rafmacalaba/datause-encoder-data huggingface dataset:
divided by GDP) was 1.29, one of the largest among FSU countries with the exception of Tajikistan (1.6) and close to Belarus (1.26).' Table 1: Source: Moldovz Economic Trends (2001) Tabls 1.1 GDP and GDP par Capft; 1993 - 28X

||199|I2|**196**|In|**1**|**1"**o|**It"**|**203**prl|
|---|---|---|---|---|---|---|---|---|
|Nominal",Lei<br>mUn1n|1,821.1|4,736.8|6,479.7|7,7976|8,917.0|9,1221|12,321.6|15,9800|
|RealGDP,year-on-year%chaMe|-1.2|**-30.9**|**-1.4**|**.59**|**1.6**|6.5|**-3.4**|**1.9**|
|**Populson,thousds**|**3,607.6**|**3,60.5**|3,603.7|**3,5090**|3,664.0|3,648.3|3,645.3|**3,63s.5**|
|A<br>**gera**<br>**EsManeRate(LeiI **USX|1.45|**4.0S**|4.49|4.59|4.63|5.38|**10.51**|12.43|
|**E,tn8**Rate,endof**pariod**(Ld_I_<br>**USS)**|**3.64**|**4.27**|**4.50**|**4.65**|**4.66**|**8.32**|_11.59_|**12.38**|
|**_GDP_**_percap'a(US$.acurltpims)_|348.1|323.3|400.5|472.0|527.1|48.8|321.6|353.5|

**Source: Depatmed Stof** and **Scdoiogy, MET** calculat*n

Furthermore, the trade intensity of the Moldova economy was concentrated on a key partner, Russia, and on other FSU countries. In 1997, about 70 percent of the exports and 52 percent of the imports were with CIS countries. In addition, besides being the most important commercial partner, Russia is also the most important direct investor (Economic Trends, 2001).

TabBe: Trade linntensity an CI[S couintries 1996-2001

|CountryName|1996|1997|1998|1999|2000|2001|
|---|---|---|---|---|---|---|
|Armenia|79.2|78.5|71.8|70.6|74.1|72.3|
|Azerbaijan|85.1|82.0|77.1|69.9|79.1|78.5|
|Belarus|96.8|125.5|123.0|120.8|137.2|82.0|
|Bulgaria|122.7|118.3|99.0|96.0|122.5|125.1|
|Croatia|89.9|97.9|88.8|89.4|95.6|101.6|
|Georgia|46.0|56.7|53.4|57.2|63.4|60.3|
|Hungary|78.8|91.0|103.3|108.5|126.5|123.1|
|Kazakhstan|71.3|72.4|65.2|82.6|106.0|98.4|
|KyrgyzRepublic|87.3|84.5|94.5|99.2|89.4|74.2|
|Moldlova|129.2|129.1|119.0|118.7|126.5|124.9|
|Romania|64.7|65.4|54.6|61.7|73.6|72.5|
|RussianFed.|45.5|45.5|57.3|71.1|68.6|61.0|
|Tajikistan|-|168.5|103.1|126.4|165.4|-|
|Turkmenistan|150.0|101.6|94.2|103.5|116.4|-|
|Ukraine|93.9|84.2|86.0|102.0|120.4|110.1|
|Uzbekistan|61.9|57.0|45.3|36.6|46.1|84.8|

Source: SIMA database.

As a comparison, a study using data for 1982 (Leamer, 1988) showed that only Singapore (1.62), French Guyana (1.25) and Brunei (1.07) have high intensities. In the study, Hungary was the only East European or non-market economy with an intensity of 0.06.

3
measure of firm-level TFP based on the cost shares from the data, as in Hau, Huang and Wang (2019). We call this TFP-CS, which appears to be less closely related to other measures of TFP.

**Table C1: The Correlation matrix of different TFP measures**

||_TFP_OP_|_TFP_OLS_|_TFP_IN_|_TFP_CS_|
|---|---|---|---|---|
|_TFP_OP_|1||||
|_TFP_OLS_|0.935|1|||
|_TFP_IN_|0.937|0.955|1||
|_TFP_CS_|0.432|0.485|0.435|1|

# **Reference**

Bentolila, S., and G. Saint-Paul. 2003. Explaining movements in the labor share. _Contributions in Macroeconomics_ 3(1).

Bloom, N., R. Sadun, and J. Van Reenen. 2012. The organization of firms across countries. _Quarterly Journal of Economics_ 127(4), 1663-1705.

Brandt, L., J. Van Biesebroeck, and Y. Zhang. 2012. Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing. _Journal of Development Economics_ 97(2), 339–351.

Hau, H., Y. Huang, and G. Wang. 2019. Firm response to competitive shocks: evidence from China’s minimum wage policy. Working paper.

Olley, G. S., and A. Pakes. 1996. The dynamics of productivity in the telecommunications equipment industry. _Econometrica_ 64(6), 1263–1297.

Perkins, D., and T. Rawski. 2008. Forecasting China’s Economic Growth to 2025. _China’s Great Economic Transformation_ , edited by Brandt, L., and T. Rawski, Cambridge University Press, 829-886.

<mark>Saint-Paul, G. and S. Bentolila. 2003. Explaining Movements in the Labor Share.</mark> _<mark>B.E. Journal of Macroeconomics</mark>_ <mark>3(1), 1-33.</mark> ``` 43 ```
the price of fuel 1 increases. This effect is stronger when the degree of fuel substitution, represented by the substitution parameter θ, is greater. This effect to some degree deters exporter taxation of fuel 1.

The second, negative, term on the right-hand side of (12) is new. It represents the effect of taxation of fuel 1 by region B, on welfare in the same region, through the effect on production and consumption of fuel 2 in region A. When s increases, region A substitutes fuel 2 for fuel 1, and thus increases its consumption of fuel 2. This increase results in increased carbon emissions. This is an externality for region B, of size αcB per unit increase in fuel 2 consumption, since region B has no direct control over the consumption of fuel 2. This effect discourages taxation of the home-produced fuel in region B.

Arguably, these additional effects are small. First, the correction of the first term depends on θ (representing the degree of substitutability of the two fuels in region A) which could be small.<sup>11</sup> More significantly, the marginal climate damage effect αc2 for region B is likely to be small. As also argued above, the coefficient α (representing marginal climate damage caused to region B, relative to that caused to region A) is reasonably small: first, as the population of region B is much smaller than that of region A (and thus the valuation of climate damages, when aggregated over all individuals in a region, much smaller for region B given equal valuation by each individual); and secondly, as the preference for avoiding climate damages per individual, as expressed through the political process, could be weaker in region B.

> 11 This may be reasonable at least when fuel 2 is interpreted as coal or natural gas, where the substitution possibilities against oil (used mainly in transport) are moderate. When fuel 2 is renewable (in particular, biofuels), our assumption here could however be less realistic. A recent meta study by Stern (2009) indicates that, while highly uncertain and variable, there is in general a high degree of substitutability between fuel types at the macro level. Average typical substitution elasticities, between oil and coal, and between oil and natural gas, are around unity; although there are many examples of estimated elasticities close to or even below zero. 13
From these findings, we qualify our previous conclusions. Households initially involved in subsistence agriculture seems to have benefited from the presence of refugees and possibly, from the Ugandan policy towards refugees. Our analysis does not lend itself to a static framework. The few who were able to switch from subsistence farming or any other initial main source of household income to commercial farming benefited more from the market advantages. Moreover, the market advantages could be more apparent for those farmers who engage in commercial vegetable production.

**Labor market participation.** Additionally, it also seems that host households who switch to wage employment also benefit from the refugee influx (Table 9). The correlation is only statistically significant when all controls are included. In an attempt to understand this potential effect, we analyze the effect of refugee presence on some individual labor market outcomes including labor force participation in the last 12 months.

Table 11 shows that there is a decreased participation into the labor force as a result of the refugee inflows (Panel A). The coefficient is statistically different from zero and implies that because of the refugee presence, hosting communities engage less on the labor market. This first result is confirmed by the decline in the number of worked months (Panel B) which indicates a negative change in the labor market both at the extensive and intensive margins. But these changes at the individual level are in contradiction with the previous findings at the household level. Also, in the absence of any variation in the received/generated income (Panels C, D and E), we further investigate the different type of employment.

Table 11: Effects of refugee presence on labor market outcomes, with an OLS analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009-2012)

||(1)|(2)|(3)|
|---|---|---|---|
||Panel A: Labor f|orce participation|(12 months)|
|Log of refugees (50km)|-0.0148*<br>(0.0083)|-0.0147*<br>(0.0082)|-0.0164*<br>(0.0085)|
|Observations|16,805|16,805|10,198|
|R-squared|0.0917|0.1470|0.1844|
||Panel B: W|orked months (12|months)|
|Log of refugees (50km)|-0.0499|-0.0288|-0.0720**|
||(0.0455)|(0.0354)|(0.0351)|
|Observations|8,010|8,010|5,577|
|R-squared|0.1230|0.2045|0.1704|

of household income is merely reported and not observed, this could imply that most households would classify themselves as commercial farmers solely based on the main purpose of planting.

32
together with the regulatory costs means that informal rms are much more prevalent in lower levels of productivity and are much smaller. Ulyssea (2018) develops and estimates an equilibrium model of informality using data from the Survey of the Urban Informal Sector (Pesquisa de Economia Informal Urbana, ECINF), a survey of informal rms conducted by IBGE in 2003. He considers two margins of informality. First, the decision of rms to not register their business (extensive margin). Second, the choice to hire workers o the books (intensive margin). The model integrates the three leading views of informal rms La Porta and Shleifer (2014) in a unied setting. The rst view argues that the informal sector is a reservoir of potentially productive entrepreneurs who are kept out of formality by high regulatory costs. The second sees informal rms as parasite rms that are productive enough to survive in the formal sector but choose to remain informal due to the cost advantages of not complying with taxes and regulations. The third argues that informality is a survival strategy for low-skill entrepreneurs who are too unproductive to ever become formal. The results show that the largest proportion of informal rms in Brazil (48.8 percent) correspond to the view that informal rms correspond to low-skill entrepreneurs who are too unproductive to ever become formal. The view that informal rms are to a large extent parasite rms also corresponds to a signicant fraction of informal rms (41.9 percent). On the other hand, the view that informal rms are potentially productive rms hindered by high regulatory costs corresponds to only 9.3 percent of informal rms in Brazil. This evidence on heterogeneity of rm productivity in the Brazilian informal sector suggests that the potential eects of formalization policies focused on the reduction of formal sector entry costs are probably limited. On the other hand, human capital policies that increase the skill of informal entrepreneurs may have signicant eects. We will return to these issues when we discuss the evidence on formalization policies in section 5.

# 3.3 Productivity dynamics

Using data from the World Bank Enterprise Survey, Barbosa Filho and Correa (2017) show that the average size of Brazilian rms is small in comparison to other emerging economies.<sup>13</sup> The average number of workers per rm in Brazil is similar to the one found in Russia, but considerably smaller than the average size in Chile, Colombia and China. The authors also note that older rms are not much larger than young rms in Brazil. Moreover, productivity does not increase with age. This suggests that the competitive selection mechanism is weak in Brazil. As a result, more productive rms do not grow and low-productivity rms stay 13This evidence was obtained from the same dataset that Barbosa Filho and Correa (2017) used to compare the distribution of labor productivity in Brazil to other emerging economies.

13
significantly the wage gap, it is not the case in the 2008 estimations. The decomposition of the gender wage differential (Table 23) shows that endowments of women actually contribute to reducing the gender wage gap: given their endowments, women should actually be paid more than men; if men had women’s endowments they would receive higher wages. The gender wage gap remains therefore “unexplained”. Given their endowments, if men were treated as women are, they would receive lower wages. In particular, there seems to be important differences in the returns to age in 2008, and to the sector of activity and to belonging to private/public sector. In particular, while the returns to being in the public sector are higher for women than men, it is the opposite in the private sector. Moreover, the returns to being in industry are lower for women than for men. The ethnicity of men and women does not significantly account for the gender wage differentials.

**Table 23: Oaxaca decomposition of gender wage differential**

||**20**<br>|**06**<br>|**2**<br>|**008**<br>|
|---|---|---|---|---|
|**Natural log of real**|**OLS**|**Heckman**|**OLS**|**Heckman**|
|**hourly wage**|||||
|Women|1.466|1.340|1.749|1.547|
|Men|1.642|1.434|1.926|1.721|
|**Wage differential**|**-0.175**|**-0.094**|**-0.177**|**-0.174**|
|_Of which:_|||||
|**Endowments**|**0.019****|**0.032*****|**0.041*****|**0.057*****|
|_Of which:_|||||
|Age|-0.002|-0.002|0.005*|0.009**|
|Job tenure|-0.006|-0.005|-0.003|-0.003|
|Job tenure squared|0.000|0.000|0.0003|0.000|
|Educational|0.028***|0.036***|0.037***|0.045***|
|attainment|||||
|Marital status|0.000|0.002|-0.002|0.000|
|Region|0.007***|0.010***|0.008***|0.010***|
|Ethnicity|0.001|0.001|0.0003|0.001|
|Sector|-0.003|-0.003|-0.003|-0.003|
|Private/Public|-0.006***|-0.006***|-0.002|-0.002|
|**Coefficients (returns)**|**-0.213*****|**-0.144**|**-0.232*****|**-0.252**|
|_Of which:_|||||
|Age|-0.080|-0.285|-0.542***|-0.663**|
|Job tenure|0.006|0.006|0.012|0.007|
|Job tenure squared|0.014|0.013|0.016|0.018|
|Educational|-0.004|0.006|0.002|-0.008|
|attainment|||||
|Marital status|0.006|0.008|0.016|0.016|
|Region|-0.001|-0.010|-0.029*|-0.032|

35
# **<u>4. Estimation Strategy</u>**

The experiment is designed to disentangle the impact of the behavioral change parenting component from the cash transfer program. This section describes the estimation strategy. It also outlines how data from the follow-up sample (Table 1) are used in the comparisons on which the estimation strategy relies.

# _<u>4.1 BCC Value-Added and Local Spillovers</u>_

Table 1 provides the composition of the follow-up sample by actual beneficiary status for the cash transfer program. In the control group, all households are non-beneficiaries. In each treatment group, there are three types of households in the sample: non-beneficiaries, beneficiaries from the baseline sample, and beneficiaries from the booster sample.

The experiment is designed to provide precise estimates of the value-added of the behavioral change parenting intervention. Based on randomized assignment of villages to the two treatment arms, we can disentangle the impact of the behavioral change component on cash transfer beneficiaries and local spillovers on households that do not benefit from cash transfers within treated villages.

First, we estimate the value-added of the BCC component for cash transfer beneficiaries by comparing outcomes between cash transfer beneficiaries in villages assigned or not to BCC. This estimate is obtained through the following regression model excluding observations from the control group: _i_ refers to an individual or household (depending on whether the outcome of interest is a household-level or individual-level outcome), _Y_ is the outcome of interest, _BCC_ takes the value of 1 for cash transfer villages randomly assigned to the behavioral change component, _Z_ is a set of randomization strata fixed effects, and _ε_ is the error term. C takes the value of 1 for observations in the control group, 0 otherwise. B captures the actual beneficiary status from the cash transfer program. Robust (White-Huber) standard-errors are clustered. To maximize power, this estimation is undertaken by including observations from the booster

> between the two teams, the list of children was extracted by the coordination team from the household data uploaded on the server (after verification). The pre-filled child questionnaires when then downloaded onto the child survey supervisors’ laptops, before being transferred to enumerators’ tablets. Child survey teams were responsible for verifying children’s age for all children identified by the household survey team and collecting data for all children in the appropriate age range. Thorough quality control procedures were put in place, including through automated checks on the tablets, as well as systematic verification of data collected by enumerators and supervisors. Additional verifications, including automated back-checks, were undertaken by the coordination and quality control teams once data were uploaded to the server.

13
# **<u>Table 8: Effects by gender and inheritance regime across surveys</u>**

||<br>**Organ**|<br>**ic manur**|**e applied**|**Ca**|**sh crop pl**|**anted**|
|---|---|---|---|---|---|---|
||**IHPS**|**IHS4**|**IHS4 rest.**|**IHPS**|**IHS4**|**IHS4 rest.**|
|Women have right to bequeath land (_β_11)|0.177**|0.047|0.016|0.062|-0.035|-0.070|
||[0.010]|[0.118]|[0.757]|[0.413]|[0.203]|[0.159]|
|Women have right to sell land (_β_12)|0.008|0.023|0.005|-0.073|0.046**|0.037|
||[0.896]|[0.497]|[0.911]|[0.533]|[0.048]|[0.314]|
|Men have right to bequeath land (_β_21)|-0.022|0.038|0.036|0.194***|0.012|0.051|
||[0.888]|[0.464]|[0.663]|[0.008]|[0.713]|[0.232]|
|Men have right to sell land (_β_22)|0.039|-0.003|-0.009|0.056|-0.026|-0.008|
||[0.753]|[0.938]|[0.882]|[0.463]|[0.279]|[0.867]|
|Women have right to bequeath land|<br>-0.062|<br>-0.109|<br>-0.035|<br>-0.108|<br>-0.016|<br>-0.083|
|*Non-matrilineal community (_β_31)|[0.583]|[0.125]|[0.629]|[0.443]|[0.749]|[0.613]|
|<br>Women have right to sell land|<br>0.200*|<br>0.031|<br>-0.010|<br>0.143|<br>-0.042|<br>-0.117|
|<br>*Non-matrilineal community (_β_32)|[0.057]|[0.698]|[0.929]|[0.357]|[0.423]|[0.139]|
|Men have right to bequeath land|0.063|0.054|0.157|-0.050|-0.058|-0.243*|
|<br>*Non-matrilineal community (_β_41)|[0.801]|[0.393]|[0.205]|[0.637]|[0.555]|[0.081]|
|<br>Men have right to sell land|<br>-0.032|<br>-0.094|<br>-0.026|<br>-0.014|<br>0.072|<br>0.077|
|<br>*Non-matrilineal community (_β_42)|[0.832]|[0.231]|[0.750]|[0.950]|[0.512]|[0.464]|
|Observations|1,932|11,201|9,439|1,932|11,201|9,439|
|R-squared|<br>0.780|<br>0.817|<br>0.833|<br>0.585|<br>0.609|<br>0.629|
|<br>Mean of dependent variable|0.233|0.215|0.218|0.070|0.054|0.054|
|Means of independent variables:|||||||
|Female bequest in matrilineal com.|0.444|0.507|0.440|0.444|0.507|0.440|
|<br>Female sale right in matrilineal com.|0.407|0.425|0.370|0.407|0.425|0.370|
|Male bequest right in matrilineal com.|0.324|0.324|0.284|0.324|0.324|0.284|
|Male sale right in matrilineal com.|0.299|0.291|0.255|0.299|0.291|0.255|
|<br>Female bequest right in non-matrilineal community|0.456|0.423|0.293|0.456|0.423|0.293|
|<br>Female sale right in non-matrilineal community|0.347|0.287|0.208|0.347|0.287|0.208|
|Male bequest right in non-matrilineal community|0.407|0.558|0.441|0.407|0.558|0.441|
|<br>Male sale right in non-matrilineal community|0.333|0.465|0.365|0.333|0.465|0.365|

_Notes_ : Regressions are for owned parcels. Household fixed effects, parcel attributes including size, soil type (sandy, between sandy and clay, clay), slope (flat, slight slope, moderate slope, hilly), and whether land is in swamp/wetland, and interactions between women’ or men’s ownership with non-matrilineal community are controlled for throughout. Robust standard errors are clustered by district. Figures in square brackets are p-values from wild cluster bootstrap (Roodman, 2018) with Rademacher weights and 1000 replications. *** significant at 1%; significant at 5%; * significant at 10%.

23
# Table 4 ).<sup>8</sup>

The national picture points to a pattern of sustained crop income growth across the years of the panel survey: 2006 to 2012. However, when crop income is disaggregated across regions, a slightly different picture emerges. First, from Figure 5 it is clear that the level of agricultural income varies substantially across regions, with much higher levels of crop income recorded in the Western region. The lowest levels of crop income are seen in the Northern region. Although crop income in the Central region is not particularly high, this reflects the fact that a much lower share of total income in the Central region comes from agriculture. In the Northern and Eastern regions agricultural income is the dominant source of income, as in the Western region, but overall levels of income are much lower.

Secondly, Figure indicates that agricultural income growth was negative between 2010 and 2011 in the Eastern and Northern regions. Although growth recovered between 2011 and 2012, the negative growth rate in the north and east resulted in both regions falling behind the center and west. In the following section we examine what drove these patterns.

**Figure 3. Real income per capita levels by source of income, all households**

<!-- Start of picture text --> 900,000<br>800,000<br>700,000<br>600,000<br>500,000<br>400,000<br>300,000<br>200,000<br>100,000<br>0<br>2005/06 2009/10 2010/11 2011/12<br>Crop Livestock Agr. Wage Non‐agr. wage Self‐emp<br>Source: UNPS 2006-2012<br>Ugandan Schillings (2011 prices)<br><!-- End of picture text -->

**Figure 4. Real income levels by source of income, bottom 40 percent**

> 8 It is worth noting that the panel analysis may overestimate national average per capita agricultural growth (and underestimate national average per capita non-agricultural growth) as households that attritted over time are probably more likely to be those that have moved out of agriculture. However, the nationally representative cross-sections undertaken during this time show that many households have stayed in agriculture, so this is unlikely to be a large source of bias.

11