rafmacalaba/datause-encoder-data huggingface dataset:
Second, analogous to labor productivity, another, less sophisticated measure of economic efficiency is the incremental capital output ratio (ICOR). Its popularity in efficiency analysis perhaps stems from the difficulty of measuring the capital stock of an economy. We recognized the merits of TFP and embodied technical progress and incorporated their effects in our projection results, but the analytical work nevertheless relied on ICORs, which were utilized in the macro models at the World Bank and elsewhere in the 1970s and 1980s. It was in the 1990s that Vikram Nehru and his associates created an original and reliably estimated cross-country database of physical and human capital stock and estimated TFPs for a large set of developing and developed countries (Nehru and Dhareshwar 1993, 1994; Nehru, Swanson, and Dube 1995).

Third, recent research has shown that investment and growth in advanced as well as emerging economies is increasingly driven by knowledge-based capital, such as computerized information, including software and databases; innovative property (patents, copyrights, designs, trademarks); and economic and financial competencies (brand equity, firm-specific human capital, networks joining highly skilled people and specialized institutions, organizational know-how that raises firms’ and other agencies’ efficiency and productivity). In some advanced economies, firms now invest more in knowledge-based capital than they do in physical capital. For firms such as Google, Microsoft, and Yahoo, investment in physical capital constitutes a small fraction of their total annual investments. By 2009, business investment in knowledge-based capital, at about 14 percent of GDP, exceeded business investment in physical capital, which amounted to less than 10 percent of GDP. According to a recent OECD report, between 1995 and 2007, knowledgebased capital investments explain as much as 27 percent of labor productivity growth in the United States but just 20–25 percent in Europe (OECD 2012b).

According to Hulten and Hao (2012), knowledge-based capital explains between 17 percent (China) and 30 percent (United States) of output growth per worker in the 2000–06 period. Including knowledge-based capital reduces the traditional estimates for labor productivity and TFP growth.

# _d. The convergence hypothesis: Limits and potential_

The convergence hypothesis states that in the long run, productivity and income levels of less developed countries converge to levels prevailing in the leading economy. This hypothesis arose 32
**Table A5:** Effect of female assignment on chat-based and admin-based purchase outcomes

||Anychat-base|d Anyad|min-based|Combine|dpurchases|
|---|---|---|---|---|---|
||(1)<br>All|(2)<br>(48h)|(3)<br>(24h)|(4)<br>(48h)|(5)<br>(24h)|
|Female|-.018<sup>_∗∗∗_</sup>|-.02<sup>_∗∗_</sup>|-.018<sup>_∗∗_</sup>|-.039<sup>_∗∗∗_</sup>|-.037<sup>_∗∗∗_</sup>|
||(.0067)|(.0097)|(.0087)|(.012)|(.011)|
|Control Mean (wt)|.033|.043|.037|.076|.070|
|N|2653|2653|2653|2653|2653|

This table shows the effect of female name assignment on purchase outcomes for administrative, non-administrative, and combined purchases. Non-administrative purchase records are recovered from chats, as they are purchases made by agents for customers. Administrative purchases are purchases made by customers observed through administrative records. Purchases are measured within 24 or 48 hours of the start of the chat. Female indicator determined in customer’s first chat of the day. Controls include agent-month, customer location, customer purchase history, and customer chat history fixed effects. The control group mean is reweighted by fixed effects cells to match the implied agent-month fixed effects-only OLS weights. Standard errors two-way clustered at the agent-day and customer-day level. _∗ p <_ 0.10, _∗∗ p <_ 0.05, _∗∗∗ p <_ 0.01.

6
largest database, the introduction of CBs elicits a job growth response among MSMEs that is over six times larger than that among large firms. Finally, MSMEs in industries with high dependence on external finance respond to the introduction of CBs with job growth rates that are almost two times larger than those of large firms in similar industries.

Our paper contributes to the growing literature on finance and labor. Several papers have used the 2007-09 Great Recession in the US as a shock to external finance to identify the causal link between finance and employment. Duygan-Bump, Levkov, and Montoriol-Gariga (2015) find that workers in small firms in the US were more likely to become unemployed during the 2007-09 financial crisis if they worked in industries with high external financing needs. Benmelech, Bergman, and Seru (2011) show that local unemployment rates in the U.S. are affected by the provision of bank credit. Chodorow-Reich (2014) finds that borrowers of banks affected by disruptions in the syndicated loan market following the collapse of Lehman Brothers in 2008 faced restrictions in credit supply, which translated into greater cuts in employment at these firms. Greenstone, Mas, and Nguyen (2012) also show that the 2007-09 lending shocks accounted for significant but modest declines in overall county-level employment.

However, it is not clear how these finance and labor market linkages in the US<sup>8</sup> translate to firms in poorer economies with completely different financial infrastructures and institutional with employment growth rates that are over 0.89 percentage points higher. Similarly, an upward movement by 1 standard deviation in the debt ratio of MSME firms is associated with an almost 0.4 percentage point higher employment growth. These are large movements in response to changes in financing when compared to the median annual employment growth rate of just under 1.5 percent.

> 8 A few studies have used data from other developed countries. Bentolila, Jansen, Jimenez, and Ruano (2013) use matched bank-firm data from the Spanish Credit Register and show that during the Great Recession, firms with credit relationships with weak banks had substantially lower employment levels than firms borrowing from nonaffected banks. Popov and Rocholl (2015) use German banks’ heterogeneous exposure to the US subprime mortgage market to show that firms with credit relationships with affected banks experienced significantly larger declines in employment and labor compensation relative to firms whose credit relationships were with healthy banks. Boeri, Garibaldi, and Moen (2012) use data from 11 OECD countries and find that highly leveraged sectors are characterized by higher job destruction rates than low-leveraged sectors during banking crises.

5
**Table 5. Regressions with Rule of Law** , **Bureaucracy, Corruption**

||**Model 1 **<br>**R**|**Model 2 **<br>**ule of La**|**Model 3 **<br>**w**|**Model 1**<br>**B**|**Model 2 **<br>**ureaucra**|**Model 3 **<br>**cy**|**Model 1 **|**Model 2 **<br>**Corrupt**|**Model 3**<br>**ion**|
|---|---|---|---|---|---|---|---|---|---|
||**(OLS)**|**(IV Aid)**|**(IV Aid) **|**(OLS)**|**(IV**|**(IV Aid) **|**(OLS)**|**(IV Aid)**|**(IV Aid)**|
||||||**Aid)**|||||
|aid|-0.08|-0.13|-0.06|-0.08|-0.05|-0.03|-0.06|-0.03|0.02|
|(log)|-0.64|-0.07|-0.06|-0.05|-0.07|-0.05|-0.06|-0.06|-0.05|
|initial institution|-.67***|-.72***|-.67***|-.40***|-.36***|-.40***|-.52***|-.51***|-.54***|
|(in year 1990)|-0.086|-0.08|-0.07|-0.07|-0.06|-0.07|-0.08|-0.08|-0.08|
|lpopulation|-1.12|-3.31|0.61|0.37|-2.7|.15*|-0.86|-3.93*|-0.44|
|(‘90~’95)|-2.61|-2.68|-2.29|-1.9|-2|-1.85|-1.82|-2.13|-1.72|
|lgnp|.63*|.69**|1.11***|.45*|0.38|.54**|0.29|0.3|.63**|
|(‘90~’95)|-0.32|-0.31|-0.26|-0.25|-0.31|-0.27|-0.26|-0.29|-0.28|
|English Legal Origin|||-0.24|||-.43*|||-.65**|
||||-0.28|||-0.26|||-0.31|
|French Legal Origin|||-.7**|||-.79***|||-.83**|
||||-0.32|||-0.26|||-0.32|
|Latitude|||.01**|||0.003|||0.008|
||||-0.006|||-0.003|||-0.004|
|Constant|3.16***|3.47***|3.21***|1.36***|1.57***|1.92***|1.96***|2.17***||
||-0.38|-0.35|-7.43|-0.26|-0.29|-0.35|-0.36|-0.35||
|# obs|79|62|62|79|62|62|79|62|62|
|R<sup>2 </sup>|<sup>0.4</sup>|0.81|0.85|0.33|0.48|0.6|0.43|0.52|0.62|

# Notes:

1) dependent variable is indicator of institutional quality.

2) instrument 1 for aid (lgnp and lpop in 1984) is used. Instrument 2 not shown but basic story similar.

3) robust standard error is reported in the brackets below the coefficient 4) ***designates significance at .01 level, ** at .05 level, * at .10 level.

18
**Table O.2** Agriculture is driving Zambia’s job growth but not GDP

|Drivers|of GDP an<br>|d formal empl<br>|oyment growth<br>|, 1991–2<br>|010<br>||
|---|---|---|---|---|---|---|
||GDP|(millions of 20|02 US$)|Empl|oyment (1,000|s people)|
||Initial|Change over|period (%)|Initial|Change ove|r period (%)|
|Sectors|1991|1991–2002|2002–2010|1991|1991–2002|2002–2010|
|Value (US$ millions<br>or 1,000s people)|8,410|1,023|6,108|2,519|1,001|865|
|Contribution (%)|100.0|100.0|100.0|100.0|100.0|100.0|
|Agriculture|15.2|30.5|8.1|65.4|87.6|51.4|
|Mining|20.1|–84.4|13.6|1.9|0.2|1.0|
|Manufacturing|10.7|21.3|6.8|4.3|1.4|1.3|
|Utilities|3.3|2.6|1.6|0.9|–1.2|0.7|
|Construction|8.4|-8.0|21.9|1.9|–0.3|4.2|
|Trade|17.3|49.3|9.8|10.3|4.8|19.6|
|Hotels, catering|1.2|14.0|2.5|0.5|1.9|1.3|
|Transport, communications|6.1|12.1|16.5|2.9|–1.5|3.7|
|Finance, business services|9.8|48.8|9.0|1.8|0.6|6.1|
|Government|7.1|11.4|8.9|5.6|6.7|6.7|
|Other services|0.7|2.3|1.4|4.5|–0.2|4.1|

Source: Resnick and Thurlow, Chapter 6 in this book.

But starting in 2002, the share of employment in agriculture began to fall, with services absorbing most of the workers who left the farm. Although the services sector is dominated by small-scale informal activity, its activities are still more productive than subsistence agriculture. Mining staged an impressive recovery, but only accounted for 1 percent of the new jobs created. And manufacturing not only failed to rally, but actually continued its decline. In the end, structural change and within-sector growth each accounted for around half of the 3.56 percent increase in labor productivity between 2002 and 2010. However, Resnick and Thurlow emphasize that the renewed growth and positive structural change have not translated into social transformation—a reality that has been reflected “in the country’s shifting political landscape,” and one that they contend can be seen playing out elsewhere in Africa, even in countries without large-scale mineral resources.

With more than half of the population engaged in low-productivity agriculture, structural change could play a significant role in Zambia’s growth going forward. But it may well be that to realize this potential, Zambia must first invest more in its fundamentals. Gross fixed capital formation as a share 24
Again, our results are not sensitive to the use of different assumptions regarding the actual FCS increase generated by social protection programs since our focus here is on targeting accuracy.

Finally, we simulate alternative selection mechanisms: random targeting and universal selection. Random targeting will consist in selecting randomly X% of the households. The threshold is adjusted to provide a relevant comparison with PMT or CBT selection in each case. Universal selection on the other hand will include all households but will deliver the same total amount of benefits as with other methods to keep program budget constant. This means that each household will receive a smaller amount of benefit compared to PMT or CBT, but everyone will receive benefits.

# _4.3 Initial conditions and descriptive statistics_

Table 2 shows initial FGT poverty indices in our nine data sets, confirming that most data sets have remarkably high levels of poverty, and poverty indices much higher than national poverty rates. This is likely, in part, a result of geographical targeting, although comparisons based on different consumption aggregates may be difficult. These initial conditions are important to keep in mind, as they may contribute to explain targeting results. Indeed, it is more difficult to select the poorest where “everyone is poor” (Ellis, 2012). The data sets are relatively homogeneous in terms of consumption and food insecurity levels<sup>25</sup> . However, Senegal 2 households are better off, followed by Burkinabe 2 households. In these two data sets, geographical targeting is not as narrow as in other data sets, which focus on smaller, poorer geographic areas.<sup>26</sup> The FCS is not as straightforward to compare across countries, but its highest values are also in Senegal 2 and Chad, followed by Niger 2. Cameroon is the country with the lowest average per-capita consumption, but food security levels are even lower in Mali (HDDS).

# **5 Results**

# _5.1 Targeting performance_

# _Performance in reaching the poorest households_

We start by presenting the performance of PMT relative to CBT targeting schemes to reach the poorest households, based on CBT selection thresholds in each database. For example, if a CBT targeting scheme selected 20 percent of the population, we adjust the PMT threshold so that 20 percent of the population is

> 25 See Figure 1 for the per-capita consumption cumulative distribution by data set. Additional descriptive statistics by data set for our key variables are presented in Table A2 in the Addendum, available upon request. 26 The Senegal 2 study was conducted to assess the targeting performance of the Unique National Registry (RNU), which was conducted nationally. The sample comes from 4 regions. Likewise, the health insurance project studied in Burkina Faso 2 was rolled out in several regions, and the sample comes from 2 distinct regions.

15
and identify two key dimensions of heterogeneity for our core set of results in our sample. The first is GDP per capita (e.g., Carleton et al. (2022)). The second is firm size, measured as the number of permanent, full-time employees ( _Emp_ ), which we consider a stable long-term proxy for firm quality. Modeling these two dimensions of heterogeneity allows us to test the hypotheses that the economic effects of climate change mostly materialize in poorer regions and that small firms may face more constraints in adapting to climate change. Including additional dimensions of heterogeneity beyond GDP and firm size does not change the relationship between temperature differences and firm performance, suggesting that GDP and firm size parsimoniously capture first-order heterogeneity (see Figure A1).

We flexibly model each weather variable using a third-order polynomial, which allows the data to drive the observed relationship between weather deviations and firm performance while preserving statistical power.<sup>3</sup> We fully saturate the model by interacting each term in the polynomial with the our two dimensions of heterogeneity. Following the recommendation of Abadie et al. (2023), we cluster our standard errors at the strata level (i.e., by subnational regions, sectors, and firm size categories). Indexing firms with _i_ , sectors with _j_ , countries with _c_ , fiscal years with _t_ , and strata with _s_ , we estimate Here, _CDiffijcst_ is the difference between the relevant climate variable for firm _i_ in year _t_ (when the Enterprise Survey was conducted) relative to that climate variable in firm _i_ ’s location from 1980–2008. The variable log( _GDPct_ ) is the logarithm of GDP for country _c_ in the year we observe firms in the Enterprise Survey and _Empijcst_ is the number of employees working at firm _i_ in year _t_ . _CHistijcs_ is the historical average of the relevant climate variable from 1980–2008, _γjct_ is a sector by country by fiscal year fixed effect, and _εjsc_ is the error

> 3Figure A2 shows that our results are robust to alternative functional forms.

13
**Table 5: Girls' Enrollment - Grade 6**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|FE|FE + Controls|Excluding mosque and<br>primaryschools|<br>Excluding mosque and<br>primary schools+ Multan<br>+Khanewal|
|Stipend District|-10.11***|||||
||(0.865)|||||
|2004|3.234***|3.896***|2.482***|2.480***|2.609***|
||(0.203)|(0.206)|(0.519)|(0.520)|(0.553)|
|2005|4.376***|5.375***|3.962***|3.967***|4.075***|
||(0.251)|(0.253)|(0.536)|(0.537)|(0.570)|
|2006|1.790***|5.856***|6.182***|6.188***|6.368***|
||(0.278)|(0.274)|(0.283)|(0.283)|(0.282)|
|2007|2.421***|3.789***|4.156***|4.154***|4.218***|
||(0.265)|(0.261)|(0.272)|(0.272)|(0.281)|
|4<br>2008|1.469***|2.921***|3.317***|3.314***|3.414***|
|0|(0.237)|(0.227)|(0.240)|(0.240)|(0.248)|
|Stipend District * 2004|0.263|0.0728|0.124|0.124|-0.0400|
||(0.294)|(0.302)|(0.302)|(0.302)|(0.313)|
|Stipend District * 2005|1.863***|1.877***|1.932***|1.929***|1.854***|
||(0.364)|(0.380)|(0.379)|(0.379)|(0.395)|
|Stipend District * 2006|2.379***|2.074***|2.072***|2.079***|1.859***|
||(0.382)|(0.419)|(0.419)|(0.419)|(0.431)|
|Stipend District * 2007|3.829***|3.657***|3.640***|3.641***|3.510***|
||(0.375)|(0.394)|(0.394)|(0.394)|(0.411)|
|Stipend District * 2008|3.511***|3.447***|3.438***|3.437***|3.259***|
||(0.335)|(0.346)|(0.346)|(0.346)|(0.361)|
|Number of Basic Facilities = 1|||-0.326|-0.324|-0.288|
||||(0.510)|(0.512)|(0.546)|
|Number of Basic Facilities = 2|||-0.954|-0.952|-0.860|
||||(0.502)|(0.504)|(0.537)|
> -25 -

Figure 8 <!-- Start of picture text --> KOREA: HOUSING UNITS PER HOUSEHOLD<br>Ratio for Cities. 1960-1985<br>65.0<br>64.0'<br>61.0<br>60.0<br>t A k<br>59 .0 $<br>58.0<br>-l<br>$;7.0- \A/-<br>54.0-<br>s3.0-<br>0 5.0<br>i960 1965 1970 1975 1980 19 SS<br>a url-ts/hiousehsolds<br><!-- End of picture text --> 57. With the achievement of price stability and pursuit of financial liberalization, the financial markets are at a threshold in terms of housing finance policies. There are major opportunities for a large, decentralized and efficient mortgage market for the first time. analyses of the new flow-of-funds data which separate the household sector from the non-incorporated business sector shows the strong growth of household net financial assets which have more than doubled in five years under stable prices. Confirming the very slow decline of the "orepression ratio" between UMM rates and banking rates, households have stayed away from monetary (or deposit) institutions and favored the investment and finance companies. Life insurance has grown very rapidly. The demand for long-term assets is growing.

58. What the housing market least needs is increased government intervention. Housing finance liberalization and the rapid expansion of the mortgage market should contribute powerfully to the rapid demise of the chonsei which has outlived its usefulness and has such negative effects on the rental markets. The pursuit of public policies based on the fragmented thinking which has characterized past directed credit policies should be changed. For instance,, recent proposals for large public rental programs ignore the large benefits of genuine housing finance reforms and'should be integrated with them. Similarly, there is ample reason to review the intent and effects of physical planning
**MONGOLIA'S INFORMAL SECTOR -**

**Appendix B: Methodologies and Descriptive Statistics**

**44**

||**(D)**<br>X)<br>(a)|I am currently registered at the employm<br>I was registered in the past, but I am not<br>I've never been registered at the employ|ent regulation office.<br>registered now.<br>ment regulation office.||||
|---|---|---|---|---|---|---|
|9.|Who|owns this kiosk?||C9<br>|(<br>0291|[|
||Q;)|I own it.|0An economic entity owns it.||||
||()|A relative owns it.|X A budget organization owns it.||||
||(D|A private person who is not a relative o|wns it.||||
||1-|If vou do not own it how are you comp|ensated?||@0)3|[<br>01|
||(D|I am not paid directly, but the income fr<br>_kiosk_goes to my family.|om this<br>(D<br>I am paid depending on h<br>time I work|ow muc|h||
||(D|I am paid a portion of the profits|i)<br>Other||||
|10.|How|much money does the_kiosk_usually earn af|ter<br>Average weekday|||1311|
||subtr<br>and r|acting the cost of purchasing inventories, tra<br>ent? (Do not subtract any wages.)|nsportation,<br>Average weekend day|||**[32]**|
|11.|How<br>|much money is usually spent in a week<br>|Purchase of inventories|||133_|
||for th|e following:|Transportation|||[341|
||||Other expenses, excluding wages|||[351|
|12.|Whic|h of the following works in this_kiosk,_|Spouse.|Y|N|**[36]**|
||exclu|ding yourself? (Select all that apply.)|Other family members or relatives.|Y|N|**[37)**|
||||People who are not related.|Y|N|1381|
|13.|Do y|ou keep written records of your transactions|(purchases, revenues, profits, etc.)?|Y|N|**[39]**|
|14.|Do y<br>_kiosk_|ou currently make Social Insurance or Healt<br> income?|h Insurance contributions from your|Y|N|**(40]**|
|15.|Do y|ou or any of your employers currently make|contributions to the Health Insurance Fund?|Y|N|**[41]**|
|16.|Do y<br>Insur|ou or any of your employers currently make<br>ance Funds?|contributions to the other Social|Y|N|**[42]**|
|17.|Does<br>of the|your spouse (if married) work for an emplo<br>Health or Social Insurance Funds?|yer who makes contributions to any|Y|N|**[43)**|
wage labor were curtailed, and respondents reflected on reduced local labor demand and wages (down by about 15 percent) alongside increases in food prices.

Further qualitative semi-structured interviews in study municipalities in 2010—with two dozen respondents (including local leaders, members of households with a migrant and returned migrants themselves) identified through a snowball sampling method—complemented many of the patterns evident the year before and revealed that the local economy had not yet fully rebounded. Several respondents continued to report diminished remittances, often due to migrants having only part-time work. While migration continued, it was lower than in the past and additional migrants had returned to weakened local economic opportunities. Respondents underscored the potential costs of failed migration or early return for which the initial investment had not yet been recouped, leaving return migrants in substantial debt, usually to another family member or local money lender.

The quantitative household panel data between 2008 and 2012 confirm a drop in migration from the region, consistent with the timing reported in the 2009 qualitative study and demonstrating a slow recovery to previous levels. Figure 1 shows the fraction of households by year who had at least one international migrant in any location and at least one international mi <mark>g</mark> rant in the United States, calculated from the retrospective information on migration experiences of all household members described in Section 2.2. The fraction of households with a <mark>mig</mark> rant in the United States rose steadily until 2008 when it dropped sharply, after which it remained fairly stable through 2012. Total international migration, however, began to recover somewhat with increases in migration to Mexico, consistent with the qualitative evidence on the changing relative advantages of that destination for which the upfront costs were substantially lower. Travel to nearby Chiapas was relatively easy and Guatemalans could enter legally, usually after indicating the name of a farm in which they intended to work.

The panel survey data also confirm a reduction in remittances deeper than seen at the national level, possibly reflecting weaker permanency of the less educated migrants from this