest to the richest and partially cumulating individual contributions to the overall poverty. Individual contributions are based on the chosen deprivation function ψ (x|z). The three I’s stand for (1) _Incidence_ , (2) _Intensity_ and (3) _Inequality_ (among the poor).

17
The control variables used in place of the vector _X(i,t)_ in equation (1) consist of a set of binary variables identifying the date of interview of the household, and individual and demographic composition variables in each round. In particular, we include the age of the household head, his/her gender, years of education, binary variables for his/her marital status, the household demographic composition (i.e., the number of children separately by age group, adult men (and women separately) aged 19 to 54, and men (and women) over the age of 55) a binary variable indicating whether this is an indigenous household and binary variables identifying whether the household receives benefits from other programs (such as _DIF_ , _Desayunos Escolares_ , and _Oportunidades_ ).<sup>24</sup> , In specification A, in addition to the control variable s _X(i,t)_ we also include two community level variables, such as the value of the estimated marginality index<sup>25</sup> for the locality, and the distance between the community and the _"cabecera municipal”_ (the governing center of the municipality and likely the largest locality of the municipality).<sup>26</sup>

# Table 2

Table 2 presents the estimates for food and total (food+nonfood) consumption. The estimates of β 1 , β 2 and β 3 , the coefficients of T1, T2 and T3, respectively, are occasionally statistically significant which implies that there are some pre-existing differences in food <u>and</u> total consumption between each one of the three different treatment groups and the control group. These findings support the use of the DiD estimator since it is able to control for these

> 24 A household is classified as indigenous if one person, older than 18 years, speaks an indigenous language.

> 25We used the CONAPO marginality index for the year 2000.

> 26 The community level variables are excluded when village- or household-level dummies are included in the regressions.

19
<!-- Start of picture text --> The Trace tests<br>2)<br>a7s<br>150<br>1350 1304 1356: 1356: 13oT 1358 195<br>THE CRITICAL VALUES ARE NOT ¥YALID<br><!-- End of picture text --> <!-- Start of picture text --> The Trace tests<br>7 Fawe<br>1.6 if AR wR A OR A A we a eR eM AM A A AR A AM A OR OA OR OM<br>14 iow ge : iat atk. on wn AR RA RR ROAR ROR Oe em oe<br>1.1<br>ae 13 1990 194 ia 1993 ag 1994 1996 1997 age<br>THE CRITICAL VALUES ARE HOT VALID<br><!-- End of picture text --> <!-- Start of picture text --> The Trace tests<br>2t)<br>2.2<br>2.0 - oe ec<br>12 - Ce ed ce ah Ae<br>1.6 fi Ge fi fide Fe ae F Be Shoe SH GN GHENSH Gk Ge Sh Ge fh Bk Fe fh ae fe =<br>1.4 Hi BR gh =: PESe: selli a: ak ge = Hi Ho eos 2 si 2h Gh EE dk ER SE HE HE =<br>1.0<br>4290 4g94 1992 1993 qa9-4 qa95 1996 1997 4998<br><!-- End of picture text -->
To compute our measure of non-resource revenue, we use data on “General Government Revenue, Percent of GDP” from WEO and GRD from 1980–2016. For each country, we choose the dataset with the longest time series available. Nonresource revenue is computed as the difference between total revenue and resource revenue (columns (9) and (10) of Appendix Table 5). The annual change in nonresource revenue as a share of MAGDP, , is computed as above (for yy yy and ). iitt iitt ΔNNCCRRC ΔGG yy The country-specific commodity price index and the half-life are estimated iitt ΔCCRRC using the same methodology applied in Section III.B for SSA. Appendix Table 4 provides the OLS estimates of the half-life of the commodity price index for the 56 commodity exporters included in the sample. The dummy variable is calculated based on the ER index constructed in Ilzetzki et al. (2019), using the “Hard Peg” classification of Eichengreen and Razo-iitt MMPPGG Garcia (2011). Ilzetzki et al. (2019) provide a time series covering 1940–2016 for 191 countries of an index that ranges from 1 (currency union, the least flexible) to 13 (freely floating, the most flexible) (see Appendix Table 6 for a more detailed description of the ER index). A country _i_ is classified as having a fixed ER regime in period _t_ = 1, F = 0) if the ER index is equal to or less than 3 = (pre-announced narrow band), and as floating ER otherwise iitt iitt (MMPPGG FFAATT 0, F = 1).<sup>50</sup> Under this metric, about one-third of the observations in the iitt (MMPPGG sample are associated with a fixed ER, and 24 out of 56 countries have at least one iitt FFAATT year in this regime (see Appendix Table 6 for details). For robustness, in Section V.I.B we report the results using a different classification where a country is classified as having a fixed ER when the ER index is equal to or less than 7 (De

> 50 We depart from Ilzetzki et al. (2019) and classify Algeria as having a fixed ER in 2000–15. This adjustment is based on Achouche and Allaoua (2018) that identify a “fear of floating” in Algeria during the same period: “(...) throughout the study period, volatility of exchange rates is lower than that of foreign reserves: relatively, the ratio remains less than unity (...) This is consistent with the results of Hausmann et al. (2000) on de jure floating countries but de facto fixed. This finding supports the idea that foreign exchange reserves are used to defund exchange rate and the Algerian economy depend on external relations; exports and imports and its exchange rate is not defined by internal inputs.”

36
**Figure 11. Uganda’s railways, like those of the rest of East Africa, are lightly used, 2005**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>Source: AICD railways database 2009.<br>Million Traffic Units per route km<br>Sudan, SR Tanzania, TRC Tanzania, TAZARA East Africa Kenya, KRC Uganda, URC Central Africa West Africa Southern Africa North Africa<br><!-- End of picture text --> _Uganda’s railways recorded relatively high traffic by East African standards, though low in absolute terms._ (figure 11). Uganda’s railway has been recording 0.8 million traffic units annually, above the East African regional average of 0.6 million, and well ahead of rail networks in Sudan and Tanzania. Nevertheless, traffic flows on East African railways are low relative to other regions of Africa, which are themselves lightly used by global standards..

_Poor operational performance has made Uganda’s railways uncompetitive relative to roads._ Uganda’s railways have recorded low labor productivity, locomotive productivity, and wagon productivity (table 5). The recent concession of the railways of both Kenya and Uganda did _Source:_ AICD railways database 2009. not lead to immediate improvements in the performance of the system, with efficiency impeded by obsolete equipment, lack of adequate maintenance, and lack of spare parts.

_Competition from the road sector has further reduced the usage of Uganda’s railways_ . The lack of a strong marketing effort in the face of competition from the road sector has contributed to declining rail freight volumes. This was compounded by the liberalization of coffee transportation in 1990, removing the government mandate that the industry use rail transportation (World Bank 2005).

**Table 5. Performance of Ugandan railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Djibouti, CDE|||||12.5|3.1|
|Ethiopia/Djibouti, CDE|71|3,037|11|156|||
|Kenya, KRC (RVRC)|203|1,159|24|218|3.8|0.6|
|Sudan, Sudan|77|329|9|225|||
|Tanzania, TRC|259|3,264|25|583|4|1.6|
|Tanzania, TAZARA|352|2,770|13|551|3|1.1|
|Uganda, URC (RVRC)|156||9|144|||
|Rail concessions|350|2,945|23|491|5|2|

_Source:_ AICD railways database.

_Note:_ Labor productivity = ‘000s traffic units per employee; locomotive productivity = millions of traffic units per locomotive; carriage productivity = ‘000s passenger-km per carriage; wagon productivity = ‘000s net tonne-km per wagon.

# Air transport

## **Achievements**

_Uganda’s efforts to liberalize the air transport market have increased international air traffic and boosted sector performance_ . In 2001, Uganda shut down its national flag carrier and opened up the market for liberalized traffic. After the initial drop in capacity, in 2007 a new carrier—Air Uganda—was 21
by many as too low to support long‐term growth rates consistent with rapid poverty reduction.

The decline of public investment is the result of several factors. Chief among them was Latin America’s shift to a market‐oriented development model after the debt crisis of the early 1980s, and the retrenchment of the public sector from direct involvement in the production of goods and services. The latter was driven mainly by the difficult financial situation of most Latin American governments, which forced a deep and protracted fiscal adjustment in many countries. The opening up of infrastructure industries to private sector participation was an important aspect of this process—although its timing and extent varied greatly across countries and infrastructure sectors.

Political economy makes adjustment difficult at the best of times. The recent backlash against free market reforms in Latin America, and the longstanding sensitivity to conditions perceived as imposed by outsiders, makes it more important than ever that adjustment programs be well conceived.

**Figure 1**

<!-- Start of picture text --> Latin America<br>Primary deficit and public infrastructure investment<br>(percent of GDP, weighted average of eight countries)<br>8% 4%<br>6%<br>3%<br>4%<br>2%<br>2%<br>0%<br>-2% 1%<br>Primary Deficit Public Investment in Infrastructure<br>Primary Deficit Infrastructure<br>Public Investment in<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Source: Calderón and Servén (2004); and FITCH database.

2
**Table 2. Comparison of Key Characteristics by Parent Matchmaking or Not**

||Par<br>Matc<br>(1|ent<br>hed<br>)|Non-p<br>Mat<br>(2|arent<br>ched<br>)|<br>(3|Difference<br>) = (2) – (|<br>1)|
|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|t-stat.|
|Harmony|0.968|0.707|1.038|0.718|0.070|0.019|3.600|
|No conflicts|0.224|0.417|0.268|0.443|0.044|0.012|3.843|
|ln(couple income)|8.226|1.031|9.020|1.183|0.795|0.030|26.379|
|Parent match norm|0.450|0.163|0.300|0.179|-0.149|0.005|32.366|
|husband's schooling|7.677|3.153|9.249|3.120|1.572|0.083|18.903|
|schooling of the mother of the husband|1.445|2.559|2.580|3.367|1.134|0.083|13.701|
|schooling of the father of the husband|3.322|3.330|4.815|3.806|1.493|0.097|15.395|
|husband being a Communist Party member|0.140|0.347|0.237|0.425|0.098|0.011|9.177|
|number of children|1.895|1.075|1.518|0.974|-0.376|0.027|14.039|
|having 3 or more children|0.241|0.428|0.122|0.327|-0.118|0.010|12.237|
|wife's schooling|5.736|3.809|8.174|3.618|2.438|0.098|24.919|
|ln(wife income)|6.935|0.852|7.369|0.765|0.433|0.021|20.208|
|Reward providing old age support|0.446|0.497|0.448|0.497|0.003|0.013|0.195|
|Wife submissiveness|1.232|0.901|0.937|0.855|-0.295|0.023|12.651|
|Wife career unimportant|0.232|0.422|0.159|0.366|-0.073|0.010|7.103|
|No good male friend|0.484|0.500|0.336|0.472|-0.148|0.013|11.525|
|Cannot reject sex|0.517|0.500|0.445|0.497|-0.072|0.013|5.430|
|ln(avg township income)|7.661|0.860|8.401|1.087|0.740|0.027|27.407|
|avgtownshipschooling|6.682|2.018|8.389|2.330|1.707|0.059|28.837|

37
to shocks and less able to cope with those shocks. Thus, we control for livelihood zone fixed effects in our regressions of TA-level aid distribution.<sup>14</sup>

# **3.4. Additional Explanatory Variables**

<u>Explicit Household Targeting Characteristics</u> At the local level, aid distribution should be targeted to less wealthy, female-headed households, households with babies and infants (we use a dummy for households with children under three years old), households where all adults are elderly (defined as all adults older than 59), and households with any disabled adult member (Babu et al., 2018; MVAC, 2016; ALNAP, 2003).

To proxy wealth, we use an index of household durables, which is created from a principal component factor analysis of consumer durables and household dwelling characteristics<sup>15</sup> ; the number of mobile phones owned by family members; an index of agricultural implements, created from a principal component factor analysis<sup>16</sup> ; and the natural logarithm of total landholdings.<sup>17</sup> Additional household demographics include size; the maximum number of years of education completed by any household member; the proportion of adult household members who are literate in English; and a dichotomous variable for whether the household had moved during survey periods.

In addition, we include a series of dichotomous variables to control for whether the household faced other idiosyncratic shocks such as having any member that lost employment or facing high food

> 14 - Livelihood zones are constructed by FEWS NET, and were accessed at <u>https://fews.net/southern africa/malawi/livelihood-zone-map/july-2015.</u>

> 15 The index is based on (i) the dichotomous variables for whether the household has any bed, table, chair, or other living room furniture; any of fan, air conditioner, clock, or solar panel; any of radio or tape/CD/DVD player; any of sewing machine, washing machine, iron; any of TV, VCR, computer, satellite dish, or generator; and (ii) the dichotomous variables for whether the household’s dwelling has improved walls; improved roof; improved floor; improved lighting fuel; electrification; access to an improved drinking water source; access to an improved latrine; insecticide treated bed nets. The number of dwelling rooms per capita is also included in the index.

> 16 The implements include hand hose, slashers, axes, knapsack sprayers, panga knives, and sickles.

> 17 Agricultural land is “held” if it was acquired by grant from local leaders, inheritance, bride price, purchase, lease, or gift. The land of the household’s dwelling is “held” if the dwelling is owned or was authorized for free.

16
**Figure 10. Cumulative spending multiplier +/- 90 confidence intervals. Using spending forecast errors. Extreme economic expansions versus recessions, and increases (** ∆G>0 **) versus decreases (** ∆G<0 **) in spending. OECD countries.**

Panel A. Expansions & ∆G<0 Panel B. Expansions & ∆G>0 <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>0 1 2 3 4<br>Semester<br>Panel C. Recessions & ∆G<0<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>0 1 2 3 4<br>Semester<br>Cumulative multiplier<br>Cumulative multiplier<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>0 1 2 3 4<br>Semester<br>Cumulative multiplier<br><!-- End of picture text --> Panel D. Recessions & ∆G>0 <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>0 1 2 3 4<br>Semester<br>Cumulative multiplier<br><!-- End of picture text -->